Software for doing computations in graded Lie algebras

We introduce the Macaulay2 package GradedLieAlgebras for doing computations in graded Lie algebras presented by generators and relations.Comment: 5 page

Industry Linkage and Spatial Co-Evolution Characteristics of Industrial Clusters Based on Natural Semantics—Taking the Electronic Information Industry Cluster in the Pearl River Delta as an Example

Identifying industrial clusters and the changes in the spatial representation of these clusters is a basic but challenging issue for understanding and promoting urban and regional development. However, the current evolution characteristics of industrial clusters pay too much attention to the spatial perspective, and some studies analyze the evolution of industrial clusters from the perspective of industrial linkages. It is very important to combine industrial linkages and spatial agglomeration to observe the evolution of industrial clusters. To solve this problem, based on the enterprise big data from 1984 to 2019, this study uses the method based on natural semantics and spatial collaborative aggregation to identify industrial linkages and spatial aggregation of industrial clusters, and takes the electronic information industry cluster in the Pearl River Delta (PRD) region as an example for empirical research. It can be seen from the results that most of the industries in the PRD cluster remain stable, and the industrial linkages and spatial agglomeration within the cluster are increasing. From the overall type of change, fewer industries can maintain high linkage–high proximity, and most industries are mainly concentrated in low linkage–high proximity. Through the combination of semantic and spatial synergy analysis, this study helps urban planners and policymakers understand the changes in industrial linkages and spatial agglomeration of industrial clusters

Industry Linkage and Spatial Co-Evolution Characteristics of Industrial Clusters Based on Natural Semantics—Taking the Electronic Information Industry Cluster in the Pearl River Delta as an Example

Identifying industrial clusters and the changes in the spatial representation of these clusters is a basic but challenging issue for understanding and promoting urban and regional development. However, the current evolution characteristics of industrial clusters pay too much attention to the spatial perspective, and some studies analyze the evolution of industrial clusters from the perspective of industrial linkages. It is very important to combine industrial linkages and spatial agglomeration to observe the evolution of industrial clusters. To solve this problem, based on the enterprise big data from 1984 to 2019, this study uses the method based on natural semantics and spatial collaborative aggregation to identify industrial linkages and spatial aggregation of industrial clusters, and takes the electronic information industry cluster in the Pearl River Delta (PRD) region as an example for empirical research. It can be seen from the results that most of the industries in the PRD cluster remain stable, and the industrial linkages and spatial agglomeration within the cluster are increasing. From the overall type of change, fewer industries can maintain high linkage–high proximity, and most industries are mainly concentrated in low linkage–high proximity. Through the combination of semantic and spatial synergy analysis, this study helps urban planners and policymakers understand the changes in industrial linkages and spatial agglomeration of industrial clusters

A Case Analysis of Dust Weather and Prediction of PM₁₀ Concentration Based on Machine Learning at the Tibetan Plateau

Dust weather is common and disastrous at the Tibetan Plateau. This study selected a typical case of dust weather and analyzed its main development mechanism in the northeast of the Tibetan Plateau, then applied six machine learning methods and a time series regression model to predict PM10 concentration in this area. The results showed that: (1) The 24-h pressure change was positive when the front intruded on the surface; convergence of vector winds with a sudden drop in temperature and humidity led by a trough on 700 hPa; a “two troughs and one ridge” weather situation appeared on 500 hPa while the cold advection behind the trough was strong and a cyclone vorticity was formed in the east of Inner Mongolia. (2) The trajectory of air mass from the Hexi Corridor was the main air mass path influencing Xining City, in this case, since a significant lag in the peak of PM10 concentration appeared in Xining City when compared with Zhangye City. (3) The Multiple Linear Regression was not only timely and effective in predicting the PM10 concentration but had great abilities for anticipating the transition period of particle concentration and the appearance date of maximum values in such dust weather. (4) The MA and MP in the clean period were much lower than that in the dust period; the PM10 of Zhangye City as an eigenvalue played an important role in predicting the PM10 of Xining City even in clean periods. Different from dust periods, the prediction effect of Random Forest Optimized by Bayesian hyperparameter was superior to Multiple Linear Regression in clean periods

A Case Analysis of Dust Weather and Prediction of PM10 Concentration Based on Machine Learning at the Tibetan Plateau

Dust weather is common and disastrous at the Tibetan Plateau. This study selected a typical case of dust weather and analyzed its main development mechanism in the northeast of the Tibetan Plateau, then applied six machine learning methods and a time series regression model to predict PM10 concentration in this area. The results showed that: (1) The 24-h pressure change was positive when the front intruded on the surface; convergence of vector winds with a sudden drop in temperature and humidity led by a trough on 700 hPa; a “two troughs and one ridge” weather situation appeared on 500 hPa while the cold advection behind the trough was strong and a cyclone vorticity was formed in the east of Inner Mongolia. (2) The trajectory of air mass from the Hexi Corridor was the main air mass path influencing Xining City, in this case, since a significant lag in the peak of PM10 concentration appeared in Xining City when compared with Zhangye City. (3) The Multiple Linear Regression was not only timely and effective in predicting the PM10 concentration but had great abilities for anticipating the transition period of particle concentration and the appearance date of maximum values in such dust weather. (4) The MA and MP in the clean period were much lower than that in the dust period; the PM10 of Zhangye City as an eigenvalue played an important role in predicting the PM10 of Xining City even in clean periods. Different from dust periods, the prediction effect of Random Forest Optimized by Bayesian hyperparameter was superior to Multiple Linear Regression in clean periods

Data-Independent Acquisition-Based Proteome and Phosphoproteome Profiling Reveals Early Protein Phosphorylation and Dephosphorylation Events in Arabidopsis Seedlings upon Cold Exposure

Protein phosphorylation plays an important role in mediating signal transduction in cold response in plants. To better understand how plants sense and respond to the early temperature drop, we performed data-independent acquisition (DIA) method-based mass spectrometry analysis to profile the proteome and phosphoproteome of Arabidopsis seedlings upon cold stress in a time-course manner (10, 30 and 120 min of cold treatments). Our results showed the rapid and extensive changes at the phosphopeptide levels, but not at the protein abundance levels, indicating cold-mediated protein phosphorylation and dephosphorylation events. Alteration of over 1200 proteins at phosphopeptide levels were observed within 2 h of cold treatment, including over 140 kinases, over 40 transcriptional factors and over 40 E3 ligases, revealing the complexity of regulation of cold adaption. We summarized cold responsive phosphoproteins involved in phospholipid signaling, cytoskeleton reorganization, calcium signaling, and MAPK cascades. Cold-altered levels of 73 phosphopeptides (mostly novel cold-responsive) representing 62 proteins were validated by parallel reaction monitoring (PRM). In summary, this study furthers our understanding of the molecular mechanisms of cold adaption in plants and strongly supports that DIA coupled with PRM are valuable tools in uncovering early signaling events in plants

Integrative Proteome and Phosphoproteome Profiling of Early Cold Response in Maize Seedlings

Cold limits the growth and yield of maize in temperate regions, but the molecular mechanism of cold adaptation remains largely unexplored in maize. To identify early molecular events during cold shock, maize seedlings were treated under 4 °C for 30 min and 2 h, and analyzed at both the proteome and phosphoproteome levels. Over 8500 proteins and 19,300 phosphopeptides were quantified. About 660 and 620 proteins were cold responsive at protein abundance or site-specific phosphorylation levels, but only 65 proteins were shared between them. Functional enrichment analysis of cold-responsive proteins and phosphoproteins revealed that early cold response in maize is associated with photosynthesis light reaction, spliceosome, endocytosis, and defense response, consistent with similar studies in Arabidopsis. Thirty-two photosynthesis proteins were down-regulated at protein levels, and 48 spliceosome proteins were altered at site-specific phosphorylation levels. Thirty-one kinases and 33 transcriptional factors were cold responsive at protein, phosphopeptide, or site-specific phosphorylation levels. Our results showed that maize seedlings respond to cold shock rapidly, at both the proteome and phosphoproteome levels. This study provides a comprehensive landscape at the cold-responsive proteome and phosphoproteome in maize seedlings that can be a significant resource to understand how C4 plants respond to a sudden temperature drop