Search

Predicting the capability of decay-heat removal for a pebble bed of the high-temperature gas-cooled reactor is a cutting-edge issue in the research of generation IV advanced reactors. In this research, numerical simulations of heat transfer in the pebble bed reactor have been conducted by developing two kinds of models, a particle-scale model and a porous media model. The aim is to estimate the effect thermal conductivity of the pebble bed and predict the internal temperature field. The former model relies on the pebble–pebble conduction and radiation at a scale as fine as every fuel pebble. The latter one is an overall model at the scale mainly on the whole feature and capability of the pebble bed reactor. These two models have generated consistent results in an intermediate case considering the effective thermal conductivity for radiation and conduction of the pebbles. Based on this consistency, the pebble bed's overall features of the internal field of the temperature field in pressurized helium have been well predicted. The simulation results validate the pretty good capability of the pebble bed reactor on decay-heat removal. The maximum temperature of the reactor core is limited to about 350 °C at the 7 MPa pressurized helium. This means the safety of the HTR-PM reactor could be reasonably guaranteed. • A new SPH kernel by combining a polynomial and a reciprocal term is proposed. • Validated by dam-break, the kernel is used for natural convection in reactor cores. • Comparing to existing kernels shows the differences and (dis)-advantages of them. • Various temperature differences, channel numbers/lengths are simulated. • A mapping method is used to transfer particles' velocity/temperature to Eulerian. [ABSTRACT FROM AUTHOR]

Based on the discrete element method (DEM) and GPU parallel computing, a particle heat transfer model is developed to simulate the heat transfer in a pebble bed of the high-temperature gas-cooled reactor (HTGR). The model is implemented based on a previously developed GPU-DEM program by our team and uses the mesh-based neighbor searching algorithm for the heat transfer calculation. This model couples the conduction and radiative heat transfer between the pebbles and incorporates neural networks and empirical fittings to calculate the radiation view factors, which can improve computational efficiency. The effective thermal conductivity of different models and experimental data are used to verify the accuracy of the model, and the influence of different radiation heat transfer models on the results is also compared. The results show that the effective thermal conductivity derived from the current model is comparable to the classical models at different temperatures, and the numerical simulation results based on the current model are in good agreement with the corresponding experimental data. Additionally, the model achieves a single-core speedup ratio of 126–395 times with GPU acceleration, significantly enhancing computational efficiency. In conclusion, the current model has been effectively verified for accuracy and computational efficiency, and it demonstrates great potential in dealing with large-scale pebble flow and heat transfer challenges in HTGRs. • A Voronoi-tessellation-free new heat transfer model is proposed for pebble beds. • View factors are calculated by neural networks to couple conduction and radiation. • GPU parallel computing is employed at a speedup ratio of 126–395 times of CPU. • An alternate-read-write method and unified Memory Access technology are used. • The model accuracy is validated and discussed by comparing it with experiments. [ABSTRACT FROM AUTHOR]

The flow, stacking, and mixing of pebbles in the High-Temperature Gas-cooled Reactor (HTGR) will affect the power distribution of the core and thus affect the economy and safety of the nuclear reactor. Simulations of pebble loading and mixing in HTR-PM based on GPU-DEM have been studied with particle numbers ranging from 230,000 to 420,000. The effects of four loading methods on pebble mixing are compared and analyzed. Mean position, segregation index (SI), Lacey's mixing index (PSMI), mixing entropy (ME), and porosity are used for quantitative analysis. In addition, an alternative approximate method is proposed to calculate the particle number fraction, which can help solve the problem that the particle number fraction is related to the mesh size. The final result shows that different physical parameters, such as mass and Young's modulus, will induce slight stratification during pebble mixing. At the same time, the simulation results with different loading methods have different mixing degrees. The reduced model mixes better than the single-pebble-loading method, but the latter is closer to engineering practice. • Loading & mixing of two kinds of pebbles in a real-scale bed are simulated • An in-house GPU-DEM program has been developed to simulate 420,000 particles • Mixing degrees are analyzed by mean position, SI, PSMI, ME, and porosity • Effect of mass and Young's modulus on the mixing of pebbles is explored. • A new method is proposed to calculate number fractions to solve the mesh effects. [ABSTRACT FROM AUTHOR]

CRISPR-based base editors (BEs) are powerful tools for precise nucleotide substitution in a wide range of organisms, but spatiotemporal control of base editing remains a daunting challenge. Herein, we develop a photoactivatable base editor (Mag-ABE) for spatiotemporally controlled genome editing in vivo for the first time. The base editing activity of Mag-ABE can be activated by blue light for spatiotemporal regulation of both EGFP reporter gene and various endogenous genes editing. Meanwhile, the Mag-ABE prefers to edit A4 and A5 positions rather than to edit A6 position, showing the potential to decrease bystander editing of traditional adenine base editors. After integration with upconversion nanoparticles as a light transducer, the Mag-ABE is further applied for near-infrared (NIR) light-activated base editing of liver in transgenic reporter mice successfully. This study opens a promising way to improve the operability, safety, and precision of base editing. [Display omitted] [ABSTRACT FROM AUTHOR]

Diverse oxygen generation strategies have been developed to overcome hypoxia in tumors for enhancing the therapeutic efficacy, but inevitably suffering from tedious synthesis process of oxygen generators in vitro before in vivo administration. Herein, we show direct injection of commercially and clinically used KMnO 4 into solid tumors enables in situ formation of MnO 2 as an oxygen depot for cascade oxidation damage and enhanced photodynamic therapy. KMnO 4 can damage tumor tissues by oxidation and generate MnO 2 , and subsequent intravenous injection of Ce6 allows MnO 2 -triggered hypoxia-modulated photodynamic therapy of tumors. Excellent cascade tumor suppression effect is realized both in vitro and in vivo based on the KMnO 4 –Ce6 system without the need of synthesis. The proposed strategy lays down a novel way with unprecedented superiors of no need of synthesis process and ultra-facile administration procedure for tumor hypoxia-modulated cascade therapy. Intratumoral injection of KMnO 4 can not only damage tumor cells by oxidation, but also generate MnO 2 as a minimalist O 2 generator, and subsequently intravenous injection of Ce6 allows hypoxia-modulated photodynamic therapy of tumors. The proposed strategy lays down a new way with unprecedented superiors of avoiding the synthesis process and ultra-facile administration procedure for tumor hypoxia-modulated cascade therapy. [Display omitted] [ABSTRACT FROM AUTHOR]

The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction. However, one challenge for deep learning-based models is appropriate and accurate representations of drugs and targets, especially the lack of effective exploration of target representations. Another challenge is how to comprehensively capture the interaction information between different instances, which is also important for predicting DTA. In this study, we propose AttentionMGT-DTA, a multi-modal attention-based model for DTA prediction. AttentionMGT-DTA represents drugs and targets by a molecular graph and binding pocket graph, respectively. Two attention mechanisms are adopted to integrate and interact information between different protein modalities and drug-target pairs. The experimental results showed that our proposed model outperformed state-of-the-art baselines on two benchmark datasets. In addition, AttentionMGT-DTA also had high interpretability by modeling the interaction strength between drug atoms and protein residues. Our code is available at https://github.com/JK-Liu7/AttentionMGT-DTA. [ABSTRACT FROM AUTHOR]

• rFSAV is a novel recombinant five-antigen Staphylococcus aureus vaccine. • rFSAV was assessed in patients undergoing elective surgery for closed fractures for the first time. • rFSAV is safe, well tolerated in patients undergoing elective surgery for closed fractures. • rFSAV elicits rapid and robust specific humoral immune responses in targeted population. • rFSAV is worth further evaluation in in phase 3 studies in a larger number of individuals. Vaccines are urgently required to control Staphylococcus aureus hospital and community infections and reduce the use of antibiotics. Here, we report the safety and immunogenicity of a recombinant five-antigen Staphylococcus aureus vaccine (rFSAV) in patients undergoing elective surgery for closed fractures. A randomized, double-blind, placebo-controlled, multicenter phase 2 clinical trial was carried out in 10 clinical research centers in China. Patients undergoing elective surgery for closed fractures, aged 18–70 years, were randomly allocated at a ratio of 1:1 to receive the rFSAV or placebo at a regimen of two doses on day 0 and another dose on day 7. All participants and investigators remained blinded during the study period. The safety endpoint was the incidence of adverse events within 180 days. The immunogenicity endpoints included the level of specific antibodies to five antigens after vaccination, as well as opsonophagocytic antibodies. A total of 348 eligible participants were randomized to the rFSAV (n = 174) and placebo (n = 174) groups. No grade 3 local adverse events occurred. There was no significant difference in the incidence of overall systemic adverse events between the experimental (40.24 %) and control groups (33.72 %) within 180 days after the first immunization. The antigen-specific binding antibodies started to increase at days 7 and reached their peaks at 10–14 days after the first immunization. The rapid and potent opsonophagocytic antibodies were also substantially above the background levels. rFSAV is safe and well-tolerated in patients undergoing elective surgery for closed fractures. It elicited rapid and robust specific humoral immune responses using the perioperative immunization procedure. These results provide evidence for further clinical trials to confirm the vaccine efficacy. China's Drug Clinical Trials Registration and Information Publicity Platform registration number: CTR20181788. WHO International Clinical Trial Registry Platform identifier: ChiCTR2200066259. [ABSTRACT FROM AUTHOR]

Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs is a necessary way to study ncRNA. Traditional biochemical methods are time-consuming and labor-intensive, and computational-based methods can help detect the location of ncRNAs on a large scale. However, many models did not consider the correlation information among multiple subcellular localizations of ncRNAs. This study proposes a radial basis function neural network based on shared subspace learning (RBFNN-SSL), which extract shared structures in multi-labels. To evaluate performance, our classifier is tested on three ncRNA datasets. Our model achieves better performance in experimental results. [ABSTRACT FROM AUTHOR]