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Abstract Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are neurodegenerative disorders characterized by the accumulation of α-synuclein aggregates. α-synuclein forms droplets via liquid-liquid phase separation (LLPS), followed by liquid-solid phase separation (LSPS) to form amyloids, how this process is physiologically-regulated remains unclear. β-synuclein colocalizes with α-synuclein in presynaptic terminals. Here, we report that β-synuclein partitions into α-synuclein condensates promotes the LLPS, and slows down LSPS of α-synuclein, while disease-associated β-synuclein mutations lose these capacities. Exogenous β-synuclein improves the movement defects and prolongs the lifespan of an α-synuclein-expressing NL5901 Caenorhabditis elegans strain, while disease-associated β-synuclein mutants aggravate the symptoms. Decapeptides targeted at the α-/β-synuclein interaction sites are rationally designed, which suppress the LSPS of α-synuclein, rescue the movement defects, and prolong the lifespan of C. elegans NL5901. Together, we unveil a Yin-Yang balance between α- and β-synuclein underlying the normal and disease states of PD and DLB with therapeutical potentials.

Table Structure Recognition (TSR) is pivotal in document analysis, especially in end-to-end automated identification, which profoundly impacts decision-making and efficiency spanning diverse fields such as power industry, education, healthcare, and scientific papers. However, accurately identifying table structures remains challenging due to complex layouts and irregular distortions. Considering the complex structure of tables in power relay protection systems, this study proposes a novel end-to-end TSR model, leveraging deep learning and innovative modules to address these challenges. Specifically, the new model TSR-Net replaces YOLOv8’s backbone with our proposed DSConv-Variants, improving accuracy and reducing parameters. Additionally, a Multi-Scale Attention Module (MAM) captures semantic features efficiently, while a Dynamic Calibration Module (DCM) enhances cross-layer fusion. Experimental results demonstrate the superior performance of our approach on public benchmarks, showcasing its utility for TSR.

Background: In traditional Mongolian or Tibetan medicine in China, Chebulae Fructus (CF) is widely used to process or combine with aconitums to decrease the severe toxicity of aconitums. Researches in this area have predominantly focused on tannins, with few research on other major CF components for cardiotoxicity mitigation. The present study aimed to clarify whether triterpenoids can attenuate the cardiotoxicity caused by mesaconitine (MA) and investigate the mechanism of cardiotoxicity attenuation.Methods: Firstly, the pharmacophore model, molecular docking, and 3D-QSAR model were used to explore the mechanism of CF components in reducing the toxicity of MA mediated by the TRPV1 channel. Then three triterpenoids were selected to verify whether the triterpenoids had the effect of lowering the cardiotoxicity of MA using H9c2 cells combined with MTT, Hoechst 33258, and JC-1. Finally, Western blot, Fluo-3AM, and MTT assays combined with capsazepine were used to verify whether the triterpenoids reduced H9c2 cardiomyocyte toxicity induced by MA was related to the TRPV1 channel.Results: Seven triterpenoids in CF have the potential to activate the TRPV1 channel. And they exhibited greater affinity for TRPV1 compared to other compounds and MA. However, their activity was relatively lower than that of MA. Cell experiments revealed that MA significantly reduced H9c2 cell viability, resulting in diminished mitochondrial membrane potential and nuclear pyknosis and damage. In contrast, the triterpenoids could improve the survival rate significantly and counteract the damage of MA to the cells. We found that MA, arjungenin (AR), and maslinic acid (MSA) except corosolic acid (CRA) upregulated the expression of TRPV1 protein. MA induced a significant influx of calcium, whereas all three triterpenoids alleviated this trend. Blocking the TRPV1 channel with capsazepine only increased the cell viability that had been simultaneously treated with MA, and AR, or MSA. However, there was no significant difference in the CRA groups treated with or without capsazepine.Conclusion: The triterpenoids in CF can reduce the cardiotoxicity caused by MA. The MSA and AR function as TRPV1 agonists with comparatively reduced activity but a greater capacity to bind to TRPV1 receptors, thus antagonizing the excessive activation of TRPV1 by MA.

Abstract Acute kidney injury (AKI) exhibits high morbidity and mortality. Kidney injury molecule-1 (KIM1) is dramatically upregulated in renal tubules upon injury, and acts as a biomarker for various renal diseases. However, the exact role and underlying mechanism of KIM1 in the progression of AKI remain elusive. Herein, we report that renal tubular specific knockout of Kim1 attenuates cisplatin- or ischemia/reperfusion-induced AKI in male mice. Mechanistically, transcription factor Yin Yang 1 (YY1), which is downregulated upon AKI, binds to the promoter of KIM1 and represses its expression. Injury-induced KIM1 binds to the ECD domain of death receptor 5 (DR5), which activates DR5 and the following caspase cascade by promoting its multimerization, thus induces renal cell apoptosis and exacerbates AKI. Blocking the KIM1-DR5 interaction with rationally designed peptides exhibit reno-protective effects against AKI. Here, we reveal a YY1-KIM1-DR5 axis in the progression of AKI, which warrants future exploration as therapeutic targets.

Camptothecin and its analogues show important antitumor activity and have been used in clinical studies. However, hydrolysis of lactone in the E ring seriously attenuates the antitumor activity. To change this situation, aromathecin alkaloids are investigated in order to replace camptothecins. Potential antitumor activity has obtained more and more attention from organic and pharmaceutical chemists. As a member of the aromathecin alkaloids, rosettacin has been synthesized via different methods. This review summarizes recent advances in the synthesis of rosettacin.

Transformations of alcohols, which are ubiquitous in chemistry and are native functionalities in many natural products and bioactive molecules, are cornerstones of organic synthesis. Here the authors describe photocatalyzed cross-couplings of activated alcohols with α-amino acids, providing a direct approach for a variety of unnatural amino acids.

A protective oxide film is the key to the corrosion resistance of the FeCrAl alloy. The mechanism of the formation of the multilayer oxide film of the FeCrAl alloy in 700 °C air was explored by studying the structure evolution of the oxide film and the oxidation kinetics of FeCrAl. The results show that a multilayer oxide layer is formed on the surface of the FeCrAl alloy after 1344 h, with a (Fe,Cr)2O3 layer, an Al-rich oxide layer, an Al-depleted zone, and a new Al-rich oxide layer sequentially arranged from the surface to the matrix. This indicates that the Al element plays an important role in the formation of the oxide film. The Al in the matrix is depleted to form the Al-rich oxide layer, resulting in the Al-depleted zone. The new Al-rich oxide layer is formed under the Al-depleted zone by internal oxidation. It should be noted that the precipitation of the AlN phase in the matrix is observed, which might be a probable factor for the Al-depleted zone in the matrix.

A rhodium(III)-catalyzed intramolecular oxidative annulation of O-substituted N-hydroxyacrylamides for the construction of indolizinones via sequential C(sp2)–H activation and C(sp3)–H amination has been developed. This approach shows excellent functional-group tolerance. The synthesized scaffold forms the core of many natural products with pharmacological relevance.

Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep learning methods driven by massive data cannot achieve the intended expectations. Semi-supervised and self-supervised algorithms have advantages in coping with this phenomenon. This paper primarily concentrates on applying self-supervised strategies to make strides in semi-supervised HSI classification. Notably, we design an effective and a unified self-supervised assisted semi-supervised residual network (SSRNet) framework for HSI classification. The SSRNet contains two branches, i.e., a semi-supervised and a self-supervised branch. The semi-supervised branch improves performance by introducing HSI data perturbation via a spectral feature shift. The self-supervised branch characterizes two auxiliary tasks, including masked bands reconstruction and spectral order forecast, to memorize the discriminative features of HSI. SSRNet can better explore unlabeled HSI samples and improve classification performance. Extensive experiments on four benchmarks datasets, including Indian Pines, Pavia University, Salinas, and Houston2013, yield an average overall classification accuracy of 81.65%, 89.38%, 93.47% and 83.93%, which sufficiently demonstrate that SSRNet can exceed expectations compared to state-of-the-art methods.

Microwave irradiation and post-Ugi reactions own their respective advantages in comparison with other strategies. The combination of microwave irradiation and post-Ugi reactions shows paramount importance in the construction of polycycles. This minireview outlines the recent developments of microwave-assisted post-Ugi reactions for the synthesis of polycycles. Through transition metal-catalyzed or transition metal-free transformations, diverse polycycles are prepared in an efficient, rapid, and step-economical manner.

With the rapid expansion of prefabricated construction in China, significant changes in safety performance are still unapparent for numerous prefabricated constructions, and safety accidents are constantly exposed in public. The ignorance of interactions among safety risks impedes efficacious improvement, which instructs the need for a thorough analysis of these interactions based on complex network theory. This paper starts with the identification of 37 safety risks refined through literature review and expert interviews, and 90 interrelationships among them verified by virtue of the questionnaire survey, laying a foundation for the establishment of a prefabricated construction safety risk network (PCSRN). The topological analysis results prove that PCSRN is a scale-free as well as a small-world network, which indicates the high-efficiency propagation and diffusion among safety risks in prefabricated constructions. Moreover, eight critical nodes are identified with four different ranking criteria, and corresponding safety strategies are proposed to address them. The developed method not only provides a novel insight to interpret the safety risks of prefabricated construction but also has the potential to advance safety performance of this sector.

The low solubility of gadolinium (Gd) limits its application in iron-based neutron shielding alloys. In this study, a high content of Gd up to 7.87 wt.% was added into 316L stainless steel by mechanical alloying (MA) of Gd2O3 with 316L powder. Neutronics calculation results showed that the shielding efficiency of 0.2 mm thick 7.87 wt.% Gd-316L alloy was equivalent to that of 33 mm thick 316L for thermal neutrons. Microstructure analysis indicated that Gd-Si-O particles with an average size of 12.5 nm were uniformly dispersed in the matrix. It revealed that Gd2O3 dissolved into the 316L matrix during MA and re-precipitated at around 780 °C as Gd2SiO5 by XRD and TG-DSC. This study provides a novel Gd-containing alloy designed to achieve integration of structural alloys with good neutron shielding function for future application.

Metro plays an indispensable role in promoting social and economic development of the big cities. However, the safety operation of the metro system is believed to be an arduous work for its operation being susceptible to various factors, especially equipment failure, which could bring about delay, interruption, and even accidents. In consideration of the seriousness of metro accident, it is becoming more and more crucial to conduct proactive, targeted, and effective risk management to reduce failures. Firstly, based on the FMECA and influence diagram theory, this paper proposes a new analytical framework to study metro equipment failure in accordance with system decomposition. Furthermore, a case study is implemented to verify the feasibility and availability of this framework in evaluating criticality of failure modes and assessing its influence factors based on the collected data from metro operation company. As indicated by the research results, wheel damage is the most serious failure mode in the bogie system, and its most paramount influence factor is the shortage of professional skills. Finally, a discussion about the research methods and results is carried out, and several recommendations are put forward to improve safety management on the basis of research findings. This study has the potential to offer suggestions in regard to metro equipment design, operation, and maintenance for raising the safety level in metro operation.

Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.

Capturing the interrelations among risks is essential to thoroughly understand and promote coal mining safety. From this standpoint, 105 risks and 135 interrelations among risks had been identified from 126 typical accidents, which were also the foundation of constructing coal mine risk network (CMRN). Based on the complex network theory and Pajek, six parameters (i.e., network diameter, network density, average path length, degree, betweenness, and clustering coefficient) were employed to reveal the topological properties of CMRN. As indicated by the results, CMRN possesses scale-free network property because its cumulative degree distribution obeys power-law distribution. This means that CMRN is robust to random hazard and vulnerable to deliberate attack. CMRN is also a small-world network due to its relatively small average path length as well as high clustering coefficient, implying that accident propagation in CMRN is faster than regular network. Furthermore, the effect of risk control is explored. According to the result, it shows that roof collapse, fire, and gas concentration exceeding limit refer to three most valuable targets for risk control among all the risks. This study will help offer recommendations and proposals for making beforehand strategies that can restrain original risks and reduce accidents.