Digital Prediction of the Purchase Price of Fresh Tea Leaves of Enshi Yulu Based on Near-Infrared Spectroscopy Combined with Multivariate Analysis.

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm-1-4751.74 cm-1, 4755.63 cm-1-5129.75 cm-1, 6262.70 cm-1-6633.93 cm-1, and 7386 cm-1-7756.32 cm-1, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (Rp2 = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R2 = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology.

Assessment of Knowledge, Attitude, and Practice Toward COVID-19 in China: An Online Cross-Sectional Survey.

To analyze the level of knowledge, attitude, and practice about COVID-19 among Chinese residents, noninterventional and anonymous survey was carried out with an online questionnaire. Among the survey respondents (n = 619), 59.9% were female, 61.1% were from 18 to 30 years of age, and 42.3% held an undergraduate's degree. The mean scores for each scale were as follows: perceived knowledge (36.3 ± 6.1), attitude (29.4 ± 4.7), practice (44.1 ± 4.8), total score (109.7 ± 13.2), barrier (0.2 ± 0.7), and cognition and behavior change score (8.5 ± 1.4). Perceived knowledge, attitude, practice, total score, and cognition and behavior changes were significantly and positively correlated, whereas barrier was negatively correlated with those scales (P < 0.001). Linear regressions revealed that those respondents who were medical professionals, civil servants, employees of state-owned enterprises and public institutions, and had relatively higher level of education were associated with a higher perceived knowledge score, attitude score, practice score, and total score. Higher mean cognition and behavior change score was associated with company employees (8.8 ± 1.3). More than half of the respondents (51.4%) were optimistic about the government's interventional measures. The respondents in China had good knowledge, positive attitude, and active practice toward COVID-19, yet, it is advisable to strengthen nationwide publicity and focus on the target undereducated population by means of We-Chat, microblog, website, and community workers for better control effect.

Predicting the Risk of Psoriatic Arthritis in Plaque Psoriasis Patients: Development and Assessment of a New Predictive Nomogram.

Objective: This study aimed to develop a risk of psoriatic arthritis (PsA) predictive model for plaque psoriasis patients based on the available features. Methods: Patients with plaque psoriasis or PsA were recruited. The characteristics, skin lesions, and nail clinical manifestations of the patients have been collected. The least absolute shrinkage was used to optimize feature selection, and logistic regression analysis was applied to further select features and build a PsA risk predictive model. Calibration, discrimination, and clinical utility of the prediction model were evaluated by using the calibration plot, C-index, the area under the curve (AUC), and decision curve analysis. Internal validation was performed using bootstrapping validation. The model was subjected to external validation with two separate cohorts. Results: Age at onset, duration, nail involvement, erythematous lunula, onychorrhexis, oil drop, and subungual hyperkeratosis were presented as predictors to perform the prediction nomogram. The predictive model showed good calibration and discrimination (C-index: 0.759; 95% CI: 0.707-0.811). The AUC of this prediction model was 0.7578092. Excellent performances of the C-index were reached in the internal validation and external cohort validation (0.741, 0.844, and 0.845). The decision curve indicated good effect of the PsA nomogram in guiding clinical practice. Conclusion: This novel PsA nomogram could assess the risk of PsA in plaque psoriasis patients with good efficiency.