Evaluation of growth adaptation of Cinnamomum camphora seedlings in ionic rare earth tailings environment

The root system is an important organ for nutrient uptake and biomass accumulation in plants, while biomass allocation directly affects essential oils content, which plays an essential role in plant growth and development and resistance to adverse environmental conditions. This study was undertaken to investigate the differences and correlation of biomass allocation, root traits and essential oil content (EOC), as well as the adaptations of camphor tree with different chemical types to the ionic rare earth tailing sand habitats. Data from 1-year old cutting seedlings of C. camphora showed that the biomass of C. camphora cuttings was mainly distributed in root system, with the ratio of root biomass 49.9-72.13% and the ratio of root to canopy 1.00-2.64. The total biomass was significantly positively correlated with root length (RL), root surface area (RSA) and dry weight of fine roots (diameter ≤ 2 mm) (P < 0.05). Root biomass and leaf biomass were negatively and positively with specific root length (SRL) and specific root surface area (SRSA), respectively. Leaf biomass presented a positive effect on EOC (P < 0.05), with the correlation coefficient of 0.808. The suitability sort of these camphor trees was as follows: C. camphora β-linalool, C. camphora α-linaloolII, C. camphora α-linaloolI being better adapted to the ionic rare earth tailings substrate, C. camphora citral being the next, and C. porrectum β-linalool and C. camphora borneol being the least adaptive. EOC played a positive role in the adaptation of C. camphora (R2 = 0.6099, P < 0.05). Therefore camphor tree with linalool type is the appropriate choice in the ecological restoration of ionic rare earth tailings. The study could provide scientific recommendations for the ecological restoration of ionic rare earth tailings area combined with industrial development.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence

OBJECTIVE

Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI).

METHODS

We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities.

RESULTS

Among the 3506 ECGs of the IS patients, 2764 ECGs (78.84%) led to an abnormal diagnosis. Then we divided ECGs in the primary cohort into three groups, normal ECGs (N-Ns), abnormal ECGs after the first ischemic stroke (A-ISs), and normal ECGs after the first ischemic stroke (N-ISs). Basic demographic and ECG parameter analyses showed that heart rate, QT interval, and P-R interval were significantly different between 673 N-ISs and 3546 N-Ns (p < 0.05). The CNN has the best performance among the three models in distinguishing A-ISs and N-Ns (AUC: 0.88, 95%CI = 0.86-0.90). The prediction scores of the A-ISs and N-ISs obtained from the all three models are statistically different from the N-Ns (p < 0.001). Futhermore, the CNN scores of the two groups (mRS > 2 and mRS ≤ 2) were significantly different (p < 0.05). Finally, Grad-CAM revealed that the V4 lead may harbor the highest probability of abnormality.

CONCLUSION

Our study showed that a high proportion of post-IS ECGs harbored abnormal changes. Our CNN model can systematically assess anomalies in and prognosticate post-IS ECGs.