BnAP2-12 overexpression delays ramie flowering: evidence from AP2/ERF gene expression.

Introduction: The APETALA2/ethylene response factor (AP2/ERF) superfamily plays a significant role in regulating plant gene expression in response to growth and development. To date, there have been no studies into whether the ramie AP2/ERF genes are involved in the regulation of flower development. Methods: Here, 84 BnAP2/ERF members were identified from the ramie genome database, and various bioinformatics data on the AP2/ERF gene family, structure, replication, promoters and regulatory networks were analysed. BnAP2-12 was transferred into Arabidopsis through the flower-dipping method. Results: Phylogenetic analysis classified the 84 BnAP2/ERF members into four subfamilies: AP2 (18), RAV (3), ERF (42), and DREB (21). The functional domain analysis of genes revealed 10 conserved motifs. Genetic mapping localised the 84 members on 14 chromosomes, among which chromosomes 1, 3, 5, and 8 had more members. Collinearity analysis revealed that 43.37% possibly resulted from replication events during the evolution of the ramie genome. Promoter sequence analysis identified classified cis-acting elements associated with plant growth and development, and responses to stress, hormones, and light. Transcriptomic comparison identified 3,635 differentially expressed genes (DEGs) between male and female flowers (1,803 and 1,832 upregulated and downregulated genes, respectively). Kyoto Encyclopaedia of Genes and Genomes pathway analysis categorised DEGs involved in metabolic pathways and biosynthesis of secondary metabolites. Gene Ontology enrichment analysis further identified enriched genes associated with pollen and female gamete formations. Of the 84 BnAP2/ERFs genes, 22 and 8 upregulated and downregulated genes, respectively, were present in female flowers. Co-expression network analysis identified AP2/ERF members associated with flower development, including BnAP2-12. Subcellular localisation analysis showed that the BnAP2-12 protein is localised in the nucleus and cell membrane. Overexpression BnAP2-12 delayed the flowering time of Arabidopsis thaliana. Conclusion: These findings provide insights into the mechanism of ramie flower development.

Exogenous plant growth regulator and foliar fertilizers for phytoextraction of cadmium with Boehmeria nivea [L.] Gaudich from contaminated field soil.

As a enrichment plant, ramie can be used for the phytoremediation of cadmium (Cd)-contaminated soil. However, it is worth exploring the role of plant growth regulators and foliar fertilizers in the process of plant growth and development and Cd adsorption. By measuring the agronomic traits, Cd content of aboveground and underground ramie, calculating the Cd transfer coefficient (TF) and Cd bioconcentration factors (BCF), and the correlation between various indicators. This study examined the effects of plant growth regulators and foliar fertilizers on ramie's capacity for Cd accumulation and transportation. Plant growth regulators and foliar fertilizers increased the Cd content of the aboveground ramie, reduced the Cd content of the underground ramie, and increased the TF. Among them, GA-1 increased the Cd content of the aboveground ramie to 3 times more than that of the control and reduced the Cd content of the underground ramie by 54.76%. Salicylic acid (SA) increased the Cd content of the aboveground ramie to three times more than that of the control. The combination of GA and foliar fertilizer reduced the Cd content of the aboveground and underground ramie and the TF and BCF of the underground ramie. After the hormones were sprayed, the TF of ramie had a significant positive correlation with the Cd content of the aboveground ramie; the BCF of the aboveground ramie had a significant positive correlation with the Cd content and TF of the aboveground ramie. The results indicate that Brassinolide (BR), gibberellin (GA), ethephon (ETH), polyamines (PAs), and salicylic acid (SA) have different effects on the enrichment and transport of Cd in ramie. This study provided an effective method to improve the capacity for ramie to adsorb heavy metals during cultivation.

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning.

BACKGROUND: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. METHODS: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People's Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. RESULTS AND CONCLUSIONS: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

CT findings of patients infected with SARS-CoV-2.

BACKGROUND: We aimed to describe the chest CT findings in sixty-seven patients infected by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We retrospectively reviewed 67 patients hospitalized in Ruian People's Hospital. All the patients received the positive diagnosis of SARS-CoV-2 infection. The CT and clinical data were collected between January 23rd, 2020 and February 10th, 2020. The CT images were analyzed by the senior radiologists. RESULTS: There are 54 patients with positive CT findings and 13 patients with negative CT findings. The typical CT findings in hospitalized patients with SARS-CoV-2 infection were ground glass opacities (42/54), lesions located in the peripheral area (50/54), multiple lesions (46/54), and lesions located in the lower lobes (42/54). There were less typical CT findings, including air bronchogram (18/54), pleural thickening or pleural effusion (14/54), consolidation (12/54), lesions in the upper lobes (12/54), interlobular septal thickening (11/54), reversed halo sign (9/54), single lesion (8/54), air cavities (4/54), bronchial wall thickening (3/54), and intrathoracic lymph node enlargement (2/54). CONCLUSIONS: CT features can play an important role in the early diagnosis and follow-up of COVID-19 patients.

Fusion of mis-registered GFP and phase contrast images with convolutional sparse representation and adaptive region energy rule.

Biomedical image fusion is the process of combining the information from different imaging modalities to get a synthetic image. Fusion of phase contrast and green fluorescent protein (GFP) images is significant to predict the role of unknown proteins, analyze the function of proteins, locate the subcellular structure, and so forth. Generally, the fusion performance largely depends on the registration of GFP and phase contrast images. However, accurate registration of multi-modal images is a very challenging task. Hence, we propose a novel fusion method based on convolutional sparse representation (CSR) to fuse the mis-registered GFP and phase contrast images. At first, the GFP and phase contrast images are decomposed by CSR to get the coefficients of base layers and detail layers. Secondly, the coefficients of detail layers are fused by the sum modified Laplacian (SML) rule while the coefficients of base layers are fused by the proposed adaptive region energy (ARE) rule. ARE rule is calculated by discussion mechanism based brain storm optimization (DMBSO) algorithm. Finally, the fused image is achieved by carrying out the inverse CSR. The proposed fusion method is tested on 100 pairs of mis-registered GFP and phase contrast images. The experimental results reveal that our proposed fusion method exhibits better fusion results and superior robustness than several existing fusion methods.

The value of the computer-aided diagnosis system for thyroid lesions based on computed tomography images.

BACKGROUND: Thyroid nodules are commonly found at palpation amounting to 4-7% of the asymptomatic population and 50% of the cases are found at autopsy. Only a small proportion of thyroid nodules are malignant. The major challenge is the differential diagnosis of benign or malignant thyroid nodules, so we aim to develop the computer-assisted diagnostic method based on computed tomography (CT) images for thyroid lesions. METHODS: In this study, we retrospectively collected 52 benign and 46 malignant thyroid nodules from 90 patients in CT examinations, together with the pathologist findings and radiology diagnosis. The first-order statistic and gray-level co-occurrence matrix features were extracted from thyroid computed tomography images. These texture features were used to assess the malignancy risk of the thyroid nodules. Several classification algorithms, including support vector machine, linear discriminant analysis, random forest, and bootstrap aggregating, were applied in the prediction. Leave-one-out cross-validation was used to evaluate the performance of thyroid cancer recognition. RESULTS: In thyroid cancer identification based on a computed tomography image, we found the system using 17 texture features and support vector machine performed well. The accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, were 0.8673, 0.9105, 0.9130, 0.8269, 0.8235 and 0.9146, respectively. CONCLUSIONS: The proposed computer-aided diagnosis system provides a good assessment of the malignancy-risk of the thyroid nodules, which may help radiologists to improve the accuracy and efficiency of thyroid diagnosis.

Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images.

Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.

Thyroid nodule recognition in computed tomography using first order statistics.

BACKGROUND: Computed tomography (CT) is one of the popular tools for early detection of thyroid nodule. The pixel intensity of thyroid in CT image is very important information to distinguish nodule from normal thyroid tissue. The pixel intensity in normal thyroid tissues is homogeneous and smooth. In the benign or malignant nodules, the pixel intensity is heterogeneous. Several studies have shown that the first order features in ultrasound image can be used as imaging biomarkers in nodule recognition. METHODS: In this paper, we investigate the feasibility of utilizing the first order texture features to identify nodule from normal thyroid tissue in CT image. A total of 284 thyroid CT images from 113 patients were collected in this study. We used 150 healthy controlled thyroid CT images from 55 patients and 134 nodule images (50 malignant and 84 benign nodules) from 58 patients who have undergone thyroid surgery. The final diagnosis was confirmed by histopathological examinations. In the presented method, first, regions of interest (ROIs) from axial non-enhancement CT images were delineated manually by a radiologist. Second, average, median, and wiener filter were applied to reduce photon noise before feature extraction. The first-order texture features, including entropy, uniformity, average intensity, standard deviation, kurtosis and skewness were calculated from each ROI. Third, support vector machine analysis was applied for classification. Several statistical values were calculated to evaluate the performance of the presented method, which includes accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area of under receiver operating characteristic curve (AUC). RESULTS: The entropy, uniformity, mean intensity, standard deviation, skewness (P < 0.05), except kurtosis (P = 0.104) of thyroid tissue with nodules have a significant difference from those of normal thyroid tissue. The optimal classification was obtained from the presented method. The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) are 0.880, 0.821, 0.933, 0.917, 0.854, and 0.953 respectively. CONCLUSION: First order texture features can be used as imaging biomarkers, and the presented system can be used to assist radiologists to recognize the nodules in CT image.

[To probe the system of performance tests and safety inspections in medical equipment].

The potential problems of QA (quality assurance) and safety control of medical equipment in hospital were analyzed. On this base, this paper developed the testing system for the performance tests and the safety inspections of medical equipment in hospital, and discusses the results and the problems existing in the running systems.

Multifocal skeletal tuberculosis: experience in diagnosis and treatment.

OBJECTIVES: Multifocal skeletal tuberculosis (MSTB) is an uncommon presentation of skeletal tuberculosis. In order to provide more clinically meaningful information on the diagnosis and management of MSTB, we present a case of MSTB with multiple tuberculous lesions in multiple locations, along with a review of 13 MSTB cases from different studies. PATIENTS AND METHODS: A 29-year-old male patient with a one-year history of back pain was initially diagnosed with ankylosing spondylitis and arthritis deformans, and received treatment with oral glucocorticosteroid and leflunomide for 24 weeks. The back pain worsened with weight loss and fever one month prior to admission to our hospital. The diagnosis, MSTB, with 26 tuberculous lesions in 19 locations, was made by clinical findings, bone scan (computed topography and Tc-99m HDP scintigraphy), and bone marrow smear. RESULT: Multiple antituberculous drugs, with supportive and immune-enhancing therapies cured the patient. CONCLUSIONS: This case indicates that MSTB may develop in patients on long-term immunosuppressive drugs. In addition, our experience, along with previously reported data, suggest that strong clinical suspicion is required for an early diagnosis of MSTB, and chemotherapy, combined with supportive and immune-based therapies is effective for the treatment of MSTB.

[Design and development of the DSA digital subtraction workstation].

According to the patient examination criterion and the demands of all related departments, the DSA digital subtraction workstation has been successfully designed and is introduced in this paper by analyzing the characteristic of video source of DSA which was manufactured by GE Company and has no DICOM standard interface. The workstation includes images-capturing gateway and post-processing software. With the developed workstation, all images from this early DSA equipment are transformed into DICOM format and then are shared in different machines.

[Design and implementation of a system for transforming the NEMA 2.0 images into DICOM 3.0 images].

This paper introduces the design and implementation of a system which can get the NEMA2.0 image data from the hard disks of the imaging equipments directly,then analyzes and transforms these image data into the DICOM3.0 image data and sends them to the image server. The design has the advantages of reliable image quality, low cost and information.