Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting.

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

La-MOFs in situ loaded Al2O3 particles for effective removal of phosphate in water: characterization, application potential analysis, and mechanism.

Excessive phosphorus in water would cause eutrophication and deterioration of the ecological environment. Herein, the La-MOFs/Al2O3 composite was successfully prepared by the in situ hydrothermal synthesis method for granulation, which was conducive to exerting the phosphate adsorption capacity and facilitating practical application. The materials were characterized by SEM, EDX, XRD, BET, FTIR, and Zeta. In addition, the adsorption performance of La-MOFs/Al2O3 was evaluated through adsorption kinetics and isotherms, showing that the Langmuir adsorption capacity was 16.34 mgP·g-1 (25 °C) and increased with the water temperature. Moreover, the batch influence experiments of intimal pH, adsorbent dosage, coexisting ions, and stability tests were performed to analyze the potential for practical applications and verified through the natural micro-polluted water samples from Houxi River and Bailu Lake (China). The results indicated that the La-MOFs/Al2O3 was suited to a wide pH range of 4 to 10 and the phosphate removal efficiency remained above 70% after continuous use for four times, exhibiting excellent stability. It also had excellent selectivity in the presence of SO42-, Cl-, NO3-, and HCO3-, only decreased to 70.24% at high HCO3- ion concentration of 60 mg/L, respectively. And the La-MOFs/Al2O3 had excellent adsorption of total phosphorus, phosphate, and organic phosphorus in the actual river and lake water and completely removed dissolved phosphorus. Finally, a phosphate adsorption mechanism model involved in electrostatic interaction and ligand exchange was proposed. Therefore, La-MOFs/Al2O3 could be considered to be an excellent phosphorus adsorbent for application in the actual water environmental remediation.

Nano-Modified Polymer Gels as Temperature- and Salt-Resistant Fluid-Loss Additive for Water-Based Drilling Fluids.

With the continuous exploration and development of oil and gas resources to deep formations, the key treatment agents of water-based drilling fluids face severe challenges from high temperatures and salinity, and the development of high temperature and salt resistance filtration reducers has always been the focus of research in the field of oilfield chemistry. In this study, a nano-silica-modified co-polymer (NS-ANAD) gel was synthesized by using acrylamide, isopropylacrylamide, 2-acrylamide-2-methyl propane sulfonic acid, diallyl dimethyl ammonium chloride, and double-bond-modified inorganic silica particles (KH570-SiO2) through free radical co-polymerization. The introduction of nanotechnology enhances the polymer's resistance to high temperature degradation, making it useful as a high-temperature-resistant fluid loss reducer. Moreover, the anions (sulfonates) and cations (quaternary ammonium) enhance the extension of the polymer and the adsorption on the surface of bentonite particles in a saline environment, which in turn improves the salt resistance of the polymer. The drilling fluids containing 2.0 wt% NS-ANAD co-polymer gels still show excellent rheological and filtration performance, even after aging in high temperature (200 °C) and high salinity (saturated salt) environments, showing great potential for application in deep and ultra-deep drilling engineering.

Temperature- and Salt-Resistant Micro-Crosslinked Polyampholyte Gel as Fluid-Loss Additive for Water-Based Drilling Fluids.

With increasing global energy consumption, oil/gas drilling has gradually expanded from conventional shallow reservoirs to deep and ultra-deep reservoirs. However, the harsh geological features including high temperature and high salinity in ultra-deep reservoirs have become a critical challenge faced by water-based drilling fluids (WDFs), which seriously deteriorate the rheology and fluid loss properties, causing drilling accidents, such as wellbore instability and formation collapse. In this study, a novel temperature- and salt-resistant micro-crosslinked polyampholyte gel was synthesized using N,N-dimethylacrylamide, diallyldimethyl ammonium chloride, 2-acrylamido-2-methylpropanesulfonic acid, maleic anhydride and chemical crosslinking agent triallylamine through free radical copolymerization. Due to the synergistic effect of covalent micro-crosslinking and the reverse polyelectrolyte effect of amphoteric polymers, the copolymer-based drilling fluids exhibit outstanding rheological and filtration properties even after aging at high temperatures (up to 200 °C) and high salinity (saturated salt) environments. In addition, the zeta potential and particle size distribution of copolymer-based drilling fluids further confirmed that the copolymer can greatly improve the stability of the base fluid suspension, which is important for reducing the fluid-loss volume of WDFs. Therefore, this work will point out a new direction for the development of temperature- and salt-resistant drilling fluid treatment agents.

Oxygen vacancies enhancing performance of Mg-Co-Ce oxide composite for the selective catalytic ozonation of ammonia in water.

Catalytic ozonation based on heterogeneous metal oxides is a promising approach to removing ammonia as gaseous nitrogen from water. Herein, MgO/Co3O4/CeO2 was prepared for catalytic ozonation of ammonia in an aqueous solution. The influence of various reaction conditions was systematically investigated and optimized, in which the reaction kinetics was also analyzed. After doping Ce, the catalyst with Mg-Co-Ce molar ratio of 4:1:1 and calcined at 700 °C for 3 h, has abundant surface oxygen vacancies and exhibited excellent performance for the selective catalytic oxidation of ammonia to gaseous nitrogen by ozone. It was found that the catalytic activity of catalysts was positively related to oxygen vacancies concentration on the composites surface, which might play a vital role in selective catalytic ozonation. Under the optimal conditions, the ammonia removal rate in MgO/Co3O4/CeO2 catalytic system was 0.03328 min-1 (R2 = 0.99942), about 2.1 times greater than that of MgO/Co3O4 (0.01597 min-1, R2 = 0.99813), and the selectivity was further enhanced from 73.57% to 86.94%. Moreover, the evolution of nitrogen and chlorine species was determined to discuss the mechanism of selective oxidation of ammonia in the low chlorine-containing solution. This study might promote the understanding of catalytic ozonation of ammonia to gaseous nitrogen selectively.

Dynamic Network Characteristics of Power-electronics-based Power Systems.

Power flow studies in traditional power systems aim to uncover the stationary relationship between voltage amplitude and phase and active and reactive powers; they are important for both stationary and dynamic power system analysis. With the increasing penetration of large-scale power electronics devices including renewable generations interfaced with converters, the power systems become gradually power-electronics-dominant and correspondingly their dynamical behavior changes substantially. Due to the fast dynamics of converters, such as AC current controller, the quasi-stationary state approximation, which has been widely used in power systems, is no longer appropriate and should be reexamined. In this paper, for a better description of network characteristics, we develop a novel concept of dynamic power flow and uncover an explicit dynamic relation between the instantaneous powers and the voltage vectors. This mathematical relation has been well verified by simulations on transient analysis of a small power-electronics-based power system, and a small-signal frequency-domain stability analysis of a voltage source converter connected to an infinitely strong bus. These results demonstrate the applicability of the proposed method and shed an improved light on our understanding of power-electronics-dominant power systems, whose dynamical nature remains obscure.

Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model.

The real-time estimation of ambient particulate matter with diameter no greater than 2.5 µm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China.

Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI).

Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 µm: PM2.5) is currently quite limited in China. By introducing NO2 and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO2 and EVI, where cross-validation R² increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO2 and EVI in GWR models could more effectively estimate PM2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China.