Effects of High Strength Concrete on Progressive Collapse Resistance of Reinforced Concrete Frame

Increasing terrorist activities in the past decade brought requirements in design buildings, especially government or commercial buildings, under extreme loading conditions. One of the devastating consequences due to extreme loading is the possibility of progressive collapse. Although extensive studies had been carried out in the past decade on load resistance mechanism of reinforced concrete (RC) frames in preventing progressive collapse, the effects of high-strength-concrete (HSC) on progressive collapse resistance capacity is still unclear. Therefore, in this paper six tests of reinforced concrete frames with different span-to-depth ratio and concrete strength were conducted in the present study. Among them, three are HSC frames and the remaining are normal strength concrete frames. It was found that the use of HSC could further enhance the compressive arch action (CAA) capacity, especially for those with low span-to-depth ratio. On the other hand, HSC can reduce the tensile catenary action (TCA) capacity at large deformation stage, primarily because of higher bond stress between concrete and rebar,leading to earlier fracture of the rebar.The analytical results from the model were compared with the test results. It is found that the existing CAA model could accurately predict the CAA capacity of HSC frames as well as NSC frames.However, existing model is hard to predict the CAA capacity of the frames with relatively small span-to-depth ratio (less than 7) accurately

Crustal structure and the seismogenic environment in Yunnan imaged by double-difference tomography

The large-scale faulting and earthquake activities that developed extensively in the Yunnan area are associated with the collision of India and Eurasia. The fine crustal structure can provide a better understanding of the crustal deformation, seismogenic environment, and rupture processes. We performed a new 3-dimensional (3D) P wave velocity structure and seismic relocation using double-difference tomography based on seismic observations. The tomography images show that large-scale low-velocity anomalies spread around the margin of the south Chuan–Dian Block, Xiaojiang fault (XJF), and the Lijiang–Xiaojinhe fault (LJ-XJHF) in the middle and lower crust. There is an obvious high-speed anomaly in the Emeishan large igneous province (ELIP). We infer that the low-velocity anomaly under the LJ-XJHF zone may be derived from the lower crustal flow extruded from the central Tibetan plateau and obstructed by the ELIP, while the velocity anomalies around the XJF might be caused by shear heating, which is associated with the large-deep strike–slip fault and the transmission of stress in the southeast direction. The inversion results also show that the Yangbi earthquake occurred at the NW–SE boundary of high and low velocity from the upper crust to the lower crust, which coincides well with the location of the Yangbi earthquake sequence and the Weixi–Qiaohou fault. Meanwhile, the earthquake relocations show that the aftershocks are mainly distributed at low velocities. All the aforementioned research results indicate that the Yangbi earthquake might be attributed to the intrusion of the soft material flow along the Weixi–Qiaohou fault in the NW–SE direction. These low-viscosity crustal materials would cause brittle fractures and result in NW–SE sinistral strike–slip faults

Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation

Effect of rubber particles and fibers on the dynamic compressive behavior of novel ultra-lightweight cement composites:Numerical simulations and metamodeling

This paper presents, first, a finite element (FE) model for a rubberized ultra-lightweight cement composite (RULCC), which uses a modified Holmquist-Johnson-Concrete (H-J-C) constitutive law that is calibrated and validated by new Split Hopkinson pressure bar (SHPB) tests on the material. The validated FE model is used then as the core of a cloud computing platform using a multi node cloud simulation framework to carry out the parametric simulations, which generate required data to develop a meta-model to predict the dynamic impact strength of the RULCC. Design of experiment (DoE) and Generic Programming techniques are the main instruments in developing meta-models with reduced size of data. Finally, a meta-model of explicit expression, which is the first of its kind and considers the effect of rubber ratio, fiber ratio and dynamic impact strain rate, is proposed to predict the dynamic impact strength of the RULCC

Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology

Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features.The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT's ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The implementation and its complete source code of the toolkit are made openly accessible at https://github.com/hrlblab/spatial_pathomics

A magnetic biocatalyst based on mussel-inspired polydopamine and its acylation of dihydromyricetin

A support made of mussel-inspired polydopamine-coated magnetic iron oxide nanoparticles (PD-MNPs) was prepared and characterized. The widely used Aspergillus niger lipase (ANL) was immobilized on the PD-MNPs (ANL@PD-MNPs) with a protein loading of 138 mg/g and an activity recovery of 83.6% under optimized conditions. For the immobilization, the pH and immobilization time were investigated. The pH and thermal and storage stability of the ANL@PD-MNPs significantly surpassed those of free ANL. The ANL@PD-MNPs had better solvent tolerance than free ANL. The secondary structure of free ANL and ANL@PD-MNPs was analyzed by infrared spectroscopy. A kinetic study demonstrated that the ANL@PD-MNPs had enhanced enzyme-substrate affinity and high catalytic efficiency. The ANL@PD-MNPs was applied as a biocatalyst for the regioselective acylation of dihydromyricetin (DMY) in DMSO and gave a conversion of 79.3%, which was higher than that of previous reports. The ANL@PD-MNPs retained over 55% of its initial activity after 10 cycles of reuse. The ANL@PD-MNPs were readily separated from the reaction system by a magnet. The PD-MNPs is an excellent support for ANL and the resulting ANL@PD-MNPs displayed good potential for the efficient synthesis of dihydromyricetin-3-acetate by enzymatic regioselective acylation

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation

A broad-spectrum gas sensor based on correlated two-dimensional electron gas

Designing a broad-spectrum gas sensor capable of identifying gas components in complex environments, such as mixed atmospheres or extreme temperatures, is a significant concern for various technologies, including energy, geological science, and planetary exploration. The main challenge lies in finding materials that exhibit high chemical stability and wide working temperature range. Materials that amplify signals through non-chemical methods could open up new sensing avenues. Here, we present the discovery of a broad-spectrum gas sensor utilizing correlated two-dimensional electron gas at a delta-doped LaAlO3/SrTiO3 interface with LaFeO3. Our study reveals that a back-gating on this two-dimensional electron gas can induce a non-volatile metal to insulator transition, which consequently can activate the two-dimensional electron gas to sensitively and quantitatively probe very broad gas species, no matter whether they are polar, non-polar, or inert gases. Different gas species cause resistance change at their sublimation or boiling temperature and a well-defined phase transition angle can quantitatively determine their partial pressures. Such unique correlated two-dimensional electron gas sensor is not affected by gas mixtures and maintains a wide operating temperature range. Furthermore, its readout is a simple measurement of electric resistance change, thus providing a very low-cost and high-efficient broad-spectrum sensing technique.</p

Extensive pyrosequencing reveals frequent intra-genomic variations of internal transcribed spacer regions of nuclear ribosomal DNA

BACKGROUND: Internal transcribed spacer of nuclear ribosomal DNA (nrDNA) is already one of the most popular phylogenetic and DNA barcoding markers. However, the existence of its multiple copies has complicated such usage and a detailed characterization of intra-genomic variations is critical to address such concerns. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we used sequence-tagged pyrosequencing and genome-wide analyses to characterize intra-genomic variations of internal transcribed spacer 2 (ITS2)regions from 178 plant species. We discovered that mutation of ITS2 is frequent, with a mean of 35 variants per species. And on average, three of the most abundant variants make up 91% of all ITS2 copies. Moreover, we found different congeneric species share identical variants in 13 genera. Interestingly, different species across different genera also share identical variants. In particular, one minor variant of ITS2 in Eleutherococcus giraldii was found identical to the ITS2 major variant of Panax ginseng, both from Araliaceae family. In addition, DNA barcoding gap analysis showed that the intra-genomic distances were markedly smaller than those of the intra-specific or inter-specific variants. When each of 5543 variants were examined for its species discrimination efficiency, a 97% success rate was obtained at the species level. CONCLUSIONS: Identification of identical ITS2 variants across intra-generic or inter-generic species revealed complex species evolutionary history, possibly, horizontal gene transfer and ancestral hybridization. Although intra-genomic multiple variants are frequently found within each genome, the usage of the major variants alone is sufficient for phylogeny construction and species determination in most cases. Furthermore, the inclusion of minor variants further improves the resolution of species identification.Jingyuan Song, Linchun Shi, Dezhu Li, Yongzhen Sun, Yunyun Niu, Zhiduan Chen, Hongmei Luo, Xiaohui Pang, Zhiying Sun, Chang Liu, Aiping Lv, Youping Deng, Zachary Larson-Rabin, Mike Wilkinson and Shilin Che

Polysaccharides from the root of Angelica sinensis promotes hematopoiesis and thrombopoiesis through the PI3K/AKT pathway

<p>Abstract</p> <p>Background</p> <p>Dozens of Traditional Chinese Medicine (TCM) formulas have been used for promotion of "blood production" for centuries, and we are interested in developing novel thrombopoietic medicines from these TCMs. Our previous studies have demonstrated the hematopoietic effects of DangGui BuXue Tong (DBT), a formula composed of <it>Radix Angelicae Sinensis </it>and <it>Radix Astragali </it>in animal and cellular models. As a step further to identify and characterize the active chemical components of DBT, we tested the hematopoietic and particularly, thrombopoietic effects of polysaccharide-enriched fractions from the root of <it>Radix Angelicae Sinensis </it>(APS) in this study.</p> <p>Methods</p> <p>A myelosuppression mouse model was treated with APS (10 mg/kg/day). Peripheral blood cells from APS, thrombopoietin and vehicle-treated samples were then counted at different time-points. Using the colony-forming unit (CFU) assays, we determined the effects of APS on the proliferation and differentiation of hematopoietic stem/progenitor cells and megakaryocytic lineages. Using a megakaryocytic cell line M-07e as model, we analyzed the cellular apoptosis progression with and without APS treatment by Annexin V, Mitochondrial Membrane Potential and Caspase 3 assays. Last, the anti-apoptotic effect of APS on cells treated with Ly294002, a Phosphatidylinositol 3-Kinse inhibitor (PI3K) was also tested.</p> <p>Results</p> <p>In animal models, APS significantly enhanced not only the recovery of platelets, other blood cells and their progenitor cells, but also the formation of Colony Forming Unit (CFU). In M-07e cells, we observed the anti-apoptotic effect of APS. Treatment by Ly294002 alone increased the percentage of cells undergoing apoptosis. However, addition of APS to Ly294002-treated cells significantly reduced the percentage of cells undergoing apoptosis.</p> <p>Conclusions</p> <p>APS promotes hematopoiesis and thrombopoiesis in the mouse model. This effect likely resulted from the anti-apoptosis activity of APS and is likely to involve the PI3K/AKT pathway.</p