Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.

Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield. Currently, high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants, such as water and nutrient content. In this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping. Four varieties of poplar saplings were cultivated, and 5 different irrigation treatments were applied. Color images of the plant samples were captured for analysis. Two tasks, including leaf posture calculation and drought stress identification, were conducted. First, instance segmentation was used to extract the regions of the leaf, petiole, and midvein. A dataset augmentation method was created for reducing manual annotation costs. The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization. Second, multitask learning models were proposed for simultaneously determining the stress level and poplar variety. The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein, respectively. Drought stress increased the horizontal angle of leaves. Moreover, using raw images as the input, the multitask MobileNet achieved the highest accuracy (99% for variety identification and 76% for stress level classification), outperforming widely used single-task deep learning models (stress level classification accuracies of <70% on the prediction dataset). The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.

Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment.

BACKGROUND: The breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment. METHODS: We extracted the real-time CT equipment status time series data such as oil temperature, of three equipment, between May 19, 2020, and May 19, 2021. Tube arcing is treated as the classification label. We propose a dictionary-based data-driven model SAX-HCBOP, where the two methods, Histogram-based Information Gain Binning (HIGB) and Coefficient improved Bag of Pattern (CoBOP), are implemented to transform the data into the bag-of-words paradigm. We compare our model to the existing predictive maintenance models based on statistical and time series classification algorithms. RESULTS: The results show that the Accuracy, Recall, Precision and F1-score of the proposed model achieve 0.904, 0.747, 0.417, 0.535, respectively. The oil temperature is identified as the most important feature. The proposed model is superior to other models in predicting CT equipment anomalies. In addition, experiments on the public dataset also demonstrate the effectiveness of the proposed model. CONCLUSIONS: The two proposed methods can improve the performance of the dictionary-based time series classification methods in predictive maintenance. In addition, based on the proposed real-time anomaly prediction system, the model assists hospitals in making accurate healthcare facilities maintenance decisions.

Anomaly prediction of CT equipment based on IoMT data.

BACKGROUND: Large-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model. METHODS: We extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models. RESULTS: The results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset. CONCLUSIONS: The proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases.

Citrullination: A modification important in the pathogenesis of autoimmune diseases.

Peptidyl arginine deiminase (PAD) which mediates citrullination catalyzes the conversion of arginine residues of protein peptide chains to citrulline residues. Citrullination can be involved in the process of apoptosis, embryo development, regulation of myelin sheath function and other physiological processes. Besides, it can regulate the process of cell death, affect the formation of neutrophil extracellular traps (NETs) and produce anti-citrullinated protein antibody (ACPA) to participate in autoimmune diseases. In this manuscript, the regulatory effects of citrullination in normal physiology and autoimmune diseases are reviewed, and the effects of citrullination on immune cells in autoimmune diseases are discussed in detail.

Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease.

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.