Development and Validation of a New High-Performance Liquid Chromatography-Ultraviolet Assay for Quantification of Mitoxantrone in Plasma of BALB/c-nu Mice.

The concentration of mitoxantrone in the blood of mice was determined by a high-performance liquid chromatography-ultraviolet method with aloe-emodin as the internal standard. The separation was performed on a Hypersil BDS2 column (4.6 × 250 mm, 5 µm) as the analytical column, the mobile Phase A was acetonitrile, and B was 20-mM potassium dihydrogen phosphate (adding 1% triethylamine and adjusting the pH to 2.8 with phosphoric acid) and 4.6-mM sodium octyl sulfonate. The flow rate was 1.0 mL·min-1, the detection wavelength was 243 nm, the column temperature is 25 ± 5°C and the injection amount was 20 µL. Finally, the linear range of mitoxantrone was 5-200 µg·mL-1, and the correlation coefficient was r = 0.9999. The recovery rate of the method was 91.93-105.5%, and the extraction recovery rate was 91.45-105.5%. The intraday precision and interday precision were <3.29% (limit of detection = 0.3 µg·mL-1). The HPLC method established in this paper was simple, rapid, sensitive and accurate, and can be used to determine the content of mitoxantrone in mouse plasma after tail vein injection.

Multi-Parameter Optimization of Rubidium Laser Optically Pumped Magnetometers with Geomagnetic Field Intensity.

Rubidium laser optically pumped magnetometers (OPMs) are widely used magnetic sensors based on the Zeeman effect, laser pumping, and magnetic resonance principles. They measure the magnetic field by measuring the magnetic resonance signal passing through a rubidium atomic gas cell. The quality of the magnetic resonance signal is a necessary condition for a magnetometer to achieve high sensitivity. In this research, to obtain the best magnetic resonance signal of rubidium laser OPMs in the Earth's magnetic field intensity, the experiment system of rubidium laser OPMs is built with a rubidium atomic gas cell as the core component. The linewidth and amplitude ratio (LAR) of magnetic resonance signals is utilized as the optimization objective function. The magnetic resonance signals of the magnetometer experiment system are experimentally measured for different laser frequencies, radio frequency (RF) intensities, laser powers, and atomic gas cell temperatures in a background magnetic field of 50,765 nT. The experimental results indicate that optimizing these parameters can reduce the LAR by one order of magnitude. This shows that the optimal parameter combination can effectively improve the sensitivity of the magnetometer. The sensitivity defined using the noise spectral density measured under optimal experimental parameters is 1.5 pT/Hz1/2@1 Hz. This work will provide key technical support for rubidium laser OPMs' product development.

Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction.

Road infrastructure has significant effects on road traffic safety and needs further examination. In terms of traffic crash prediction, recent studies have started to develop deep learning classification algorithms. However, given the uncertainty of traffic crashes, predicting the traffic risk potential of different road sections remains a challenge. To bridge this knowledge gap, this study investigated a real-world expressway and collected its traffic crash data between 2013 and 2020. Then, according to the time-spatial density ratio (Pts), road sections were assigned into three classes corresponding to low, medium, and high risk levels of traffic. Next, different classifiers were compared that were trained using the transformed and resampled feature data to construct a traffic crash risk prediction model. Last, but not least, partial dependence plots (PDPs) were employed to interpret the results and analyze the importance of individual features describing the geometry, pavement, structure, and weather conditions. The results showed that a variety of data balancing algorithms improved the performance of the classifiers, the ensemble classifier superseded the others in terms of the performance metrics, and the combined SMOTEENN and random forest algorithms improved the classification accuracy the most. In the future, the proposed traffic crash risk prediction method will be tested in more road maintenance and design safety assessment scenarios.