This paper improves the combined model I, which directly adds the predicted value of the ARIMA model and the predicted value of the BP neural network model. The linear fitting of the ARIMA model and ...the nonlinear fitting of the BP model are taken as the independent variable, and the per capita coal consumption sequence is taken as the dependent variable. By multiple linear regression, a new combined model II is constructed. Based on the analysis of the change rule of China’s per capita coal consumption over the years, the combined model II is used to fit the per capita coal consumption from 2014 to 2018. The result shows that the fitting errors are 0.62%, 0.17%, 0.04%, 0.04% and 0.07%, respectively. Compared with the combined model I, the combined model II improves the prediction accuracy. Finally, the combined model II is used to predict China’s per capita coal consumption from 2019 to 2023, which are 2878 kg, 2893 kg, 2906 kg, 2919 kg and 2926 kg. It is concluded that the per capita coal consumption increases slightly but the growth rate slows down, which provides reference for relevant government departments to formulate reasonable energy development policies.
In this paper, the participants' interaction input emotion is assessed to analyze the current student interaction relationship, and two influencing factors, friendliness and empathy, are extracted. ...Secondly, the psychological game process of interpersonal interaction is simulated to model the emotion generation process of participants and students, and the sub-game perfect equilibrium strategy of the embedded game is used as the optimal emotion selection strategy. Finally, the student’s emotional states are updated according to the optimal emotional strategy, and the spatial coordinates of students’ emotional states after being stimulated by emotions are labeled with the spatial coordinates of six basic emotions. The results show that the happiness enhancement strategy based on the game model increases from 0 to 0.9, and the effectiveness increases to 1.0.
Abstract
Background
Coronavirus disease 2019 (COVID-19) is a pandemic with no specific antiviral treatments or vaccines. There is an urgent need for exploring the neutralizing antibodies from ...patients with different clinical characteristics.
Methods
A total of 117 blood samples were collected from 70 COVID-19 inpatients and convalescent patients. Antibodies were determined with a modified cytopathogenic neutralization assay (NA) based on live severe acute respiratory syndrome coronavirus 2 and enzyme-linked immunosorbent assay (ELISA). The dynamics of neutralizing antibody levels at different time points with different clinical characteristics were analyzed.
Results
The seropositivity rate reached up to 100.0% within 20 days since onset, and remained 100.0% till days 41–53. The total geometric mean titer was 1:163.7 (95% confidence interval CI, 128.5–208.6) by NA and 1:12 441.7 (95% CI, 9754.5–15 869.2) by ELISA. The antibody level by NA and ELISA peaked on days 31–40 since onset, and then decreased slightly. In multivariate generalized estimating equation analysis, patients aged 31–45, 46–60, and 61–84 years had a higher neutralizing antibody level than those aged 16–30 years (β = 1.0470, P = .0125; β = 1.0613, P = .0307; β = 1.3713, P = .0020). Patients with a worse clinical classification had a higher neutralizing antibody titer (β = 0.4639, P = .0227).
Conclusions
The neutralizing antibodies were detected even at the early stage of disease, and a significant response was shown in convalescent patients.
The neutralizing antibody responses to severe acute respiratory syndrome coronavirus 2 in patients with COVID-19 depends on time since onset and severity of disease.
Plant drought tolerance is a complex trait that requires a global view to understand its underlying mechanism. The proteomic aspects of plant drought response have been extensively investigated in ...model plants, crops and wood plants. In this review, we summarize recent proteomic studies on drought response in leaves to reveal the common and specialized drought-responsive mechanisms in different plants. Although drought-responsive proteins exhibit various patterns depending on plant species, genotypes and stress intensity, proteomic analyses show that dominant changes occurred in sensing and signal transduction, reactive oxygen species scavenging, osmotic regulation, gene expression, protein synthesis/turnover, cell structure modulation, as well as carbohydrate and energy metabolism. In combination with physiological and molecular results, proteomic studies in leaves have helped to discover some potential proteins and/or metabolic pathways for drought tolerance. These findings provide new clues for understanding the molecular basis of plant drought tolerance.
ROS and ROS-Mediated Cellular Signaling Zhang, Jixiang; Wang, Xiaoli; Vikash, Vikash ...
Oxidative medicine and cellular longevity,
01/2016, Letnik:
2016, Številka:
1
Journal Article
Recenzirano
Odprti dostop
It has long been recognized that an increase of reactive oxygen species (ROS) can modify the cell-signaling proteins and have functional consequences, which successively mediate pathological ...processes such as atherosclerosis, diabetes, unchecked growth, neurodegeneration, inflammation, and aging. While numerous articles have demonstrated the impacts of ROS on various signaling pathways and clarify the mechanism of action of cell-signaling proteins, their influence on the level of intracellular ROS, and their complex interactions among multiple ROS associated signaling pathways, the systemic summary is necessary. In this review paper, we particularly focus on the pattern of the generation and homeostasis of intracellular ROS, the mechanisms and targets of ROS impacting on cell-signaling proteins (NF-κB, MAPKs, Keap1-Nrf2-ARE, and PI3K-Akt), ion channels and transporters (Ca2+ and mPTP), and modifying protein kinase and Ubiquitination/Proteasome System.
Soil microorganisms play a pivotal role in carbon mineralization and their diversity is crucial to the function of soil ecosystems. However, the effects of long-term fertilization on ...microbial-mediated carbon mineralization are poorly understood. To identify the relative roles of microbes in carbon mineralization of yellow paddies, we investigated the long-term fertilization effects on soil properties and microbial communities and their relationships with carbon mineralization. The treatments included: no fertilization (CK), chemical fertilizer (NPK), organic fertilizer (M), and constant organic-inorganic fertilizer (MNPK). NPK treatment significantly increased soil water content (WC), while M and MNPK treatments significantly increased the content of soil organic carbon (SOC), total nitrogen (TN), soil microbial biomass carbon (SMBC), soil microbial biomass nitrogen (SMBN), and WC. Strong increases in CO2 emissions, potential mineralized carbon, and turnover rate constant were observed in both organic-fertilizer treatments (M and MNPK), relative to the CK treatment. These changes in soil properties can be attributed to the variation in microbial communities. NPK treatment had no significant effect. Different fertilization treatments changed soil microbial community; SOC and SMBN were the most important contributors to the variance in microbial community composition. The variations in community composition did not significant influence carbon mineralization; however, carbon mineralization was significantly influenced by the abundance of several non-dominant bacteria. The results suggest that SOC, SMBN, and non-dominant bacteria (Gemmatimonadetes and Latescibacteria), have a close relationship to carbon mineralization, and should be preferentially considered in predicting carbon mineralization under long-term fertilization.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Think tanks play a fundamental role in shaping policy agendas in Western countries, especially in the US. As international biosecurity is turning from a moderate to a serious concern, the convergence ...of biosecurity subjects and think tanks is evidently increasing. Examining the involvement and implication of think tanks in biosecurity policy formulation domestically and internationally is, therefore, of great value. This article takes a brief look at the intellectual output of over 30 think tanks during the last five years, before and after the outbreak of COVID-19, and tries to build an understanding of the extent to which these think tanks informed strategic, operational, and tactical decisions, with the aim of providing a better basis for dealing with sophisticated biological threats.
Student behavior analysis can reflect students' learning situation in real time, which provides an important basis for optimizing classroom teaching strategies and improving teaching methods. It is ...an important task for smart classroom to explore how to use big data to detect and recognize students behavior. Traditional recognition methods have some defects, such as low efficiency, edge blur, time-consuming, etc. In this paper, we propose a new students behaviour recognition method based on spatio-temporal attention fusion model. It makes full use of key spatio-temporal information of video, the problem of spatio-temporal information redundancy is solved. Firstly, the channel attention mechanism is introduced into the spatio-temporal network, and the channel information is calibrated by modeling the dependency relationship between feature channels. It can improve the expression ability of features. Secondly, a time attention model based on convolutional neural network (CNN) is proposed, which uses fewer parameters to learn the attention score of each frame, focusing on the frames with obvious behaviour amplitude. Meanwhile, a multi-spatial attention model is presented to calculate the attention score of each position in each frame from different angles, extract several saliency areas of behaviour, and fuse the spatio-temporal features to further enhance the feature representation of video. Finally, the fused features are input into the classification network, and the behaviour recognition results are obtained by combining the two output streams according to different weights. Experiment results on HMDB51, UCF101 datasets and eight typical classroom behaviors of students show that the proposed method can effectively recognize the behaviours in videos. The accuracy of HMDB51 is higher than 90%, that of UCF101 and real data are higher than 90%.
Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open ...nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.
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