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This study introduces a GPU-based parallel computing approach that combines the phase-field model (PF) and the lattice Boltzmann model (LBM). By establishing a coupled multiphase field model incorporating physical external fields such as flow field, temperature field, and solute field, the research simulates the growth of single grains and multiple grains under the influence of natural convection. The variations in dendritic morphology, flow field, and solute field during dendritic solidification processes are observed. Initially, the study analyzes the morphology of equiaxed dendrites and the growth patterns of primary dendrites arms under natural convection conditions. The evolution of equiaxed dendrites in single grains and multiple grains under various conditions is investigated. Furthermore, the study explores the impact of different anisotropy strengths on the growth of single grains and multiple grains under natural convection. Notably, a distinct “necking” phenomenon is observed when the anisotropy strength of a single grain is 0.05. In the case of multiple grains, where competition between dendrites is present in addition to the influence of natural convection, a pronounced “necking” phenomenon is evident at an anisotropy strength of 0.03. Moreover, OpenCL parallel technology is designed on the GPU platform to accelerate the solution of the model. The parallelization of the phase-field model coupled with the LBM model on the GPU demonstrates a clear advantage. The parallel computation based on GPU not only exhibits absolute superiority but also shows more significant acceleration effects as the computational domain increases.

17β-Estradiol (E2) is a widely present trace pollutant in aquatic environments. However, its impact on microbial communities in aerobic lake waters, which are crucial for methane (CH4) production, remains unclear. This study conducted an E2 contamination experiment by constructing laboratory-simulated aerobic microecosystems. Using 16S rRNA high-throughput sequencing, the effects of E2 on bacterial and archaeal communities were systematically examined. Combined with gas chromatography, the patterns and mechanisms of E2’s impact on CH4 emissions in aerobic aquatic systems were uncovered for the first time. Generally, E2 contamination increased the randomness of bacterial and archaeal community assemblies and weakened microbial interactions. Furthermore, changes occurred in the composition and ecological functions of bacterial and archaeal communities under E2 pollution. Specifically, two days after exposure to E2, the relative abundance of Proteobacteria in the low-concentration (L) and high-concentration (H) groups decreased by 6.99% and 4.01%, respectively, compared to the control group (C). Conversely, the relative abundance of Planctomycetota was 1.81% and 1.60% higher in the L and H groups, respectively. E2 contamination led to an increase in the relative abundance of the methanogenesis functional group and a decrease in that of the methanotrophy functional group. These changes led to an increase in CH4 emissions. This study comprehensively investigated the ecotoxicological effects of E2 pollution on microbial communities in aerobic water bodies and filled the knowledge gap regarding aerobic methane production under E2 contamination.

Viruses, as the most prolific entities on Earth, constitute significant ecological groups within freshwater lakes, exerting pivotal ecological roles. In this study, we selected Chaohu Lake, a representative eutrophic freshwater lake in China, as our research site to explore the community distribution, driving mechanisms, and potential ecological functions of diverse viral communities, the intricate virus–host interaction systems, and the overarching influence of viruses on global biogeochemical cycling.

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

Global warming caused by excess carbon dioxide (CO2) emission has been a focus of the world. The development of neutral carbon technologies becomes a strategic choice for the sustainable human society. Integrating CO2 capture and conversion (iCCC) technology can simultaneously convert the captured CO2 from flue gas into value-added chemicals, which saves great energies and expenses incurred in CO2 compression and transportation processes of conventional carbon capture, utilization, and storage (CCUS) technology. The present review critically discusses the dual-function materials (DFMs) and the iCCC technology at intermediate temperature for methane production and high temperature for syngas production. The design of reactor and optimization of operation conditions are emphasized from the perspective of industrial applications. The dual-fixed-bed reactors mode by switching the flue gas and reactant gases, and the dual-fluidized-bed reactors mode by the circulation of DFMs particles are comparatively reviewed. We hope this review can stimulate further studies including designing and fabricating feasible DFMs, exploring realistic catalytic process for CO2 conversion to high value-added chemicals, developing workable reactor modes and optimizing operation conditions, and establishing industrial demonstration for real applications of iCCC technology in the future.

Imaging of multiples, as a supplement to imaging of primaries, can provide a wider range of subsurface illumination. Therefore, it can provide more detailed information on subsurface structures. However, imaging of multiples suffers from crosstalk issues generated by unrelated events. Many strategies have been proposed to attenuate crosstalk, among which the angle domain Radon crosstalk attenuation algorithm achieves good application effect. In the angle domain, the true imaging is flat, while the crosstalk events have moveouts. Therefore, it is convenient to identify the crosstalk in angle gathers using the Radon transform. However, the conventional Radon transform lacks a quantitative description for crosstalk in angle gathers, which would affect the accuracy of crosstalk attenuation. In this paper, residual moveout kernels are derived with a Radon transform to attenuate crosstalk in angle gathers for imaging of multiples. First, two types of residual moveout (RMO) equations are derived based on the causality of crosstalk. A three-layer model is used to verify the correctness of the analytical solutions. Then, based on the derived equations, the two types of crosstalk can be attenuated respectively in the Radon domain. Synthetic experiments demonstrate that the derived RMO equations can effectively attenuate the crosstalk events in imaging of multiples.

An increasing number of studies have reported that microbiome can affect drug response by altering pharmacokinetics and pharmacodynamics of formation of toxic metabolites. With the development of metagenomic sequencing, gut microbial composition as well as the metabolic function are drawing more and more attention for the patient stratification. The established microbiota databases provide useful information about the gut microbe-drug interactions. However, these databases generally lacked the detailed effects on substance and the metabolites, which are helpful in elucidating the mechanisms underlying drug biotransformation and personalized medicine. To address these issues, in this study, we developed Metabolic action of gut Microbiota to Drugs (MagMD), a database and a web-service covering 32, 678 records of interactions between 2,146 gut microbes, 36 enzymes and 219 substrates (mainly drugs). The detailed annotations for each entry, including the taxonomic level of microbes, the molecular form and PubChem ID of drugs from PubChem Compound Database, types of microbial secreted enzymes and the original reference links can also be accessed from the web service. Availability and implementation: MagMD is a publicly available resource, constantly updated. It has an intuitive web interface and can be freely accessed at http://www.unimd.org/magmd.

The microtubule-stabilizing drug paclitaxel (PTX) is a chemotherapeutic agent widely prescribed for the treatment of various tumor types. The main adverse effect of PTX-mediated therapy is chemotherapy-induced peripheral neuropathy (CIPN) and neuropathic pain, which are similar to the adverse effects associated with other chemotherapeutic agents. Dorsal root ganglia (DRG) contain primary sensory neurons; any damage to these neurons or their axons may lead to neuropathic pain. To gain molecular and neurobiological insights into the peripheral sensory system under conditions of PTX-induced neuropathic pain, we used transcriptomic analysis to profile mRNA and non-coding RNA expression in the DRGs of adult male C57BL/6 mice treated using PTX. RNA sequencing and in-depth gene expression analysis were used to analyze the expression levels of 67,228 genes. We identified 372 differentially expressed genes (DEGs) in the DRGs of vehicle- and PTX-treated mice. Among the 372 DEGs, there were 8 mRNAs, 3 long non-coding RNAs (lncRNAs), 16 circular RNAs (circRNAs), and 345 microRNAs (miRNAs). Moreover, the changes in the expression levels of several miRNAs and circRNAs induced by PTX have been confirmed using the quantitative polymerase chain reaction method. In addition, we compared the expression levels of differentially expressed miRNAs and mRNA in the DRGs of mice with PTX-induced neuropathic pain against those evaluated in other models of neuropathic pain induced by other chemotherapeutic agents, nerve injury, or diabetes. There are dozens of shared differentially expressed miRNAs between PTX and diabetes, but only a few shared miRNAs between PTX and nerve injury. Meanwhile, there is no shared differentially expressed mRNA between PTX and nerve injury. In conclusion, herein, we show that treatment with PTX induced numerous changes in miRNA expression in DRGs. Comparison with other neuropathic pain models indicates that DEGs in DRGs vary greatly among different models of neuropathic pain.

A multi-phase model was established to imitate the growth of algal and dendritic grains during directional solidification. We studied the effects of temperature on the growth of bi-crystals and quantitatively analyzed the influence of anisotropic strength, thermal gradient, and pulling velocity on the evolution of bi-crystals. The results show that both weaker anisotropy strength and smaller pulling velocity can maintain the formation of seaweed tissue. The increase in the pulling velocity can degenerate the seaweed grains into dendrites and improve the growth rate of the dendrites, which make grain B produce more spindles, thereby accelerating the elimination of grain A. The thermal gradient is inversely proportional to the average initial spacing of dendrites. When the thermal gradient is too small, dendritic dendrites produce developed secondary dendrite arms, which, in turn, develop into tertiary dendrite arms to occupy the grain boundary, accelerating the elimination of seaweed grains. In addition, the multi-phase field model is solved by using central processing unit serial computation, single MPI (message passing interface) parallel programming method calculation, and MPI+OpenMP hybrid parallel programming structure, and the relevant factors affecting the efficiency of program operation are analyzed and tested. By comparing the computational efficiency of the three methods, it can be seen that the MPI+OpenMP hybrid parallel programming technology can make full use of computing resources in the case of large computing scale, further optimize the MPI parallel model, and obtain a higher acceleration ratio.

Background. MSC-derived extracellular vehicles (EVs) exhibit a protective functional role in renal ischemia/reperfusion injury (RIRI). Recent studies have revealed that mitophagy could be a potential target process in the treatment of RIRI. However, whether MSC-derived EVs are involved in the regulation of mitophagy in RIRI remains largely unknown to date. Methods. RIRI model was established in vivo in mice by subjecting them to renal ischemia/reperfusion. TCMK-1 cells were subjected to hypoxia/reoxygenation (H/R) stimulation to mimic RIRI in vitro. BMSCs and BMSC-derived EVs were isolated and identified. Renal injury was assessed using H&E staining. The qPCR and western blot analyses were conducted to detect the mRNA and protein levels. Apoptosis was evaluated using the TUNEL assay and flow cytometry analysis. The EVs, autophagosomes, and mitochondria were observed using TEM. The colocalization of autophagosomes with mitochondria was confirmed through the confocal assay. The direct binding of miR-223-3p to NLRP3 was validated through the dual-luciferase assay. Results. BMSCs and BMSC-derived EVs were successfully isolated from mice and identified. The protective effect of BMSC-derived EVs against RIRI was validated both in vitro and in vivo, which was indicated by a decrease in apoptosis and inflammasome activation and an increase in mitophagy. However, this protective effect was impaired in the miR-223-3p-depleted EVs, suggesting that miR-223-3p mediated this protective effect. Further mechanistic investigation revealed that miR-223-3p suppressed inflammasome activation to enhance mitophagy by directly targeting NLRP3. Conclusion. In conclusion, the protective role of BMSC-derived EVs and exosome-delivered miR-223-3p in RIRI was validated. Exogenous miR-223-3p directly targeted NLRP3 to attenuate inflammasome activation, thereby promoting mitophagy.

This paper studies the microstructure and mechanical properties of MIG (Melt Inert Gas) lap welded 6005 aluminum alloy plates. Microstructure analysis (OM) of the joint showed that 15~30 μm small grains were observed at the fusion line. Mechanical analysis shows that the small grains are broken by shielding gas and molten pool flow force. Hardness test shows that there is a softening zone (41~43 HV) in HAZ much lower than BM and WZ. The low cycle fatigue test showed that the performance of lap joint decreased sharply, and the fatigue strength of weld decreased significantly, which was only 27.34% of the base metal. The fatigue fracture (SEM) of the weld observed slip band cracking and a large number of brittle fracture characteristics. Using the stress concentration factor Kt for analysis, it was found that the cause of brittle fracture was mostly stress concentration. Lap joint stress concentration model appears in two ways: firstly, at the weld toe, the weld is subjected to eccentric force, secondly, there is a small gap between the two plates at the weld root, which cracks along the direction of 45° of the maximum shear stress.

Akkermansia muciniphila (A. muciniphila) is a mucin-degrading bacterium that resides in the mucus layer, but its potential in intestinal inflammatory diseases has sparked controversy. It is well known that both the consumption of fructose-containing beverages and psychological stress increase the risk of intestinal disease. Our results revealed that a high-fructose diet aggravated the damage to the jejunal mucosal barrier caused by restraint stress, reduced tight junction protein expression and the intestinal digestion and absorption capacity, disrupted the ability of Paneth cells to secrete antimicrobial peptides, and promoted the expression of inflammatory cytokines. A. muciniphila colonization enhanced the defense function of the mucosal barrier by enhancing the function of the NLRP6, promoting autophagy, maintaining the normal secretion of antimicrobial peptides in Paneth cells, promoting the expression of tight junction proteins, negatively regulating the NF-kB signaling pathway and inhibiting the expression of inflammatory cytokines. Our work indicates that A. muciniphila ameliorates the disruption of the intestinal mucosal barrier under high fructose and restraint stress. These results provided a rationale for the development of probiotic colonization for the prevention or treatment of intestinal diseases.

In this study, metal inert gas welding (MIG) was applied to 4 mm thick 6005A-T6 aluminum alloy welding. Compared with other parts, the hardness of the weld zone (WZ) was the lowest, about 67 HV. There was the Softening in WZ, which might make WZ the weakest zone. Then, fatigue tests were carried out on MIG welded joints. All the fatigue specimens fractured at the weld toe of the lap joint, and the fracture was characterized by a cleavage fracture. Crack closure induced by oxide was observed during the steady propagation of the fatigue crack. Impurities hindered crack propagation, changed the direction of crack propagation, and appeared in stepped fatigue strip distribution morphology; in the process of the main crack propagation, the initiation and propagation of small cracks were easily restricted and hindered by the main crack, which slowed down the propagation rate and even stopped the propagation directly.

PurposeAlpha-adrenergic receptor blockers can be effectively used in the context of medical expulsion therapy (MET) to treat ureteric stones. This study was designed to evaluate the safety and efficacy of doxazosin in MET relative to placebo or tamsulosin.MethodsWe systematically searched the PubMed, the Cochrane Library, EMBASE, Chinese Academic Database, and Web of Science databases to select randomized controlled trials (RCT) that compared the use of doxazosin with placebo or tamsulosin to treat ureteric stones. All patients we included were limited to those diagnosed with visible stones in the distal ureter. The diameter of ureteric stones does not exceed 10 mm.ResultsEight trials comparing doxazosin with placebo or tamsulosin containing 667 patients were assessed in the final analysis. The meta-analysis showed that doxazosin effectively treated ureteric stones and was better than placebo in terms of efficacy. Relative to the placebo group, the expulsion rate of stones from the distal ureter (OR = 3.00, 95% CI [2.15, 4.19], I2 = 0%, P < 0.00001) was significantly increased, and the expulsion time (days) was shortened (mean difference) (MD) = -4.03, 95% CI [-4.53, -3.53], P < 0.00001). The doxazosin group experienced fewer pain episodes (MD = -0.78, CI = [-0.94, -0.23], I2 = 0%, P < 0.00001) than the placebo group. A subgroup analysis showed that the doxazosin group had a higher expulsion rate (of 5-10 mm stones) compared with the placebo group. Although doxazosin resulted in significantly more adverse effects compared with the placebo, the patient's symptoms were mild and no further medical interventions were required. Moreover, the expulsion time (days) was shorter for patients receiving doxazosin (MD = -0.61, 95% CI [-0.97, -0.24], I2 = 39%, P = 0.001) than those receiving tamsulosin.ConclusionCompared with the placebo group, patients receiving doxazosin had a greater expulsion rate, a reduced expulsion time, and fewer pain episodes. The expulsion time of doxazosin was shorter than that of tamsulosin.

In this paper, 316L stainless steel powder was processed and formed by selective laser melting (SLM). The microstructure of the sample was studied using an optical microscope, and the fatigue failure of the sample and the characteristics of crack initiation and propagation were analyzed, providing a research basis for the application of SLM-316L. Due to the influence of microstructure and SLM process defects, the fatigue cracks of SLM-316L mainly emerged due to defects such as lack of fusion and pores, while the cracks of rolled 316L initiated at the inclusions near the surface of the specimen. After fatigue microcrack initiation of the SLM-316L specimen, due to the existence of shear stress and tear stress, the crack tip was passivated and Z-shaped propagation was formed. The existence of internal defects in SLM-316L made the microcrack initiation random and diverse. At the same time, the existence of defects affected the crack propagation in the form of bending, bifurcation and bridge, which made the main crack propagation deviate from the maximum load direction.

In the application of additive manufacturing, it is inevitable to use some formed parts with a smaller height. The analysis of various mechanical properties of these formed parts is the focus of research. In this paper, the fatigue performance of a titanium alloy specimen with a 10 mm height and electron beam selective melting is studied. By analyzing the structure and phase of the overall fatigue specimen, and then cutting the overall specimen parallel and perpendicular to the additive manufacturing direction, the tensile and fatigue tests in different directions are performed to detect the fatigue cracks at the fracture propagation the way to research. It is found that there are two ways of secondary crack propagation between fatigue bands, and three cracks are generated during the fatigue secondary crack propagation.

In this study, a phase field model is established to simulate the microstructure formation during the solidification of dendrites by taking the Al-Cu-Mg ternary alloy as an example, and machine learning and deep learning methods are combined with the Kim-Kim-Suzuki (KKS) phase field model to predict the quasi-phase equilibrium. The paper first uses the least squares method to obtain the required data and then applies eight machine learning methods and five deep learning methods to train the quasi-phase equilibrium prediction models. After obtaining different models, this paper compares the reliability of the established models by using the test data and uses two evaluation criteria to analyze the performance of these models. This work find that the performance of the established deep learning models is generally better than that of the machine learning models, and the Multilayer Perceptron (MLP) based quasi-phase equilibrium prediction model achieves the best performance. Meanwhile the Convolutional Neural Network (CNN) based model also achieves competitive results. The experimental results show that the model proposed in this paper can predict the quasi-phase equilibrium of the KKS phase-field model accurately, which proves that it is feasible to combine machine learning and deep learning methods with phase-field model simulation. [ABSTRACT FROM AUTHOR]