[Impacts of Urban Form on Carbon Emissions Under the Goal of Carbon Emission Peak and Carbon Neutrality: A Case Study of the Yangtze River Economic Belt].

Clarifying the mechanism of influence of urban form on carbon emissions is an important prerequisite for achieving urban carbon emission reduction. Taking the Yangtze River Economic Belt as an example, this study elaborated on the general mechanism of urban form on carbon emissions, used multi-source data to quantitatively evaluate the urban form, and explored the impacts of urban form indicators on carbon emissions from 2005 to 2020 at global and sub-regional scales with the help of spatial econometric models and geodetector, respectively. The results showed that:① The carbon emissions of the Yangtze River Economic Belt increased from 2 365.31 Mt to 4 230.67 Mt, but the growth rate gradually decreased. Its spatial distribution pattern was bipolar, with high-value areas mainly distributed in core cities such as Shanghai and Chongqing and low-value areas concentrated in the western regions of Sichuan and Yunnan. ② The area of construction land in the study area expanded over the past 15 years, but the population density of construction land had been decreasing. The degree of urban fragmentation was decreasing, and the difference between cities was also progressively narrowing. The average regularity of urban shape improved, and the compactness increased significantly. ③ All indicators of urban scale had significant positive effects on carbon emissions at the global scale, urban fragmentation had a significant negative effect in 2005, and the effective mesh size (MESH) indicator of urban compactness showed a significant negative correlation with carbon emissions in the study period. ④ Total class area, patch density, and effective mesh size had the most significant impacts on carbon emissions in upstream cities. Effective mesh size, mean perimeter-area ratio, and total class area had higher influences in midstream cities. Effective mesh size, percentage of like adjacencies, and largest patch index were the key factors to promote carbon reduction in downstream cities. Cities in different regions should comprehensively consider the impacts of various urban form indicators on carbon emissions and then optimize their urban form to promote sustainable development.

Spatiotemporal variations and influencing factors of methane emissions from livestock in China: A spatial econometric analysis.

In recent years, China has been implementing policies to improve the livestock industry in response to the global trend toward green and low-carbon development. These policies include the establishment of demonstration zones for high-standard agriculture, the relocation of farms to the north, etc. This study aims to investigate the impact of changes in the spatial structure of the livestock industry on methane emissions. It used panel data from 31 provinces in China from 2001 to 2021 and applied the IPCC methodology to quantify methane emissions at both the national and provincial levels. In addition, a spatial econometric model was used to analyze the impact of changes in the spatial structure of the livestock industry on methane emissions. The results show that methane from livestock in China decreased from 13.85 million tons in 2001 to 11.82 million tons in 2021. In addition, methane emissions from livestock in China show a significant spatial gradient and correlation. The Southwest has the highest methane emissions, accounting for 24 % of the total emissions. After controlling for spatial correlation and other factors in the model, it was found that the spatial structure of the livestock industry has a different influence on methane emissions both in the province and in neighboring provinces. To improve methane emission efficiency in the future, policies such as establishing functional zones for livestock farming, strengthening technological innovation and sharing for green development in agriculture, and promoting the optimization of agricultural and rural management structures should be implemented.

Semiquantitative assessment of preganglionic nerves for chronic immune-mediated neuropathies using brachial plexus magnetic resonance imaging.

Background: Brachial plexus magnetic resonance imaging (MRI) is an important noninvasive supplementary diagnostic method of chronic immune peripheral neuropathies, but few MRI studies on the preganglionic nerves have been conducted. This retrospective cross-sectional study aimed to establish a reliable assessment for brachial plexus preganglionic nerve thickness and to use this method to assess and compare nerve characteristics in various types of peripheral neuropathies. Methods: Hospitalized patients diagnosed as positive for anti-neurofascin-155 (NF155)-positive autoimmune nodopathy (AN) (NF155+), chronic inflammatory demyelinating polyneuropathy (CIDP), or multifocal motor neuropathy (MMN) at Huashan Hospital of Fudan University in Shanghai, China, who underwent brachial plexus MRI between October 2011 and August 2023 were consecutively recruited for this study. We also recruited participants who underwent brachial plexus MRI during this period with no history of trauma, inflammation, tumors, compression, or degenerative conditions as healthy controls. According to our self-developed semiquantitative assessment of preganglionic nerves, we assessed the bilateral preganglionic C5-C8 nerves individually and scored the enlargement degree from 0 to 4 points. Furthermore, a sum score ≥20 was defined as definite enlargement. Results: A total of 122 participants were enrolled, including 28 with NF155+, 40 with CIDP, 15 with MMN, and 39 healthy controls. In the comparison of the single-nerve scores, we found that there was a significant difference distribution among the four groups (χ2 test; P<0.001), with the patients with NF155+ exhibiting the highest scores in each of the bilateral C5-C8 nerves. In the comparison of the sum scores, a descending tendency was observed in patients NF155+, CIDP, and MMN, with median scores of 11, 4, and 0 points, respectively (Kruskal-Wallis test; P=0.003, P<0.001, and P=0.005, respectively for NF155+ vs. CIDP, NF155+ vs. MMN, and CIDP vs. MMN). The proportion of definite enlargement in those with NF155+ was greater than that in healthy controls (21% vs. 0%; χ2 test; P=0.004), and the sum score at 0 points was lower in the NF155+ group than in CIDP, MMN, and healthy control groups (7% vs. 37%, 87%, and 41%, respectively; χ2 test; P<0.001). Conclusions: This semiquantitative assessment can be a valuable tool for measuring preganglionic nerve enlargement, which was found to be decreased, respectively, in those with NF155+, CIDP, and MMN. Presence of definite enlargement could be a strong indicator of NF155+ in clinic.

Spatialization and driving factors of carbon budget at county level in the Yangtze River Delta of China.

The county is the basic administrative unit of China, and the spatialization of carbon budget at the county scale plays an irreplaceable role in deepening the understanding of the carbon emission mechanism and spatial pattern. Yueqing County, an economically developed county in the Yangtze River Delta of China, was selected as the study area, the spatial pattern of the carbon budget and the optimal resolution of the spatialization at the county level were dissected on the basis of accurate accounting, and driving factors of carbon emissions were further identified using the geographically weighted regression model. The results indicated that (1) the carbon emissions were mainly generated from fossil fuel combustion related to energy, accounting for 98.8% of the total carbon budget in the study area; (2) the optimal resolution of spatialization was 200 m and carbon emissions were concentrated in the southeast of the study area; (3) energy intensity, energy structure, per capita GDP, and urbanization rate were positively correlated with carbon emissions, while population played a bidirectional role in carbon emissions. This study not only strengthens the understanding of the patterns and drivers of the carbon budget but also establishes a theoretical framework and operational tools for policymakers to formulate solutions to mitigate the carbon crisis.

Environmental protection tax and green innovation of heavily polluting enterprises: A quasi-natural experiment based on the implementation of China's environmental protection tax law.

Environmental protection tax is an important tool for directing environmentally friendly growth in heavily polluting enterprises, but existing research has yet to provide consistent conclusions on whether and how environmental protection tax can promote green innovation in heavily polluting industries. The paper uses a double difference model based on data from Chinese listed companies in heavily polluting industries from 2012 to 2021 to empirically investigate whether environmental protection tax drives green innovation behavior of heavily polluting enterprises. The findings show that the environmental protection tax increases the degree of green innovation in heavily polluting enterprises, primarily through the anti-driving effect, in which an increase in environmental management expenses forces firms to increase their R&D investment, which improves the degree of green technical innovation. Furthermore, the environmental protection tax has a strong promotion effect on heavy polluters' green innovation for state-owned enterprises and those in growing period or located in high marketization regions. However, this promotion effect is insignificant for non-state-owned enterprises and those in recession period, and environmental protection tax hinders green innovation of enterprises in mature period and those located in low marketization regions. Accordingly, it is suggested to improve preferential tax policies, increase investment in corporate green innovation and strengthen the supervision of environmental tax.

Assessment of adaptive immune responses of dairy cows with Burkholderia contaminans-induced mastitis.

Burkholderia contaminans, an emerging pathogen related to cystic fibrosis, is known to cause potentially fatal infections in humans and ruminants, especially in immunocompromised individuals. However, the immune responses in cows following its infection have not been fully elucidated. In this study, T- and B-lymphocytes-mediated immune responses were evaluated in 15 B. contaminans-induced mastitis cows and 15 healthy cows with multi-parameter flow cytometry. The results showed that infection with B. contaminans was associated with a significant decrease in the number and percentage of B lymphocytes but with a significant increase in the proportion of IgG+CD27+ B lymphocytes. This indicated that humoral immune response may not be adequate to fight intracellular infection, which could contribute to the persistent bacterial infection. In addition, B. contaminans infection induced significant increase of γδ T cells and double positive (DP) CD4+CD8+ T cells but not CD4+ or CD8+ (single positive) T cells in blood. Phenotypic analysis showed that the percentages of activated WC1+ γδ T cells in peripheral blood were increased in the B. contaminans infected cows. Interestingly, intracellular cytokine staining showed that cattle naturally infected with B. contaminans exhibited multifunctional TNF-α+IFN-γ+IL-2+ B. contaminans-specific DP T cells. Our results, for the first time, revealed a potential role of IgG+CD27+ B cells, CD4+CD8+ T cells and WC1+ γδ T cells in the defense of B. contaminans-induced mastitis in cows.

Cost Volume Pyramid Based Depth Inference for Multi-View Stereo.

We propose a cost volume-based neural network for depth inference from multi-view images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner instead of constructing a cost volume at a fixed resolution leads to a compact, lightweight network and allows us inferring high resolution depth maps to achieve better reconstruction results. To this end, we first build a cost volume based on uniform sampling of fronto-parallel planes across the entire depth range at the coarsest resolution of an image. Then, given current depth estimate, we construct new cost volumes iteratively to perform depth map refinement. We show that working on cost volume pyramid can lead to a more compact, yet efficient network structure compared with existing works. We further show that the (residual) depth sampling can be fully determined by analytical geometric derivation, which serves as a principle for building compact cost volume pyramid. To demonstrate the effectiveness of our proposed framework, we extend our cost volume pyramid structure to handle the unsupervised depth inference scenario. Experimental results on benchmark datasets show that our model can perform 6x faster with similar performance as state-of-the-art methods for supervised scenario and demonstrates superior performance on unsupervised scenario. Code is available at https://github.com/JiayuYANG/CVP-MVSNet.

Heavy metal contamination risk assessment and correlation analysis of heavy metal contents in soil and crops.

Heavy metal pollution is a notable threat to agricultural production. Soil heavy metal pollution can cause potential ecological risk (ERI), and crop heavy metal pollution can cause human health risk (HRI). However, most previous studies partially focused on heavy metal pollution in soil or crop but often neglected the relationship between them. Actually, soil heavy metal can pollute crops to some extent, while not all heavy metal pollution in crops comes from soil. The inner relationship of pollution risk in soil-crop system is worth attention. In this study, we selected Ningbo as the study region and used sample data to assess both soil and crop heavy metal risks, in order to explore the differences between heavy metal contamination risks in soil and crops as well as the relationships between heavy metal contents in soil and crops. Our results showed that Hg was the most polluted heavy metal in soil, which led to the highest ecological risk in Jiangbei (Comprehensive ERI = 567) with the maximum ERI of Hg (430). However, As in crops contributed the most to health risk and caused the highest health risk in Fenghua (HRI = 10) with the largest contribution of 64.5%. Such differences of pollution risk assessment indicated that the contents of the same heavy metal were inconsistent in soil and crops. Our results further showed that the heavy metals in soil had the greatest influence on Zn in crops. Pb and Cr in soil had synergistic effects on the crop absorption of Zn, whereas As, Hg and Cu played antagonistic roles in the crop absorption of Zn. Our study confirms that heavy metals in soil would variously influence heavy metals in crops and the interaction of heavy metals is very important for pollution risk control, which have been largely ignored yet.

Assessment of Heavy Metal Pollution in Soil and Classification of Pollution Risk Management and Control Zones in the Industrial Developed City.

Soil heavy metal pollution threatens ecological health and food security. It is significant to classify pollution risk management and control zones, which can effectively cope with soil pollution and scientifically carry out soil remediation projects. In this study, based on 665 soil samples collecting from Ningbo (southeast China), single pollution index and Nemerow composite pollution index (NCPI) were measured to assess soil pollution risk, and self-organization mapping model was applied to classify management and control zones. Results showed that the heavy metal pollution in the northwest part was more serious, while the east part was less polluted. Although more than 75% soil samples had negligible risks, the Hg and Cu pollution was greatly influential and notable as their polluted samples accounted for 24.21% and 12.48% respectively. Moreover, about 55.34% soil samples and more than half study region had pollution grades, and NCPI values were obviously high with the center of northwest study area. Results also showed that the study region could be classified into four zones with good spatial variabilities. Specifically, Monitored Zone with High-risk Pollution had the highest NCPI caused by human activities, while Controlled Zone with Severe Pollution had relatively high NCPI caused by industrial and agricultural production. Protected Zone with Ecological Conservation and Restricted Zone with Potential Pollution had low NCPIs attributing to historical or natural factors. Our study implies that the classified zones can provide fundamental and momentous information for establishing appropriate priorities of heavy metal risk management and control.