Regional disparities and drivers of carbon emissions in China: A temporal-spatial production-theoretical decomposition analysis.

Given the increasing interest in keeping global warming below 1.5°C, carbon emissions reduction has become a hot topic. However, the regional disparities and the driving factors were not paid enough attention. This article established an indicator to describe the catch-up effort of different regions and proposed a temporal-spatial production-theoretical decomposition model using meta-frontier and global-frontier to capture the driving forces of the catch-up effort of different provinces to benchmarking provinces. The new model was applied to analyze China's regional carbon emissions during 2007 to 2018. The main findings from the empirical study are: (1) Overall, the regional carbon emissions and their spatial variation kept increasing during the study period. (2) Economic activity, potential carbon factor, carbon-abatement technology efficiency and regional carbon-abatement technology gap were the main drivers. (3) The improvement efforts of carbon-abatement, energy-saving technical efficiency, and potential energy intensity were the dominant factors inhibiting the growth of carbon emissions. (4) The improvement efforts of advanced technology and potential energy intensity helped to reduce the regional gaps, but their impacts varied considerably across regions in China.

Significant difference in the efficacies of silicon application regimes on cadmium species and environmental risks in rice rhizosphere.

Silicon (Si) is commonly applied as base-fertilizer or foliar-topdressing to palliate the uptake-translocation-accumulation of cadmium (Cd) in rice through Si-Cd antagonism. However, little is known about the fate of Cd in rice rhizosphere soil and its eco-environmental effects under different Si treatments. Here, systematic works had been carried out to elucidate the Cd species, soil properties, and environmental risks in rice rhizosphere driven by different Si soil-fertilization regimes including CK (without Si-addition), TSi (added before transplanting stage), JSi (added at jointing stage), and TJSi (split into two equal parts, added half before transplanting and another half at jointing stage). Results showed that TJSi outperformed the rest of fertilization regimes. The solid-phase-Cd concentrations treated with TSi, TJSi and JSi were increased by 4.18%, 5.73% and 3.41%, respectively, when compared to CK. The labile Cd (F1+F2) proportion of TJSi was reduced by 16.30%, 9.30% and 6.78%, respectively, when compared to CK, TSi, and JSi. Simultaneously, the liquid-phase-Cd concentration was appreciably suppressed by TJSi throughout the rice lifecycle, while TSi mainly abated Cd dissociation during the vegetative period, and JSi attenuated it during the grain-filling stage. The mobility factor of Cd treated with TJSi was the lowest, which was significantly lower than that of TSi (9.30%) and JSi (6.78%), respectively. Similarly, the oral exposure risk of TJSi was reduced by 4.43% and 32.53%; and the food-chain exposure risk of TJSi was decreased by 13.03% and 42.78%. Additionally, TJSi was the most effective in promoting enzyme activities and nutrient content in rhizosphere soil. Overall, TJSi is more positive and sustainable than TSi and JSi in reconstructing Cd-contaminated rhizosphere environments and abating the environmental risks of Cd. Agronomic practices in Cd-contaminated paddy soils can be informed by applying Si-fertilizer separately before transplanting and at jointing stage to achieve soil welfare and food security.

The Cd sequestration effects of rice roots affected by different Si management in Cd-contaminated paddy soil.

The application of exogenous silicon (Si) reportedly is one of the eco-friendly practices to mitigate cadmium (Cd) phytotoxicity and regulate the chemical behaviors of Cd in the soil-rice system. But the efficiency of Si on the Cd retention by rice root varies with the Si fertilizer management. The objective of this paper was to interpret the differences in Cd immobilization by rice roots and relevant mechanisms under different ways of Si application (T-Si, supplied at transplanting stage; TJ-Si, split at transplanting and jointing stage with the ratio of 50 % to 50 %; J-Si, supplied at jointing stage and CK, none of Si application) in Cd-contaminated paddy soils. The results showed that the Cd-retention capacity of rice root was increased by 0.60 % ~ 3.06 % under different Si management when compared to CK. The concentrations of monosilicic acid in soils and in apoplast and symplast of roots were increased significantly by Si application, while Cd concentrations in apoplast and symplast of root were decreased by 28.50 % (T-Si), 40.64 % (TJ-Si) and 30.26 % (J-Si), respectively. The distribution of Cd in rice cell wall was increased significantly by TJ-Si. The Cd concentrations of inert fractions (F3, F4 and F6) in root of TJ-Si were raised obviously. Si application downregulated the expression of OsIRT2 and OsNramp5 while upregulated OsHMA3, and the expression of OsHMA3 treated by TJ-Si was obviously higher than CK and J-Si. The distributions of the passive Cd in roots bound with thiol compounds (NPT, GSH and PCs) and polysaccharide components (pectin, hemicelluloses 1 and hemicellulose 2) were raised much more by TJ-Si than by T-Si and J-Si. On the whole, compared with T-Si and J-Si, TJ-Si could more easily replenish soil available Si and enhance Cd sequestration in roots as the result of the decrease of Cd transport factor in roots. This study unravels some mechanisms about different Si management on increasing Cd retention and decreasing Cd migration in rice roots, and TJ-Si is worthy of being recommended.

Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection.

Misinformation has become a frightening specter of society, especially fake news that concerning Covid-19. It massively spreads on the Internet, and then induces misunderstandings of information to the national and global communities during the pandemic. Detecting massive misinformation on the Internet is crucial and challenging because humans have struggled against this phenomenon for a long time. Our research concerns detecting fake news related to covid-19 using augmentation [random deletion (RD), random insertion (RI), random swap (RS), synonym replacement (SR)] and several graph neural network [graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE (SAmple and aggreGatE)] model. We constructed nodes and edges in the graph, word-word node, and word-document node to graph neural network. Then, we tested those models in different amounts of sample training data to obtain accuracy for each model and compared them. For our fake news detection task, we found training accuracy steadily increasing for GCN, GAT, and SAGE models from the beginning to the end of the epochs. This result proved that the performance of GNN, whether GCN, GAT, or SAGE gained an entirely insignificant difference precision result.

Abatement of Cd in rice grain and toxic risks to human health by the split application of silicon at transplanting and jointing period.

Silicon (Si) has the potential to ameliorate the toxic effects of cadmium (Cd) on rice growth and mitigate Cd-uptake by rice under Cd-contaminated soil. However, it is not completely clear whether there are differences in the impacts of different Si management on the chemical behavior of Cd in soil-rice system under Cd-contaminated paddy field. Here, pot trials were conducted to explore the effects of three modes of Si application (T-applying Si at transplanting stage, J-applying Si at jointing stage, TJ-applying Si at transplanting stage and jointing stage with a ratio of 50% to 50%) on the accumulation of Cd in rice grain and the toxic risk of Cd on human health in rice consumption under Cd-polluted soil (4.21 mg·kg-1), and that without Si application was used as control (CK). Results showed that rice growth and Cd-retention in root were enhanced by Si application, and the retention of Cd in TJ root was the highest, reaching 82.36%∼84.06% of total Cd absorbed by rice plant. TJ also elevated soil pH and CEC value significantly during the whole growth period, diminished Cd availability and converted exchangeable-Cd into residual-Cd in soil. Moreover, Si application reduced Cd concentration in iron plaque, while TJ had the lowest concentration of DCB-Cd and the highest molar ratios of Fe/Cd and Mn/Cd. The bioaccessibility of Cd from grains and cooked rice were decreased by Si application. Compared with T and J, the hazard quotient of digestion from cooked white rice of TJ in gastric phase was reduced by 19.61% and 21.94%, respectively. In brief, TJ had more efficiency on reducing the Cd availability in soil during the rice growing period, promoting the retention of Cd in root, decreasing Cd uptake by rice plant and distribution to grains, as well as the bioaccessibility of Cd from cooked rice. These results also provide a novel strategy of Si application to decrease the risk of Cd migration in the soil-rice-humans system and simultaneously promote rice yields.

Backers investment behavior on explicit and implicit factors in reward-based crowdfunding based on ELM theory.

The aim of this study is to identify the dynamic explicit and implicit information factors which displayed on the webpage of platforms that influence backers' investment decision-making behavior. We analyze the connections among these factors by collecting the longitudinal dataset from reward-based crowdfunding platform. Based on ELM model, we establish Fixed Estimation Panel Data Model respectively according to explicit and implicit factors and take Funding Status (crowdfunding results) as the moderating variable to observe the goal gradient effect. Results indicate that most variables in the central route affect backers' investment behavior positively, while most variables in the periphery route have a negative impact on backers' investment behavior. The Funding Status has a significant negative moderating effect on the explicit variables, and has no significant moderating effect on the implicit information variables of the project. In addition, we upgrade the econometric method used by previous scholars, which could improve the accuracy of the FE model. Furthermore, we find strong support for the herding effect in reward-based crowdfunding and the intensity tends to decrease before the funding goal draws near.

Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach.

Recombination presents a nonuniform distribution across the genome. Genomic regions that present relatively higher frequencies of recombination are called hotspots while those with relatively lower frequencies of recombination are recombination coldspots. Therefore, the identification of hotspots/coldspots could provide useful information for the study of the mechanism of recombination. In this study, a new computational predictor called SVM-EL was proposed to identify hotspots/coldspots across the yeast genome. It combined Support Vector Machines (SVMs) and Ensemble Learning (EL) based on three features including basic kmer (Kmer), dinucleotide-based auto-cross covariance (DACC), and pseudo dinucleotide composition (PseDNC). These features are able to incorporate the nucleic acid composition and their order information into the predictor. The proposed SVM-EL achieves an accuracy of 82.89% on a widely used benchmark dataset, which outperforms some related methods.

iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance.

Meiotic recombination presents an uneven distribution across the genome. Genomic regions that exhibit at relatively high frequencies of recombination are called hotspots, whereas those with relatively low frequencies of recombination are called coldspots. Therefore, hotspots and coldspots would provide useful information for the study of the mechanism of recombination. In this study, we proposed a computational predictor called iRSpot-DACC to predict hot/cold spots across the yeast genome. It combined Support Vector Machines (SVMs) and a feature called dinucleotide-based auto-cross covariance (DACC), which is able to incorporate the global sequence-order information and fifteen local DNA properties into the predictor. Combined with Principal Component Analysis (PCA), its performance was further improved. Experimental results on a benchmark dataset showed that iRSpot-DACC can achieve an accuracy of 82.7%, outperforming some highly related methods.

Protein Remote Homology Detection Based on an Ensemble Learning Approach.

Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods.

Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.

Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC.

[Synthesis and antibacterial activity of 7-(3-amino-4-alkoxyimino-1 -piperidyl) -quinolones].

To explore new agents of quinolone derivatives with high activity against Gram-positive and Gram-negative microorganisms, 7-(3-amino-4-alkoxyimino-1-piperidyl) quinolones were designed and synthesized, and their activity against Gram-positive and Gram- negative microorganisms were tested in vivo and in vitro. Twenty one target compounds were obtained. Their structures were established by 1H NMR, HRMS and X-ray crystallographic analysis. The target compounds possess different antimicrobial activities against both Gram-negative and Gram-positive microorganisms. Compounds 14a and 14m have broad spectral antibacterial activities. They show better antibacterial activities against 12 strains Gram-positive bacteria than three references. In particular, their activities against S. aureus and S. epidermidis (including MRSA and MRSE) were 4 - 16 times than that of gemifloxacin and balofloxacin, and 8 - 64 times than that of levofloxacin. The MIC values to S. aureus strains of compounds 14a and 14m were 0.25 - 1 mg x L(-1) and 0.125 - 1 mg x L(-1), to S. epidermidis strains were 0.5 - 4 mg x L(-1) and 1 - 8 mg x L(-1) respectively. The in vivo results showed that they have as good internal protection as gemifloxacin and moxifloxacin against systemic infection model in mice (P > 0.05).

[Comparative research of the influence factors of DNA extraction of bloodstain on the filter paper with Chelex-100 method].

OBJECTIVE: To improve DNA extraction from bloodstain on the filter paper and to establish a rapid, simple, and cost-effective method for DNA extraction suitable for database construction. METHODS: Seven hundred and fifty two aged bloodstains on filter paper were randomly divided into four groups. The four different DNA extraction methods were compared with each other, and two DNA extraction methods used for 63 fresh bloodstains on filter paper were also compared with each other. RESULTS: There were no statistically significant differences observed among the four DNA extraction methods (P > 0.05) for aged bloodstains on filter paper; But the difference between the two DNA extraction methods for fresh bloodstains on filter paper was obviously (P < 0.05). CONCLUSION: Extraction of DNA samples from aged bloodstains on filter paper can be accomplished by using Chlex-100 methodology directly with no need to wash the bloodstains.