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Steel-concrete composite beam has been increasingly applied to large span cable-stayed bridges. It takes full advantage of the material properties of steel and concrete. However, the concrete deck bears tension in the negative moment zone, such as zero block, which is disadvantageous to structures. Aiming at this problem, a finite element model of the zero block in the negative moment zone of a semi-floating cable-stayed bridge is built, and the local mechanical performance of the bridge deck under completed status is studied. Based on the analysis results, three improvement measures have been proposed. The improvement effect of each method and composed of three methods has been studied. The numerical results show that the whole zero block zone is in the compressed state under the combined action of the bending moment and axial force of the stay cable. However, the local negative moment effect in the zero block zone is very prominent under the support of the diaphragm plate. Removing parts of the diaphragm plate at the bearing position can significantly improve local mechanical behavior in the concrete deck, which transfers the local support to the adjacent two diaphragm plates. The composed improvement effect is prominent when the three measures are adopted simultaneously.

Abstract Deficiency of citrin, the liver-type aspartate-glutamate carrier, arises from biallelic mutations of the gene SLC25A13. Although citrin deficiency (CD) is associated with higher risk of hepatocellular carcinoma (HCC) in adult patients, this association remains inconclusive in pediatric cases. The patient in this paper had been diagnosed to have CD by SLC25A13 analysis at the age 10 months, and then in response to dietary therapy, her prolonged jaundice and marked hepatosplenomegaly resolved gradually. However, she was referred to the hospital once again due to recurrent abdominal distention for 2 weeks at her age 4 years and 9 months, when prominently enlarged liver and spleen were palpated, along with a strikingly elevated serum alpha-fetoprotein (AFP) level of 27605 ng/mL as well as a large mass in the right liver lobe and a suspected tumor thrombus within the portal vein on enhanced computed tomography. After 4 rounds of adjuvant chemotherapy, right hepatic lobectomy and portal venous embolectomy were performed at her age 5 years and 3 months, and metastatic hepatoblastoma was confirmed by histopathological analysis. Afterwards, the patient underwent 5 additional cycles of chemotherapy and her condition remained stable for 7 months after surgery. Unfortunately, hepatoblastoma recurred in the left lobe at the age 5 years and 10 months, which progressed rapidly into liver failure, and led to death at the age 6 years and 1 month. As far as we know, this is the the first case of hepatoblastoma in a patient with CD, raising the possibility of an association between these two conditions.

Abstract This study focuses on the application of deep learning for transforming semantic point clouds into semantic Building Information Models (BIM) to create a Heritage Digital Twin, centering on Taoping Village, a site of historical and cultural significance in Sichuan, China. Utilizing advanced technologies such as unmanned aerial vehicles and terrestrial laser scanning, we capture detailed point cloud data of the village. A pivotal element of our methodology is the KP-SG neural network, which exhibits outstanding overall performance, particularly excelling in accurately identifying 11 categories. Among those categories, buildings and vegetation, achieves recognition rates of 81% and 83% respectively, and a 2.53% improvement in mIoU compared to KP-FCNN. This accuracy is critical for constructing detailed and accurate semantic BIM models of Taoping Village, facilitating comprehensive architecture and landscape analysis. Additionally, the KP-SG’s superior segmentation capability contributes to the creation of high-fidelity 3D models, enriching virtual reality experiences. We also introduce a digital twin platform that integrates diverse datasets, their semantic information, and visualization tools. This platform is designed to support process automation and decision-making and provide immersive experiences for tourists. Our approach, integrating semantic BIM models and a digital twin platform, marks a significant advancement in preserving and understanding traditional villages like Taoping and demonstrates the transformative potential of deep learning in cultural heritage conservation.

Abstract This study aims to develop a trigger operator based on the Overlap Volume Histogram (OVH) and examined its effectiveness in enhancing plan quality to minimize radiation-induced lung injury in postoperative radiotherapy for breast cancer. This trigger operator was applied for plan re-optimization to the previous Volumetric Modulated Arc Therapy (VMAT) plans of 16 left breast conserving surgery cases. These cases were categorized into a Contiguous Group (CG) and a Separated Group (SG) based on the relative position between the target and the Left-Lung (L-Lung). We investigated the changes in Vx, mean dose, and Normal Tissue Complication Probability (NTCP) values of organs-at-risk (OARs) before and after using the trigger operator. The Pairwise Sample T test was employed to evaluate the differences in indices between the two groups before and after optimizations. The trigger operator effectively initiated plan re-optimization. The values of V5, V10, V20, V30, and V40 of the L-Lung, as well as the mean dose of the heart, all decreased after re-optimization. The Pairwise Sample T test results showed statistically significant differences in the V20, V30, and V40 of the L-Lung in the CG (P

Abstract For improving performance of the towed array sonar during the manoeuvring period, a processing framework is proposed based on joint estimation of direction of arrival (DOA) and the array shape. The sparse Bayesian learning (SBL) algorithm is utilised to accurately estimate the DOAs of interesting targets. Meanwhile, the towed array shape is modelled as a parabola array, and the array element positions are estimated using the bow of the parabola. Then, an approximate posterior covariance is implemented to the SBL for a fast converging speed required by practical applications. Besides, the co‐prime array structure is considered for the uniform linear array to further reduce the computational load. The numerical simulations have verified the effectiveness of the fast converging SBL method. MAPEX2000 experimental data processing results have shown that the proposed framework performs well in detection of weak targets during slow turns.

Abstract Depth maps are acquirable and irreplaceable geometric information that significantly enhances traditional color images. RGB and Depth (RGBD) images have been widely used in various image analysis applications, but they are still very limited due to challenges from different modalities and misalignment between color and depth. In this paper, a Fully Aligned Fusion Network (FAFNet) for RGBD semantic segmentation is presented. To improve cross‐modality fusion, a new RGBD fusion block is proposed, features from color images and depth maps are first fused by an attention cross fusion module and then aligned by a semantic flow. A multi‐layer structure is also designed to hierarchically utilize the RGBD fusion block, which not only eases issues of low resolution and noises for depth maps but also reduces the loss of semantic features in the upsampling process. Quantitative and qualitative evaluations on both the NYU‐Depth V2 and the SUN RGB‐D dataset demonstrate that the FAFNet model outperforms state‐of‐the‐art RGBD semantic segmentation methods.

IntroductionDigestive system pan-cancer is one of the lethal malignant tumors, which have the propensity for poor prognosis and difficult treatment. Endoplasmic reticulum (ER) stress has served as a pivotal role in the progression of the tumor, while the implication of ER stress on digestive system pan-cancers still needs elucidation, especially from the perspective of clinical outcome and that of genomic features.MethodsFirst, Among the ER STRESS factors from the REACTOME_UNFOLDED_PROTEIN_RESPONSE_UPR (113 genes) and HALLMARK_UNFOLDED_PROTEIN_RESPONSE (92 genes) terms, 153 ER STRESS regulators were identified after removing replicates. The somatic mutation data and copy number variation data of gastrointestinal pan-cancer were downloaded from The Cancer Genome Atlas (TCGA) database. Then, we explored the clinical outcome and genetic mutation of ER stress-related differentially expressed genes (DEGs) by multiple bioinformatics analysis. Subsequently, we analyzed the Spearman correlation between the drug sensitivity of 179 gastrointestinal anticancer drugs and the transcriptional expression of 153 ER stress factors in 769 cancer cell lines of the GDSC2 cohort. Next, ssGSEA method was used to quantify the immune cell infiltration scores in the tumor microenvironment, and Spearman correlation was used to calculate the correlation between ER stress scores and immune cell infiltration. Finally, we analyzed the cellular origin of ER stress factor dysregulation.ResultsWe analyzed the genomic changes and clinical outcomes of ER stress factors in different tumors of gastrointestinal pan-cancer. Endoplasmic reticulum stress factor (ER) in digestive tract tumors showed high SNV mutation frequency, less methylation dysregulation and was associated with multiple oncogenic pathways. Endoplasmic reticulum stress factor (ER) is a risk factor for many cancers, but the effect on overall survival in rectal adenocarcinoma is opposite to that in other gastrointestinal tumors. And ER stress factors are highly correlated with drugs that target important pathways.DiscussionBased on the clinical prognosis and genomic analysis of ER stress-related factors in patients with gastrointestinal pan-cancer, this study provides a new direction for further research on gastrointestinal pan-cancer.

The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions.

Abstract Background Colorectal cancer is a common malignancy occurring in the digestive system and ranks second in cancer mortality worldwide. In colorectal cancer, hydrogen sulfide (H2S) is selectively upregulated, resulting in the further exacerbation of the disease. Therefore, the clearance of H2S and the regulation of the enzymes on the H2S pathways are of great significance for colorectal cancer therapy. Methods Here, we investigated the H2S content in various clinical tumor tissues from patients and confirmed that overproduced concentration of H2S in colorectal cancer. Accordingly, we developed an H2S-responsive nanoplatform based on zinc oxide coated virus-like silica nanoparticles (VZnO) for the therapy of colorectal cancer. Results Owing to its excellent H2S scavenging ability, VZnO could effectively reduce H2S content in colorectal cancer to prohibit the growth of CT26 and HCT116 colorectal cancer cells. Moreover, the removal of H2S in colorectal cancer also leads to tumor inhibition through activating ferroptosis, a non-apoptotic form of cell death. The biosafety-related toxicological and pathological analysis confirmed the low toxicity and high safety of VZnO in colorectal cancer treatment. Furthermore, as an H2S-responsible nanosystem, VZnO appears to have no therapeutic effect on other non H2S rich cancers, such as the 4T1 breast cancer model. Conclusions We anticipate that the H2S-depletion-induced ferroptosis strategy using zinc oxide-based nanomaterials would provide insights in designing nanomedicines for colorectal cancer-target theranostics and may offer clinical promise. Graphic abstract

PurposeFinding a better treatment position (prone or supine) for whole-breast irradiation for Chinese female patients diagnosed with breast cancer by identify the associations between predictive somatotype parameters and dosimetric gains.Materials and methodsTwo volumetric-modulated arc therapy (VMAT) plans were deployed for whole-breast irradiation in supine and prone position with a total dose of 50 Gy in 25 fractions. Dose-volume parameters were compared and analysed both in the target volume and organs at risk, and equivalent uniform dose-based figure-of-merit (fEUD) models were further used to quantitatively evaluate the overall merits of the two plans. Body shape parameters, including body mass index (BMI), body surface area (BSA), breast shape, cup size, bust size and chest size, were collected. Anatomic features such as the central heart distance (CHD) were measured on supine CT. Spearman’s correlation analysis, receiver operating characteristic (ROC) curve analysis, and the linear regression models were conducted.ResultsDoses to the heart and left anterior descending coronary artery (LADCA) are greater in left-sided breast cancer (BC) patients in the prone position than in the supine position, and the opposite was true for right-sided BC patients (p

Soccer participation among children and adolescents is low in China. To achieve a coordinated development of soccer in all regions and to promote the physical health of children and adolescents, this study aims to identify the influencing factors regarding the participation of children and adolescents in soccer programs through a cross-sectional analysis of the “soccer population” of children and adolescents. A total of 38,258 children and adolescents aged 7–18 years were included in this study. In addition, the analysis was conducted by dividing the regions where the children and adolescents live into three parts according to socioeconomic status, and by incorporating five dimensions, including environment, family, school, community, and individual levels to find the influencing factors of children and adolescents’ participation in soccer. Chi-square test, Pearson’s correlation, and one-way logistic regression analyses were used. The results showed that the area (r = 0.487) and the average annual precipitation (r = −0.367) were associated with the participation of children and adolescents in soccer programs. Moreover, the percentage of children and adolescents who participated in soccer programs (24.5%) was higher than those who liked soccer or were proficient in it (14.4%). Meanwhile, parental encouragement and support (OR = 0.627; 95% CI, 1.102–3.179), as well as the accessibility (OR = 0.558; 95% CI, 1.418–2.155), availability (OR = 1.419; 95% CI, 1.179–1.707), and safety of sports facilities (OR = 0.316; 95% CI, 0.614–0.865), influence children and adolescents’ participation in soccer programs.

The numerical investigation of simultaneous methanation and water gas shift reactions (shift–methanation) for biogas upgrading in a fixed-bed reactor is conducted in this work. The catalyst bed was represented as a porous media model, and the reaction kinetics were coupled with heat and mass transfer in the whole simulation process in order to visualize the composition change and temperature profile in the reactor. The effect of reaction temperature, H2/CO ratio of feed gas, and initial CH4 content on the process was separately investigated. It was found that the biogas upgrading with highest CO conversion and CH4 mole fraction could be realized under a reaction temperature of 630 K. Larger H2/CO and initial CH4 content was beneficial to the biogas upgrading process but, in the meantime, lead to a higher temperature rise in the catalyst bed zone. The mass fraction of each component showed uniformity in the radial direction but presented an axial gradient due to the isotropy of porous media, while the temperature profile displayed both radial and axial gradients, which were caused by the temperature difference between the catalyst bed and the reactor wall. The results of experimental validation were in good accordance with the expected ones via numerical simulation.

Aquaculture plays a key role in achieving Sustainable Development Goals (SDGs), while it is difficult to accurately extract single-object aquaculture ponds (SOAPs) from medium-resolution remote sensing images (Mr-RSIs). Due to the limited spatial resolutions of Mr-RSIs, most studies have aimed to obtain aquaculture areas rather than SOAPs. This study proposed an object-oriented method for extracting SOAPs. We developed an iterative algorithm combining grayscale morphology and edge detection to segment water bodies and proposed a segmentation degree detection approach to select and edit potential SOAPs. Then a classification decision tree combining aquaculture knowledge about morphological, spectral, and spatial characteristics of SOAPs was constructed for object filter. We selected a 707.26 km2 study region in Sri Lanka and realized our method on Google Earth Engine (GEE). A 25.11 km2 plot was chosen for verification, where 433 SOAPs were manually labeled from 0.5 m high-resolution RSIs. The results showed that our method could extract SOAPs with high accuracy. The relative error of total areas between extracted result and the labeled dataset was 1.13%. The MIoU of the proposed method was 0.6965, representing an improvement of between 0.1925 and 0.3268 over the comparative segmentation algorithms provided by GEE. The proposed method provides an available solution for extracting SOAPs over a large region and shows high spatiotemporal transferability and potential for identifying other objects.

BL Lacertae underwent a series of historical high flux activity over a year from 2020 August in the optical to VHE γ -rays. In this paper, we report on optical flux and spectral variability of the first historical maxima outburst event during October–November in the g , r , and i bands with the 1.26 m telescope at the Xinglong Observatory, China. We detected significant intranight variations with amplitude rising up to ∼30%, where the fastest variability timescale was found to be a few tens of minutes, giving an emitting region size of the order 10 ^−3 pc, which corresponds to ∼100 Schwarzschild radius of the central black hole, likely coming from some jet mini-structures. Unlike on the intranight timescale, a clear frequency-dependent pattern along symmetric timescales (∼11 days) of flux variation is detected on a long timescale. The spectral evolution was predominated by flattening of the spectra with increasing brightness i.e., a bluer-when-brighter trend in 96% of the cases. On the night before the outburst peak, the color indices shown in a color–magnitude diagram, clustered into two distinct branches, within a period of ∼6 hr, which is connected to a hard-soft-hard spectral evolution trend extracted from time-resolved spectra. To the best of our knowledge, such a trend has never been seen in BL Lac or any other blazars before. The results obtained in this study can be explained in the context of shock-induced particle acceleration or magnetic reconnection in the jet where turbulent processes most likely resulted in the asymmetric flux variation on a nightly timescale.

Purpose:Wolfiporia cocos is frequently used in traditional Chinese medicine to treat depression. However, antidepressant-like effects of the main active ingredients of Wolfiporia cocos, total triterpenes of Wolfiporia cocos (TTWC), are not well studied. This study aimed to investigate those effects and explore their specific mechanisms of action in depth.Methods: Chemical components of TTWC were analyzed using LC-MS. Depression-like behavior in rats were induced by chronic unpredictable mild stress (CUMS). The suppressive effects of TTWC (60, 120, 240 mg/kg) against CUMS-induced depression-like behavior were evaluated using the forced swimming test (FST), open field test (OFT) and sucrose preference test (SPT). Levels of 5-hydroxytryptamine (5-HT), glutamate (GLU), corticotropin-releasing hormone (CRH), interleukin-1 beta (IL-1beta), interleukin-18 (IL-18), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-alpha) in different groups were determined by ELISA. Western blotting (WB) was used to detect the expression of NLRP3, ASC, pro-caspase-1, caspase-1, pro-IL-1beta, IL-1beta, pro-IL-18, and IL-18 in the prefrontal cortex. Additionally, the mRNA levels of NLRP3, ASC, caspase-1, IL-1beta and IL-18 were detected by RT-PCR.Results: A total of 69 lanostane-type triterpene acids of TTWC were identified. The results showed that TTWC exhibited an antidepressant-like effect in CUMS rats, reversed the decreased sugar preference in the SPT, reduction of immobility time in the FST, reduced the rest time, increased the total moving distance in the OFT. TTWC increased 5-HT levels and decreased GLU levels in the hippocampus. Moreover, TTWC decreased CRH levels in serum, indicating the regulation of over-activation of the hypothalamic-pituitary-adrenal (HPA) axis. In addition, reduced serum levels of IL-1beta, IL-18, IL-6, and TNF-alpha. The WB results implied that TTWC inhibited the expression of NLRP3, ASC, caspase-1, IL-1beta, and IL-18 in the prefrontal cortex and enhanced the expression of pro-caspase-1, pro-IL-1beta, and pro-IL-18. Although most of the results were not significant, PCR results showed that TTWC inhibited the expression of NLRP3, ASC, caspase-1, IL-1beta, and IL-18 in the prefrontal cortex.Conclusion: TTWC treatment exerted an antidepressant-like effect and regulates neurotransmitters, HPA axis and NLRP3 signaling pathway. These results indicated the potential of TTWC in preventing the development of depression.

Objective and Impact Statement. Distinguishing tumors from normal tissues is vital in the intraoperative diagnosis and pathological examination. In this work, we propose to utilize Raman spectroscopy as a novel modality in surgery to detect colorectal cancer tissues. Introduction. Raman spectra can reflect the substance components of the target tissues. However, the feature peak is slight and hard to detect due to environmental noise. Collecting a high-quality Raman spectroscopy dataset and developing effective deep learning detection methods are possibly viable approaches. Methods. First, we collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with the Raman shift ranging from 385 to 1545 cm −1. Second, a one-dimensional residual convolutional neural network (1D-ResNet) architecture is designed to classify the tumor tissues of colorectal cancer. Third, we visualize and interpret the fingerprint peaks found by our deep learning model. Results. Experimental results show that our deep learning method achieves 98.5% accuracy in the detection of colorectal cancer and outperforms traditional methods. Conclusion. Overall, Raman spectra are a novel modality for clinical detection of colorectal cancer. Our proposed ensemble 1D-ResNet could effectively classify the Raman spectra obtained from colorectal tumor tissues or normal tissues.

Background: Obesity is a worldwide health problem with many disadvantages for children and adolescents, especially for the sexual development of boys. Exercise and caloric restriction are effective in improving obesity; however, the factors influencing these improvements should be studied in detail. We conducted a randomized controlled study of factors affecting changes in body composition and physical capacity in males aged 13–15 years. Methods: Subjects were divided into three groups based on body fat percentage (BFP), and randomly divided into control and intervention groups. The experimental conditions of this study were based on an exercise camp with a 12-week closed training period. The intervention group received a combination of aerobic exercise, resistance exercise, and caloric restriction. All subjects were uniformly housed and had a standard time for diet and rest, whereas the control group performed their coursework during the exercise intervention time and had no dietary restrictions. The subjects were tested for body composition and physical capacity before and after the intervention. Results: The effect of exercise training and energy-restricted diets on fat loss and physical capacity was limited to relatively lower extremity explosive strength and cardiorespiratory endurance, with a significant effect on body composition. Fat mass reduction was influenced by obesity with a high level of individual variability, with higher levels of obesity resulting in greater reductions in fat mass. The reduction in BFP was not affected by the level of obesity, and there was a mild causal relationship with salivary testosterone (ST) in the intervention group but not in the control group. ST was predictive of future BFP in exercisers; individuals with high ST had a greater reduction in BFP under conditions of prolonged exercise and caloric restriction. Conclusions: More obese male adolescents can obtain greater fat mass reduction with the intervention, but fat-free mass and exercise quality are minimally affected by the level of obesity. Adolescent males with higher ST ground tend to gain improvements in body composition, and ST can be predictive of future BFP but needs to be in the context of an exercise intervention.

Deep-learning-based radar echo extrapolation methods have achieved remarkable progress in the precipitation nowcasting field. However, they suffer from a common notorious problem—they tend to produce blurry predictions. Although some efforts have been made in recent years, the blurring problem is still under-addressed. In this work, we propose three effective strategies to assist deep-learning-based radar echo extrapolation methods to achieve more realistic and detailed prediction. Specifically, we propose a spatial generative adversarial network (GAN) and a spectrum GAN to improve image fidelity. The spatial and spectrum GANs aim at penalizing the distribution discrepancy between generated and real images from the spatial domain and spectral domain, respectively. In addition, a masked style loss is devised to further enhance the details by transferring the detailed texture of ground truth radar sequences to extrapolated ones. We apply a foreground mask to prevent the background noise from transferring to the outputs. Moreover, we also design a new metric termed the power spectral density score (PSDS) to quantify the perceptual quality from a frequency perspective. The PSDS metric can be applied as a complement to other visual evaluation metrics (e.g., LPIPS) to achieve a comprehensive measurement of image sharpness. We test our approaches with both ConvLSTM baseline and U-Net baseline, and comprehensive ablation experiments on the SEVIR dataset show that the proposed approaches are able to produce much more realistic radar images than baselines. Most notably, our methods can be readily applied to any deep-learning-based spatiotemporal forecasting models to acquire more detailed results.