Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling.

Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique-Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources.

Laparoscopically Resected Ectopic Hepatocellular Carcinoma Mimicking Subepithelial Tumor of Stomach: A Case Report.

Ectopic hepatocellular carcinoma (HCC) can be defined as an HCC arising from hepatic parenchyma located in an extrahepatic organ or tissue. Fewer than 100 cases of ectopic liver have been reported. In 30 of these cases, HCC was detected in ectopic tissue. The author describes a case of ectopic HCC mimicking subepithelial tumor (SET) of the stomach successfully treated by laparoscopic resection. This case report also provides a pertinent review of the literature. A 61-year-old male was referred to our department for the management of an intra-abdominal mass found on abdominal computed tomography performed at a local medical center due to a 2-month history of vague abdominal pain. Abdominal magnetic resonance imaging and computed tomography showed a 7.2-cm sized heterogenous mass over the left subphrenic space adjacent to the stomach. Esophagogastroduodenoscopy showed a huge subepithelial mass at the fundus of the stomach. Based on the above findings, the diagnosis of SET of the stomach such as gastrointestinal stromal tumor or leiomyoma was suspected. Laparoscopic surgery was performed for definite diagnosis and treatment of the huge SET of the stomach. Histologic diagnosis was ectopic HCC of the trabecular type. Its histologic grade was consistent with Edmondson grade II. Although ectopic HCC is quite rare tumor of the stomach, it should be considered in the differential diagnosis of other abdominal mass-like lesions. Currently, with advances of laparoscopic surgery, laparoscopic resection for complete excision of ectopic HCC is possible with acceptable results.

The Fidelity of Artificial Intelligence to Multidisciplinary Tumor Board Recommendations for Patients with Gastric Cancer: A Retrospective Study.

PURPOSE: Due to significant growth in the volume of information produced by cancer research, staying abreast of recent developments has become a challenging task. Artificial intelligence (AI) can learn, reason, and understand the enormous corpus of literature available to the scientific community. However, large-scale studies comparing the recommendations of AI and a multidisciplinary team board (MTB) in gastric cancer treatment have rarely been performed. Therefore, a retrospective real-world study was conducted to assess the level of concordance between AI and MTB treatment recommendations. METHODS: Treatment recommendations of Watson for Oncology (WFO) and an MTB were retrospectively analyzed 322 patients with gastric cancer from January 2015 to December 2018 and the degree of agreement between them was compared. The patients were divided into concordance and non-concordance groups and factors affecting the concordance rate were analyzed. RESULTS: The concordance rate between the AI and MTB was 86.96%. The concordance rates for each stage were 96.93% for stage I, 88.89% for stages II, 90.91% for stage III, and 45.83% for stage IV, respectively. In the multivariate analysis, age (p-value = 0.000), performance status (p-value = 0.003 for performance score 1; p-value = 0.007 for performance score 2; p-value = 0.000 for performance score 3), and stage IV (p-value = 0.017) had a significant effect on concordance between the MTB and WFO. CONCLUSION: Factors affecting the concordance rate were age, performance status, and stage IV gastric cancer. To increase the validity of future medical AI systems for gastric cancer treatment, their supplementation with local guidelines and the ability to comprehensively understand individual patients is essential.

Is vagotomy necessary in palliative surgery for incurable advanced gastric cancer?: a retrospective case-control study.

BACKGROUND: The interplay between the nervous system and cancer plays an important role in the initiation and progression of gastric cancer. Few studies have presented evidence that the sympathetic nervous system inhibits the occurrence and development of gastric cancer while the parasympathetic nervous system promotes the growth of gastric cancer. To investigate the effect of vagotomy, which is the resection of a parasympathetic nerve innervating the stomach, on the progression of gastric cancer, a retrospective study was conducted comparing the prognosis of simple palliative gastrojejunostomy (PGJ) and palliative gastrojejunostomy with vagotomy (PGJV). METHODS: From January 01, 2000, to December 31, 2021, the medical records of patients who underwent PGJ or PGJV because of gastric outlet obstruction due to incurable advanced gastric cancer at the Yeungnam University Medical Center were retrospectively reviewed. Patients were divided into two groups: locally unresectable gastric cancer (LUGC) or gastric cancer with distant metastasis (GCDM), according to the reason for gastrojejunostomy, and factors affecting overall survival (OS) were analyzed. RESULTS: There was no significant difference in surgical outcomes and postoperative complications between the patients with PGJV and patients with PGJ. In univariate analysis, vagotomy was not a significant factor for OS in the GCDM group (HR 1.14, CI 0.67-1.94, p value 0.642), while vagotomy was a significant factor for OS in the LUGC group (HR 0.38, CI 0.15-0.98, p value 0.045). In multivariate analysis, when vagotomy is performed together with PGJ for LUGC, the OS can be significantly extended (HR 0.25, CI 0.09-0.068, p value 0.007). CONCLUSIONS: When PGJ for LUGC was performed with vagotomy, additional survival benefits could be achieved with low complication risk. However, to confirm the effect of vagotomy on the growth of gastric cancer, further prospective studies using large sample sizes are essential.

Intraabdominal abscess mimicking gastric cancer recurrence: a case report.

Surgical site infection is a common healthcare-associated infection that rarely occurs several months after surgery. Herein, a case is described in which an abdominal mass lesion was found at a 6-month follow-up visit after gastrectomy was performed for early gastric cancer. Positron emission tomography-computed tomography revealed a 2.5 cm-sized mass with a high maximal standard uptake value (8.32), located above a previous anastomosis site. Locoregional recurrence of gastric cancer was diagnosed by multidisciplinary team discussion, and explorative laparotomy was performed. However, surgical and pathologic findings revealed that the mass was an intraabdominal abscess. In conclusion, differential diagnosis of delayed abscess formation should be considered if the possibility of tumor recurrence is low, especially after early gastric cancer surgery.

Synthesis and applications of bismuth-impregnated biochars originated from spent coffee grounds for efficient adsorption of radioactive iodine: A mechanism study.

The adsorption of radioactive iodine, which is capable of presenting high mobility in aquatic ecosystems and generating undesirable health effects in humans (e.g., thyroid gland dysfunction), was comprehensively examined using pristine spent coffee ground biochar (SCGB) and bismuth-impregnated spent coffee ground biochar (Bi@SCGB) to provide valuable insights into the variations in the adsorption capacity and mechanisms after pretreatment with Bi(NO3)3. The greater adsorption of radioactive iodine toward Bi@SCGB (adsorption capacity (Qe) = 253.71 µg/g) compared to that for SCGB (Qe = 23.32 µg/g) and its reduced adsorption capability at higher pH values provide evidence that the adsorption of radioactive iodine with SCGB and Bi@SCGB is strongly influenced by the presence of bismuth materials and the electrostatic repulsion between their negatively charged surfaces and negatively charged radioactive iodine (IO3-). The calculated R2 values for the adsorption kinetics and isotherms support that chemisorption plays a crucial role in the adsorption of radioactive iodine by SCGB and Bi@SCGB in aqueous phases. The adsorption of radioactive iodine onto SCGB was linearly correlated with the contact time (h1/2), and the diffusion of intra-particle predominantly determined the adsorption rate of radioactive iodine onto Bi@SCGB (Cstage II (129.20) > Cstage I (42.33)). Thermodynamic studies revealed that the adsorption of radioactive iodine toward SCGB (ΔG° = -8.47 to -7.83 kJ/mol; ΔH° = -13.93 kJ/mol) occurred exothermically and that for Bi@SCGB (ΔG° = -15.90 to -13.89 kJ/mol; ΔH° = 5.88 kJ/mol) proceeded endothermically and spontaneously. The X-ray photoelectron spectroscopy (XPS) analysis of SCGB and Bi@SCGB before and after the adsorption of radioactive iodine suggest the conclusion that the change in the primary adsorption mechanism from electrostatic attraction to surface precipitation upon the impregnation of bismuth materials on the surfaces of spent coffee ground biochars is beneficial for the adsorption of radioactive iodine in aqueous phases.

Downstream Transport of Geosmin Based on Harmful Cyanobacterial Outbreak Upstream in a Reservoir Cascade.

Understanding water quality events in a multiple-impoundment series is important but seldom presented comprehensively. Therefore, this study was conducted to systematically understand the explosion event of geosmin (GSM) in the North Han River (Chuncheon, Soyang, Euiam, and Cheongpyeong Reservoirs) and Han River (Paldang Reservoir), which consists of a cascade reservoir series, the largest drinking water source system in South Korea. We investigated the spatiotemporal relationship of harmful cyanobacterial blooms in the upstream reservoir (Euiam) with the water quality incident event caused by the GSM in the downstream reservoir (Paldang) from January to December 2011. The harmful cyanobacterial bloom occurred during August−September under a high water temperature (>20 °C) after a heavy-rainfall-based flood runoff event. The high chlorophyll-a (Chl-a) concentration in the upper Euiam Reservoir was prolonged for two months with a maximum concentration of 1150.5 mg m−3, in which the filamentous Dolichospermum circinale Kütz dominated the algal community at a rate of >99%. These parameters remarkably decreased (17.3 mg Chl-a m−3) in October 2011 when the water temperature decreased (5 °C) and soluble reactive phosphorus was depleted. However, high and unprecedented GSM concentrations, with a maximum value of 1640 ng L−1, were detected in the downstream reservoirs (Cheongpyeong and Paldang); the level was 11 times higher than the value (10 ng L−1) recommended by the World Health Organization. The concentrations of GSM gradually decreased and had an adverse effect on the drinking water quality until the end of December 2011. Our study indicated that the time lag between the summer−fall cyanobacterial outbreak in the upstream reservoir and winter GSM explosion events in the downstream reservoirs could be attributed to the transport and release of GSM through the effluent from hydroelectric power generation in this multiple-reservoir system. Therefore, we suggest that a structural understanding of the reservoir cascade be considered during water quality management of drinking water sources to avoid such incidents in the future.

Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method.

Many countries have attempted to monitor and predict harmful algal blooms to mitigate related problems and establish management practices. The current alert system-based sampling of cell density is used to intimate the bloom status and to inform rapid and adequate response from water-associated organizations. The objective of this study was to develop an early warning system for cyanobacterial blooms to allow for efficient decision making prior to the occurrence of algal blooms and to guide preemptive actions regarding management practices. In this study, two machine learning models: artificial neural network (ANN) and support vector machine (SVM), were constructed for the timely prediction of alert levels of algal bloom using eight years' worth of meteorological, hydrodynamic, and water quality data in a reservoir where harmful cyanobacterial blooms frequently occur during summer. However, the proportion imbalance on all alert level data as the output variable leads to biased training of the data-driven model and degradation of model prediction performance. Therefore, the synthetic data generated by an adaptive synthetic (ADASYN) sampling method were used to resolve the imbalance of minority class data in the original data and to improve the prediction performance of the models. The results showed that the overall prediction performance yielded by the caution level (L1) and warning level (L2) in the models constructed using a combination of original and synthetic data was higher than the models constructed using original data only. In particular, the optimal ANN and SVM constructed using a combination of original and synthetic data during both training (including validation) and test generated distinctively improved recall and precision values of L1, which is a very critical alert level as it indicates a transition status from normalcy to bloom formation. In addition, both optimal models constructed using synthetic-added data exhibited improvement in recall and precision by more than 33.7% while predicting L-1 and L-2 during the test. Therefore, the application of synthetic data can improve detection performance of machine learning models by solving the imbalance of observed data. Reliable prediction by the improved models can be used to aid the design of management practices to mitigate algal blooms within a reservoir.

Temporal triggers of N2O emissions during cyclical and seasonal variations of a full-scale sequencing batch reactor treating municipal wastewater.

To investigate the major triggers of nitrous oxide (N2O) production in a full-scale wastewater treatment plant, N2O emissions and wastewater characteristics (ammonia, nitrite, nitrate, total nitrogen, dissolved inorganic carbon, dissolved organic carbon, pH, temperature, dissolved oxygen and specific oxygen uptake rate), the results of variations in the cycling of a sequential batch reactor (SBR, where only full nitrification was performed), were monitored seasonally for 16 months. Major triggers of N2O production were investigated based on a seasonal measured database using a random forest (RF) model and sensitivity analysis, which was applied to identify important input variables. As the result of seasonal monitoring in the full-scale SBR, the N2O emission factor relative to daily total nitrogen removal ranged from 0.05 to 2.68%, corresponding to a range of N2O production rate from 0.02 to 0.70 kg-N/day. Results from the RF model and sensitivity analysis revealed that emissions during nitrification were directly or indirectly related to nitrite accumulation, temperature, ammonia loading rate and the specific oxygen uptake rate ratio between ammonia oxidizing bacteria and nitrite oxidizing bacteria (sOUR-ratio). However, changes in the microbial community did not significantly impact N2O emissions. Based on these results, the sOUR-ratio could represent the major trigger for N2O emission in a full-scale BNR system: a higher sOUR-ratio value with an average of 3.13 ± 0.23 was linked to a higher N2O production rate with an average value of 1.27 ± 0.12 kg-N/day (corresponding to 3.96 ± 1.20% of N2O emission factor relative to daily TN removal), while a lower sOUR-ratio with an average value of 2.39 ± 0.27 was correlated with a lower N2O production average rate of 0.17 ± 0.11 kg-N/day (corresponding to 0.74 ± 0.69% of N2O emission factor) (p-value = 0.00001, Mann-Whitney test).

Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models.

Hydrodynamic and water quality modeling have provided valuable simulation results that have enhanced the understanding of the spatial and temporal distribution of algal blooms. Typical model simulations are performed with point-based observational data that are used to configure initial and boundary conditions, and for parameter calibration. However, the application of such conventional modeling approaches is limited due to cost, labor, and time constraints that preclude the retrieval of high-resolution spatial data. Thus, the present study applied fine-resolution algal data to configure the initial conditions of a hydrodynamic and water quality model and compared the accuracy of short-term algal simulations with the results simulated using conventional point-based initial conditions. The environmental fluid dynamics code (EFDC) model was calibrated to simulate Chlorophyll-a (Chl-a) concentrations. Hyperspectral images were used to generate Chl-a maps based on a two-band ratio algorithm for configuring the initial condition of the EFDC model. The model simulation with hyperspectral-based initial conditions returned relatively accurate results for Chl-a, compared to the simulation based on point-based initial conditions. The simulations exhibited percent bias values of 9.93 and 14.23, respectively. Therefore, the results of this study demonstrate how hyperspectral-based initial conditions could improve the reliability of short-term algal bloom simulations in a hydrodynamic model.

Changes in adsorption mechanisms of radioactive barium, cobalt, and strontium ions using spent coffee waste biochars via alkaline chemical activation: Enrichment effects of O-containing functional groups.

The single adsorption of radioactive barium (Ba(II)), cobalt (Co(II)), and strontium (Sr(II)) ions using pristine (SCWB-P) and chemically activated spent coffee waste biochars with NaOH (SCWB-A) were thoroughly explored in order to provide deeper insights into the changes in their adsorption mechanisms through alkaline chemical activation. The greater removal efficiencies of SCWB-A (76.6-97.3%) than SCWB-P (45.6-75.2%) and the consistency between the adsorptive removal patterns (Ba(II) > Sr(II) > Co(II)) and oxygen bond dissociation enthalpies (BaO (562 kJ/mol) > SrO (426 kJ/mol) > CoO (397 kJ/mol)) of radioactive species supported the assumption that the adsorption removal of radioactive species with spent coffee waste biochars highly depended on the abundances of O-containing functional groups. The calculated R2 values of the pseudo-first-order (SCWB-P = 0.998-0.999; SCWB-A = 0.850-0.921) and pseudo-second-order kinetic models (SCWB-P = 0.988-0.998; SCWB-A = 0.935-0.966) are evident that the physisorption mainly controlled the adsorption of radioactive species toward SCWB-P and the chemisorption played a crucial role in their adsorptive removal with SCWB-A. From the calculated intra-particle diffusion, isotherm, thermodynamic parameters, it can be concluded that the intra-particle diffusion and monolayer adsorption primarily governed the adsorption of radioactive species using SCWB-P and SCWB-A, and their adsorption processes occurred spontaneously and endothermically. The dominant adsorption mechanism of spent coffee waste biochars was changed from physisorption (ΔH° of SCWB-P = 21.6-29.8 kJ/mol) to chemisorption (ΔH° of SCWB-A = 42.4-81.3 kJ/mol) through alkaline chemical activation. The distinctive M-OH peak in the O1s XPS spectra of SCWB-A directly corresponding to the decrease in the abundances of O-containing functional groups confirms again that the enrichment of O-containing functional groups markedly facilitated the adsorption removal of radioactive species by chemisorption occurred at the inner and outer surfaces of spent coffee waste biochars.

A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir.

Understanding the dynamics of harmful algal blooms is important to protect the aquatic ecosystem in regulated rivers and secure human health. In this study, artificial neural network (ANN) and support vector machine (SVM) models were used to predict algae alert levels for the early warning of blooms in a freshwater reservoir. Intensive water-quality, hydrodynamic, and meteorological data were used to train and validate both 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 models, respectively. The results indicated that the two models well reproduced the algae alert level based on the time-lag input and output data. In particular, the ANN model showed a better performance than the SVM model, displaying a higher performance value in both training and validation steps. Furthermore, a sampling frequency of 6- and 7-day were determined as efficient early-warning intervals for the freshwater reservoir. Therefore, this study presents an effective early-warning prediction method for algae alert level, which can improve the eutrophication management schemes for freshwater reservoirs.

Effects of physicochemical properties of biochar derived from spent coffee grounds and commercial activated carbon on adsorption behavior and mechanisms of strontium ions (Sr2+).

This study examined differences in the adsorption isotherms, kinetic equations, and thermodynamics of Sr2+ by biochar from spent coffee grounds (SCG) and powdered activated carbon (PAC). The specific surface area (957.6 m2/g) and pore volume (0.676 cm3/g) of PAC were much greater than those of SCG biochar (specific surface area = 11.0 m2/g, pore volume = 0.009 cm3/g). However, SCG biochar showed a higher maximum adsorption capacity of Sr2+ (Qmax = 51.81 mg/g) compared with PAC (Qmax = 32.79 mg/g) due to its abundance of O-containing functional groups. The negligible removal efficiencies of Sr2+ by SCG biochar and PAC under acidic conditions (pH = 1.0-3.0) are evidence that the electrostatic repulsion might hinder severely the adsorption of Sr2+ by the carbonaceous adsorbents. The higher R2 values of the pseudo-second-order model (R2 ≥ 0.999) compared with the pseudo-first-order model (R2 ≥ 0.815) suggest that chemisorption governed the removal of Sr2+ using SCG biochar and PAC. Furthermore, the better description of the adsorption behavior of Sr2+ by the Langmuir isotherm model (R2 ≥ 0.994) than the Freundlich isotherm model (R2 ≥ 0.982) supports the assumption that the monolayer adsorption played critical roles in the removal of Sr2+ using SCG biochar and PAC. The thermodynamic studies revealed that adsorption of Sr2+ onto SCG biochar and PAC was endothermic and happened spontaneously. Despite the significant inhibitory effects of DOM, SCG biochar exhibited the higher removal efficiencies of Sr2+ compared with PAC. Hence, SCG biochar could be considered as an alternative to PAC for the removal of Sr2+ from aqueous solutions.

Competitive adsorption of pharmaceuticals in lake water and wastewater effluent by pristine and NaOH-activated biochars from spent coffee wastes: Contribution of hydrophobic and π-π interactions.

This study investigated the competitive adsorption mechanisms of pharmaceuticals (i.e., naproxen, diclofenac, and ibuprofen) toward the pristine and NaOH-activated biochars from spent coffee wastes (SCW) in lake water and wastewater effluent. The kinetic and isotherm studies revealed that the improved physicochemical characteristics and physically homogenized surfaces of the pristine SCW biochar through the chemical activation with NaOH were beneficial to the adsorption of pharmaceuticals (competitive equilibrium adsorption capacity (Qe, exp): NaOH-activated SCW biochar (61.25-192.07 µmol/g) > pristine SCW biochar (14.81-20.65 µmol/g)). The adsorptive removal of naproxen (Qe, exp = 14.81-18.81 µmol/g), diclofenac (Qe, exp = 15.73-20.00 µmol/g), and ibuprofen (Qe, exp = 16.20-20.65 µmol/g) for the pristine SCW biochar showed linear correlations with their hydrophobicity (log D at pH 7.0: ibuprofen (1.71) > diclofenac (1.37) > naproxen (0.25)). However, their Qe, exp values for the NaOH-activated SCW biochar (naproxen (176.39-192.07 µmol/g) > diclofenac (78.44-98.74 µmol/g) > ibuprofen (61.25-80.02 µmol/g)) were inversely correlated to the order of their log D values. These results suggest that the reinforced aromatic structure of the NaOH-activated SCW biochar facilitated the π-π interaction. The calculated thermodynamic parameters demonstrated that the competitive adsorption of pharmaceuticals on the NaOH-activated SCW biochar compared to pristine SCW biochar occurred more spontaneously over the entire pH (5.0-11.0) and ionic strength (NaCl: 0-0.125 M) ranges. These observations imply that the NaOH-activated SCW biochar might be potentially applicable for the removal of pharmaceuticals in lake water and wastewater effluent.

Single and competitive adsorptions of micropollutants using pristine and alkali-modified biochars from spent coffee grounds.

This study investigated the single and competitive adsorption characteristics of micropollutants using the pristine and alkali-modified spent coffee grounds (SCG) biochars. The alkali modification substantially improved the physicochemical characteristics of the SCG biochars (specific surface area and pore volume), which may have led to differences in the adsorption behaviors of the micropollutants. The pseudo second order model (R2 ≥ 0.990) better described the single and competitive adsorption kinetics than the pseudo first order model (R2 ≥ 0.664). It is evident that chemisorption played a key role in the removal of the micropollutants by the pristine and alkali-modified SCG biochars. The single and competitive adsorptions of the micropollutants were highly dependent on the solution pH and ionic strength since the pore-filling effects, electrostatic and hydrophobic interactions governed their removal by the pristine and alkali-modified SCG biochars. The higher removal efficiencies of the micropollutants by the alkali-modified SCG biochars (≥ 44.5%) in the presence of dissolved organic matter compared to the pristine SCG biochars (≤ 18.5%) support the assumption that alkali modification could markedly reinforce the surface structural properties of the SCG biochars related to the adsorption capacities.

Jejunogastric intussusception prone to misdiagnosis as gastric cancer.

The authors report a case of a 78-year-old female with a history of gastric surgery 35 years ago. She was initially misdiagnosed as gastric cancer bleeding and underwent an emergency laparotomy under the diagnosis of jejunogastric intussusception (JGI), 23 hours after the onset of symptoms. We also reviewed 116 JGI case reports and analyzed clinical features and outcomes. Compared to the past, diagnosis of JGI is easier with diagnostic examinations such as an endoscopy, computed tomography, and the upper gastrointestinal series. And a good prognosis can be expected with proper fluid resuscitation and surgical reduction, even if the symptoms persist more than 48 hours.

Assessment of a green roof practice using the coupled SWMM and HYDRUS models.

Green roof can mitigate urban stormwater and improve environmental, economic, and social conditions. Various modeling approaches have been effectively employed to implement a green roof, but previous models employed simplifications to simulate water movement in green roof systems. To address this issue, we developed a new modeling tool (SWMM-H) by coupling the stormwater management and HYDRUS-1D models to improve simulations of hydrological processes. We selected green roof systems to evaluate the coupled model. Rainfall-runoff experiments were conducted for a pilot-scale green roof and urban subbasin. Soil moisture in the green roof and runoff volume in the subbasin were simulated more accurately by using SWMM-H instead of SWMM. The scenario analysis showed that SWMM-H selected sandy loam for controlling runoff whereas SWMM recommended sand. In conclusion, SWMM-H could be a useful tool for accurately understanding hydrological processes in green roofs.

Factors Associated with Operation Time of Laparoscopic Gastric Wedge Resection for Gastric Subepithelial Tumors.

Purpose: Gastric subepithelial tumor (GST) is a disease entity that includes all gastric subepithelial lesions. The oncologically safe surgical technique is complete resection with adequate resection margins. Most of the studies about laparoscopic gastric wedge rsection (LGWR) in GST focus on oncologic curability or surgical effectiveness. However, studies on the factors associated with the operation time are rare. Therefore, this study was conducted to analyze and compare the factors associated with the operation time of LGWR. Methods: From 2010 to 2019, 145 consecutive patients undergoing LGWR were reviewed retrospectively. Clinical characteristics of GST and operation time were analyzed and compared. Results: A total of 145 patients was enrolled and reviewed. There were 59 males (40.7%) and 86 females (59.3%) with a mean age of 53.6 years and mean body mass index (BMI) of 23.9 kg/m2. Mean tumor size was 2.9 cm and mean operation time was 66.0 minutes. In statistically, the mean operation time showed significant association with tumor size, BMI, longitudinal tumor location and tumor location between lesser and greater curvature. In multivariate analysis, tumor size, BMI and longitudinal classification of tumor location are statistically significant. Conclusion: A shorter operation time is expected when there is a small tumor, low BMI and mid portion of the stomach GST. Preoperative evaluation for tumor size and body weight is important. In patients with large GST, obesity and both end stomach GST, we think that pre-operative preparation for long operation time should be considered.

Clinicopathologic features of remnant gastric cancer after curative distal gastrectomy according to previous reconstruction method: a retrospective cohort study.

BACKGROUND: Survival rate of patients treated for gastric cancer has increased due to early detection and improvements of surgical technique and chemotherapy. Increase in survival rate has led to an increase in the risk for remnant gastric cancer (RGC). The purpose of this study was to investigate clinicopathologic features of RGC according to previous reconstruction method and factors affecting the interval from previous curative distal gastrectomy for gastric cancer to RGC occurrence. METHODS: Medical records of patients diagnosed with RGC at Yeungnam University Medical Center from January 2000 to December 2017 who had a history of distal gastrectomy with D2 LN dissection due to gastric cancer were reviewed retrospectively. RESULTS: Forty-eight patients were enrolled in this study. The mean interval of 48 RGC patients was 105.6 months (8.8 years). RGC after Billroth II reconstruction recurred more often at anastomosis site than RGC after Billroth I reconstruction (p = 0.001). The mean interval of RGC after Billroth I reconstruction was 67 months, shorter than 119 months of RGC after Billroth II reconstruction (p = 0.003). On the contrary, interval showed no difference according to stage of previous gastric cancer, remnant gastric cancer, or sex (p = 0.810, 0.145, and 0.372, respectively). CONCLUSIONS: RGC after Billroth I reconstruction tends to arise earlier at non-anastomosis site than RGC after Billroth II. Therefore, we should examine non-anastomosis site carefully from the beginning of surveillance after gastric cancer surgery with Billroth I reconstruction for better outcome.

Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea.

Microbial contamination in beach water poses a public health threat due to waterborne diseases. To reduce the risk of exposure to fecal contamination, informing beachgoers in advance about the microbial water quality is important. Currently, determining the level of fecal contamination takes 24 h. The objective of this study is to predict the current level of fecal contamination (enterococcus [ENT] and ) using readily available environmental variables. Artificial neural network (ANN) and support vector regression (SVR) models were constructed using data from the Haeundae and Gwangalli Beaches in Busan City. The input variables included the tidal level, air and water temperature, solar radiation, wind direction and velocity, precipitation, discharge from the wastewater treatment plant, and suspended solid concentration in beach water. The dependence of fecal contamination on the input variables was statistically evaluated; precipitation, discharge from the wastewater treatment plant, and wind direction at the two beaches were positively correlated to the changes in the two bacterial concentrations ( < 0.01), whereas solar radiation was negatively correlated ( < 0.01). The performance of the ANN model for predicting ENT and at Gwangalli Beach was significantly higher than that of the SVR model with the training dataset ( < 0.05). Based on the comparison of residual values between the predicted and observed fecal indicator bacteria concentrations in two models, the ANN demonstrated better performance than SVR. This study suggests an effective prediction method to determine whether a beach is safe for recreational use.