Explore the vast range of titles available.

The mechanism and origins of site-selectivity of Rh2(S-tfpttl)4-catalyzed C(sp3)–H bond aminations were studied using density functional theory (DFT) calculations. The synergistic combination of the dirhodium complex Rh2(S-tfpttl)4 with tert-butylphenol sulfamate TBPhsNH2 composes a pocket that can access both tertiary and benzylic C–H bonds. The nonactivated tertiary C–H bond was selectively aminated in the presence of an electronically activated benzylic C–H bond. Both singlet and triplet energy surfaces were investigated in this study. The computational results suggest that the triplet stepwise pathway is more favorable than the singlet concerted pathway. In the hydrogen atom abstraction by Rh–nitrene species, which is the rate- and site-selectivity-determining step, there is an attractive π–π stacking interaction between the phenyl group of the substrate and the phthalimido group of the ligand in the tertiary C–H activation transition structure. By contrast, such attractive interaction is absent in the benzylic C–H amination transition structure. Therefore, the DFT computational results clearly demonstrate how the synergistic combination of the dirhodium complex with sulfamate overrides the intrinsic preference for benzylic C–H amination to achieve the amination of the nonactivated tertiary C–H bond.

By using the finite element method and a “coarse to fine” two-stage genetic algorithm as the forward calculation method and the inverse search scheme, respectively, we perform both the unconstrained and constrained optimal design of the unit cell topology of the two-dimensional square-latticed solid phononic crystals (PnCs), to maximize the relative widths of the gaps between the adjacent energy bands of the PnCs. In the constrained optimizations, the maximization is subjected to the constraint of a predefined average density. In the numerical results, the variation patterns of the optimized structures with the order of the bandgap for both the out-plane shear and the in-plane mixed elastic wave modes are presented, and the effects of both the material contrast and the predefined average density on the obtained optimal structures are discussed.

Autophagy, a conserved cellular degradation and recycling process, can be enhanced by nutrient depletion, oxidative stress or other harmful conditions to maintain cell survival. 6-Hydroxydopamine/ascorbic acid (6-OHDA/AA) is commonly used to induce experimental Parkinson’s disease (PD) lesions by causing oxidative damage to dopaminergic neurons. Activation of autophagy has been observed in the 6-OHDA-induced PD models. However, the mechanism and exact role of autophagy activation in 6-OHDA PD model remain inconclusive. In this study, we report that autophagy was triggered via mucolipin 1/calcium/calcineurin/TFEB (transcription factor EB) pathway upon oxidative stress induced by 6-OHDA/AA. Interestingly, overexpression of TFEB alleviated 6-OHDA/AA toxicity. Moreover, autophagy enhancers, Torin1 (an mTOR-dependent TFEB/autophagy enhancer) and curcumin analog C1 (a TFEB-dependent and mTOR-independent autophagy enhancer), significantly rescued 6-OHDA/AA-induced cell death in SH-SY5Y cells, iPSC-derived DA neurons and mice nigral DA neurons. The behavioral abnormality of 6-OHDA/AA-treated mice can also be rescued by Torin 1 or C1 administration. The protective effects of Torin 1 and C1 can be blocked by autophagy inhibitors like chloroquine (CQ) or by knocking down autophagy-related genes TFEB and ATG5. Taken together, this study supports that TFEB-mediated autophagy is a survival mechanism during oxidative stress and pharmacological enhancement of this process is a neuroprotective strategy against oxidative stress-associated PD lesions.

Currently, the Global Navigation Satellite System (GNSS) mainly uses the satellites in Medium Earth Orbit (MEO) to provide position, navigation, and timing (PNT) service. The weak navigation signals limit its usage in deep attenuation environments, and make it easy to interference and counterfeit by jammers or spoofers. Moreover, being far away to the Earth results in relatively slow motion of the satellites in the sky and geometric change, making long time needed for achieved centimeter positioning accuracy. By using the satellites in Lower Earth Orbit (LEO) as the navigation satellites, these disadvantages can be addressed. In this contribution, the advantages of navigation from LEO constellation has been investigated and analyzed theoretically. The space segment of global Chinese BeiDou Navigation Satellite System consisting of three GEO, three IGSO, and 24 MEO satellites has been simulated with a LEO constellation with 120 satellites in 10 orbit planes with inclination of 55 degrees in a nearly circular orbit (eccentricity about 0.000001) at an approximate altitude of 975 km. With simulated data, the performance of LEO constellation to augment the global Chinese BeiDou Navigation Satellite System (BeiDou-3) has been assessed, as one of the example to show the promising of using LEO as navigation system. The results demonstrate that the satellite visibility and position dilution of precision have been significantly improved, particularly in mid-latitude region of Asia-Pacific region, once the LEO data were combined with BeiDou-3 for navigation. Most importantly, the convergence time for Precise Point Positioning (PPP) can be shorted from about 30 min to 1 min, which is essential and promising for real-time PPP application. Considering there are a plenty of commercial LEO communication constellation with hundreds or thousands of satellites, navigation from LEO will be an economic and promising way to change the heavily relay on GNSS systems.

This study establishes a novel machine learning paradigm integrating physical mechanisms. It aims to address the limitations of traditional methods in predicting the concrete fatigue life of high-speed railway (HSR) track slab, particularly their insufficient accuracy and poor interpretability due to overlooking microstructure characteristics and load randomness. The research method innovatively develops a multi-task learning framework combining the Graph Neural Network (GNN) and Transformer architectures. GNN abstracts concrete microstructure as topological graphs to capture spatial damage propagation paths, while Transformer analyzes random load spectra to identify critical temporal patterns determining fatigue damage. The multi-task framework simultaneously predicts fatigue life, damage evolution rate, and residual strength. Experimental results demonstrate the outstanding performance of the proposed GNN-Transformer model. After 150 training epochs, the proposed model achieves a Mean Squared Error of 0.224, Root Mean Square Error of 0.332, and Coefficient of Determination reaching 0.939. Meanwhile, based on the computational architecture used in the experiments, the model achieves an average single-sample prediction latency of 0.86 s and a GPU utilization rate of 48.3%, demonstrating its strong potential for online deployment. All indicators significantly outperform existing models. The study concludes that this framework successfully constructs a high-accuracy, efficient, and physically interpretable prediction model by effectively integrating microstructure topology with macroscopic load sequence features. This study provides robust algorithmic support for predictive maintenance and intelligent health management of HSR infrastructure.

In areas where loess is distributed, landslides represent a significant geohazard with severe implications. Among these events, loess-mudstone landslides are particularly prevalent, posing substantial risks to the safety and property of local residents, and moisture plays a pivotal role as a key factor in causing these disasters. In this study, the hydraulic properties of the soils along the longitudinal section of an ongoing loess-mudstone landslide are investigated through the variation of soil water characteristic curves, which are subsequently fitted by utilizing van Genuchten model. Moreover, a comprehensive experimental investigation was conducted on the loess, mudstone, and loess-mudstone mixtures to facilitate analysis, including X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM) observation, particle size distribution (PSD) analysis, along with fundamental geotechnical tests for parameter determination. It is found that mudstone and loess have distinct SWCC distribution. The SWCC of loess at various depths exhibits a similar distribution pattern due to the occurrence of landslide. The SWCC distribution of loess-mudstone mixture displays a transitional trend between the SWCC of mudstone and that of loess, and the water retention capacity increases as the mudstone content increases. The experimental findings have demonstrated notable agreement between each other and exhibited a satisfactory level of concurrence with the observed phenomena in geological surveys.

Background Flavonoids have strong free radical scavenging and antioxidant capacity. The high abundance of flavonoids in Chinese prickly ash peels have many benefits to human health. In this study, ‘Hancheng Dahongpao’, a main cultivar, was taken as materials to investigate the flavonoids biosynthesis mechanism of Zanthoxylum bungeanum Maxim at three key development stages by integration of metabolomics and transcriptomics analysis. Results A total of 19 differentially accumulated metabolites were identified, the key flavonoids compounds were kaempferol, quercetin and their glycoside derivatives, and two major anthocyanins (peonidin O-hexoside and peonidin 3-O-glucoside). 5 gene networks/modules including 15 important candidate genes were identified, which was highly correlated with flavonoids. Among these genes, ZM-163828 and ZM-184209 were strongly correlated with kaempferol and quercetin, and ZM-125833 and ZM-97481 were controlled the anthocyanins biosynthesis. Moreover, it was shown that MYB-ZM1, MYB-ZM3, MYB-ZM5, MYB-ZM6 and MYB-ZM7 coordinately controlled flavonoids accumulation through regulating the structural genes. Conclusions Generally, this study systematically revealed the flavonoids metabolic pathways and candidate genes involved in flavonoids biosynthesis and laid a foundation for the potential targets for the breeding of new valuable Chinese prickly ash cultivars.

The construction projects in Pakistan have inherent problems of erroneous planning and schedule development. This dilemma has led to the failure of the majority of construction projects in Pakistan. Earlier researches have tried to curtail the increasing spectrum of inaccurate planning and schedule development. But not many research studies have shed light on the major factor of 2D CAD drawings interpretation problems, which are playing a key role in defective planning and scheduling. Moreover, the role of BIM, i.e., Building Information Modeling with respect to efficient planning, has not been also discussed in detail. This study has concluded a positive impact of BIM on effective planning and scheduling in the construction industry of Pakistan. About 210 responses were obtained from experienced construction professionals of Pakistan and tested via regression analysis in order to validate this argument. It has been analyzed that 2D CAD drawings are now getting primitive and 3D technology BIM is taking its place. BIM is an efficient tool for planning and scheduling. With the start of CPEC and Multi-Billion Dollar investment on infrastructure, construction industry of Pakistan needs to uplift its existing standard in order to meet with international requirements. For this purpose, BIM has been recommended. It will not only assist in attaining flawless planning but will also increase the success rate of CPEC projects in Pakistan.

Background: Traditional Chinese medicine (TCM) has been widely used in the treatment of human diseases. However, the synergistic effects of multiple TCM prescriptions in the treatment of stroke have not been thoroughly studied. Objective of the study: This study aimed to reveal the mechanisms underlying the synergistic effects of these TCM prescriptions in stroke treatment and identify the active compounds. Methods: Herbs and compounds in the Di-Tan Decoction (DTD), Xue-Fu Zhu-Yu Decoction (XFZYD), and Xiao-Xu-Ming Decoction (XXMD) were acquired from the TCMSP database. SEA, HitPick, and TargetNet web servers were used for target prediction. The compound-target (C-T) networks of three prescriptions were constructed and then filtered using the collaborative filtering algorithm. We combined KEGG enrichment analysis, molecular docking, and network analysis approaches to identify active compounds, followed by verification of these compounds with an oxygen-glucose deprivation and reoxygenation (OGD/R) model. Results: The filtered DTD network contained 39 compounds and 534 targets, the filtered XFZYD network contained 40 compounds and 508 targets, and the filtered XXMD network contained 55 compounds and 599 targets. The filtered C-T networks retained approximately 80% of the biological functions of the original networks. Based on the enriched pathways, molecular docking, and network analysis results, we constructed a complex network containing 3 prescriptions, 14 botanical drugs, 26 compounds, 13 targets, and 5 pathways. By calculating the synergy score, we identified the top 5 candidate compounds. The experimental results showed that quercetin, baicalin, and ginsenoside Rg1 independently and synergistically increased cell viability. Conclusion: By integrating pharmacological and chemoinformatic approaches, our study provides a new method for identifying the effective synergistic compounds of TCM prescriptions. The filtered compounds and their synergistic effects on stroke require further research.