Joon Ha Kim

Linking land-use type and stream water quality using spatial data of fecal indicator bacteria and heavy metals in the Yeongsan river basin

This study reveals land-use factors that explain stream water quality during wet and dry weather conditions in a large river basin using two different linear models-multiple linear regression (MLR) models and constrained least squares (CLS) models. Six land-use types and three topographical parameters (size, slope, and permeability) of the watershed were incorporated into the models as explanatory variables. The suggested models were then demonstrated using a digitized elevation map in conjunction with the land-use and the measured concentration data for Escherichia coli (EC), Enterococci bacteria (ENT), and six heavy metal species collected monthly during 2007-2008 at 50 monitoring sites in the Yeongsan Watershed, Korea. The results showed that the MLR models can be a powerful tool for predicting the average concentrations of pollutants in stream water (the Nash-Sutcliffe (NS) model efficiency coefficients ranged from 0.67 to 0.95). On the other hand, the CLS models, with moderately good prediction performance (the NS coefficients ranged 0.28-0.85), were more suitable for quantifying contributions of respective land-uses to the stream water quality. The CLS models suggested that industrial and urban land-uses are major contributors to the stream concentrations of EC and ENT, whereas agricultural, industrial, and mining areas were significant sources of many heavy metal species. In addition, the slope, size, and permeability of the watershed were found to be important factors determining the extent of the contribution from each land-use type to the stream water quality. The models proposed in this paper can be considered useful tools for developing land cover guidelines and for prioritizing locations for implementing management practices to maintain stream water quality standard in a large river basin.

Use of barcoded pyrosequencing and shared OTUs to determine sources of fecal bacteria in watersheds

While many current microbial source tracking (MST) methods rely on the use of specific molecular marker genes to identify sources of fecal contamination, these methods often fail to determine all point and nonpoint contributors of fecal inputs into waterways. In this study, we developed a new library-dependent MST method that uses pyrosequencing-derived shared operational taxonomy units (OTUs) to define sources of fecal contamination in waterways. A total 56,841 pyrosequencing reads of 16S rDNA obtained from the feces of humans and animals were evaluated and used to compare fecal microbial diversity in three freshwater samples obtained from the Yeongsan river basin in Jeonnam Province, South Korea. Sites included an urbanized agricultural area (Y1) (Escherichia coli counts ≥ 1600 CFU/100 mL), an open area (Y2) with no major industrial activities (940 CFU/100 mL), and a typical agricultural area (Y3) (≥ 1600 CFU/100 mL). Data analyses indicated that the majority of bacteria in the feces of humans and domesticated animals were comprised of members of the phyla Bacteroidetes or Firmicutes, whereas the majority of bacteria in wild goose feces and freshwater samples were classified to the phylum Proteobacteria. Analysis of OTUs shared between the fecal and environmental samples suggested that the potential sources of the fecal contamination at the sites were of human and swine origin. Quantification of fecal contamination was also examined by comparing the density of pyrosequencing reads in each fecal sample within shared OTUs. Taken together, our results indicated that analysis of shared OTUs derived from barcoded pyrosequencing reads provide the necessary resolution and discrimination to be useful as a next generation platform for microbial source tracking studies.

Meteorological effects on the levels of fecal indicator bacteria in an urban stream: a modeling approach.

Gwangju Creek (GJC) in Korea, which drains a highly urbanized watershed, has suffered from substantial fecal contamination, thereby limiting the beneficial use of the water in addition to threatening public health. In this study, to quantitatively estimate the sinks and sources of fecal indicator bacteria (FIB) in GJC under varying meteorological conditions, two FIB (i.e., Escherichia coli and enterococci bacteria) were monitored hourly for 24h periods during both wet and dry weather conditions at four sites along GJC, and the collected data was subsequently used to develop a spatiotemporal FIB prediction model. The monitoring data revealed that storm washoff and irradiational die-off by sunlight are the two key processes controlling FIB populations in wet and dry weather, respectively. FIB populations significantly increased during precipitation, with greater concentrations occurring at higher rainfall intensity. During dry weather, FIB populations decreased in the presence of sunlight in daytime but quickly recovered at nighttime due to continuous point-source inputs. In this way, the contributions of the key processes (i.e., irradiational die-off by sunlight, settling, storm washoff, and resuspension) to the FIB levels in GJC under different meteorological conditions were quantitatively estimated using the developed model. The modeling results showed that the die-off by sunlight is the major sink of FIB during the daytime in dry weather with a minor contribution from the settling process. During wet weather, storm washoff and resuspension are equally important processes that are responsible for the substantial increase of FIB populations.

Foxg1 promotes olfactory neurogenesis by antagonizing Gdf11

Foxg1, a winged-helix transcription factor, promotes the development of anterior neural structures; in mice lacking Foxg1, development of the cerebral hemispheres and olfactory epithelium (OE) is severely reduced. It has been suggested that Foxg1 acts by positively regulating the expression of growth factors, such as Fgf8, which support neurogenesis. However, Foxg1 also binds Smad transcriptional complexes, allowing it to negatively regulate the effects of TGFβ family ligands. Here, we provide evidence that this latter effect explains much of the ability of Foxg1 to drive neurogenesis in the OE. We show that Foxg1 is expressed in developing OE at the same time as the gene encoding growth differentiation factor 11 (Gdf11), a TGFβ family member that mediates negative-feedback control of OE neurogenesis. Mutations in Gdf11 rescue, to a considerable degree, the major defects in Foxg1-/- OE, including the early, severe loss of neural precursors and olfactory receptor neurons, and the subsequent collapse of both neurogenesis and nasal cavity formation. Rescue is gene-dosage dependent, with loss of even one allele of Gdf11 restoring substantial neurogenesis. Notably, we find no evidence for a disruption of Fgf8 expression in Foxg1-/- OE. However, we do observe both a failure of expression of follistatin (Fst), which encodes a secreted Gdf11 antagonist normally expressed in and around OE, and an increase in the expression of Gdf11 itself within the remaining OE in these mutants. Fst expression is rescued in Foxg1-/-;Gdf11-/- and Foxg1-/-;Gdf11+/- mice. These data suggest that the influence of Foxg1 on Gdf11-mediated negative feedback of neurogenesis may be both direct and indirect. In addition, defects in development of the cerebral hemispheres in Foxg1-/- mice are not rescued by mutations in Gdf11, nor is Gdf11 expressed at high levels within these structures. Thus, the pro-neurogenic effects of Foxg1 are likely to be mediated through different signaling pathways in different parts of the nervous system.

The modified SWAT model for predicting fecal coliforms in the Wachusett Reservoir Watershed, USA

This study assessed fecal coliform contamination in the Wachusett Reservoir Watershed in Massachusetts, USA using Soil and Water Assessment Tool (SWAT) because bacteria are one of the major water quality parameters of concern. The bacteria subroutine in SWAT, considering in-stream bacteria die-off only, was modified in this study to include solar radiation-associated die-off and the contribution of wildlife. The result of sensitivity analysis demonstrates that solar radiation is one of the most significant fate factors of fecal coliform. A water temperature-associated function to represent the contribution of beaver activity in the watershed to fecal contamination improved prediction accuracy. The modified SWAT model provides an improved estimate of bacteria from the watershed. Our approach will be useful for simulating bacterial concentrations to provide predictive and reliable information of fecal contamination thus facilitating the implementation of effective watershed management.

Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network

The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. As groundwater resources are one of most important freshwater sources for water supplies in Southeast Asian countries, it is important to investigate the spatial distribution of As contamination and evaluate the health risk of As for these countries. The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. Therefore, modeling approaches for As concentration using conventional on-site measurement data can be an alternative to quantify the As contamination. The objective of this study is to evaluate the predictive performance of four different models; specifically, multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), and the combination of principal components and an artificial neural network (PC-ANN) in the prediction of As concentration, and to provide assessment tools for Southeast Asian countries including Cambodia, Laos, and Thailand. The modeling results show that the prediction accuracy of PC-ANN (Nash-Sutcliffe model efficiency coefficients: 0.98 (traning step) and 0.71 (validation step)) is superior among the four different models. This finding can be explained by the fact that the PC-ANN not only solves the problem of collinearity of input variables, but also reflects the presence of high variability in observed As concentrations. We expect that the model developed in this work can be used to predict As concentrations using conventional water quality data obtained from on-site measurements, and can further provide reliable and predictive information for public health management policies.

Follistatin modulates a BMP autoregulatory loop to control the size and patterning of sensory domains in the developing tongue

The regenerative capacity of many placode-derived epithelial structures makes them of interest for understanding the molecular control of epithelial stem cells and their niches. Here, we investigate the interaction between the developing epithelium and its surrounding mesenchyme in one such system, the taste papillae and sensory taste buds of the mouse tongue. We identify follistatin (FST) as a mesenchymal factor that controls size, patterning and gustatory cell differentiation in developing taste papillae. FST limits expansion and differentiation of Sox2-expressing taste progenitor cells and negatively regulates the development of taste papillae in the lingual epithelium: in Fst-/- tongue, there is both ectopic development of Sox2-expressing taste progenitors and accelerated differentiation of gustatory cells. Loss of Fst leads to elevated activity and increased expression of epithelial Bmp7; the latter effect is consistent with BMP7 positive autoregulation, a phenomenon we demonstrate directly. We show that FST and BMP7 influence the activity and expression of other signaling systems that play important roles in the development of taste papillae and taste buds. In addition, using computational modeling, we show how aberrations in taste papillae patterning in Fst-/- mice could result from disruption of an FST-BMP7 regulatory circuit that normally suppresses noise in a process based on diffusion-driven instability. Because inactivation of Bmp7 rescues many of the defects observed in Fst-/- tongue, we conclude that interactions between mesenchyme-derived FST and epithelial BMP7 play a central role in the morphogenesis, innervation and maintenance of taste buds and their stem/progenitor cells.

Absence of Escherichia coli Phylogenetic Group B2 Strains in Humans and Domesticated Animals from Jeonnam Province, Republic of Korea

ABSTRACT Multiplex PCR analyses of DNAs from genotypically unique Escherichia coli strains isolated from the feces of 138 humans and 376 domesticated animals from Jeonnam Province, South Korea, performed using primers specific for the chuA and yjaA genes and an unknown DNA fragment, TSPE4.C2, indicated that none of the strains belonged to E. coli phylogenetic group B2. In contrast, phylogenetic group B2 strains were detected in about 17% (8 of 48) of isolates from feces of 24 wild geese and in 3% (3 of 96) of isolates obtained from the Yeongsan River in Jeonnam Province, South Korea. The distribution of E. coli strains in phylogenetic groups A, B1, and D varied depending on the host examined, and there was no apparent seasonal variation in the distribution of strains in phylogenetic groups among the Yeongsan River isolates. The distribution of four virulence genes (eaeA, hlyA, stx1, and stx2) in isolates was also examined by using multiplex PCR. Virulence genes were detected in about 5% (38 of 707) of the total group of unique strains examined, with 24, 13, 13, and 9 strains containing hlyA, eaeA, stx2, and stx1, respectively. The virulence genes were most frequently present in phylogenetic group B1 strains isolated from beef cattle. Taken together, results of these studies indicate that E. coli strains in phylogenetic group B2 were rarely found in humans and domesticated animals in Jeonnam Province, South Korea, and that the majority of strains containing virulence genes belonged to phylogenetic group B1 and were isolated from beef cattle. Results of this study also suggest that the relationship between the presence and types of virulence genes and phylogenetic groupings may differ among geographically distinct E. coli populations.

Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea

Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vector machine (SVM) were used to predict Chl-a concentration for the early warning in the Juam Reservoir and Yeongsan Reservoir, which are located in an upstream region (freshwater reservoir) and downstream region (estuarine reservoir), respectively. Weekly water quality data and meteorological data for a 7-year period were used to train and validate both the ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the two models, respectively. Results revealed that the two models well-reproduced the temporal variation of Chl-a based on the weekly input variables. In particular, the SVM model showed better performance than the ANN model, displaying a higher prediction accuracy in the validation step. The Williams-Kloot test and sensitivity analysis demonstrated that the SVM model was superior for predicting Chl-a in terms of prediction accuracy and description of the cause-and-effect relationship between Chl-a concentration and environmental variables in both the Juam Reservoir and Yeongsan Reservoir. Furthermore, a 7-day interval was determined as an efficient early warning interval in the two reservoirs. As such, this study suggested an effective early-warning prediction method for Chl-a concentration and improved the eutrophication management scheme for reservoirs.

Characteristics of wet and dry weather heavy metal discharges in the Yeongsan Watershed, Korea

A comprehensive water quality monitoring program was conducted in the Yeongsan (YS) River, Korea from 2005 to present to investigate wet and dry weather pollutant discharge in an attempt to establish point and non-point pollution management strategies. As part of this monitoring program, 11 heavy metal species were measured during dry and wet weather conditions in the YS River, where Gwangju City (GJ), a subcatchment of the YS River, was further monitored to clarify the responsibility of different metal species discharged into the mainstream. Monthly grab water samples showed that greater amounts of metals along the YS River were discharged during the wet summer months due largely to storm runoff. In addition, further monitoring results revealed that GJ, a highly urbanized area, was a significant contributor of the heavy metals being discharged into the YS River during both wet and dry weather. The most abundant metal species discharged from GJ were manganese, aluminum and iron with different contributions of wet and dry weather flows to the total discharge load. Wet weather flow was a significant contributor to the annual dissolved metal loads, accounting for 44-93% of the annual load depending on the metal species, with the exception of chromium and cadmium (9% and 27%, respectively). Mostly, metal loads during wet weather were shown to be proportional to the rainfall depth and antecedent dry period. A substantial fraction of metals were also associated with solids, suggesting that sedimentation might be an appropriate management practice for reducing the metal load generated in GJ. Overall, although dissolved metal concentrations in YS River were at an acceptable level for aquatic community protection, continual metal discharge throughout the year was considered to be a potential problem in the long-term due to gradual water quality degradation as well as continuous metal accumulation in the system.