Analysis of Factors Influencing the Corporate Performance of Listed Companies in China's Agriculture and Forestry Sector Based on a Panel Threshold Model

The global food crisis caused by COVID-19 and the Russia-Ukraine conflict have made many countries around the world realize the significance of agroforestry to a country's food security. However, China's agroforestry R&D innovation is currently lagging behind in development, and some agricultural seeds are heavily dependent on foreign countries, which seriously affects China's national food security. It is especially important to explore the reasons why China's agroforestry R&D and innovation is lagging behind. As listed agroforestry companies face the market demand directly, there is an urgent need to study the R&D innovations of listed agroforestry companies at present. This paper analyzes the impacts of R&D innovation, corporate management and supply chain management on the corporate performance of listed agroforestry companies using the entropy weighting method, GMM estimation and panel threshold model, mainly by selecting annual panel data from CSMAR for the period 2010 to 2021. The following conclusions were drawn: (1) There is a nonlinear relationship between R&D innovation and firm performance, and a 'U';-shaped relationship. This indicates that there is an entrance threshold for R&D innovation in the agroforestry industry, below which corporate performance does not improve. (2) There is a nonlinear relationship between corporate management and corporate performance, and a U-shaped relationship. (3) There is a nonlinear relationship between supply chain management and firm performance, with an inverted-U-shaped relationship. This paper explains the reasons for the slow development of R&D innovation in China's agriculture and forestry industry and fills the gap in the theoretical study of the nonlinear relationship between R&D innovation and corporate performance of listed companies in China's agriculture and forestry industry. Finally, this paper provides a theoretical basis for the decision making of government departments related to agriculture and forestry, and offers some suggestions for listed companies in agriculture and forestry to improve their corporate performance.

Asymmetric Effects of Economic Development, Agroforestry Development, Energy Consumption, and Population Size on CO2 Emissions in China

The COVID-19 epidemic and the Russian–Ukrainian conflict have led to a global food and energy crisis, making the world aware of the importance of agroforestry development for a country. Modern agriculture mechanization leads to massive energy consumption and increased CO2 emissions. At the same time, China is facing serious demographic problems and a lack of consumption in the domestic market. The Chinese government is faced with the dilemma of balancing environmental protection with economic development in the context of the “double carbon” strategy. This article uses annual World Bank statistics from 1990 to 2020 to study the asymmetric relationships between agroforestry development, energy consumption, population size, and economic development on CO2 emissions in China using the partial least squares path model (PLS-PM), the autoregressive VAR vector time series model, and the Granger causality test. The results are as follows: (1) The relationship between economic development and carbon dioxide emissions, agroforestry development and carbon dioxide emissions, energy consumption and carbon dioxide emissions, and population size and carbon dioxide emissions are both direct and indirect, with an overall significant positive effect. There is a direct negative relationship between population size and carbon dioxide emissions. (2) The results of the Granger causality test show that economic development, energy consumption, and CO2 emissions are the causes of the development of agroforestry;economic development, agroforestry development, population size, and CO2 emissions are the causes of energy consumption;energy consumption is the cause of economic development and CO2 emissions;and agroforestry development is the cause of population size and energy consumption. (3) In the next three years, China’s agroforestry development will be influenced by the impulse response of economic development, energy consumption, and CO2 emission factors, showing a decreasing development trend. China’s energy consumption will be influenced by the impulse response of economic development, agroforestry development, population size, and CO2 emission factors, showing a decreasing development trend, followed by an increasing development trend. China’s CO2 emission will be influenced by the impulse response of energy consumption and agroforestry development. China’s CO2 emissions will be influenced by the impulse response of energy consumption and agroforestry development factors, showing a downward and then an upward development trend.

Pathogen-sugar interactions revealed by universal saturation transfer analysis.

Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily modified pathogen proteins can be confounded by overlapping sugar signals and/or compounded with known experimental constraints. Universal saturation transfer analysis (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin-lineage severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike trimer binds sialoside sugars in an "end-on" manner. uSTA-guided modeling and a high-resolution cryo-electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar binding in SARS-CoV-2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins deep in the human lung as potentially relevant to virulence and/or zoonosis.

The Architecture of Inactivated SARS-CoV-2 with Postfusion Spikes Revealed by Cryo-EM and Cryo-ET.

The ongoing global pandemic of coronavirus disease 2019 (COVID-19) resulted from the outbreak of SARS-CoV-2 in December 2019. Currently, multiple efforts are being made to rapidly develop vaccines and treatments to fight COVID-19. Current vaccine candidates use inactivated SARS-CoV-2 viruses; therefore, it is important to understand the architecture of inactivated SARS-CoV-2. We have genetically and structurally characterized ß-propiolactone-inactivated viruses from a propagated and purified clinical strain of SARS-CoV-2. We observed that the virus particles are roughly spherical or moderately pleiomorphic. Although a small fraction of prefusion spikes are found, most spikes appear nail shaped, thus resembling a postfusion state, where the S1 protein of the spike has disassociated from S2. Cryoelectron tomography and subtomogram averaging of these spikes yielded a density map that closely matches the overall structure of the SARS-CoV postfusion spike and its corresponding glycosylation site. Our findings have major implications for SARS-CoV-2 vaccine design, especially those using inactivated viruses.