ABSTRACT
The aim of this study was to investigate the rheological properties, particle size distribution, color change, and stability of lily juice under different ultrasonic treatment conditions (152 W, 304 W, 456 W, 608 W, and 760 W). The results showed that the lily juice exhibited non-Newtonian shear thinning behavior, and the viscosity decreased with the increase in ultrasonic power. Under ultrasonic treatment conditions, there was no significant change in the pH value and zeta potential value of the samples. The content of cloudy value and total soluble solids (TSS) increased gradually. However, both the sedimentation components and centrifugal sedimentation rate showed a downward trend and an asymptotic behavior. In addition, high-power ultrasound changed the color index (L* value decreased, a* value increased), tissue structure, and particle distribution of the sample, and small particles increased significantly. To sum up, ultrasonic treatment has great potential in improving the physical properties and suspension stability of lily juice.
ABSTRACT
RATIONALE AND OBJECTIVES: Adrenal venous sampling (AVS) is the primary method for differentiating between primary aldosterone (PA) subtypes. The aim of study is to develop prediction models for subtyping of patients with PA using computed tomography (CT) radiomics and clinicobiochemical characteristics associated with PA. MATERIALS AND METHODS: This study retrospectively enrolled 158 patients with PA who underwent AVS between January 2014 and March 2021. Neural network machine learning models were developed using a two-stage analysis of triple-phase abdominal CT and clinicobiochemical characteristics. In the first stage, the models were constructed to classify unilateral or bilateral PA; in the second stage, they were designed to determine the predominant side in patients with unilateral PA. The final proposed model combined the best-performing models from both stages. The model's performance was evaluated using repeated stratified five-fold cross-validation. We employed paired t-tests to compare its performance with the conventional imaging evaluations made by radiologists, which categorize patients as either having bilateral PA or unilateral PA on one side. RESULTS: In the first stage, the integrated model that combines CT radiomic and clinicobiochemical characteristics exhibited the highest performance, surpassing both the radiomic-alone and clinicobiochemical-alone models. It achieved an accuracy and F1 score of 80.6% ± 3.0% and 74.8% ± 5.2% (area under the receiver operating curve [AUC] = 0.778 ± 0.050). In the second stage, the accuracy and F1 score of the radiomic-based model were 88% ± 4.9% and 81.9% ± 6.2% (AUC=0.831 ± 0.087). The proposed model achieved an accuracy and F1 score of 77.5% ± 3.9% and 70.5% ± 7.1% (AUC=0.771 ± 0.046) in subtype diagnosis and lateralization, surpassing the accuracy and F1 score achieved by radiologists' evaluation (p < .05). CONCLUSION: The proposed machine learning model can predict the subtypes and lateralization of PA. It yields superior results compared to conventional imaging evaluation and has potential to supplement the diagnostic process in PA.
ABSTRACT
To clarify the rapid formation and evolutionary mechanisms of an extremely severe and persistent haze and fog (HF) episode that occurred in central-eastern China from Dec 20 to 25, 2015, a novel campaign was conducted and vertical profiles of wind, temperature, light extinction coefficient (LEC) and PM2.5 concentration were used to analyze the rapid formation and evolutionary mechanisms of this HF episode. The substantial downward transportation of regional pollution from high layers and stagnant weather conditions favorable for the local pollution accumulation were the two main causes of the rapid increase in pollutant concentration. Southwest wind speeds of 4m/s between 300 and 600m and obvious downward flows were observed, whereas the southwest wind speeds were low below 300m, and strong temperature inversion with intensity of 4.5°C/100m expanded vertically to a height of 600m. Two peaks of PM2.5 concentration were observed at 200 and 700m, corresponding to 235 and 215µg/m3, respectively. The frequent change in wind direction and wind speeds resulted in the fluctuation of PM2.5 concentration. The turbulence within lower layers of the troposphere was enhanced by easterly and northerly winds which decreased the pollution level; however, the strength and stretching height of the winds were insufficient to fully clear the air of pollutants. The PM2.5 concentration revealed 2-high concentration layers in the vertical direction. The maximum concentration layer was below 100m, while the second high-concentration layer was at 400m.
