Graph-based deep learning for predictions on changes in microbiomes and biogas production in anaerobic digestion systems.

Anaerobic digestion (AD), which relies on a complex microbial consortium for efficient biogas generation, is a promising avenue for renewable energy production and organic waste treatment. However, understanding and optimising AD processes are challenging because of the intricate interactions within microbial communities and the impact of volatile fatty acids (VFAs) on biogas production. To address these challenges, this study proposes the application of graph convolutional networks (GCNs) to comprehensively model AD processes.

Early Left Ventricular Unloading after Venoarterial Extracorporeal Membrane Oxygenation: One-Year Outcomes of the EARLY-UNLOAD Randomized Clinical Trial.

Background: The long-term effects of early left ventricular unloading after venoarterial extracorporeal membrane oxygenation (VA-ECMO) remain unclear.

Methods: The EARLY-UNLOAD trial was a single-center, investigator-initiated, open-label, randomized clinical trial involving 116 patients with cardiogenic shock (CS) undergoing VA-ECMO. The patients were randomly assigned to undergo either early routine left ventricular unloading by transseptal left atrial cannulation within 12 hours after randomization or the conventional approach, which permitted rescue transseptal cannulation in case of an increased left ventricular afterload.

Architecting Ultra-Robust Zr(IV) Metal-Organic Framework for Energy-Efficient Desiccant Air Conditioning.

Air-conditioning systems, composed mainly of humidity control and heat reallocation units, play a pivotal role in upholding superior air quality and human well-being across diverse environments ranging from international space stations and pharmacies to granaries and cultural relic preservation sites, and to commercial and residential buildings. The adoption of sorbent water as the working pair and low-grade renewable or waste heat in adsorption-driven air-conditioning presents a state-of-the-art solution, notably for its energy efficiency and eco-friendliness vis-à-vis conventional electricity-driven vapor compression cycles. Here, we introduce a rational π-extension strategy to engineer an ultrarobust and highly porous zirconium metal-organic framework (Zr-MOF).

Revolutionizing cesium monitoring in seawater through electrochemical voltammetry and machine learning.

Monitoring radioactive cesium ions (Cs) in seawater is vital for environmental safety but remains challenging due to limitations in the accessibility, stability, and selectivity of traditional methods. This study presents an innovative approach that combines electrochemical voltammetry using nickel hexacyanoferrate (NiHCF) thin-film electrode with machine learning (ML) to enable accurate and portable detection of Cs. Optimizing the fabrication of NiHCF thin-film electrodes enabled the development of a robust sensor that generates cyclic voltammograms (CVs) sensitive to Cs⁺ concentrations as low as 1 ppb in synthetic seawater and 10 ppb in real seawater, with subtle changes in CV patterns caused by trace Cs⁺ effectively identified and analyzed using ML.

Differential statin intensity and outcomes in patients following myocardial infarction with very low low-density lipoprotein cholesterol.

Background: Despite increasing evidence on the benefits of statin therapy for acute myocardial infarction (AMI), differential outcomes in accordance with statin intensity have not been evaluated in patients with AMI and low-density lipoprotein cholesterol (LDL-C) levels < 55 mg/dL. Therefore, this study aimed to compare the clinical outcomes of high- and moderate-intensity statin therapy in this population.

Methods: A total of 752 participants with AMI and LDL-C levels < 55 mg/dL from a Korean nationwide multicenter observational cohort (2016-2020) were included and categorized into two groups: high-intensity statin group (n = 384) and moderate-intensity statin group (n = 368).

The Effects of Nicotine on Re-endothelialization, Inflammation, and Neoatherosclerosis After Drug-Eluting Stent Implantation in a Porcine Model.

Background And Objectives: Cigarette smoking is a major risk factor for atherosclerosis. Nicotine, a crucial constituent of tobacco, contributes to atherosclerosis development and progression. However, evidence of the association between nicotine and neointima formation is limited.

Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity in multi-layered aquifers spanning the entire Mekong Delta (MD) region using machine learning (ML) models based on an in situ dataset and using three indicators (Cl, pH, and HCO). We applied nine different decision tree-based models and evaluated their prediction performances.

Modeling freshwater plankton community dynamics with static and dynamic interactions using graph convolution embedded long short-term memory.

Given the frequent association between freshwater plankton and water quality degradation, several predictive models have been devised to understand and estimate their dynamics. However, the significance of biotic and abiotic interactions has been overlooked. In this study, we aimed to address the importance of the interaction term in predicting plankton community dynamics by applying graph convolution embedded long short-term memory networks (GC-LSTM) models, which can incorporate interaction terms as graph signals.

Optimal timing of revascularization for patients with non-ST segment elevation myocardial infarction and severe left ventricular dysfunction.

Optimal timing of revascularization for patients who presented with non-ST segment elevation myocardial infarction (NSTEMI) and severe left ventricular (LV) dysfunction is unclear. A total of 386 NSTEMI patients with severe LV dysfunction from the nationwide, multicenter, and prospective Korea Acute Myocardial Infarction Registry V (KAMIR-V) were enrolled. Severe LV dysfunction was defined as LV ejection fraction ≤ 35%.

Enhancing Familial Hypercholesterolemia Detection in South Korea: A Targeted Screening Approach Integrating National Program and Genetic Cascade Screening.

Background And Objectives: Familial hypercholesterolemia (FH) increases the risk of premature cardiovascular disease through disrupted low-density lipoprotein cholesterol (LDL-C) metabolism. Although FH is a severe condition, it remains widely underdiagnosed, which can be attributed to barriers in genetic testing and a lack of awareness. This study aims to propose and evaluate a targeted screening program for FH in South Korea by integrating the General Health Screening Program (GHSP) with cascade genetic screening.

Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data.

The distribution coefficient (K) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, K depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict K values for radionuclide adsorption in soils surrounding nuclear facilities in Republic of Korea.

Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model.

Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or battery electrode deionization (BDI). However, this simple conversion from effluent conductivity to ion concentration is often incorrect, thereby necessitating a more congruent method for performing real-time measurements of effluent ion concentrations. In this study, a random forest (RF)-based artificial intelligence (AI) model was developed to address this shortcoming.

Autonomous real-time control for membrane capacitive deionization.

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy.

Prediction of hydroxyl radical exposure during ozonation using different machine learning methods with ozone decay kinetic parameters.

The abatement of micropollutants by ozonation can be accurately calculated by measuring the exposures of molecular ozone (O) and hydroxyl radical (OH) (i.e., ∫[O]dt and ∫[OH]dt).

Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data.

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations.