Digital carbon neutrality: evidence of carbon emission reduction based on digital inclusive finance.

As a powerful engine for economic reform and curbing carbon emissions, digital inclusive finance provides solid support for achieving the goal of digital carbon neutrality. This study reveals the positive effect of digital inclusive finance on carbon emission reduction and the deeper reasons behind it by digging deeper into the panel data of 213 cities in China. The study adopts advanced empirical analysis methods to rigorously test the association between digital inclusive finance and carbon emissions. The results show that there is a strong positive correlation between the booming development of digital inclusive finance and the significant decline in carbon emissions. This finding remains solid after several rounds of robustness tests, which fully proves the reliability of the research results. Further mechanism analysis reveals the multiple paths of digital financial inclusion on carbon emission reduction. First, it promotes the optimization and upgrading of industrial structure by optimizing the allocation of financial resources, thus reducing the proportion of high-carbon emission industries. Second, digital inclusive finance attracts more foreign capital inflows and introduces advanced low-carbon technologies and management experience, further promoting the development of low-carbon economy. In addition, the study also found that the differences between different cities in terms of geographic location and city size have a significant impact on the carbon emission reduction effect of digital inclusive finance. In particular, the carbon emission reduction effect of digital inclusive finance is particularly significant in western regions, central cities, and first-tier cities. In response to these findings, this paper proposes a series of targeted policy recommendations. First, the financial service system should be further optimized to increase the coverage and penetration of digital inclusive finance, especially in less developed regions and small- and medium-sized cities. Second, regional policy synergies should be strengthened to form a strong synergy to promote the development of a low-carbon economy. In addition, it should guide capital flows to low-carbon industries and encourage enterprises to increase green technology research and development and application, while actively promoting low-carbon consumption concepts and guiding consumers to form green consumption habits. Through the implementation of these measures, it is expected that the potential of digital inclusive finance in the development of a low-carbon economy will be further stimulated, making a greater contribution to the realization of the goals of carbon peaking and carbon neutrality.

The relationship between anxiety and smartphone addiction in the context of Covid-19: The mediating effect of attentional control and executive dysfunction.

In the context of Covid-19, the present study examined the relationship between anxiety and smartphone addiction and tested the mediation role of attentional control and executive dysfunction. Four hundred and twenty-one Chinese undergraduate students completed anxiety, attentional control, executive dysfunction, and smartphone addiction measures. The findings of correlation analysis indicated that anxiety was negatively associated with attentional control, and positively with executive dysfunction and smartphone addiction. The results of structural equation model showed that attentional control and executive dysfunction played a mediation role between anxiety and smartphone addiction in series. Moreover, anxiety did not directly predict smartphone addiction in the final model including attentional control and executive dysfunction as mediators, suggesting that attentional control and executive dysfunction were full mediators in the relation between anxiety and smartphone addiction.

[Soil physical and chemical properties and vegetation characteristics of different types of grassland in Qilian Mountains, China]. / 祁连山不同类型草地的土壤理化性质与植被特征.

Grasslands in Qilian Mountains plays an important role in maintaining the ecological security of western China. To understand soil physical and chemical properties and the distribution characteristics of vegetation, as well as their correlation in different types of grasslands in Qilian Mountains, we measured soil moisture, nutrient content, bulk density, particle composition, and vegetation characteristics in seven types of grassland in Qilian Mountains. The fractal dimension of soil particles, soil organic carbon, total nitrogen and total phosphorus storages in 0-40 cm soil layer, and plant diversity index were calculated. The results showed that there were significant differences in soil physical and chemical properties and vegetation characteristics among different grassland types. Compared with other types of grassland, alpine meadow had higher soil water, nutrient and clay content, but lower bulk density and sand content. Soil organic carbon, total nitrogen and total phosphorus storages in 0-40 cm layer ranged from 3084 to 45247, 164 to 2358 and 100 to 319 g·m-2, respectively, with high contents of organic carbon and total nitrogen and low content of total phosphorus. There was a significant positive correlation between soil total phosphorus storage and plant diversity index, indicating that soil total phosphorus content was the key factor affec-ting grassland plant diversity in Qilian Mountains. Compared with other grassland types, alpine meadow in Qilian Mountains had better vegetation status, soil moisture, and nutrient conditions.

Natural terpenoids with anti-inflammatory activities: Potential leads for anti-inflammatory drug discovery.

Natural products are mainly secondary metabolites produced by plants, microorganisms, and animals, which are still abundant in modern drug discovery. Terpenoids are the most diverse group of natural products, attracting extensive attention owing to their various biological activities. This manuscript reviewed the chemical structures, anti-inflammatory activities, and mechanisms of action of 281 terpenoid natural products reported from 2010 to the present. Their biological targets and both in vitro and in vivo screening models were also surveyed and statistically summarized. This review will provide potential anti-inflammatory lead compounds and helpful information to researchers engaged in natural products and anti-inflammatory drug discovery.

Discovery of small-molecule compounds and natural products against Parkinson's disease: Pathological mechanism and structural modification.

Parkinson's disease (PD) is the second common neurodegenerative disease characterized by movement disorder. The symptoms of PD harm both the physical and mental health of patients. However, the current treatment strategies for PD only alleviate the symptoms but cannot recover the degenerative process of dopaminergic neurons. Therefore, it is necessary to develop novel and safe drugs for the treatment of PD. In this review, we comprehensively summarized the detailed pathological mechanisms and potential drugable targets of PD. The approved anti-PD drugs in clinical use and the drug candidates under clinical trials were also listed. More importantly, the compounds in the drug discovery phase with in vivo anti-PD activities in the recent two decades (2000-2020) were summarized. The structure-activity relationships (SARs) were also analyzed. Additionally, we predicted all the reviewed compounds' blood-brain barrier (BBB) permeability and statistically analyzed their pharmacological targets and in vivo anti-PD testing models. It is hoped that this review can provide practical information for researchers in the field of anti-PD drug discovery and promote their research work.

HDAC1/MAO-B dual inhibitors against Alzheimer's disease: Design, synthesis and biological evaluation of N-propargylamine-hydroxamic acid/o-aminobenzamide hybrids.

A series of N-propargylamine-hydroxamic acid/o-aminobenzamide hybrids inhibitors combining the typical pharmacophores of hydroxamic acid/o-aminobenzamide and propargylamine were designed and synthesized as HDAC1/MAO-B dual inhibitors for the treatment of Alzheimer's disease. Most of the hybrids displayed moderate to good MAO-B inhibitory activities. Among them, Hybrid If exhibited the most potent activity against MAO-B and HDAC1 (MAO-B, IC50 = 99.0 nM; HDAC1, IC50 = 21.4 nM) and excellent MAO selectively (MAO-A, IC50 = 9923.0 nM; SI = 100.2). Moreover, compound If significantly reversed Aß1-42-induced PC12 cell damage and decreased the production of intracellular ROS, exhibiting favorable antioxidant activity. More importantly, hybrid If instantly penetrated the BBB and accumulated in brain tissue as well as markedly ameliorated cognitive dysfunction in a Morris water maze ICR mice model. In summary, HDAC1/MAO-B dual inhibitor If is a promising potential agent for the therapy of Alzheimer's disease.

The relationship between anxiety and depression with smartphone addiction among college students: The mediating effect of executive dysfunction.

Smartphone addiction symptom is increasing globally. Many studies have found that negative emotion is associated with smartphone addiction, but few explore the mediating effect of executive dysfunction. In a large-scale, cross-sectional survey, 421 Chinese college students completed measures on anxiety, depression, smartphone addiction, and executive dysfunction. We surveyed the prevalence of depression, impaired executive function, and smartphone addiction. A confirmatory factor analysis was performed on the questionnaire structure, and the mediation models were used to examine the relationship between anxiety, depression, impaired executive function, and smartphone addiction. The main finding indicated that anxiety, depression, and executive dysfunction were positively and significantly associated with smartphone addiction. Executive dysfunction plays a mediation role between anxiety and depression with smartphone addiction. Specifically, executive dysfunction completely mediates the pathway of anxiety and smartphone addiction and partly mediates the path of depression and smartphone addiction. Depression directly predicted smartphone addiction positively but anxiety did not. The sample consisted of Chinese college students, which limits generalizability and self-reported lack of objectivity. The result suggests that we should pay more attention to the mediating role of executive dysfunction between negative emotion and smartphone addiction.

Impact of public support and government's policy on climate change in China.

Climate change is a global environmental issue that would damage the natural and biological systems. Although there are many controversies about climate change, the temperature rise has become more and more obvious in the world since the 1990s. It is worthwhile to understand whether the public supports the government's policy on climate change and how public support affects climate change. A case study of the first-tier cities in China is done to explore these questions through questionnaire surveys. 3468 valid questionnaires from four first-tier cities in China were screened out are used for individual behavior analysis. The results show that the respondents' perceptions towards climate change in China are laxer than those in other countries, meanwhile, they hope that the government would respond to climate change actively. The influencing factors mainly include the demographic characteristics and their perception of climate change. Demographic characteristics are about gender, income, marital status, age, and whether they have children. Moreover, respondents' perception of climate change has a significant impact on their attitudes towards the government's policies for mitigating climate change. The conclusions are drawn based on the comparative analysis of the survey results and suggestions are put forward for making climate change policies.

N-Propargylamine-hydroxypyridinone hybrids as multitarget agents for the treatment of Alzheimer's disease.

AD is a progressive brain disorder. Because of the lack of remarkable single-target drugs against neurodegenerative disorders, the multitarget-directed ligand strategy has received attention as a promising therapeutic approach. Herein, we rationally designed twenty-nine hybrids of N-propargylamine-hydroxypyridinone. The designed hybrids possessed excellent iron-chelating activity (pFe3+ = 17.09-22.02) and potent monoamine oxidase B inhibitory effects. Various biological evaluations of the optimal compound 6b were performed step by step, including inhibition screening of monoamine oxidase (hMAO-B IC50 = 0.083 ± 0.001 µM, hMAO-A IC50 = 6.11 ± 0.08 µM; SI = 73.5), prediction of blood-brain barrier permeability and mouse behavioral research. All of these favorable results proved that the N-propargylamine-hydroxypyridinone scaffold is a promising structure for the discovery of multitargeted ligands for AD therapy.

Discovery of natural anti-inflammatory alkaloids: Potential leads for the drug discovery for the treatment of inflammation.

Inflammation is an adaptive response of the immune system to tissue malfunction or homeostatic imbalance. Corticosteroids and non-steroidal anti-inflammatory drugs (NSAIDs) are frequently applied to treat varieties of inflammatory diseases but are associated with gastrointestinal, cardiovascular, and kidney side effects. Developing more effective and less toxic agents remain a challenge for pharmaceutical chemist due to the complexity of the different inflammatory processes. Alkaloids are widely distributed in plants with diverse anti-inflammatory activities, providing various potential lead compounds or candidates for the design and discovery of new anti-inflammatory drug candidates. Therefore, re-examining the anti-inflammatory alkaloid natural products is advisable, bringing more opportunities. In this review, we summarized and described the recent advances of natural alkaloids with anti-inflammatory activities and possible mechanisms in the period from 2009 to 2020. It is hoped that this review of anti-inflammatory alkaloids can provide new ideas for researchers engaged in the related fields and potential lead compounds for the discovery of anti-inflammatory drugs.

Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level.

Effective computational prediction of complex or novel molecule syntheses can greatly help organic and medicinal chemistry. Retrosynthetic analysis is a method employed by chemists to predict synthetic routes to target compounds. The target compounds are incrementally converted into simpler compounds until the starting compounds are commercially available. However, predictions based on small chemical datasets often result in low accuracy due to an insufficient number of samples. To address this limitation, we introduced transfer learning to retrosynthetic analysis. Transfer learning is a machine learning approach that trains a model on one task and then applies the model to a related but different task; this approach can be used to solve the limitation of few data. The unclassified USPTO-380K large dataset was first applied to models for pretraining so that they gain a basic theoretical knowledge of chemistry, such as the chirality of compounds, reaction types and the SMILES form of chemical structure of compounds. The USPTO-380K and the USPTO-50K (which was also used by Liu et al.) were originally derived from Lowe's patent mining work. Liu et al. further processed these data and divided the reaction examples into 10 categories, but we did not. Subsequently, the acquired skills were transferred to be used on the classified USPTO-50K small dataset for continuous training and retrosynthetic reaction tests, and the pretrained accuracy data were simultaneously compared with the accuracy of results from models without pretraining. The transfer learning concept was combined with the sequence-to-sequence (seq2seq) or Transformer model for prediction and verification. The seq2seq and Transformer models, both of which are based on an encoder-decoder architecture, were originally constructed for language translation missions. The two algorithms translate SMILES form of structures of reactants to SMILES form of products, also taking into account other relevant chemical information (chirality, reaction types and conditions). The results demonstrated that the accuracy of the retrosynthetic analysis by the seq2seq and Transformer models after pretraining was significantly improved. The top-1 accuracy (which is the accuracy rate of the first prediction matching the actual result) of the Transformer-transfer-learning model increased from 52.4% to 60.7% with greatly improved prediction power. The model's top-20 prediction accuracy (which is the accuracy rate of the top 20 categories containing actual results) was 88.9%, which represents fairly good prediction in retrosynthetic analysis. In summary, this study proves that transferring learning between models working with different chemical datasets is feasible. The introduction of transfer learning to a model significantly improved prediction accuracy and, especially, assisted in small dataset based reaction prediction and retrosynthetic analysis.

Carbon sinks and output of China's forestry sector: An ecological economic development perspective.

Forestry has a dual role in mitigating climate change and increasing regional output value. Therefore, forestry is meaningful for the anthroposphere and the atmosphere. In this study, slacks-based measure (SBM) approach and Malmquist-Luenberger Index are adopted to measure the static efficiency and dynamic changes in forestry productivity in thirty regions in China. Ecological development is measured by setting forest carbon sinks as desirable output and economic development is evaluated by forestry output value. Moreover, using the three-stage DEA model, the economic and environmental factors are introduced to adjust regional forest carbon sinks and forestry output slacks. Finally, from timely evolution and spatial non-equilibrium perspectives, the ecological-economic efficiency and total factor productivity of forests are analyzed according to the results. The results revealed that the estimators of ecological efficiency and productivity are greater than the economic development of the forest. The highest ecological economic efficiency and productivity is in the southwest region of China. Eight economic or environmental factors adjusting the output have influence on the total factor productivity of forestry, and the results show that harvest has no clear effect on environmental improvement. Policy implications are further proposed to develop environmental management to mitigate climate change.