Impacts of dreissenid mussel invasions on chlorophyll and total phosphorus in 25 lakes in the USA

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94854/1/fwb.12050.pd

Recent Trends in Water Quality Management in Korea: An Introduction to Korea’s Total Maximum Daily Load (TMDL) Program

In 1999, the Korean Ministry of Environment adopted the Total Maximum Daily Load (TMDL) management system to resolve the problems of traditional water quality management policies represented by concentration regulations, land-use regulations, and environmental treatment facilities. A special act established in 1999 recommends the autonomous implementation of the program in the Han River watershed. Also, a series of special acts established in 2002 require the compulsory implementation for three other major river watersheds. Accordingly, as of 2007, four major river watersheds are implementing the first phase of the TMDLs as well as preparing for a second phase set to begin in 2011. Although the introduction of the TMDL program can be evaluated as a paradigm shift in water quality management, the current system addresses issues that originated specifically in Korea’s political and economic context

Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model

There have been increasing reports of harmful algal blooms (HABs) worldwide. However, the factors that influence cyanobacteria dominance and HAB formation can be site‐specific and idiosyncratic, making prediction challenging. The drivers of cyanobacteria blooms in Lake Paldang, South Korea, the summer climate of which is strongly affected by the East Asian monsoon, may differ from those in well‐studied North American lakes. Using the observational data sampled during the growing season in 2007–2011, a Bayesian hurdle Poisson model was developed to predict cyanobacteria abundance in the lake. The model allowed cyanobacteria absence (zero count) and nonzero cyanobacteria counts to be modeled as functions of different environmental factors. The model predictions demonstrated that the principal factor that determines the success of cyanobacteria was temperature. Combined with high temperature, increased residence time indicated by low outflow rates appeared to increase the probability of cyanobacteria occurrence. A stable water column, represented by low suspended solids, and high temperature were the requirements for high abundance of cyanobacteria. Our model results had management implications; the model can be used to forecast cyanobacteria watch or alert levels probabilistically and develop mitigation strategies of cyanobacteria blooms. Key Points A Bayesian hurdle Poisson model predicted cyanobacteria abundance Temperature, flushing rate, and water column stability were key factors The model forecasted cyanobacteria watch and alert levels probabilisticallyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106958/1/wrcr20820.pd

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

Is the mechanism of synchronous cardiocerebral infarction (CCI) different from that of metachronous CCI?

Background Cardiocerebral infarction (CCI) is the simultaneous occurrence of acute ischemic stroke (AIS) and myocardial infarction (MI) at the same time (synchronous), or one after another (metachronous). This study aimed to investigate the differences in the underlying mechanisms between synchronous and metachronous CCI. Methods This study analyzed patients with AIS registered in the Clinical Research Collaboration for Stroke in Korea Prospective Registry at a single Stroke Center from January 2019 to December 2022. Patients with synchronous and metachronous CCI (MI within 72 hours after AIS) were included. Severity at admission and modified Rankin Scale scores 3 months after treatment were assessed. Results Among 3,319 AIS patients, 12 (0.36%) were diagnosed with acute CCI (male, 8; mean age, 69.6±14.0 years). Of these, six (0.18%) had synchronous CCI, while the other six had metachronous CCI. The synchronous CCI group exhibited lower neurological severity at admission than the metachronous CCI group (median National Institutes of Health Stroke Scale, 3.5 vs. 12.5). Among the 12 patients, seven (58%) had ST-elevation myocardial infarction (STEMI), with five (83%) of the synchronous CCI cases presenting as STEMI. Two cases of new-onset atrial fibrillation occurred exclusively in patients with synchronous CCI. Also, one case with synchronous CCI had a thrombus in the left ventricle. Conclusion Acute CCI is rare and manifests with varying degrees of severity. Our study suggests that AIS in synchronous CCI may be secondary to embolism caused by a preceding MI. In contrast, metachronous CCI exhibits diverse mechanisms, including secondary myocardial injury resulting from a preceding severe AIS

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

Do Invasive Mussels Restrict Offshore Phosphorus Transport in Lake Huron?

Dreissenid mussels were first documented in the Laurentian Great Lakes in the late 1980s. Zebra mussels (Dreissena polymorpha) spread quickly into shallow, hard-substrate areas; quagga mussels (Dreissena rostriformis bugensis) spread more slowly and are currently colonizing deep, offshore areas. These mussels occur at high densities, filter large water volumes while feeding on suspended materials, and deposit particulate waste on the lake bottom. This filtering activity has been hypothesized to sequester tributary phosphorus in nearshore regions reducing offshore primary productivity. We used a mass balance model to estimate the phosphorus sedimentation rate in Saginaw Bay, a shallow embayment of Lake Huron, before and after the mussel invasion. Our results indicate that the proportion of tributary phosphorus retained in Saginaw Bay increased from approximately 4670% when dreissenids appeared, reducing phosphorus export to the main body of Lake Huron. The combined effects of increased phosphorus retention and decreased phosphorus loading have caused an approximate 60% decrease in phosphorus export from Saginaw Bay to Lake Huron. Our results support the hypothesis that the ongoing decline of preyfish and secondary producers including diporeia (Diporeia spp.) in Lake Huron is a bottom-up phenomenon associated with decreased phosphorus availability in the offshore to support primary production

Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

Abstract Recently, weather data have been applied to one of deep learning techniques known as “long short-term memory (LSTM)” to predict streamflow in rainfall-runoff relationships. However, this approach may not be suitable for regions with artificial water management structures such as dams and weirs. Therefore, this study aims to evaluate the prediction accuracy of LSTM for streamflow depending on the availability of dam/weir operational data across South Korea. Four scenarios were prepared for 25 streamflow stations. Scenarios #1 and #2 used weather data and weather and dam/weir operational data, respectively, with the same LSTM model conditions for all stations. Scenarios #3 and #4 used weather data and weather and dam/weir operational data, respectively, with the different LSTM models for individual stations. The Nash–Sutcliffe efficiency (NSE) and the root mean squared error (RMSE) were adopted to assess the LSTM’s performance. The results indicated that the mean values of NSE and RMSE were 0.277 and 292.6 (Scenario #1), 0.482 and 214.3 (Scenario #2), 0.410 and 260.7 (Scenario #3), and 0.592 and 181.1 (Scenario #4), respectively. Overall, the model performance was improved by the addition of dam/weir operational data, with an increase in NSE values of 0.182–0.206 and a decrease in RMSE values of 78.2–79.6. Surprisingly, the degree of performance improvement varied according to the operational characteristics of the dam/weir, and the performance tended to increase when the dam/weir with high frequency and great amount of water discharge was included. Our findings showed that the overall LSTM prediction of streamflow was improved by the inclusion of dam/weir operational data. When using dam/weir operational data to predict streamflow using LSTM, understanding of their operational characteristics is important to obtain reliable streamflow predictions

Fabrication of mechanically-tunable multilayered 3D cell co-culture hydrogel system for high-throughput invetigation of complex cellular behavior

Hydrogels are widely used as a 3D cell coculture platform, as they can be tailored to provide suitable microenvironments to induce cellular phenotypes with physiological significance. Herein, programmable multilayer photolithography is employed to develop a 3D hydrogel-based co-culture system in an efficient and scalable manner, which consists of an inner microgel array containing one cell type covered by an outer hydrogel overlay containing another cell type. In particular, the mechanical properties of microgel array and hydrogel overlay are independently controlled in a wide range, with elastic moduli ranging from 1.7 to 31.6 kPa, allowing the high-throughput investigation of both individual hydrogel mechanics and mechanical gradients generated at their interface. Utilizing this system, it was demonstrated that macrophage phenotypical changes (i.e. proliferation, spheroid formation and M ?? polarization) were substantially influenced by the direction and degree of mechanical gradient, as well as the presence of co-cultured fibroblasts in the vicinity. Furthermore, the paracrine effect between the macrophages in different microgels was clearly mediated by their inter-distance

Programmable multilayer printing of a mechanically-tunable 3D hydrogel co-culture system for high-throughput investigation of complex cellular behavior

Hydrogels are widely used as a 3D cell culture platform, as they can be tailored to provide suitable microenvironments to induce cellular phenotypes with physiological significance. Hydrogels are especially deemed attractive as a co-culture platform, in which two or more different types of cells are cultured together in close proximity, since the spatial distribution of different cell types can be rendered possible by advanced microfabrication schemes. Herein, programmable multilayer photolithography is employed to develop a 3D hydrogel-based co-culture system in an efficient and scalable manner, which consists of an inner microgel array containing one cell type covered by an outer hydrogel overlay containing another cell type. In particular, the mechanical properties of microgel array and hydrogel overlay are independently controlled in a wide range, with elastic moduli ranging from 1.7 to 31.6 kPa, allowing the high-throughput investigation of both individual hydrogel mechanics and mechanical gradients generated at their interface. Utilizing this system, phenotypical changes (i.e. proliferation, spheroid formation and M?? polarization) of macrophages encapsulated in microgel array, in response to complex mechanical microenvironment and co-cultured fibroblasts, are comprehensively explored