Numerical simulation of ultra-strength concrete-filled steel columns

Researches into the constitutive relation of the super high strength and high performance concrete and the stress strain relationship of the ultra-strength concrete-filled steel columns are rare. Therefore, this paper based on continuous mechanics presents the relationship of mathematical description to the concrete deformation behaviors. The compressive behaviors of steel-reinforced super high-strength concrete columns under axial loading were studied with a series of experiments. Two specimens with concrete strengths ranging from 130,1MPa to 137,3MPa and with 121mm circular hollow stub columns with wall thicknesses of 5 mm were manufactured. At the same time, a three dimensional non-linear FE analysis of axial compression was conducted using the finite element program ABAQUS/Standard solver. The numerical results were validated through comparison with experimental data in terms of axial loading and deformation modes

MTANS:Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images

GAN-based multi-style photo cartoonization

Cartoon is a common form of art in our daily life and automatic generation of cartoon images from photos is highly desirable. However, state-of-the-art single-style methods can only generate one style of cartoon images from photos and existing multi-style image style transfer methods still struggle to produce high-quality cartoon images due to their highly simplified and abstract nature. In this paper, we propose a novel multi-style generative adversarial network (GAN) architecture, called MS-CartoonGAN, which can transform photos into multiple cartoon styles. We develop a multi-domain architecture, where the generator consists of a shared encoder and multiple decoders for different cartoon styles, along with multiple discriminators for individual styles. By observing that cartoon images drawn by different artists have their unique styles while sharing some common characteristics, our shared network architecture exploits the common characteristics of cartoon styles, achieving better cartoonization and being more efficient than single-style cartoonization. We show that our multi-domain architecture can theoretically guarantee to output desired multiple cartoon styles. Through extensive experiments including a user study, we demonstrate the superiority of the proposed method, outperforming state-of-the-art single-style and multi-style image style transfer methods

Research on Modern Marine Environmental Governance in China: Subject Identification, Structural Characteristics, and Operational Mechanisms

Under the guidance of modern environmental governance concepts, there have been profound changes in the subject, structure, and operational mechanism of the modern marine environmental governance in China. This paper first classifies the subjects of modern marine environmental governance in China, as well as their relationships; analyses the structural characteristics from the three levels of rights, society, and region; explores the operational mechanism; and builds the framework of the modern marine environmental governance system in China. Both the central and local governments act as the leaders of the modern marine environmental governance system in China, and there have been many new changes in their relationships. On the one hand, the interest and goals of the central and local governments have gradually converged under the pressure system. On the other hand, local governments follow the principles of comprehensive governance regarding the coastline and collaborative cooperation is gradually beginning to occur. Different governance subjects are interrelated and intertwined to form a complete modern marine environmental governance structure, which includes the following three levels: the governmental power structure; the social structure, which involves collaboration between multiple entities; and the regional structure, which involves land-sea coordination in environmental governance. These structures each play their parts in the overall process of the marine environmental governance’s institutional arrangements, process coordination, and feedback adjustments and ultimately constitute a dynamic and complete modern marine environmental governance operational system

HyOASAM: A Hybrid Open API Selection Approach for Mashup Development

At present, Mashup development has attracted much attention in the field of software engineering. It is the focus of this article to use existing open APIs to meet the needs of Mashup developers. Therefore, how to select the most appropriate open API for a specific user requirement is a crucial problem to be solved. We propose a Hybrid Open API Selection Approach for Mashup development (HyOASAM), which consists of two basic approaches: one is a user-story-driven open API discovery approach, and the other is multidimensional-information-matrix- (MIM-) based open API recommendation approach. The open API discovery approach introduces user stories in agile development to capture Mashup requirements. First, it extracts three components from user stories, and then, it extracts three corresponding properties from open API descriptions. Next, the similarity calculation is performed on two sets of data. The open API recommendation approach first uses MIM to store open APIs, Mashups, and the invoking relationship between them. Second, it enters the matrix obtained in the previous step into a factorization machine model to calculate the association scores between the Mashups and the open APIs, and TOP-N open API lists for creating the Mashup are obtained. Finally, experimental comparison and analysis are carried out on the PWeb dataset. The experimental results show that our approach has improved significantly

Characterization of a Flavonoid 3’/5’/7-O-Methyltransferase from Citrus reticulata and Evaluation of the In Vitro Cytotoxicity of Its Methylated Products

O-methylation of flavonoids is an important modification reaction that occurs in plants. O-methylation contributes to the structural diversity of flavonoids, which have several biological and pharmacological functions. In this study, an O-methyltransferase gene (CrOMT2) was isolated from the fruit peel of Citrus reticulata, which encoding a multifunctional O-methyltransferase and could effectively catalyze the methylation of 3’-, 5’-, and 7-OH of flavonoids with vicinal hydroxyl substitutions. Substrate preference assays indicated that this recombinant enzyme favored polymethoxylated flavones (PMF)-type substrates in vitro, thereby providing biochemical evidence for the potential role of the enzyme in plants. Additionally, the cytotoxicity of the methylated products from the enzymatic catalytic reaction was evaluated in vitro using human gastric cell lines SGC-7901 and BGC-823. The results showed that the in vitro cytotoxicity of the flavonoids with the unsaturated C2-C3 bond was increased after being methylated at position 3’. These combined results provide biochemical insight regarding CrOMT2 in vitro and indicate the in vitro cytotoxicity of the products methylated by its catalytic reaction

Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers

ObjectivesTo apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI.MethodsA total of 244 patients were analyzed, 99 in training dataset scanned at 1.5 T and 83 in testing-1 and 62 in testing-2 scanned at 3 T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2-), HER2+, and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the training dataset, by using a conventional CNN and the convolutional long short-term memory (CLSTM). Then, transfer learning was applied to re-tune the model using testing-1(2) and evaluated in testing-2(1).ResultsIn the training dataset, the mean accuracy evaluated using tenfold cross-validation was higher by using CLSTM (0.91) than by using CNN (0.79). When the developed model was applied to the independent testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%.ConclusionsThe recurrent network using CLSTM could track changes in signal intensity during DCE acquisition, and achieved a higher accuracy compared with conventional CNN during training. For datasets acquired using different settings, transfer learning can be applied to re-tune the model and improve accuracy.Key points• Deep learning can be applied to differentiate breast cancer molecular subtypes. • The recurrent neural network using CLSTM could track the change of signal intensity in DCE images, and achieved a higher accuracy compared with conventional CNN during training. • For datasets acquired using different scanners with different imaging protocols, transfer learning provided an efficient method to re-tune the classification model and improve accuracy

Table_1_The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis.XLSX

BackgroundPoor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain.MethodsWe used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted “brain age gap” (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis.ResultsWe found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10−4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60–73 (p-value = 2.37 × 10−8).ConclusionPoor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60–73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.</p

Data_Sheet_1_The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis.DOCX

BackgroundPoor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain.MethodsWe used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted “brain age gap” (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis.ResultsWe found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10−4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60–73 (p-value = 2.37 × 10−8).ConclusionPoor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60–73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.</p