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Green transformation has become a central goal of China’s development strategy in response to mounting environmental pressure and long-term growth needs. Improving green innovation efficiency (GIE) is essential to achieving this transformation while sustaining economic momentum. This study evaluates the impact of the Pilot Policy for Innovative Industrial Clusters on GIE across Chinese cities. Using panel data from 280 prefecture-level cities between 2007 and 2021, we apply difference-in-differences and spatial difference-in-differences (SDID) models to estimate policy effects, spatial spillovers, and transmission mechanisms. The results are as follows: (1) The pilot policy significantly improves GIE in the pilot cities, with robust results after various tests. (2) The policy enhances urban green innovation through four main channels: reducing energy consumption intensity, upgrading industrial structure, fostering green technological innovation, and accelerating digital infrastructure development. (3) In addition to its direct impact on pilot cities, the policy also boosts the GIE of neighboring cities via spatial spillover effects. (4) Heterogeneity analysis reveals that the policy’s effect is more pronounced in central cities, non-resource cities, and cities with a strong environmental protection focus. This study contributes to the understanding of innovative industrial cluster policies in enhancing GIE and offers valuable policy insights for promoting urban green development.

Background Polycystic ovary syndrome (PCOS) is a pivotal cause of anovulatory infertility and the pathogenesis remains elusive. Cellular senescence and sphingolipid metabolism disorder are closely intertwined, and both have been demonstrated present within the granulosa cells of PCOS, while research on the combined impact of senescence and sphingolipids on PCOS-related anovulation is scarce. Methods Here, we leveraged four datasets of PCOS and executed differential gene expression analysis, engaged in WGCNA, and harnessed machine learning algorithms—including RF, SVM-RFE, and LASSO—to deeply explore the key genes that interact with senescence and sphingolipid metabolism in granulosa cells of PCOS. These key genes were subjected to further analysis to construct a diagnostic model, forecast immune cell infiltration, and identify potential agents. Additionally, within the testosterone-stimulated granulosa cells, we validated the expression of key genes, confirmed senescence and sphingolipids dysregulation, and evaluated the therapeutic efficacy of the candidate agent. Results Our research pinpointed a set of genes ( LYN, PLCG2, STAT5B, MMP9, and IL6R ) that showed promise as biomarkers for PCOS-related anovulation and the diagnostic nomogram was developed. These biomarkers were linked to various immune cell types infiltration. In testosterone-stimulated granulosa cells, we observed increased expression of these biomarkers, accompanied by signs of senescence and changes in sphingolipids. Importantly, the potential agent aspirin displayed the ability to ameliorate these two processes. Conclusion This study highlighted the important value of genes associated with senescence and sphingolipids dysregulation in PCOS. Aspirin targeting senescence could be a promising therapeutic drug for addressing anovulation associated with PCOS.

The workforce is aging with the population aging. How to effectively manage and motivate older workers is significant for elderly human resources development and the sustainable development of enterprises in organizations. Age stereotypes of older workers refer to people’s beliefs and expectations about a specific group of 45–65-year-olds in the workplace. This paper examines the effect of age stereotypes of older workers on job performance and intergenerational knowledge transfer intention. This study carried out two research designs, a questionnaire survey and an experimental study, to explore the effects of positive and negative age stereotypes of older workers on job performance and intergenerational knowledge transfer intention within an organizational context and underlying mediating mechanisms. The results showed that positive stereotypes of older workers significantly positively affected job performance and intergenerational knowledge transfer intention. In comparison, negative stereotypes of older workers significantly negatively affected job performance and intergenerational knowledge transfer intention, and self-perception of aging substantially mediates the effects. This study broadens the research field on the impact of positive and negative age stereotypes on older workers in organizational contexts. This study guides organizations in reducing age discrimination, creating an inclusive workplace environment, and achieving the successful aging of older workers.

This study investigated how marital power influence adolescents’ social adjustment in China, based on a sample of 759 middle school students. While prior research has largely focused on marital relationships and co-parenting, few studies have examined adolescent adjustment through the lens of marital power. Using latent profile analysis in Mplus, four marital power types were identified: shared power, contested power, wife-dominant, and husband-dominant. Shared-power families represented the largest proportion and reported the highest levels of couple satisfaction with power distribution. Different marital power structures were significantly associated with distinct adolescent social adjustment outcomes. Adolescents from shared-power families exhibited significantly higher levels of self, interpersonal, behavioral, and environmental adjustment compared to those from the other three family types. They also demonstrated better functional outcomes, characterized by higher positive adjustment and lower negative adjustment levels, particularly in contrast to adolescents from contested-power and husband-dominant families. Notably, contested-power families were associated with the poorest adjustment across all domains. To further investigate the underlying mechanisms, mediation analyses showed that perceived marital power—by both husbands and wives—positively predicted adolescents’ positive adjustment and negatively predicted maladjustment. Furthermore, parental co-parenting quality emerged as a statistically significant partial mediator, suggesting that marital power influences adolescent adjustment in part by fostering more unified parenting practices, which in turn enhance adolescent social adjustment.

We focus on the causes of fluctuations in wintertime PM 10 in nine regional core cities of China using two machine learning models, Random Forest (RF) and Recurrent Neural Network (RNN). RF and RNN both show high performance in predicting hourly PM 10 using only gaseous air pollutants (SO 2 , NO 2 and CO) as inputs, showing the predominance of the secondary inorganic aerosol and implying the existence of thermodynamic equilibrium between gaseous air pollutants and PM 10 . Also, we find the following results. The correlation of gaseous air pollutants and PM 10 were more relevant than that of meteorological conditions and PM 10 . CO was the predominant factor for PM 10 in the Beijing-Tianjin-Hebei Plain and the Yangtze River Delta while SO 2 and NO 2 were also important features for PM 10 in the Pearl River Delta and Sichuan Basin. The spatial heterogeneity and temporal homogeneity of PM 10 in China are revealed. The long-range transported PM 10 was substantiated to be insignificant, except in the sandstorms. The severity of PM 10 was attributable to the lopsided shift of thermodynamic equilibrium and the phenology of indigenous flora.

Since the reform and opening up, China’s rapid economic growth mainly depends on the industrial development mode of “high energy consumption and high pollution,” which has caused serious haze pollution. In order to achieve the goal of haze control and sustainable development, we need to give full play to the role of technological innovation. Empirical analysis of the haze control effect of technological innovation has theoretical significance and practical value. Based on the panel data of 30 provinces in China from 2005 to 2018 and the PM2.5 concentration data published by the atmospheric composition analysis group of Dalhousie University, this study selects R&D personnel input and technology market turnover to represent the level of technological innovation and uses the panel data model, threshold effect model, and spatial Durbin model to empirically analyze the impact of technological innovation on haze pollution control. The empirical results show that 1) technological innovation can significantly reduce the PM2.5 concentration of the province, showing a positive haze control effect; 2) technological innovation indicates a negative indirect effect on PM2.5 concentration, confirming the “technology spillover effect,” that is, technological innovation also has a haze control effect on the surrounding provinces; 3) with the increase in the province’s economic aggregate, the haze control effect of technological innovation shows a trend of “high low high,” and the role of technological innovation is the lowest in the stage of economic transformation; and 4) from the perspective of regional differentiation, the haze control effect of technological innovation is the largest in the central region, and the smallest in the western region. Technological innovation indicates a positive haze control effect on all regions at all stages of economic development. This study provides policy suggestions for the government and enterprises to use innovation for cleaner production and sustainable development.

In rice (Oryza sativa), the presence of a dominant Badh2 allele encoding betaine aldehyde dehydrogenase (BADH2) inhibits the synthesis of 2-acetyl-1-pyrroline (2AP), a potent flavor component in rice fragrance. By contrast, its two recessive alleles, badh2-E2 and badh2-E7, induce 2AP formation. Badh2 was found to be transcribed in all tissues tested except for roots, and the transcript was detected at higher abundance in young, healthy leaves than in other tissues. Multiple Badh2 transcript lengths were detected, and the complete, full-length Badh2 transcript was much less abundant than partial Badh2 transcripts. 2AP levels were significantly reduced in cauliflower mosaic virus 35S-driven transgenic lines expressing the complete, but not the partial, Badh2 coding sequences. In accordance, the intact, full-length BADH2 protein (503 residues) appeared exclusively in nonfragrant transgenic lines and rice varieties. These results indicate that the full-length BADH2 protein encoded by Badh2 renders rice nonfragrant by inhibiting 2AP biosynthesis. The BADH2 enzyme was predicted to contain three domains: NAD binding, substrate binding, and oligomerization domains. BADH2 was distributed throughout the cytoplasm, where it is predicted to catalyze the oxidization of betaine aldehyde, 4-aminobutyraldehyde (AB-ald), and 3-aminopropionaldehyde. The presence of null badh2 alleles resulted in AB-ald accumulation and enhanced 2AP biosynthesis. In summary, these data support the hypothesis that BADH2 inhibits 2AP biosynthesis by exhausting AB-ald, a presumed 2AP precursor.

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The objective of this study is to develop a model that incorporates clinical measurements with 3D radiomic signatures extracted from CT images of oral and maxillofacial surgery patients to evaluate mask ventilation. A prospective cohort trial was conducted to enroll patients scheduled for oral and maxillofacial surgery. After obtaining informed consent, clinical measurements and head and neck CT images were collected. The anesthesiologist who managed the airway graded the mask ventilation. Difficult mask ventilation was defined as mask ventilation that required assistance or the use of an oral airway or other adjuvant by the anesthesiologist. For radiomics analysis, 3D airway segmentation was extracted and calculated 3D radiomic signatures and corresponding radiological features. Subsequently, features in the clinical measurements model and radiomic signatures model were determined using the least absolute shrinkage and selection operator (LASSO) classifier. A mixed model was developed that incorporated both radiomic signature features and clinical measurement features. A total of 716 patients were enrolled in the study. The mixed model combined the five 3D radiomic signatures and six clinical measurements, and was found to have the highest predictive accuracy. In the validation group, the mixed group had an area under the curve (AUC) of 0.851, which was higher than the AUC of 0.812 in the clinical measurements model and 0.827 in the radiomic signatures model. This study developed a mixed model that combines 3D radiomic signatures and clinical measurements. Its application in clinical practice can assist in identifying patients at risk of experiencing difficult mask ventilation during oral and maxillofacial surgeries.