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Low-carbon economy has become the current global economic development trend, and corporate carbon disclosure has attracted more and more attention from scholars and investors. This study creatively explores the mechanism of corporate carbon disclosure on total factor productivity with financing structure as a mediating variable. The content analysis method is employed to assess carbon disclosure that is suitable for Chinese enterprises. Through the mediating effect model and Sobel test, the internal mechanism of carbon disclosure affecting total factor productivity is analyzed, with Chinese heavy polluting enterprises from 2015 to 2018 as research samples. The results show that, firstly, carbon disclosure has a positive effect on the improvement of total factor productivity. The effect of monetary carbon disclosure on the improvement of total factor productivity is higher than that of non-monetary carbon disclosure. Secondly, the financing structure has a mediating effect between carbon disclosure and total factor profductivity, and the mediating effect of internal financing capabilities is better than those of external financing costs. Finally, external financing costs and internal financing capabilities have mediating effects in both heterogeneous carbon disclosure and total factor productivity. The mediating effect of internal financing capabilities is significantly higher than the mediating effect of external financing costs. The effect of monetary carbon disclosure on total factor productivity indirectly through internal financing capabilities is higher than that of non-monetary carbon disclosure.

Background Heat shock transcription factors ( Hsf s) are present in majority of plants and play central roles in thermotolerance, transgenerational thermomemory, and many other stress responses. Our previous paper identified at least 82 H sf members in a genome-wide study on wheat ( Triticum aestivum L.). In this study, we analyzed the Hsf expression profiles in the advanced development stages of wheat, isolated the markedly heat-responsive gene TaHsfA2–10 (GenBank accession number MK922287), and characterized this gene and its role in thermotolerance regulation in seedlings of Arabidopsis thaliana (L. Heynh.) . Results In the advanced development stages, wheat Hsf family transcription profiles exhibit different expression patterns and varying heat-responses in leaves and roots, and Hsf s are constitutively expressed to different degrees under the normal growth conditions. Overall, the majority of group A and B Hsf s are expressed in leaves while group C Hsf s are expressed at higher levels in roots. The expression of a few Hsf genes could not be detected. Heat shock (HS) caused upregulation about a quarter of genes in leaves and roots, while a number of genes were downregulated in response to HS. The highly heat-responsive gene TaHsfA2–10 was isolated through homeologous cloning. qRT-PCR revealed that TaHsfA2–10 is expressed in a wide range of tissues and organs of different development stages of wheat under the normal growth conditions. Compared to non-stress treatment, TaHsfA2–10 was highly upregulated in response to HS, H 2 O 2, and salicylic acid (SA), and was downregulated by abscisic acid (ABA) treatment in two-leaf-old seedlings. Transient transfection of tobacco epidermal cells revealed subcellular localization of TaHsfA2–10 in the nucleus under the normal growth conditions. Phenotypic observation indicated that TaHsfA2–10 could improve both basal thermotolerance and acquired thermotolerance of transgenic Arabidopsis thaliana seedlings and rescue the thermotolerance defect of the T-DNA insertion mutant athsfa2 during HS. Compared to wild type (WT) seedlings, the TaHsfA2–10 -overexpressing lines displayed both higher chlorophyll contents and higher survival rates. Yeast one-hybrid assay results revealed that TaHsfA2–10 had transactivation activity. The expression levels of thermotolerance-related AtHsps in the TaHsfA2–10 transgeinc Arabidopsis thaliana were higher than those in WT after HS. Conclusions Wheat Hsf family members exhibit diversification and specificity of transcription expression patterns in advanced development stages under the normal conditions and after HS. As a markedly responsive transcriptional factor to HS, SA and H 2 O 2 , TaHsfA2–10 involves in thermotolerance regulation of plants through binding to the HS responsive element in promoter domain of relative Hsps and upregulating the expression of Hsp genes.

Background Tracheobronchomegaly, also known as Mounier-Kuhn syndrome (MKS), is a rare congenital condition characterized by significant dilation of the trachea and main bronchi along with an abnormal wall structure. Diagnosis can be confirmed through computed tomography, pulmonary function tests, and diagnostic bronchoscopy. Currently, there is no curative treatment for MKS; thus, symptomatic and supportive care remain the primary therapeutic approaches. Early diagnosis, effective infection control, and individualized management are crucial for improving patient outcomes. Methods This case report describes a middle-aged woman who presented with chronic cough, expectoration, and wheezing. She had been misdiagnosed with chronic obstructive pulmonary disease (COPD) at a local hospital for an extended period and was subsequently referred to our institution for fiberoptic bronchoscopy, which confirmed the diagnosis of MKS. By reviewing the literature via PubMed, we conducted a retrospective analysis of 29 previously reported cases of MKS, including the present case, totaling 30 cases (21 males and 9 females), predominantly middle-aged and elderly individuals. Conclusions Based on our literature review, the misdiagnosis rate of MKS remains high, often accompanied by significant diagnostic delays. Additionally, the proportion of secondary MKS cases has increased, challenging the traditional notion that MKS is exclusively congenital. Despite its rarity, clinicians should consider MKS in patients presenting with recurrent lower respiratory tract infections, abnormal tracheobronchial morphology., poor response to antibiotic therapy, or refractory COPD-like symptoms. Early imaging and bronchoscopic evaluations are essential to confirm the diagnosis and prevent delayed treatment.

Citrus is an important commercial crop in many areas. The management and planning of citrus growing can be supported by timely and efficient monitoring of citrus-growing regions. Their complex planting structure and the weather are likely to cause problems for extracting citrus-growing regions from remote sensing images. To accurately extract citrus-growing regions, deep learning is employed, because it has a strong feature representation ability and can obtain rich semantic information. A novel model for extracting citrus-growing regions by UNet that incorporates an image pyramid structure is proposed on the basis of the Sentinel-2 satellite imagery. A pyramid-structured encoder, a decoder, and multiscale skip connections are the three main components of the model. Additionally, atrous spatial pyramid pooling is used to prevent information loss and improve the ability to learn spatial features. The experimental results show that the proposed model has the best performance, with the precision, the intersection over union, the recall, and the F1-score reaching 88.96%, 73.22%, 80.55%, and 84.54%, respectively. The extracted citrus-growing regions have regular boundaries and complete parcels. Furthermore, the proposed model has greater overall accuracy, kappa, producer accuracy, and user accuracy than the object-oriented random forest algorithm that is widely applied in various fields. Overall, the proposed method shows a better generalization ability, higher robustness, greater accuracy, and less fragmented extraction results. This research can support the rapid and accurate mapping of large-scale citrus-growing regions.

Accurate estimation of the drag forces generated by vegetation stems is crucial for the comprehensive assessment of the impact of aquatic vegetation on hydrodynamic processes in aquatic environments. The coupling relationship between vegetation layer flow velocity and vegetation drag makes precise prediction of submerged vegetation drag forces particularly challenging. The present study utilized published data on submerged vegetation drag force measurements and employed a genetic programming (GP) algorithm, a machine learning technique, to establish the connection between submerged vegetation drag forces and flow and vegetation parameters. When using the bulk velocity, U , as the reference velocity scale to define the drag coefficient, C d , and stem Reynolds number, the GP runs revealed that the drag coefficient of submerged vegetation is related to submergence ratio ( H *), aspect ratio ( d *), blockage ratio ( ψ *), and vegetation density ( λ ). The relation between vegetation stem drag forces and flow velocity is implicitly embedded in the definition of C d . Comparisons with experimental drag force measurements indicate that using the bulk velocity as the reference velocity, as opposed to using the vegetation layer average velocity, U v , eliminates the need for complex iterative processes to estimate U v and avoids introducing additional errors associated with U v estimation. This approach significantly enhances the model’s predictive capabilities and results in a simpler and more user-friendly formula expression.

Copper (Cu) is an essential trace element for key biochemical reactions. Dietary or genetic copper deficiencies are associated with anemia, cardiomyopathy, and neurodegeneration. The essential requirement for copper in humans is illustrated by Menkes disease, a fatal neurodegenerative disorder of early childhood caused by mutations in the ATP7A copper transporter. Recent groundbreaking studies have demonstrated that a copper delivery small molecule compound, elesclomol (ES), is able to substantially ameliorate pathology and lethality in a mouse model of Menkes disease when injected as an ES-Cu 2+ complex. It is well appreciated that drugs administered through oral means are more convenient with better efficacy than injection methods. Here we show, using genetic models of copper-deficient C. elegans and mice, that dietary ES supplementation fully rescues copper deficiency phenotypes. Worms lacking either the homolog of the CTR1 copper importer or the ATP7 copper exporter showed normal development when fed ES. Oral gavage with ES rescued intestine-specific Ctr1 knockout mice from early postnatal lethality without additional copper supplementation. Our findings reveal that ES facilitates copper delivery from dietary sources independent of the intestinal copper transporter CTR1 and provide insight into oral administration of ES as an optimal therapeutic for Menkes disease and possibly other disorders of copper insufficiency.

Signaling circuits crucial to systemic physiology are widespread, yet uncovering their molecular underpinnings remains a barrier to understanding the etiology of many metabolic disorders. Here, we identified a copper-linked signaling circuit activated by disruption of mitochondrial function in the murine liver or heart that resulted in atrophy of the spleen and thymus and caused a peripheral white blood cell deficiency. We demonstrated that the leukopenia was caused by α-fetoprotein, which required copper and the cell surface receptor CCR5 to promote white blood cell death. We further showed that α-fetoprotein expression was upregulated in several cell types upon inhibition of oxidative phosphorylation. Collectively, our data argue that α-fetoprotein may be secreted by bioenergetically stressed tissue to suppress the immune system, an effect that may explain the recurrent or chronic infections that are observed in a subset of mitochondrial diseases or in other disorders with secondary mitochondrial dysfunction.

The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems.

The management of water levels in wetlands is of great importance for the wetland ecosystem, including the conservation and revitalization of plants. However, the water level requirements (WLRs) of wetland plants have not been well investigated. In this study, Typha angustifolia was selected as an experimental plant species. Combining field investigation and simulation experiments, the relationship between the development status of this species and water level fluctuations (WLFs) in different life-history stages were analyzed. The results show that populations in the Yangtze floodplain, China, had two phenotypic forms ‘tall’ and ‘short’, and that these were distributed in lakes with intermittent or quasi-natural fluctuations and reservoir-like fluctuations, respectively. Lakes with high amplitude (>3.2 m) water fluctuations did not contain T. angustifolia. We investigated the distribution and growth of T. angustifolia in lakes of varying hydrology across the Yangtze floodplain, seeking to define its tolerance of water-level fluctuations and submergence at different stages in its life cycle. The upper tolerance limit of static submerged water depth was bounded by 1.5 times the height of plants in the seedling stage, and the upper tolerance limit of the submergence rate in the seedling stage was the average growth rate of seedling, 1.5 cm/d. The plant height had a positive linear correlation with amplitude and water depth from June to July. The autumn biomass was significantly negatively correlated with amplitude and water depth from January to May. This paper is perhaps the first case study on water level fluctuation requirements (WLFRs) of emergent macrophytes. It systematically assessed the WLFRs of T. angustifolia in each life-history stage, and established a comprehensive WLFR conceptual model. The results of this study could provide a quantitative operational basis for the protection and restoration of this species in Yangtze floodplain lakes.

This study analyzes the spatiotemporal evolution and agglomeration characteristics of the scale and intensity of carbon emission scale and intensity in 21 low-carbon cities from 2005 to 2016 by kernel density estimations and Moran’s I. Based on the revealed comparative advantage index, environmental protection expenditure and science and technology expenditure are symmetrically treated as green fiscal expenditure proxy variables. Simultaneously, four models are constructed, involving non-time and non-space effect, time effect, spatial effect, and spatiotemporal effect, to investigate the effect of green fiscal expenditure on urban carbon emission. The results demonstrate that: First, the carbon emission scale continues to increase and does not present spatial agglomeration characteristics. Conversely, carbon emission intensity continues to decline and manifests spatial agglomeration heterogeneity. The uncoordinated regional economic development is a primary factor for spatial differences in carbon emission scale and intensity. Second, green fiscal expenditure enhances the effectiveness of emission reduction and generates spatiotemporal effects. In the short term, science and technology expenditure is more effective in carbon reduction than environmental protection expenditure. The former can resolve the emergency. Additionally, due to the radiation-driven effect, the latter has a negative spatial spillover effect. In the long term, environmental protection expenditure consistently restrains carbon emissions, and its reduction effect is sustainable. Third, economy and population are the drivers of carbon emission growth, and the structural effect is greater than the scale effect. An optimized energy structure can achieve carbon reductions. Technological innovation should not be ignored.