To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R2) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds.

The identification of human remains is of utmost importance in a variety of scenarios. One of the primary identification methods is DNA. DNA extraction from human remains could be difficult, particularly in situations where the remains have been exposed to environmental conditions and other insults. Several studies tried to improve extraction by applying different approaches. ForensicGEM Universal (MicroGem) is a single-tube approach to DNA extraction and a temperature-driven method that could have some advantages with respect to previous techniques, among them, reducing the risk of contamination, not requiring specialized equipment, or several steps to perform. The aim of this study was to assess, for the first time, the efficiency of DNA extraction and quality of STR profiles applying the MicroGem protocol and modifications of this protocol from tooth samples in comparison with automatic extraction (AE). Our results indicated that AE and MicroGem performed similar, though with variability depending on the MicroGem modifications, increasing the DNA yield and STR profile quality when DNA is concentrated with Microcon. These findings demonstrated the efficiency of this methodology for DNA extraction from human remains while also providing a simple and quick technique suitable to apply in a variety of forensic scenarios.

Widely used agricultural greenhouses are critical in the development of facility agriculture because of not only their huge capacity in food and vegetable supplies, but also their environmental and climatic effects. Therefore, it is important to obtain the spatial distribution of agricultural greenhouses for agricultural production, policy making, and even environmental protection. Remote sensing technologies have been widely used in greenhouse extraction mainly in small or local regions, while large-scale and high-resolution (~ 1-m) greenhouse extraction is still lacking. In this study, agricultural greenhouses in an important agricultural province (Shandong, China) are extracted by the combination of high-resolution remote sensing images from Google Earth and deep learning algorithm with high accuracy (94.04% for mean intersection over union over test set). The results demonstrated that the agricultural greenhouses cover an area of 1755.3 km2, accounting for 1.11% of the total province and 2.31% of total cultivated land. The spatial density map of agricultural greenhouses also suggested that the facility agriculture in Shandong has obviously regional aggregation characteristics, which is vulnerable in both environment and economy. The results of this study are useful and meaningful for future agriculture planning and environmental management.

Greenhouses are an important part of modern facility-based agriculture. While creating well-being for human society, greenhouses also bring negative impacts such as air pollution, soil pollution, and water pollution. Therefore, it is of great significance to obtain information such as the area and quantity of greenhouses. It is still a challenging task to find a low-cost, high-efficiency, and easy-to-use method for the dual extraction of greenhouse area and quantity on a large scale. In this study, relatively easy-to-obtain high-resolution Google Earth remote sensing images are used as the experimental data source, and an area and quantity simultaneous extraction framework (AQSEF) is constructed to extract both the area and quantity of greenhouses. The AQSEF uses UNet and YOLO v5 series networks as core operators to complete model training and prediction, and main components such as SWP, OSW&NMS and GCA complete data postprocessing. To evaluate the feasibility of our method, we take Beijing, China, as the research area and select multiple accuracy evaluation indicators in the two branches for accuracy verification. The results show that the mIoU, OA, Kappa, Recall and Precision with the best performance model in the area extraction branch can reach 0.931, 0.987, 0.867, 0.91 and 0.914, respectively. Additionally, the Recall, Precision, AP@0.5 and mAP@0.5: 0.95 values of the best performance model are 0.781, 0.891, 0.812 and 0.509, respectively, in the extraction of the quantity of greenhouses. Finally, in Beijing, the area covered by greenhouses is approximately 85.443 km2, and the quantity of greenhouses is approximately 155,464. With the proposed method, the time consumed for area extraction and quantity extraction is 6.73 h and 12.97 h, respectively. The experimental results show that AQSEF helps to overcome the spatiotemporal diversity of greenhouses and quickly and accurately map a high-spatial-resolution greenhouse distribution product within the research area.

The extraction of the foveal avascular zone (FAZ) from optical coherence tomography angiography (OCTA) images has been used in many studies in recent years due to its association with various ophthalmic diseases. In this study, we investigated the utility of a dataset for deep learning created using Kanno Saitama Macro (KSM), a program that automatically extracts the FAZ using swept-source OCTA. The test data included 40 eyes of 20 healthy volunteers. For training and validation, we used 257 eyes from 257 patients. The FAZ of the retinal surface image was extracted using KSM, and a dataset for FAZ extraction was created. Based on that dataset, we conducted a training test using a typical U-Net. Two examiners manually extracted the FAZ of the test data, and the results were used as gold standards to compare the Jaccard coefficients between examiners, and between each examiner and the U-Net. The Jaccard coefficient was 0.931 between examiner 1 and examiner 2, 0.951 between examiner 1 and the U-Net, and 0.933 between examiner 2 and the U-Net. The Jaccard coefficients were significantly better between examiner 1 and the U-Net than between examiner 1 and examiner 2 (p < 0.001). These data indicated that the dataset generated by KSM was as good as, if not better than, the agreement between examiners using the manual method. KSM may contribute to reducing the burden of annotation in deep learning.

BACKGROUND: Reliable and interpretable automatic extraction of clinical phenotypes from large electronic medical record databases remains a challenge, especially in a language other than English.
OBJECTIVE: We aimed to provide an automated end-to-end extraction of cohorts of similar patients from electronic health records for systemic diseases.
METHODS: Our multistep algorithm includes a named-entity recognition step, a multilabel classification using medical subject headings ontology, and the computation of patient similarity. A selection of cohorts of similar patients on a priori annotated phenotypes was performed. Six phenotypes were selected for their clinical significance: P1, osteoporosis; P2, nephritis in systemic erythematosus lupus; P3, interstitial lung disease in systemic sclerosis; P4, lung infection; P5, obstetric antiphospholipid syndrome; and P6, Takayasu arteritis. We used a training set of 151 clinical notes and an independent validation set of 256 clinical notes, with annotated phenotypes, both extracted from the Assistance Publique-Hôpitaux de Paris data warehouse. We evaluated the precision of the 3 patients closest to the index patient for each phenotype with precision-at-3 and recall and average precision.
RESULTS: For P1-P4, the precision-at-3 ranged from 0.85 (95% CI 0.75-0.95) to 0.99 (95% CI 0.98-1), the recall ranged from 0.53 (95% CI 0.50-0.55) to 0.83 (95% CI 0.81-0.84), and the average precision ranged from 0.58 (95% CI 0.54-0.62) to 0.88 (95% CI 0.85-0.90). P5-P6 phenotypes could not be analyzed due to the limited number of phenotypes.
CONCLUSIONS: Using a method close to clinical reasoning, we built a scalable and interpretable end-to-end algorithm for extracting cohorts of similar patients.

Diagnostic statements for pituitary adenomas (PAs) are complex and unstandardized. We aimed to determine the most commonly used elements contained in the statements and their combination patterns and variations in real-world clinical practice, with the ultimate goal of promoting standardized diagnostic recording and establishing an efficient element extraction process.
Patient medical records from 2012 to 2020 that included PA among the first three diagnoses were included. After manually labeling the elements in the diagnostic texts, we obtained element types and training sets, according to which an information extraction model was constructed based on the word segmentation model \"Jieba\" to extract information contained in the remaining diagnostic texts.
A total of 576 different diagnostic statements from 4010 texts of 3770 medical records were enrolled in the analysis. The first ten diagnostic elements related to PA were histopathology, tumor location, endocrine status, tumor size, invasiveness, recurrence, diagnostic confirmation, Knosp grade, residual tumor, and refractoriness. The automated extraction model achieved F1-scores that reached 100% for all ten elements in the second round and 97.3-100.0% in the test set consisting of an additional 532 diagnostic texts. Tumor location, endocrine status, histopathology, and tumor size were the most commonly used elements, and diagnoses composed of the above elements were the most frequent. Endocrine status had the greatest expression variability, followed by Knosp grade. Among all the terms, the percentage of loss of tumor size was among the highest (21%). Among statements where the principal diagnoses were PAs, 18.6% did not have information on tumor size, while for those with other diagnoses, this percentage rose to 48% (P < 0.001).
Standardization of the diagnostic statement for PAs is unsatisfactory in real-world clinical practice. This study could help standardize a structured pattern for PA diagnosis and establish a foundation for research-friendly, high-quality clinical information extraction.

Remote sensing technology has the advantages of fast information acquisition, short cycle, and a wide detection range. It is frequently used in surface resource monitoring tasks. However, traditional remote sensing image segmentation technology cannot make full use of the rich spatial information of the image, the workload is too large, and the accuracy is not high enough. To address these problems, this study carried out atmospheric calibration, band combination, image fusion, and other data enhancement methods for Landsat 8 satellite remote sensing data to improve the data quality. In addition, deep learning is applied to remote-sensing image block segmentation. An asymmetric convolution-CBAM (AC-CBAM) module based on the convolutional block attention module is proposed. This optimization module of the integrated attention and sliding window prediction method is adopted to effectively improve the segmentation accuracy. In the experiment of test data, the mIoU, mAcc, and aAcc in this study reached 97.34%, 98.66%, and 98.67%, respectively, which is 1.44% higher than that of DNLNet (95.9%). The AC-CBAM module of this research provides a reference for deep learning to realize the automation of remote sensing land information extraction. The experimental code of our AC-CBAM module can be found at https://github.com/LinB203/remotesense.

This study aimed to determine the optimal image reconstruction method for preoperative computed tomography (CT) angiography for pulmonary segmentectomy. This study enrolled 20 patients who underwent contrast-enhanced CT examination for pulmonary segmentectomy. The optimal image reconstruction algorithm among four different reconstruction algorithms (filtered back projection, hybrid iterative reconstruction, model- based iterative reconstruction, and deep learning reconstruction [DLR]) was investigated by assessing the CT numbers, vessel extraction ratios, and misclassification ratios. The vessel extraction ratios for main and subsegment branches reconstructed using DLR were significantly higher than those using other reconstruction algorithms (96.7% and 90.8% for pulmonary artery and vein, respectively). The misclassification ratios at the right upper lobe pulmonary vessels (V1 and V2) were especially high because they were close to the superior vena cava, and their CT numbers were similar in all four reconstructions. In conclusion, the DLR allows a high extraction rate of pulmonary blood vessels and a low misclassification rate of automatic extraction.

OBJECTIVE: Current conventional algorithms used for 3-dimensional simulation in virtual hepatectomy still have difficulties distinguishing the portal vein (PV) and hepatic vein (HV). The accuracy of these algorithms was compared with a new deep-learning based algorithm (DLA) using artificial intelligence.
METHODS: A total of 110 living liver donor candidates until 2017, and 46 donor candidates until 2019 were allocated to the training group and validation groups for the DLA, respectively. All PV or HV branches were labeled based on Couinaud\'s segment classification and the Brisbane 2000 Terminology by hepato-biliary surgeons. Misclassified and missing branches were compared between a conventional tracking-based algorithm (TA) and DLA in the validation group.
RESULTS: The sensitivity, specificity, and Dice coefficient for the PV were 0.58, 0.98, and 0.69 using the TA; and 0.84, 0.97, and 0.90 using the DLA (P < .001, excluding specificity); and for the HV, 0.81, 087, and 0.83 using the TA; and 0.93, 0.94 and 0.94 using the DLA (P < .001 to P = .001). The DLA exhibited greater accuracy than the TA.
CONCLUSIONS: Compared with the TA, artificial intelligence enhanced the accuracy of extraction of the PV and HVs in computed tomography.