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In this study, five different dissolved organic matter (DOM) fractions, defined based on a size exclusion chromatography with simultaneous detection of organic carbon (OCD) and ultraviolet (UVD), were quantitatively tracked with a treatment train of coagulation/flocculation–sand filtration–ozonation–granular activated carbon (GAC) filtration in a full-scale advanced drinking water treatment plant (DWTP). Five DOM samples including raw water were taken after each treatment process in the DWTP every month over the period of three years. A higher abundance of biopolymer (BP) fraction was found in the raw water during spring and winter than in the other seasons, suggesting an influence of algal bloom and/or meltwater on DOM composition. The greater extent of removal was observed upon the coagulation/flocculation for high-molecular-weight fractions including BP and humic substances (HS) and aromatic moieties, while lower sized fractions were preferentially removed by the GAC filtration. Ozone treatment produced the fraction of low-molecular-weight neutrals probably resulting from the breakdown of double-bonded carbon structures by ozone oxidation. Coagulation/flocculation was the only process that revealed significant effects of influent DOM composition on the treatment efficiency, as revealed by a high correlation between the DOM removal rate and the relative abundance of HS for the raw water. Our study demonstrated that SEC-OCD-UVD was successful in monitoring size-based DOM composition for the advanced DWTP, providing an insight into optimizing the treatment options and the operational conditions for the removal of particular fractions within the bulk DOM.

Exposure to air pollutants, such as PM2.5 and ozone, has a serious adverse effect on health, with more than 4 million deaths, including early deaths. Air pollution in ports is caused by exhaust gases from various elements, including ships, and to reduce this, the International Maritime Organization (IMO) is also making efforts to reduce air pollution by regulating the sulfur content of fuel used by ships. Nevertheless, there is a lack of measures to identify and minimize the effects of air pollution. The Community Multiscale Air Quality (CMAQ) model is the most used to understand the effects of air pollution. In this paper, we propose a hybrid model combining the CMAQ model and RNN-LSTM, an artificial neural network model. Since the RNN-LSTM model has very good predictive performance, combining these two models can improve the spatial distribution prediction performance of a large area at a relatively low cost. In fact, as a result of prediction using the hybrid model, it was found that IOA improved by 0.235~0.317 and RMSE decreased by 4.82~8.50 μg/m3 compared to the case of using only CMAQ. This means that when PM2.5 is predicted using the hybrid model, the accuracy of the spatial distribution of PM2.5 can be improved. In the future, if real-time prediction is performed using the hybrid model, the accuracy of the calculation of exposure to air pollutants can be increased, which can help evaluate the impact on health. Ultimately, it is expected to help reduce the damage caused by air pollution through accurate predictions of air pollution.

Understanding the roles of inland reservoirs becomes increasingly important with respect to global carbon cycling as well as water resource management due to the unprecedented demand for construction in recent decades. In this study, the dissolved organic matter (DOM) quantity and quality in a newly constructed dam reservoir and its tributaries were monitored monthly during the initial impounding period (July to November 2014) using a size exclusion chromatography (SEC) with online organic carbon detector (OCD). The highest values were observed in the month of August with the highest precipitation for the bulk dissolved organic carbon (DOC), specific UV absorbance (SUVA), and most of the assigned size fractions (except for biopolymers) in the tributaries, indicating that allochthonous sources of DOM were dominant in the feeding stream waters of the reservoir. The bulk DOC and high molecular weight humic substance fraction (∼1 kDa) were generally co-varied with the monthly precipitation in the tributaries, while building blocks (350–500 Da), and low molecular weight (LMW) acids and neutrals showed different trends. In a dam site, the smaller molecular fractions became more abundant during the dry season (September to November), presumably due to the in-reservoir processes such as photo- and bio-degradation. Our results also revealed that storms mobilized a large amount of highly aromatic soil-derived DOM to the reservoir. A depth profile at the dam site showed the water is well mixed up to a depth of ∼20 m. The SEC-OCD data coupled with non-metric multidimensional scaling provided a clear visualization of the spatiotemporal variations in DOM composition, which shed new light on the DOM composition formed in a newly constructed dam reservoir and also on the strategies for future water treatment options.

The unpleasant odor of drinking water is one of the major problems in many water utilities in the world. Actinomycetes have long been associated with odorous compounds. Considering the paucity of research on Actinomycetes producing odorous compounds in South Korea, presence of Actinomycetes, their molecular characteristics and ability to produce odorous compounds were investigated in this study. Findings confirmed the presence of Actinomycetes in surface soil, sediment, and water samples from four sites: two artificial lakes [Paldang and Cheongpyeong (CP)], and two streams [Gyeongan (GA) and Yangpyeong]. Surface soil and sediment from CP area had the greatest concentration of Actinomycetes (8.2 × 107 and 6.8 × 106 colony forming units (CFUs)/gram, dry weight, respectively). When water samples are considered, samples from GA had the highest concentration (1.9 × 102 CFU/mL). 16S rRNA sequencing and molecular phylogenetic analysis showed that Streptomyces was the dominant genus (64.1%). In addition, the isolated Actinomycetes synthesized 5.4 ng/L geosmin as demonstrated by thermal desorption unit-gas chromatograph/mass spectrometry analysis.

Due to the tragic disaster that happened in Japan and crippled the Fukushima nuclear power plant, serious concerns have been raised regarding the contamination of drinking water as a result of the radioactive materials that were released. Even though the quantities of radioactive material in rain were relatively low, people were concerned about the drinking water. Therefore, there is a need to know the removal efficiency of the unit process of water treatment and to prepare a safety plan to protect the public's health from radioactive materials. In this study, the laboratory scale removal rates were estimated for the coagulation/flocculation, adsorption, and ion exchange processes. The reference standard materials which are stable elements, Cesium-133 (Cs-133) and Iodine-127 (I-127), were used for the typical and advanced water treatment processes at the laboratory scale. For the coagulation/flocculation process, three major coagulants were assessed for this process. However, the removal rates of this process were low. For the adsorption process, powdered activated carbon and zeolites were investigated. The powdered activated carbon showed insignificant removal rates for both reference materials. However, synthetic zeolite was an effective process for Cs-133, and the ion exchange method showed high removal rates for both Cs-133 and I-127.

Identification of cancer prognostic genes is important in that it can lead to accurate outcome prediction and better therapeutic trials for cancer patients. Many computational approaches have been proposed to achieve this goal; however, there is room for improvement. Recent developments in deep learning techniques can aid in the identification of better prognostic genes and more accurate outcome prediction, but one of the main problems in the adoption of deep learning for this purpose is that data from cancer patients have too many dimensions, while the number of samples is relatively small. In this study, we propose a novel network-based deep learning method to identify prognostic gene signatures via distributed gene representations generated by G2Vec, which is a modified Word2Vec model originally used for natural language processing. We applied the proposed method to five cancer types including liver cancer and showed that G2Vec outperformed extant feature selection methods, especially for small number of samples. Moreover, biomarkers identified by G2Vec was useful to find significant prognostic gene modules associated with hepatocellular carcinoma.

A new concept for copper (Cu) metal leaching by the simultaneous use of cupric ions (Cu2+) and oxygen (O2) was proposed to improve Cu metal leaching in sulfuric acid. According to this concept, Cu2+ oxidizes Cu metal into cuprous ion (Cu+), and O2 oxidizes Cu+ into Cu2+. The improvement in Cu leaching efficiency from Cu metal was investigated experimentally in the sulfuric acid solution using Cu2+ and O2 simultaneously. Furthermore, the result was compared with that for the sulfuric acid solution containing neither Cu2+ nor O2 and with the sulfuric acid solution without Cu2+ and O2. When both Cu2+ and O2 were used in the leaching solution, the leaching rate of Cu from Cu metal powder was higher than at other leaching conditions, and the leaching efficiency of Cu increased to more than 99.9% in 1 mol/L sulfuric acid solution at 400 rpm and 50 °C with ≤75 µm Cu metal powder, 1% pulp density, 10,000 mg/L initial Cu2+ concentration, and 100 cc/min O2 introduction. These results indicated that the leaching of Cu from Cu metal could be accelerated by adding Cu2+ and O2 in the sulfuric acid solution.

Mechanochromic sensors are promising for structural health monitoring as they can visually monitor the deformation caused by discoloration. Most studies have focused on the large deformation problems over 100% strain; however, it is necessary to investigate the discoloration characteristics in a small deformation range to apply it to engineering structures, such as reinforced concrete. In this study, a photonic crystal-based discoloration sensor was investigated to determine the discoloration characteristics of the red, green, and blue (RGB) as well as hue, saturation, and value (HSV) color spaces according to displacement levels. B and S showed the highest sensitivity and linear discoloration at displacements < 1 mm, whereas R and H showed significant discoloration characteristics at displacements > 1 mm. The Vision Transformer model based on RGB and HSV channels was linearly predictable up to 4 mm displacement with an accuracy of R2 0.89, but errors were found at the initial displacement within 2 mm.

Background: Liposomal irinotecan (nal-IRI) plus 5-fluorouracil and leucovorin (5-FU/LV) was effective and well-tolerated in patients with metastatic pancreatic adenocarcinoma (mPAC) that progressed on gemcitabine-based therapy in the global NAPOLI-1 trial. Real-world data may further clarify the outcomes and safety profile of nal-IRI + 5-FU/LV in clinical practice. Methods: This retrospective analysis included patients with mPAC who received nal-IRI + 5-FU/LV following gemcitabine-based therapy under a Managed Access Program in Korea. Results: From January 2017 to April 2018, 86 patients across 10 institutions received nal-IRI + 5-FU/LV (median age, 61 years; 60% male; ECOG performance status, 0–1). A total of 35 (41%) and 51 (59%) patients had received less than two and two or more lines of chemotherapy before inclusion, respectively. At a median follow up of 6.4 months, median overall survival (OS) was 9.4 months (95% confidence interval [CI] 7.4–11.4) and median progression-free survival (PFS) was 3.5 months (95% CI 1.3–5.7). Six-month OS and PFS rates were 65.1% and 37.5%, respectively. Objective response and disease control rates were 10% and 55%, respectively. Most common grade 3–4 toxicities were neutropenia (37.2%), nausea (10.5%), vomiting (9.3%), anorexia (8.1%) and diarrhoea (4.7%). Conclusion: Real-life data for Korean patients indicate that, consistent with NAPOLI-1, nal-IRI + 5-FU/LV is effective and well-tolerated in patients with mPAC that progressed on gemcitabine-based therapy.

Cobalt and nickel have become important strategic resources because they are widely used for renewable energy technologies and rechargeable battery production. Cementation, an electrochemical deposition of noble metal ions using a less noble metal as an electron donor, is an important option to recover Co and Ni from dilute aqueous solutions of these metal ions. In this study, cementation experiments for recovering Co2+ and Ni2+ from sulfate and chloride solutions (pH = 4) were conducted at 298 K using Al powder as electron donor, and the effects of additives such as activated carbon (AC), TiO2, and SiO2 powders on the cementation efficiency were investigated. Without additives, cementation efficiencies of Co2+ and Ni2+ were almost zero in both sulfate and chloride solutions, mainly because of the presence of an aluminum oxide layer (Al2O3) on an Al surface, which inhibits electron transfer from Al to the metal ions. Addition of nonconductor (SiO2) did not affect the cementation efficiencies of Co2+ and Ni2+ using Al as electron donor, while addition of (semi)conductors such as AC or TiO2 enhanced the cementation efficiencies significantly. The results of surface analysis (Auger electron spectroscopy) for the cementation products when using TiO2/Al mixture showed that Co and Ni were deposited on TiO2 particles attached on the Al surface. This result suggests that conductors such as TiO2 act as an electron pathway from Al to Co2+ and Ni2+, even when an Al oxide layer covered on an Al surface.