Impact of urban land use tax on carbon emission efficiency of urban construction land--Analysis based on panel data of 30 provinces in China.

Reducing urban carbon emissions is an important path for ecological civilization construction, which can be achieved through the adjustment of urban land use tax. Using provincial Panel data from 2011 to 2021, based on the analysis of urban carbon emission efficiency values using a non radial SBM model, the Tobit random effects panel model is used to explore the institutional impact of urban land use tax. The study found that urban land use tax has a significant positive promoting effect on carbon emission efficiency and shows certain regional differences. The eastern region is higher in overall efficiency and technical efficiency than the central and western regions, but the central region has the highest overall scale efficiency. At the same time, factors such as population urbanization, industrial structure, and energy-saving technology level will also have a certain impact on this effect. Based on the institutional effect of improving carbon emission efficiency, the article proposes corresponding countermeasures and suggestions from aspects such as tax rate levels, tax system adjustments, tax incentives, and differentiated regional arrangements.

Limited conservation in cross-species comparison of GLK transcription factor binding suggested wide-spread cistrome divergence.

Non-coding cis-regulatory variants in animal genomes are an important driving force in the evolution of transcription regulation and phenotype diversity. However, cistrome dynamics in plants remain largely underexplored. Here, we compare the binding of GOLDEN2-LIKE (GLK) transcription factors in tomato, tobacco, Arabidopsis, maize and rice. Although the function of GLKs is conserved, most of their binding sites are species-specific. Conserved binding sites are often found near photosynthetic genes dependent on GLK for expression, but sites near non-differentially expressed genes in the glk mutant are nevertheless under purifying selection. The binding sites' regulatory potential can be predicted by machine learning model using quantitative genome features and TF co-binding information. Our study show that genome cis-variation caused wide-spread TF binding divergence, and most of the TF binding sites are genetically redundant. This poses a major challenge for interpreting the effect of individual sites and highlights the importance of quantitatively measuring TF occupancy.

Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis.

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Due to the lack of early diagnosis methods and warning signals of CRC and its strong heterogeneity, the determination of accurate treatments for CRC and the identification of specific early warning signals are still urgent problems for researchers. In this study, the expression profiles of cancer tissues and the expression profiles of tumor-adjacent tissues in 28 CRC patients were combined into a human protein-protein interaction (PPI) network to construct a specific network for each patient. A network propagation method was used to obtain a mutant giant cluster (GC) containing more than 90% of the mutation information of one patient. Next, mutation selection rules were applied to the GC to mine the mutation sequence of driver genes in each CRC patient. The mutation sequences from patients with the same type CRC were integrated to obtain the mutation sequences of driver genes of different types of CRC, which provide a reference for the diagnosis of clinical CRC disease progression. Finally, dynamic network analysis was used to mine dynamic network biomarkers (DNBs) in CRC patients. These DNBs were verified by clinical staging data to identify the critical transition point between the pre-disease state and the disease state in tumor progression. Twelve known drug targets were found in the DNBs, and 6 of them have been used as targets for anticancer drugs for clinical treatment. This study provides important information for the prognosis, diagnosis and treatment of CRC, especially for pre-emptive treatments. It is of great significance for reducing the incidence and mortality of CRC.