Biomolecule labeling in living systems is crucial for understanding biological processes and discovering therapeutic targets. A variety of labeling warheads have been developed for multiple biological applications, including proteomics, bioimaging, sequencing, and drug development. Quinone methides (QMs), a class of highly reactive Michael receptors, have recently emerged as prominent warheads for on-demand biomolecule labeling. Their highly flexible functionality and tunability allow for diverse biological applications, but remain poorly explored at present. In this regard, we designed, synthesized, and evaluated a series of new QM probes with a trifluoromethyl group at the benzyl position and substituents on the aromatic ring to manipulate their chemical properties for biomolecule labeling. The engineered QM warhead efficiently labeled proteins both in vitro and under living cell conditions, with significantly enhanced activity compared to previous QM warheads. We further analyzed the labeling efficacy with the assistance of density functional theory (DFT) calculations, which revealed that the QM generation process, rather than the reactivity of QM, contributes more predominantly to the labeling efficacy. Noteworthy, twelve nucleophilic residues on the BSA were labeled by the probe, including Cys, Asp, Glu, His, Lys, Asn, Gln, Arg, Ser, Thr, Trp and Tyr. Given their high efficiency and tunability, these new QM warheads may hold great promise for a broad range of applications, especially spatiotemporal proteomic profiling for in-depth biological studies.
The aggregation behavior of ferrihydrite nanoparticles (FNPs) can control the fate of associated aqueous contaminants, trace elements, and organic compounds. However, FNP aggregation is difficult to predict in the presence of organic matter (OM), given the heterogeneity in the OM properties. Five model OMs based on (poly)acrylic acid (PAA or AA) and polyethylene glycol with or without terminal carboxyl groups (PEG or PEGbis, respectively) were chosen to probe the influence of key OM properties─specifically, carboxyl richness and molecular weight (MW)─and the dominant mechanisms by which they influence OM adsorption onto FNPs and the resulting aggregation. For OMs with similar MWs, those with a higher carboxyl richness adsorbed more extensively onto FNPs: PAA2k > PEGbis > PEG. Meanwhile, for OMs with the same carboxyl richness, higher MW OMs adsorbed more: PAA25k > PAA2k > AA. Furthermore, the subsequent aggregation of FNPs was largely controlled by the adsorbed mass. OMs with negligible adsorption (i.e., PEG and AA) did not change the aggregation behavior of FNPs. For OMs with low carboxyl richness (PEGbis), accelerated aggregation occurred through a bridging effect with low adsorbed mass. For OMs with high carboxyl richness (PAA2k and PAA25k), aggregation was accelerated at moderate adsorbed OM masses by patch-charge attraction and was inhibited with high adsorbed OM mass due to steric repulsion. This study provided new insights into understanding and predicting the transport and fate of FNPs and natural organic matter (NOM) in natural environments with various NOM compositions.
In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic’s progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers.
Public transportation is important for older adults to meet their mobility needs and obtain external support. However, little is known about the elderly population with disabilities using public transportation. Using a nationally representative sample from the China Family Panel Studies (CFPS) for 2016, 2018, and 2020, we examined the elderly and public transportation disability (E&PTD) rate and its determinants in Chinese adults aged 45 years and above; the E&PTD rate in this population was 9.65%. The results showed that factors such as being female, age, and family size increased the likelihood of E&PTD in older adults. Married older adults with more education, better intelligence, higher incomes, receiving a pension, and living in urban areas and pilot provinces of healthcare reform were less likely to have E&PTD. This study provides an important policy reference for providing better public transportation services for E&PTDs.
Advanced oxidation processes (AOPs) have a broad range of potential applications in the treatment of emerging refractory emerging pollutants. However, due to the presence of highly reactive substances such as free radicals that are difficult to capture, it is challenging to investigate the mechanism of AOPs at the elementary reaction level. The conventional methods, such as electron spin resonance (ESR), free radical quantification, and free radical quenching, are plagued by systematic issues that have led to bottlenecks in the field of AOP studies. The development of computational chemistry theory and computer performance provides a new method to study the mechanism of AOPs through density functional theory (DFT) calculation. Due to its excellent cost–performance benefit, DFT calculations for aperiodic small molecules have become popular in the field of AOPs. In this paper, a comprehensive review is presented on the applications of DFT calculations for predicting active sites and exploring reaction selectivity and oxidant activation mechanisms. A systematic classification of methods related to molecular descriptors and transition states is provided. Furthermore, some current research issues are identified, and future development prospects and challenges are discussed.
Persistent green innovation helps enterprises save energy, reduce pollution, and continue to gain economic benefits. However, existing studies explored the economic and organizational factors influencing firms’ persistent green innovation while neglecting peer influence in the digital economy. This study examines the impact of digital economy and peer influence on persistent green innovation using data of Chinese-listed companies from 2011 to 2019. The results show that digital economy and peer influence positively affect persistent green innovation. Moreover, digital economy plays a competitive mediating role between peer influence and persistent green innovation. The results of further research show that both the time lag term of peer influence and spatial lag term of digital economy affect persistent green innovation. This study incorporates the three-level elements of enterprise, peer, and city into a unified framework, providing theoretical reference and practical guidance for green innovation to enhance the competitive advantage of enterprises.
There is growing interest in the relationship between digitalization and environmental, social, and governance (ESG) performance, but existing research focuses on the one-way relationship and ignores the two-way mechanism. Based on a sample of 3335 listed companies in China in 2020, this study adopts a spatial simultaneous equation model to investigate the bidirectional mechanism between companies’ digitalization and ESG performance. The results show that digitalization and ESG performance have a significant positive two-way mechanism; digitalization enhances ESG performance, while ESG performance promotes digitalization. The results also indicate a significant positive intra-industry spillover effect for both digitization and ESG performance. Further research shows that the relationship between digitization and ESG performance does not differ depending on the definition of spatial weights, however, the spillover effects do differ depending on the definition of spatial weights.
Plastisphere, characterized by microbial colonization on plastic debris, has attracted concern with its adverse environmental effects. The microbial features have been increasingly investigated; however, there lacks direct evidence for microplastics serving as carbon sources and enriching plastic-degrading microorganisms. Here, we obtained microbial communities from soil microplastics, analyzed the dissimilarity compared with soil, and characterized the plastic-degrading potential of isolates from plastisphere. Results showed the plastisphere communities significantly differed from soil communities and exhibited a higher relative abundance of Nocardia and Rhodococcus. To verify the selective enrichment of plastic-degrading microorganisms in the plastisphere, culture-based strategies were employed to evaluate the polyethylene (PE) degradation potential of two isolates Nocardia asteroides No.11 and Rhodococcus hoagii No.17. They could grow solely on PE and led to significant weight loss. FTIR and SEM analysis revealed the formation of new functional groups and the destruction of structural integrity on PE surfaces. Genes related to PE biodegradation were identified by genome-wide sequencing thus recognizing relevant enzymes and elucidating potential pathways. Overall, this report combined culture-free and culture-based approaches to confirm the plastic degradation potential of selectively enriched microorganisms in soil plastisphere, providing a positive perspective toward promoting microplastic biodegradation in farmland soil by enhancing natural microbial processes.
Microplastics, recognized as some of the most pervasive and enduring pollutants, have emerged as a potential threat to environmental eco-health. While much is known about the effects of microplastics on soil microorganisms, our understanding of how they interact with terrestrial organisms and the underlying mechanisms remains limited. In this study, the effects of polyethylene microplastics at a concentration of 0.5 % (w/w) on the antioxidant enzymes, gut microbiota of Eisenia fetida and the soil microbiota on days 1, 3, 7, 15, and 30 were investigated. The results indicated that exposure to microplastics slightly increased the activities of superoxide dismutase (1.22-fold on day 3, 1.12-fold on day 7) and catalase (1.10-fold on day 3, 1.09-fold on day 7) in E. fetida, while exposure markedly decreased peroxidase activity (1.33- to 1.79-fold) throughout the whole period. Both the soil microbiota and the gut microbiota of E. fetida in terms of diversity and composition were significantly affected by the microplastic amendment, and their structure tended to be similar throughout the exposure time. The family Nocardiaceae was significantly enriched in both the soil and E. fetida gut biota with microplastic exposure. Our results demonstrated that the antioxidant enzyme response of E. fetida was closely related to both the microbiota, although this relationship with the gut microbiota may have been weakened by microplastic exposure. Overall, this study furnishes new perspectives on the ecotoxicity of microplastics, revealing significant implications for the vitality of soil-dwelling organisms and the overarching health of terrestrial ecosystems.
The multiple-channel[1] sound source enhancement methods have made a great progress in recent years, especially when combined with the learning-based algorithms. However, the performance of these techniques is limited by the completeness of the training dataset, which may degrade in mismatched environments. In this paper, we propose a reconstruction Model based Self-supervised Learning (RMSL) method for sound source enhancement. A reconstruction module is used to integrate the estimated target signal and noise components to regenerate the multi-channel mixed signals, and it is connected with a separating model to form a closed loop.In this case, the optimization of the separation model can be achieved by continuously iterating the separation-reconstruction process. We use the separation error, the reconstruction error, and the signal-noise independence error as lossfunctions in the self-supervised learning process. This method is applied to the state-of-the-art sound source separation model (ADL-MVDR) and evaluated under different scenarios. Experimental results demonstrate that the proposed method can improve the performance of ADL-MVDR algorithm under different number of sound sources, bringing about 0.5 dB to 1 dB Si-SNR gain, while maintaining good clarity and intelligibility in practical application.
A visa-free policy is a tool for attracting foreign tourists, but existing studies evaluating its effects do not consider spatial spillover. This study, therefore, examined the effects of visa policies on urban inbound tourism with spatial spillover. This study used the implementation of the 72-hr visa-free transit policy in China as a natural experiment and employed a spatial difference-in-differences approach. The results show that the implementation of the 72-hr visa-free transit policy had no significant effect on the number of foreign tourists, but can reduce the average length of stay for foreign tourists. It also shows that there is a significant spatial spillover effect on the flow of foreign tourists. The results indicate the importance of spatial spillover in the evaluation of the impact of visa policy on inbound tourism, providing empirical evidence for further improving visa-free policies and urban tourism development.