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ABSTRACT Loropetalum chinense (R.Br.) Oliv., a species that was previously documented to only be distributed in China, Japan, and India, with all distribution records north of the Tropic of Cancer, has now been recorded for the flora of Vietnam. The newly discovered population from the Son La Province in northwestern Vietnam, south of the Tropic of Cancer, represents the southernmost extent of the distribution of L. chinense. Detailed morphological descriptions, field photos, and a distribution map were provided. [ABSTRACT FROM AUTHOR]

Abstract Background To investigate the morphological parameters of the vastus medialis obliquus (VMO) muscle and delineate its importance in the maintenance of patellofemoral joint stability. Methods The magnetic resonance imaging data of seventy-five knees (fifty-four patients) with recurrent lateral patella dislocation (LPD) and seventy-five knees (seventy patients) without recurrent LPD were retrospectively analysed. Five morphological parameters related to the VMO (elevation in the sagittal plane and coronal plane, craniocaudal extent, muscle-fibre angulation, cross-sectional area ratio) and two patella tilt parameters (patella tilt angle, bisect offset ratio) were measured in MR images. The independent-samples t test or chi-square test was used for statistical comparisons. Results The mean ages of the patients in the recurrent LPD group and control group were 22.1 ± 9.9 years and 24.0 ± 6.5 years, respectively. Eighteen out of seventy-five (24%) patients MRI showed VMO injuries. Compared with the control group, the patients with recurrent LPD showed significantly higher sagittal VMO elevation (10.4 ± 2.3 mm vs. 4.1 ± 1.9 mm), coronal VMO elevation (15.9 ± 5.7 mm vs. 3.9 ± 3.7 mm), muscle-fibre angulation (35.4 ± 8.0° vs. 27.9 ± 6.3°), patella tilt angle (25.9 ± 10.7° vs. 9.1 ± 5.2°), and bisect offset ratio values (0.9 ± 0.3 vs. 0.5 ± 0.1) and significantly lower craniocaudal extent (13.7 ± 5.3 mm vs. 16.7 ± 5.1 mm) and cross-sectional area ratio values (0.05 ± 0.02 vs. 0.07 ± 0.02). Conclusions The results showed that abnormalities in the VMO and patella tilt were clearly present in recurrent LPD patients compared with normal people.

Abstract Background COVID-19 is a highly infectious respiratory disease. No therapeutics have yet been proven effective for treating severe COVID-19. Objectives To determine whether human umbilical cord mesenchymal stem cell infusion may be effective and safe for the treatment of severe COVID-19. Methods Patients with severe COVID-19 were randomly divided into 2 groups: the standard treatment group and the standard treatment plus hUC-MSC infusion group. The incidence of progression from severe to critical illness, 28-day mortality, clinical symptom improvement, time to clinical symptom improvement, hematologic indicators including C-reactive protein, lymphocyte number, and interleukin 6, and imaging changes were observed and compared between the two groups. Measurements and main results The incidence of progression from severe to critical illness and the 28-day mortality rate were 0 in the hUC-MSC treatment group, while 4 patients in the control group deteriorated to critical condition and received invasive ventilation; 3 of them died, and the 28-day mortality rate was 10.34%. In the hUC-MSC treatment group, the time to clinical improvement was shorter than that in the control group. Clinical symptoms of weakness and fatigue, shortness of breath, and low oxygen saturation obviously improved beginning on the third day of stem cell infusion and reached a significant difference on day 7. CRP and IL-6 levels were significantly lower from day 3 of infusion, the time for the lymphocyte count to return to the normal range was significantly faster, and lung inflammation absorption was significantly shorter on CT imaging in the hUC-MSC group than in the control group. Conclusions Intravenous transplantation of hUC-MSCs is a safe and effective method that can be considered a salvage and priority treatment option for severe COVID-19. Trial registration Chinese Clinical Trial Registration; ChiCTR2000031494; Registered on 2 April 2020; http:// www.medresman.org

Abstract Background A comparative analysis of the strengths and weaknesses of three different methods for radiologic evaluation of patellofemoral instability (PFI). Methods Computed tomography (CT) and magnetic resonance imaging (MRI) were performed in 47 patients with or without PFI. The tibial tubercle-trochlear groove (TT-TG) distance was measured by two observers through conventional CT and three-dimensional CT reconstruction (TDR-TT-TG) respectively and the tibial tubercle-posterior cruciate ligament (TT-PCL) distance with MRI. The intraclass correlation coefficient (ICC) was used to evaluate the interobserver reliability. In addition, the differences of three measurements between different patients were compared. The consistency of TT-TG and TDR-TT-TG was analyzed by the Bland-Altman method. Results The ICCs of three measurements were high between two observers; the results were TT-TG (ICC = 0.852), TDR-TT-TG (ICC = 0.864), and TT-PCL (ICC = 0.758). The values of PFI patients were significantly higher than those of non-PFI patients, and the mean TT-TG, TDR-TT-TG, and TT-PCL distance in patients with PFI were 19.0 ± 3.8 mm, 19.0 ± 3.7 mm, and 25.1 ± 3.6 mm, respectively. There was no statistically significant difference between the TT-TG distance and the TDR-TT-TG distance, we found no significant difference. The Bland-Altman analysis showed that the TDR-TT-TG distance was in good agreement with the TT-TG distance. Conclusion All three methods can be used to assess PFI; the TDR-TT-TG measurement method has superior operability and better interobserver consistency. It may be an alternative method to the conventional TT-TG distance measurement.

A high resolution dataset is one of the prerequisites for tea chrysanthemum detection with deep learning algorithms. This is crucial for further developing a selective chrysanthemum harvesting robot. However, generating high resolution datasets of the tea chrysanthemum with complex unstructured environments is a challenge. In this context, we propose a novel tea chrysanthemum – generative adversarial network (TC-GAN) that attempts to deal with this challenge. First, we designed a non-linear mapping network for untangling the features of the underlying code. Then, a customized regularization method was used to provide fine-grained control over the image details. Finally, a gradient diversion design with multi-scale feature extraction capability was adopted to optimize the training process. The proposed TC-GAN was compared with 12 state-of-the-art generative adversarial networks, showing that an optimal average precision (AP) of 90.09% was achieved with the generated images (512 × 512) on the developed TC-YOLO object detection model under the NVIDIA Tesla P100 GPU environment. Moreover, the detection model was deployed into the embedded NVIDIA Jetson TX2 platform with 0.1 s inference time, and this edge computing device could be further developed into a perception system for selective chrysanthemum picking robots in the future.

This research presents a comprehensive review of the research on smart urban energy retrofit decision-making. Based on the analysis of 91 journal articles over the past decade, the study identifies and discusses five key categories of approaches to retrofit decision-making, including simulation, optimization, assessment, system integration, and empirical study. While substantial advancements have been made in this field, opportunities for further growth remain. Findings suggest directions for future research and underscore the importance of interdisciplinary collaboration, data-driven evaluation methodologies, stakeholder engagement, system integration, and robust and adaptable retrofit solutions in the field of urban energy retrofitting. This review provides valuable insights for researchers, policymakers, and practitioners interested in advancing the state of the art in this critical area of research to facilitate more effective, sustainable, and efficient solutions for urban energy retrofits.

Background: Delivery of inhalable nanoparticles through metered-dose inhalers (MDI) is a promising approach to treat lung disease such as asthma and chronic obstructive pulmonary disease. Nanocoating of the inhalable nanoparticles helps in stability and cellular uptake enhancement but complicates the production process. Thus, it is meaningful to accelerate the translation process of MDI encapsulating inhalable nanoparticles with nanocoating structure. Methods: In this study, solid lipid nanoparticles (SLN) are selected as a model inhalable nanoparticle system. An established reverse microemulsion strategy was utilized to explore the industrialization potential of SLN-based MDI. Three categories of nanocoating with the functions of stabilization (by Poloxamer 188, encoded as SLN(0)), cellular uptake enhancement (by cetyltrimethylammonium bromide, encoded as SLN(+)), and targetability (by hyaluronic acid, encoded as SLN(−)) were constructed upon SLN, whose particle size distribution and zeta-potential were characterized. Subsequently, SLN were loaded into MDI, and evaluated for the processing reliability, physicochemical nature, formulation stability, and biocompatibility. Results: The results elucidated that three types of SLN-based MDI were successfully fabricated with good reproducibility and stability. Regarding safety, SLN(0) and SLN(−) showed negligible cytotoxicity on cellular level. Conclusions: This work serves as a pilot study for the scale-up of SLN-based MDI, and could be useful for the future development of inhalable nanoparticles.

Pseudomonas aeruginosa (PA) is an important pathogen that has been proven to colonize and cause infection in the respiratory tract of patients with structural lung diseases and to lead to bronchial fibrosis. The development of pulmonary fibrosis is a complication of PA colonization of the airway, resulting from repeated infection, damage and repair of the epithelium. Bronchial epithelial cell epithelial-mesenchymal transition (EMT) plays a vital role in bronchial fibrosis. To date, research on bronchial epithelial cell EMT caused by PA-secreted virulence factors has not been reported. Here, we found that PA3611 protein stimulation induced bronchial epithelial cell EMT with mesenchymal cell marker upregulation and epithelial cell marker downregulation. Moreover, integrin αvβ6 expression and TGF-β1 secretion were markedly increased, and p38 MAPK phosphorylation and NF-κB p65 subunit phosphorylation were markedly enhanced. Further research revealed that PA3611 promoted EMT via integrin αvβ6-mediated TGF-β1-induced p38/NF-κB pathway activation. The function of PA3611 was also verified in PA-infected rats, and the results showed that ΔPA3611 reduced lung inflammation and EMT. Overall, our results revealed that PA3611 promoted EMT via integrin αvβ6-mediated TGF-β1-induced p38/NF-κB pathway activation, suggesting that PA3611 acts as a crucial virulence factor in bronchial epithelial cell EMT and is a potential target for the clinical treatment of bronchial EMT and fibrosis caused by chronic PA infection.

Soybean yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. The earlier the prediction during the growing season the better. Accurate soybean yield prediction is important for germplasm innovation and planting environment factor improvement. But until now, soybean yield has been determined by weight measurement manually after soybean plant harvest which is time-consuming, has high cost and low precision. This paper proposed a soybean yield in-field prediction method based on bean pods and leaves image recognition using a deep learning algorithm combined with a generalized regression neural network (GRNN). A faster region-convolutional neural network (Faster R-CNN), feature pyramid network (FPN), single shot multibox detector (SSD), and You Only Look Once (YOLOv3) were employed for bean pods recognition in which recognition precision and speed were 86.2, 89.8, 80.1, 87.4%, and 13 frames per second (FPS), 7 FPS, 24 FPS, and 39 FPS, respectively. Therefore, YOLOv3 was selected considering both recognition precision and speed. For enhancing detection performance, YOLOv3 was improved by changing IoU loss function, using the anchor frame clustering algorithm, and utilizing the partial neural network structure with which recognition precision increased to 90.3%. In order to improve soybean yield prediction precision, leaves were identified and counted, moreover, pods were further classified as single, double, treble, four, and five seeds types by improved YOLOv3 because each type seed weight varies. In addition, soybean seed number prediction models of each soybean planter were built using PLSR, BP, and GRNN with the input of different type pod numbers and leaf numbers with which prediction results were 96.24, 96.97, and 97.5%, respectively. Finally, the soybean yield of each planter was obtained by accumulating the weight of all soybean pod types and the average accuracy was up to 97.43%. The results show that it is feasible to predict the soybean yield of plants in situ with high precision by fusing the number of leaves and different type soybean pods recognized by a deep neural network combined with GRNN which can speed up germplasm innovation and planting environmental factor optimization.