Does place-based green policy improve air pollution? Evidence from China's National Eco-Industrial Demonstration Park Policy.

Air pollution is one of the serious environmental problems facing the world. This paper systematically investigates the impact and transmission mechanism of the construction of national eco-industrial parks (NEDPs) on urban air pollution based on Chinese city-level panel data from 2003 to 2021 using a staggered difference-in-differences (staggered DID) model. It is found that the construction of NEDP significantly reduces urban air pollution, a conclusion supported by the negative weight diagnostic test and two types of robust DID estimators. Mechanism analyses indicate that NEDP construction reduces urban air pollution mainly by improving regional environmental regulation, promoting green technology innovation and improving energy structure. In addition, the mitigation effect of NEDP construction on urban air pollution is heterogeneous by policy intensity, city resource endowment, city size and administrative status. Further tests show that the institutional environment enhances the air pollution mitigation effect of NEDP construction and that the better the degree of marketization, property rights system, legal system and market development in the place where the policy is implemented, the more conducive it is to amplify the air pollution suppression effect brought about by NEDP construction. Developing economies should take complete account of the characteristics of different regions when implementing place-based green policies to achieve synergistic development of the environment and the economy.

High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing.

Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide-level retrieval could be more intuitive and practical in clinical applications, most methods are designed for patch-level retrieval. A few recently unsupervised slide-level methods only focus on integrating patch features directly, without perceiving slide-level information, and thus severely limits the performance of WSI retrieval. To tackle the issue, we propose a High-Order Correlation-Guided Self-Supervised Hashing-Encoding Retrieval (HSHR) method. Specifically, we train an attention-based hash encoder with slide-level representation in a self-supervised manner, enabling it to generate more representative slide-level hash codes of cluster centers and assign weights for each. These optimized and weighted codes are leveraged to establish a similarity-based hypergraph, in which a hypergraph-guided retrieval module is adopted to explore high-order correlations in the multi-pairwise manifold to conduct WSI retrieval. Extensive experiments on multiple TCGA datasets with over 24,000 WSIs spanning 30 cancer subtypes demonstrate that HSHR achieves state-of-the-art performance compared with other unsupervised histology WSI retrieval methods.

Quantity or quality? The impact of financial geo-density on firms' green innovation.

Green innovation is an essential support for environmentally sustainable development. However, little attention has been given to the impact of financial expansion on green innovation in the existing literature, and there is a lack of studies based on the perspective of the financial geographical supply structure. This study uses latitude and longitude information to construct firm-level financial geo-density data in China. It examines the impact of financial geo-density on a firm's green innovation and mechanisms. The results reveal that as financial geo-density increases, green innovation quantity increases, but green innovation quality decreases. The findings of the mechanism test indicate that an increase in financial geo-density decreases the cost of financing and boosts bank competition in the vicinity of the firm, hence resulting in a rise in the firms' green innovation quantity. Nevertheless, the degree of bank competition increased by financial geo-density increase negatively affects firms' green innovation quality. Heterogeneity analysis shows that financial geo-density has a more significant positive impact on a firm's green innovation quantity in high environmental regulation areas and high-pollution industries. Firms with low innovation capabilities are the main group responsible for the decline in green innovation quality. For firms located in low environmental regulation areas and medium- to light-pollution industries, financial geo-density has a more significant inhibition effect on green innovation quality. Further tests have shown that the extent to which financial geo-density enhances a firm's green innovation quantity diminishes as market segmentation increases. A new concept of financial development policies based on green development and innovation is presented in this paper for developing economies.

Circulating FGF21 and GDF15 as Biomarkers for Screening, Diagnosis, and Severity Assessment of Primary Mitochondrial Disorders in Children.

Background: Primary mitochondrial disorders (PMDs) are a diagnostic challenge for paediatricians, and identification of reliable and easily measurable biomarkers has become a high priority. This study aimed to investigate the role of serum fibroblast growth factor 21 (FGF21) and growth differentiation factor 15 (GDF15) in children with PMDs. Methods: We analysed serum FGF21 and GDF15 concentrations by enzyme-linked immunosorbent assay (ELISA) in children with PMDs, patients with non-mitochondrial neuromuscular disorders (NMDs), and aged-matched healthy children, and compared them with serum lactate and ratio of lactate and pyruvate (L/P). We also evaluated correlations between these biomarkers and the phenotype, genotype, and severity of PMDs. Results: The median serum GDF15 and FGF21 concentrations were significantly elevated in fifty-one patients with PMDs (919.46 pg/ml and 281.3 pg/ml) compared with those of thirty patients with NMDs (294.86 pg/ml and 140.51 pg/ml, both P < 0.05) and fifty healthy controls (221.21 pg/ml and 85.02 pg/ml, both P < 0.05). The area under the curve of GDF15 for the diagnosis of PMDs was 0.891, which was higher than that of the other biomarkers, including FGF21 (0.814), lactate (0.863) and L/P ratio (0.671). Calculated by the maximum Youden index, the critical value of GDF15 was 606.369 pg/ml, and corresponding sensitivity and specificity were 74.5and 100%. In the PMD group, FGF21 was significantly correlated with International Paediatric Mitochondrial Disease Scale (IPMDS) score. The levels of GDF15 and FGF21 were positively correlated with age, critical illness condition, and multisystem involvement but were not correlated with syndromic/non-syndromic PMDs, different mitochondrial syndromes, nuclear DNA/mitochondrial DNA pathogenic variants, gene functions, or different organ/system involvement. Conclusion: Regardless of clinical phenotype and genotype, circulating GDF15 and FGF21 are reliable biomarkers for children with PMDs. GDF15 can serve as a screening biomarker for diagnosis, and FGF21 can serve as a severity biomarker for monitoring.

Hypoparathyroidism, neutropenia and nephrotic syndrome in a patient with mitochondrial trifunctional protein deficiency: A case report and review of the literature.

INTRODUCTION: Mitochondrial trifunctional protein (TFP) deficiency is an autosomal recessive disorder that causes a clinical spectrum of diseases ranging from severe infantile cardiomyopathy to mild chronic progressive neuromyopathy, however, parathyroid glands, hematologic system and kidney damage are not the common presentations of this disease. METHODS: We describe the clinical, biochemical and molecular features of the TFP deficiency patient at our institution. We also provide an extensive literature review of previous published cases with emphasis on the clinical/biochemical phenotype-genotype correlation of this disorder. RESULTS: Our case is a complete TFP deficiency patient dominated presented with hypoparathyroidism, neutropenia and nephrotic syndrome, which caused by compound heterozygoues variants in HADHB gene. Based on the retrospective study of 157 cases, TFP patients presented with diverse clinical, biochemical and molecular features. The onset age is typically before early childhood. Neuromuscular system is more vulnerable involved. Severe form is generally characterized by multiorgan involvement. A notable feature of severe and intermediate form is respiratory failure. Neuropathy and rhabdomyolysis are the typical manifestations of mild form. Increased long-chain 3-OH-acylcarnitines (C16-OH, C18:1-OH) are the most common biochemical finding. The mortality of the present study is as high as 57.9%, which is linked with the onset age, phenotype, mutation type and muscular histology. Mutations in HADHB are more frequent in Asian descent with complete TFP deficiency and usually presented with atypical presentations. The type of mutation, rather than residual enzyme activity seem to be more related to the phenotype and prognosis. The most common HADHA variant is 1528G > C, no common HADHB variant were detected. CONCLUSIONS: TFP deficiency is heterogeneous at both the molecular and phenotypic levels, generally a high mortality. Although there is no strict clinical/biochemical phenotype-genotype correlation, difference in ethnic and subunit mutations still have certain characteristics.

Hypergraph learning for identification of COVID-19 with CT imaging.

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.