Virus-induced gene silencing in the perennial woody Paeonia ostii

Tree peony is a perennial deciduous shrub with great ornamental and medicinal value. A limitation of its current functional genomic research is the lack of effective molecular genetic tools. Here, the first application of a Tobacco rattle virus (TRV)-based virus-induced gene silencing (VIGS) in the tree peony species Paeonia ostii is presented. Two different approaches, leaf syringe-infiltration and seedling vacuum-infiltration, were utilized for Agrobacterium-mediated inoculation. The vacuum-infiltration was shown to result in a more complete Agrobacterium penetration than syringe-infiltration, and thereby determined as an appropriate inoculation method. The silencing of reporter gene PoPDS encoding phytoene desaturase was achieved in TRV-PoPDS-infected triennial tree peony plantlets, with a typical photobleaching phenotype shown in uppermost newly-sprouted leaves. The endogenous PoPDS transcripts were remarkably down-regulated in VIGS photobleached leaves. Moreover, the green fluorescent protein (GFP) fluorescence was detected in leaves and roots of plants inoculated with TRV-GFP, suggesting the capability of TRV to silence genes in various tissues. Taken together, the data demonstrated that the TRV-based VIGS technique could be adapted for high-throughput functional characterization of genes in tree peony

Multiscale Simulation of Laser-Based Direct Energy Deposition (DED-LB/M) Using Powder Feedstock for Surface Repair of Aluminum Alloy

Laser-based direct energy deposition (DED-LB/M) has been a promising option for the surface repair of structural aluminum alloys due to the advantages it offers, including a small heat-affected zone, high forming accuracy, and adjustable deposition materials. However, the unequal powder particle size during powder-based DED-LB/M can cause unstable flow and an uneven material flow rate per unit of time, resulting in defects such as pores, uneven deposition layers, and cracks. This paper presents a multiscale, multiphysics numerical model to investigate the underlying mechanism during the powder-based DED-LB/M surface repair process. First, the worn surfaces of aluminum alloy components with different flaw shapes and sizes were characterized and modeled. The fluid flow of the molten pool during material deposition on the worn surfaces was then investigated using a model that coupled the mesoscale discrete element method (DEM) and the finite volume method (FVM). The effect of flaw size and powder supply quantity on the evolution of the molten pool temperature, morphology, and dynamics was evaluated. The rapid heat transfer and variation in thermal stress during the multilayer DED-LB/M process were further illustrated using a macroscale thermomechanical model. The maximum stress was observed and compared with the yield stress of the adopted material, and no relative sliding was observed between deposited layers and substrate components

Learning Generative Neural Networks for 3D Colorization

Automatic generation of 3D visual content is a fundamental problem that sits at the intersection of visual computing and artificial intelligence. So far, most existing works have focused on geometry synthesis. In contrast, advances in automatic synthesis of color information, which conveys rich semantic information of 3D geometry, remain rather limited. In this paper, we propose to learn a generative model that maps a latent color parameter space to a space of colorizations across a shape collection. The colorizations are diverse on each shape and consistent across the shape collection. We introduce an unsupervised approach for training this generative model and demonstrate its effectiveness across a wide range of categories. The key feature of our approach is that it only requires one colorization per shape in the training data, and utilizes a neural network to propagate the color information of other shapes to train the generative model for each particular shape. This characteristics makes our approach applicable to standard internet shape repositories

PTHrP Modulates the Proliferation and Osteogenic Differentiation of Craniofacial Fibrous Dysplasia-Derived BMSCs

Fibrous dysplasia (FD) is a skeletal stem cell disease caused by mutations in the guanine nucleotide-binding protein, alpha-stimulating activity polypeptide (GNAS) gene, which results in the abnormal accumulation of cyclic adenosine monophosphate (cAMP) and hyperactivation of downstream signaling pathways. Parathyroid hormone-related protein (PTHrP) is secreted by the osteoblast lineage and is involved in various physiological and pathological activities of bone. However, the association between the abnormal expression of PTHrP and FD, as well as its underlying mechanism, remains unclear. In this study, we discovered that FD patient-derived bone marrow stromal cells (FD BMSCs) expressed significantly higher levels of PTHrP during osteogenic differentiation and exhibited greater proliferation capacity but impaired osteogenic ability compared to normal control patient-derived BMSCs (NC BMSCs). Continuous exogenous PTHrP exposure on the NC BMSCs promoted the FD phenotype in both in vitro and in vivo experiments. Through the PTHrP/cAMP/PKA axis, PTHrP could partially influence the proliferation and osteogenesis capacity of FD BMSCs via the overactivation of the Wnt/β-Catenin signaling pathway. Furthermore, PTHrP not only directly modulated cAMP/PKA/CREB transduction but was also demonstrated as a transcriptional target of CREB. This study provides novel insight into the possible pathogenesis involved in the FD phenotype and enhances the understanding of its molecular signaling pathways, offering theoretical evidence for the feasibility of potential therapeutic targets for FD

Clinical models to predict lymph nodes metastasis and distant metastasis in newly diagnosed early esophageal cancer patients: A population‐based study

Abstract Background Patients with early esophageal cancer (EC) receive individualized therapy based on their lymph node metastasis (LNM) and distant metastasis (DM) status; however, deficiencies in current clinical staging techniques and the issue of cost‐effectiveness mean LNM and DM often go undetected preoperatively. We aimed to develop three clinical models to predict the likelihood of LNM, DM, and prognosis in patients with early EC. Method The Surveillance, Epidemiology, and End Results database was queried for T1 EC patients from 2004 to 2015. Multivariable logistic regression and Cox proportional hazards models were used to recognize the risk factors of LNM and DM, predict overall survival (OS), and develop relevant nomograms. Receiver operating characteristic (ROC)/concordance index and calibration curves were used to evaluate the discrimination and accuracy of the three nomograms. Decision curve analyses (DCAs), clinical impact curves, and subgroups based on model scores were used to determine clinical practicability. Results The area under the curve of the LNM and DM nomograms were 0.668 and 0.807, respectively. The corresponding C‐index of OS nomogram was 0.752. Calibration curves and DCA showed an effective predictive accuracy and clinical applicability. In patients with T1N0M0 EC, surgery alone (p < 0.01) proved a survival advantage. Chemotherapy and radiotherapy indicated a better prognosis in the subgroup analysis for T1 EC patients with LNM or DM. Conclusions We created three nomograms to predict the likelihood of LNM, DM, and OS probability in patients with early EC using a generalizable dataset. These useful visual tools could help clinical physicians deliver appropriate perioperative care

Validation of Artificial Intelligence in the Classification of Adolescent Idiopathic Scoliosis and the Compairment to Clinical Manual Handling

Objective The accurate measurement of Cobb angles is crucial for the effective clinical management of patients with adolescent idiopathic scoliosis (AIS). The Lenke classification system plays a pivotal role in determining the appropriate fusion levels for treatment planning. However, the presence of interobserver variability and time‐intensive procedures presents challenges for clinicians. The purpose of this study is to compare the measurement accuracy of our developed artificial intelligence measurement system for Cobb angles and Lenke classification in AIS patients with manual measurements to validate its feasibility. Methods An artificial intelligence (AI) system measured the Cobb angle of AIS patients using convolutional neural networks, which identified the vertebral boundaries and sequences, recognized the upper and lower end vertebras, and estimated the Cobb angles of the proximal thoracic, main thoracic, and thoracolumbar/lumbar curves sequentially. Accordingly, the Lenke classifications of scoliosis were divided by oscillogram and defined by the AI system. Furthermore, a man–machine comparison (n = 300) was conducted for senior spine surgeons (n = 2), junior spine surgeons (n = 2), and the AI system for the image measurements of proximal thoracic (PT), main thoracic (MT), thoracolumbar/lumbar (TL/L), thoracic sagittal profile T5–T12, bending views PT, bending views MT, bending views TL/L, the Lenke classification system, the lumbar modifier, and sagittal thoracic alignment. Results In the AI system, the calculation time for each patient's data was 0.2 s, while the measurement time for each surgeon was 23.6 min. The AI system showed high accuracy in the recognition of the Lenke classification and had high reliability compared to senior doctors (ICC 0.962). Conclusion The AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons

Improving ATMS Imagery Visualization Using Limb Correction and AI Resolution Enhancement

The advanced technology microwave sounder (ATMS) is an important satellite instrument that provides vital data on atmosphere temperature and moisture for weather forecasting and climate research, and helps us plan for extreme weather. However, its coarse resolution and angular dependence have long been a challenge for improving image visualization. This article proposes a method to enhance the imagery visualization for ATMS, combining limb correction (LC) with artificial intelligence (AI) resolution enhancement (RE). Measurement data from the ATMS onboard NOAA-20 were utilized to train the LC method, which were then validated using newly acquired NOAA-21 ATMS data. The AI RE was performed using enhanced super-resolution generative adversarial networks, which increased the pixel resolution by a factor of four. The high-resolution (HR) Advanced Microwave Scanning Radiometer 2 data served as a reference to initially and quantitatively evaluate the RE method. The combined method of LC and AI RE produced an angular-dependence-free and HR ATMS image, resulting in a significant improvement in image visualization, including surface and atmosphere information, and allows for clear identification of severe weather events. For the swift identification and analysis of tropical cyclones in the upcoming season, as of this writing, this proposed method has been routinely employed to produce high-quality global ATMS images, and these images are showcased and tested in the NOAA internal HR imagery visualization system&#x2014;JSTAR Mapper. Moreover, concentrated efforts are being made to further enhance these images in preparation for an official release

Additional file 1 of Nonlinear relationship between oxidative balance score and hyperuricemia: analyses of NHANES 2007–2018

Supplementary Material