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In this work, a total of nine ultra-high performance concrete (UHPC) filled steel tubular columns (UHPCFSTs) were fabricated and tested under cyclic loading. The results indicated that UHPCFSTs mainly exhibited flexural failure mode, compression-flexure failure mode and welding line fracture, and it changed from flexural failure mode to compression-flexure failure mode when the axial compression ratios exceeded 0.3. The elastic and ultimate drift ratios of UHPCFSTs were within the ranges of 1.37–2.05% and 2.38–4.32%, respectively, and the equivalent viscous damping coefficients were within the range of 0.197–0.358 when they failed, UHPCFSTs could sever as good choices in high seismic intensity areas. [ABSTRACT FROM AUTHOR]

Lysine‐specific demethylase 1 (LSD1) inhibitors are being developed for cancer therapy, but their bioeffects on vasculatures are not clear. In this study, we compared the influences of ORY‐1001 (an LSD1 inhibitor being advanced into clinical trials) and 199 (a novel LSD1 inhibitor recently developed by us) to human umbilical vein endothelial cells (HUVECs) in vitro and further verified the bioeffects of ORY‐1001 to zebrafish (Danio rerio) larvae in vivo. The results showed that up to 10 μM ORY‐1001 or 199 did not significantly affect the cellular viability of HUVECs but substantially reduced the release of inflammatory interleukin‐8 (IL‐8) and IL‐6. The signaling molecule in vasculatures, NO, was also increased in HUVECs. As the mechanism, the protein levels of endothelial NO synthase (eNOS) or p‐eNOS, and their regulators Kruppel‐like factor 2 (KLF2) or KLF4, were also increased after drug treatment. In vivo, 24 h treatment with up to 100 nM ORY‐1001 reduced blood speed without changing morphologies or locomotor activities in zebrafish larvae. ORY‐1001 treatment reduced the expression of il8 but promoted the expression of klf2a and nos in the zebrafish model. These data show that LSD1 inhibitors were not toxic but capable to inhibit inflammatory responses and affect the function of blood vessels through the up‐regulation of the NOS‐KLF pathway. It is necessary to investigate the influences of LSD1 inhibitors on vascular systems. This study reported that LSD1 inhibitors ORY‐1001 or 199 were not cytotoxic to HUVECs but reduced inflammatory mediator release and induced NO release through the up‐regulation of the eNOS‐KLF2/4 signaling pathway. However, LSD1 inhibitors also modestly induced oxidative stress. Under in vivo conditions, ORY‐1001 did not induce developmental toxicity but reduced il8 expression and blood speed through the up‐regulation of nos‐klf gene expression. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) technology has been widely utilized for collecting three-dimensional (3D) point cloud data on forest scenes, enabling the generation of high-accuracy digital elevation models (DEMs) for the efficient investigation and management of forest resources. Point cloud filtering serves as the crucial initial step in DEM generation, directly influencing the accuracy of the resulting DEM. However, forest filtering presents challenges in dealing with sparse point clouds and selecting appropriate initial ground points. The introduction of full-waveform LiDAR data offers a potential solution to the problem of sparse point clouds. Additionally, advancements in multi-source data integration and machine learning algorithms have created new avenues that can address the issue of initial ground point selection. To tackle these challenges, this paper proposes a novel filtering method for forest scenes utilizing full-waveform LiDAR data and hyperspectral image data. The proposed method consists of two main steps. Firstly, we employ the improved dynamic graph convolutional neural network (IDGCNN) to extract initial ground points. In this step, we utilize three types of low-correlation features: LiDAR features, waveform features, and spectral features. To enhance its accuracy and adaptability, a self-attention module was incorporated into the DGCNN algorithm. Comparative experiments were conducted to evaluate the effectiveness of the algorithm, demonstrating that the IDGCNN algorithm achieves the highest classification accuracy with an overall accuracy (OA) value of 99.38% and a kappa coefficient of 95.95%. The second-best performer was the RandLA-net algorithm, achieving an OA value of 98.73% and a kappa coefficient of 91.68%. The second step involves refining the initial ground points using the cloth simulation filter (CSF) algorithm. By employing the CSF algorithm, non-ground points present in the initial ground points are effectively filtered out. To validate the efficacy of the proposed filtering method, we generated a DEM with a resolution of 0.5 using the ground points extracted in the first step, the refined ground points obtained with the combination of the first and second steps, and the ground points obtained directly using the CSF algorithm. A comparative analysis with 23 reference control points revealed the effectiveness of our proposed method, as evidenced by the median error of 0.41 m, maximum error of 0.75 m, and average error of 0.33 m. [ABSTRACT FROM AUTHOR]

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species., Competing Interests: Conflicts of interest. The author(s) declare no conflict of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping., Competing Interests: There is conflict of interest regarding this article with Apple Inc., Microsoft Corporation Inc., and Parametrix.ai Inc.., (Copyright © 2022 Jialin Yuan et al.)