Additive Manufacturing Of (MgCoNiCuZn)O High-entropy Oxide Using A 3D Extrusion Technique And Oxide Precursors

This report presents an additive manufacturing approach, for the first time, to producing high-entropy oxides (HEOs) using a 3D extrusion-based technique with oxide precursors. The precursors were prepared by a wet chemical method from sulfates. Additives were utilized to optimize the rheological properties of the printing inks with these precursors, and the properties of the printed HEOs were improved by increasing the solid content of the inks. When ink with a solid content of 78 wt% was used for printing, the resulting HEO exhibited a relative density of 92% and a high dielectric constant after undergoing pressure less sintering at 800 °C. Compared to traditional methods of manufacturing HEOs, the 3D extrusion technique is a very promising method for producing HEOs with complex geometries

Relationship between serum irisin level, all-cause mortality, and cardiovascular mortality in peritoneal dialysis patients

Introduction: This study aimed to investigate the prospective role of serum irisin-a novel adipo-myokine-in all-cause mortality and cardiovascular (CV) mortality in patients on peritoneal dialysis (PD). Methods: A prospectively observational study was conducted with 154 PD patients. Baseline clinical data were collected from the medical records. Serum irisin concentrations were determined using enzyme-linked immunosorbent assay. Patients were divided into the high irisin group (serum irisin ≥ 113.5ng/mL) and the low irisin group (serum irisin < 113.5ng/mL) based on the median value of serum irisin. A Body Composition Monitor was used to monitor body composition. Cox regression analysis was utilized to find the independent risk factors of all-cause and CV mortality in PD patients. Results: The median serum irisin concentration was 113.5 ng/mL (interquartile range, 106.2–119.8 ng/mL). Patients in the high irisin group had significantly higher muscle mass and carbon dioxide combining power (CO2CP) than those in the low irisin group (p < 0.05). Serum irisin was positively correlated with pulse pressure, CO2CP, and muscle mass, while negatively correlated with body fat percentage (p < 0.05). During a median of follow-up for 60.0 months, there were 55 all-cause deaths and 26 CV deaths. Patients in high irisin group demonstrated a higher CV survival rate than those in low irisin group (p = 0.016). Multivariate Cox regression analysis showed that high irisin level [hazard ratio (HR), 0.341; 95% confidence interval (CI), 0.135–0.858; p = 0.022], age, and diabetic mellitus were independently associated with CV mortality in PD patients. However, serum irisin level failed to demonstrate a statistically significant relationship with all-cause mortality. Conclusion: Low serum irisin levels at baseline were independently predictive of CV mortality but not all-cause mortality in PD patients. Therefore, serum irisin could be a potential target for monitoring CV outcomes in PD patients

Boosting Oxygen and Peroxide Reduction Reactions on PdCu Intermetallic Cubes

Palladium‐based nanocatalysts have the potential to replace platinum‐based catalysts for fuel‐cell reactions in alkaline electrolytes, especially PdCu intermetallic nanoparticles with high electrochemical activity and stability. However, unlike the synthetic methods for obtaining the nanoparticles, the effect of PdCu shape on the performance is relatively less well studied. Here, we demonstrate the facet dependence of PdCu intermetallics on the oxygen reduction reaction (ORR) and peroxide reduction, and reveal that the {100} dominant PdCu cubes have a much higher ORR mass activity and specific activity than spheres at 0.9 V vs. RHE, which is four and five times that of commercial Pd/C and Pt/C catalysts, respectively, and show only a 31.7 % decay after 30 000 cycles in the stability test. Moreover, cubic PdCu nanoparticles show higher peroxide electroreduction activity than Pd cubes and PdCu spheres. Density functional theory (DFT) calculation reveals that the huge difference originates from the reduction in oxygen adsorption energy and energy barrier of peroxide decomposition on the ordered {100} PdCu surface. Given the relationship between the shape and electrochemical performance, this study will contribute to further research on electrocatalytic improvements of catalysts in alkaline environments.Shape the future: PdCu intermetallic cubes and spheres are synthesized to investigate the facet dependence on the oxygen reduction reaction and peroxide reduction. The cubes show large improvements in mass activity towards both reactions, compared with the spheres. DFT calculation uncovers that the dominant {100} faces of the cubes offer more appropriate oxygen adsorption and are thermodynamically favorable for peroxide reduction compared to the surface of spheres.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/1/celc202000381.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/2/celc202000381_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/3/celc202000381-sup-0001-misc_information.pd

Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric survey facility under construction jointly by the University of Science and Technology of China and Purple Mountain Observatory. It is equipped with a primary mirror of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73 Gpix on the main focus plane to achieve high-quality imaging over a field of view of 6.5 square degrees. The installation of WFST in the Lenghu observing site is planned to happen in the summer of 2023, and the operation is scheduled to commence within three months afterward. WFST will scan the northern sky in four optical bands (u, g, r, and i) at cadences from hourly/daily to semi-weekly in the deep high-cadence survey (DHS) and the wide field survey (WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and 22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during a photometric night, respectively, enabling us to search tremendous amount of transients in the low-z universe and systematically investigate the variability of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23 and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate explorations of energetic transients in demand for high sensitivity, including the electromagnetic counterparts of gravitational-wave events detected by the second/third-generation GW detectors, supernovae within a few hours of their explosions, tidal disruption events and luminous fast optical transients even beyond a redshift of 1. Meanwhile, the final 6-year co-added images, anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS, will be of significant value to general Galactic and extragalactic sciences. The highly uniform legacy surveys of WFST will also serve as an indispensable complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP

Population pharmacokinetics of Amisulpride in Chinese patients with schizophrenia with external validation: the impact of renal function

Introduction: Amisulpride is primarily eliminated via the kidneys. Given the clear influence of renal clearance on plasma concentration, we aimed to explicitly examine the impact of renal function on amisulpride pharmacokinetics (PK) via population PK modelling and Monte Carlo simulations.Method: Plasma concentrations from 921 patients (776 in development and 145 in validation) were utilized.Results: Amisulpride PK could be described by a one-compartment model with linear elimination where estimated glomerular filtration rate, eGFR, had a significant influence on clearance. All PK parameters (estimate, RSE%) were precisely estimated: apparent volume of distribution (645 L, 18%), apparent clearance (60.5 L/h, 2%), absorption rate constant (0.106 h−1, 12%) and coefficient of renal function on clearance (0.817, 10%). No other significant covariate was found. The predictive performance of the model was externally validated. Covariate analysis showed an inverse relationship between eGFR and exposure, where subjects with eGFR= 30 mL/min/1.73 m2 had more than 2-fold increase in AUC, trough and peak concentration. Simulation results further illustrated that, given a dose of 800 mg, plasma concentrations of all patients with renal impairment would exceed 640 ng/mL.Discussion: Our work demonstrated the importance of renal function in amisulpride dose adjustment and provided a quantitative framework to guide individualized dosing for Chinese patients with schizophrenia

Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response

The SARS-CoV-2 virus, the causative agent of COVID-19, is undergoing constant mutation. Here, we utilized an integrative approach combining epidemiology, virus genome sequencing, clinical phenotyping, and experimental validation to locate mutations of clinical importance. We identified 35 recurrent variants, some of which are associated with clinical phenotypes related to severity. One variant, containing a deletion in the Nsp1-coding region (D500-532), was found in more than 20% of our sequenced samples and associates with higher RT-PCR cycle thresholds and lower serum IFN-beta levels of infected patients. Deletion variants in this locus were found in 37 countries worldwide, and viruses isolated from clinical samples or engineered by reverse genetics with related deletions in Nsp1 also induce lower IFN-beta responses in infected Calu-3 cells. Taken together, our virologic surveillance characterizes recurrent genetic diversity and identified mutations in Nsp1 of biological and clinical importance, which collectively may aid molecular diagnostics and drug design.Peer reviewe

An Intrusion Detection Model Based on Feature Selection and Improved One-Dimensional Convolutional Neural Network

The problem of intrusion detection has new solutions, thanks to the widespread use of machine learning in the field of network security, but it still has a few issues at this time. Traditional machine learning techniques to intrusion detection rely on expert experience to choose features, and deep learning approaches have a low detection efficiency. In this paper, an intrusion detection model based on feature selection and improved one-dimensional convolutional neural network was proposed. This model first used the extreme gradient boosting decision tree (XGboost) algorithm to sort the preprocessed data, and then it used comparison to weed out 55 features with a higher contribution. Then, the extracted features were fed into the improved one-dimensional convolutional neural network (I1DCNN), and this network training was used to complete the final classification task. The feature selection and improved one-dimensional convolutional neural network (FS-I1DCNN) intrusion detection model not only solved the traditional machine learning method of relying on expert experience to extract features but also improved the detection efficiency of the model, reduced the training time while reducing the dimension, and increased the overall accuracy. In comparison to the I1DCNN model without feature extraction and the conventional one-dimensional convolutional neural network (1DCNN) model, the experimental results demonstrate that the FS-I1DCNN model’s overall accuracy increases by 0.67% and 2.94%, respectively. Its accuracy, precision, recall, and F1-score were significantly better than those of the other intrusion detection models, including SVM and DBN