Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine.

Leaf nitrogen (N) content and nonstructural carbohydrate (NSC) content are 2 important physiological indicators that reflect the growth state of trees. Rapid and accurate measurement of these 2 traits multitemporally enables dynamic monitoring of tree growth and efficient tree breeding selection. Traditional methods to monitor N and NSC are time-consuming, are mostly used on a small scale, and are nonrepeatable. In this paper, the performance of unmanned aerial vehicle multispectral imaging was evaluated over 11 months of 2021 on the estimation of canopy N and NSC contents from 383 slash pine trees. Four machine learning methods were compared to generate the optimal model for N and NSC prediction. In addition, the temporal scale of heritable variation for N and NSC was evaluated. The results show that the gradient boosting machine model yields the best prediction results on N and NSC, with R 2 values of 0.60 and 0.65 on the validation set (20%), respectively. The heritability (h 2) of all traits in 11 months ranged from 0 to 0.49, with the highest h 2 for N and NSC found in July and March (0.26 and 0.49, respectively). Finally, 5 families with high N and NSC breeding values were selected. To the best of our knowledge, this is the first study to predict N and NSC contents in trees using time-series unmanned aerial vehicle multispectral imaging and estimating the genetic variation of N and NSC along a temporal scale, which provides more reliable information about the overall performance of families in a breeding program.

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery.

Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis. In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild.

Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging.

Traditional methods used to monitor the aboveground biomass (AGB) and belowground biomass (BGB) of slash pine (Pinus elliottii) rely on on-ground measurements, which are time- and cost-consuming and suited only for small spatial scales. In this paper, we successfully applied unmanned aerial vehicle (UAV) integrated with structure from motion (UAV-SfM) data to estimate the tree height, crown area (CA), AGB, and BGB of slash pine for in slash pine breeding plantations sites. The CA of each tree was segmented by using marker-controlled watershed segmentation with a treetop and a set of minimum three meters heights. Moreover, the genetic variation of these traits has been analyzed and employed to estimate heritability (h 2). The results showed a promising correlation between UAV and ground truth data with a range of R 2 from 0.58 to 0.85 at 70 m flying heights and a moderate estimate of h 2 for all traits ranges from 0.13 to 0.47, where site influenced the h 2 value of slash pine trees, where h 2 in site 1 ranged from 0.13~0.25 lower than that in site 2 (range: 0.38~0.47). Similar genetic gains were obtained with both UAV and ground truth data; thus, breeding selection is still possible. The method described in this paper provides faster, more high-throughput, and more cost-effective UAV-SfM surveys to monitor a larger area of breeding plantations than traditional ground surveys while maintaining data accuracy.

Land use change simulation and spatial analysis of ecosystem service value in Shijiazhuang under multi-scenarios.

Simulating changes in the value of ecosystem services caused by land use changes in large cities under multiple scenarios is of great significance for cities to formulate land use policies and improve ecosystem services. Take Shijiazhuang, which is in the process of rapid urbanization, as an example. Based on the remote sensing image data and statistical yearbook of 1988, 1998, 2008, and 2018 as the basic data to analyze and estimate the 30 years of land use and ecosystem service value changes in Shijiazhuang. According to this, the CA-Markov model was used to simulate the land use change in Shijiazhuang under three scenarios in 2030 and estimate the value of ecosystem services under each scenario, using grid tools to visually express the spatial distribution of ecosystem service values and the degree of agglomeration under three scenarios. The results indicate that the most obvious feature of land use change in Shijiazhuang from 1988 to 2018 was that the farmland area decreased year by year, the built-up expanded rapidly, the farmland area decreased by 86,874.75 hm2 in 30 years, and the built-up increased by 154,711.90 hm2. In 1988, 1998, 2008, and 2018, the ecosystem service value of Shijiazhuang was 32.578 billion yuan, 32.799 billion yuan, 29.944 billion yuan, and 31.251 billion yuan respectively. In 2030, under three scenarios of natural development, farmland protection, and ecological protection, the value of ecosystem services is 331.111 billion yuan, 33.670 billion yuan, and 33.891 billion yuan in order. The hot spots are mainly concentrated in the northwest and southwest of Shijiazhuang, and cold spots are concentrated in the eastern cities, counties, and districts. Based on changes in land use brought about by urban expansion, simulating the value of ecosystem services under multiple scenarios in the future, providing scientific guidance for building urban ecological networks, and realizing sustainable urban ecological development.

The investigation of factors influencing the measurement of thyroid stimulating hormone in dried blood spots on filter paper.

We investigated sample collection variables that may influence the measurement of the thyroid stimulating hormone (TSH) including: time after birth, season, different ways for blood spot drying and varied elution time from filter paper. TSH was measured with an enzyme-linked immunoassay (EIA) method on dried blood spots collected from newborns and/or external quality control materials from CDC. We found that TSH results were stable if specimens were collected from newborns 72 hours after birth. We obtained different results when TSH was measured during different seasons. The results also changed as the specimens were dried in different ways. The length of time for eluting from the DBS also exerted influence on the TSH measurement. In order to assure newborn screening quality, all factors influencing the results should be considered and the best condition for testing chosen. The specimen should be collected from babies at 3-6 days of age and air-dried at room temperature. Different cut-offs may be necessary for different seasons of the years.

[The classification and treatment of lamina papyracea blow-out fracture].

OBJECTIVE: To investigate the classification criterion and the treatment of lamina papyracea blow-out fracture. METHOD: A clinic analysis was made on 108 case with lamina papyracea blow-out fracture. According to the classification, different treatments were taken differently. RESULT: The classification of 108 cases were reported as follows: 71 cases were classified as level 1, 33 cases as level 2, 4 cases as level 3. There was no special treat-ment for 43 cases (level 1) who had no diplopia. 28 cases (level 1) who had symptom of diplopia were treated conservatively. Its cure rate was 92. 9% (26/28). Other 34 cases were operated. Among them, 33 cases were classified as at least level 2, 1 case who had no satisfactory curative effect with conservative therapy was classified as level 1. The cure rate was 88. 2% (30/34). The other 4 cases classified as level 2 or 3 who didn't accept operation were treated conservatively, the cure rate was 25% (1/4). CONCLUSION: The classification of lamina papyracea blowout fracture facilitates the judgement of patient's condition and the selection of treatment. The transnasal endoscopic operation on serious lamina papyracea blow-out fracture cases produces a good curative effect.