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As an intellectual property product that is highly farmer-friendly, geographical indication (GI) products have always garnered significant attention. In recent years, research on how GI products promote agricultural development has been increasing, yet the academic community remains divided on this issue. On one hand, some studies point out that GI products can drive agricultural development; on the other hand, other studies suggest that the impact of GI products is not significant or varies. Meta-analysis is a method that leverages statistical techniques to integrate the findings of multiple studies with a common research objective, addressing controversial issues and arriving at generalizable conclusions. Therefore, to more precisely uncover the intrinsic relationship between GI products and agricultural development and to delve deeper into the root causes of the aforementioned discrepancies, this study employed a meta-analytic approach. We extracted 478 correlation coefficients (r) as effect sizes from 82 empirical articles worldwide. Using these coefficients, we calculated the overall effect size and moderating effects of GI products on promoting agricultural development. Research indicates that GI products exert a positive influence on agricultural development. There is a low positive correlation between the two (r = 0.197). Further analysis reveals that various factors at the sample, data, literature, and methodology levels all impact the outcomes of GI products' promotion of agricultural development. Research has shown that, in pursuit of sustainable agricultural development goals, it is further recommended that governments should accord high priority to the cultivation and development of GI products. This is aimed at providing practical insights to facilitate the sustainable advancement of GI products and bolster agricultural competitiveness. [ABSTRACT FROM AUTHOR]

In China, where the corporate social responsibility (CSR) process is relatively underdeveloped, the government has promulgated a series of laws and regulations on CSR disclosure in recent years to promote the sustainable transformation of the economy. Using data from A-share listed Chinese firms from 2009 to 2021, this study empirically examines the relationship between CSR disclosure quality and firm investment efficiency in China. The results indicate that (1) improvements in CSR disclosure quality significantly mitigate firms' underinvestment and overinvestment, thereby enhancing investment efficiency. (2) Further analysis shows that high-quality CSR information also promotes investment efficiency by reducing agency costs and financing constraints and improving media evaluations of firms. (3) A heterogeneity analysis suggests that the positive effect of CSR disclosure on investment efficiency is stronger for firms with lower equity incentives, more severe financing constraints, and higher media attention. Our study extends the understanding of the mechanisms through which CSR disclosure affects firms' investment efficiency, potentially providing insights for research in related fields and guiding future CSR disclosure practices in other developing countries. [ABSTRACT FROM AUTHOR]

Using Chinese A-share listed companies from 2006 to 2013 as the research sample, this paper empirically examines the impact and mechanism of the mandatory CSR disclosure policy on Chinese firms' risk-taking by combining the difference-in-differences (DID) approach. This study finds that the implementation of the policy increases firms' operating costs and leads to an increase in their financing constraints, which ultimately creates a disincentive for firms to take risks. Second, we also find that, relative to firms that do not disclose CSR reports, the sales revenues, R&D investment and investment levels of firms subject to CSR disclosure are significantly reduced, which may be the result of firms' tendency to operate conservatively. In addition, heterogeneity analyses suggest that the dampening effect of mandatory CSR disclosure policies on risk-taking is stronger for firms with higher financing costs and for non-state-owned firms. This study further explores the mechanism of the impact of mandatory CSR disclosure on firms' risk-taking, which will help the government to formulate and improve the information disclosure policy regarding driving the transformation of corporate development in the future. [ABSTRACT FROM AUTHOR]

Cross-lingual text summarization is a highly desired service for overseas report editing tasks and is formulated in a distributed application to facilitate the cooperation of editors. The multilanguage pre-trained language model (MPLM) can generate high-quality cross-lingual text summaries with simple fine-tuning. However, the MPLM does not adapt to complex variations, like the word order and tense in different languages. When the model performs on these languages with separate syntactic structures and vocabulary morphologies, it will lead to the low-level quality of the cross-lingual summary. The matter worsens when the cross-lingual summarization datasets are low-resource. We use a knowledge distillation framework for the cross-lingual summarization task to address the above issues. By learning the monolingual teacher model, the cross-lingual student model can effectively capture the differences between languages. Since the teacher and student models generate summaries in two languages, their representations lie on different vector spaces. In order to construct representation relationships across languages, we further propose a similarity metric, which is based on bidirectional semantic alignment, to map different language representations to the same space. In order to improve the quality of cross-lingual summaries further, we use contrastive learning to make the student model focus on the differentials among languages. Contrastive learning can enhance the ability of the similarity metric for bidirectional semantic alignment. Our experiments show that our approach is competitive in low-resource scenarios on cross-language summarization datasets in pairs of distant languages. [ABSTRACT FROM AUTHOR]

The development of cancer vaccines has been intensively pursued over the past 50 years with modest success. However, recent advancements in the fields of genetics, molecular biology, biochemistry, and immunology have renewed interest in these immunotherapies and allowed the development of promising cancer vaccine candidates. Numerous clinical trials testing the response evoked by tumour antigens, differing in origin and nature, have shed light on the desirable target characteristics capable of inducing strong tumour-specific non-toxic responses with increased potential to bring clinical benefit to patients. Novel delivery methods, ranging from a patient’s autologous dendritic cells to liposome nanoparticles, have exponentially increased the abundance and exposure of the antigenic payloads. Furthermore, growing knowledge of the mechanisms by which tumours evade the immune response has led to new approaches to reverse these roadblocks and to re-invigorate previously suppressed anti-tumour surveillance. The use of new drugs in combination with antigen-based therapies is highly targeted and may represent the future of cancer vaccines. In this review, we address the main antigens and delivery methods used to develop cancer vaccines, their clinical outcomes, and the new directions that the vaccine immunotherapy field is taking.

This study attempts to determine whether improving the quality of mandatory corporate social responsibility (CSR) information disclosure can have a positive impact on a firm's development. To this end, an empirical analysis is conducted to establish a relationship between the quality of mandatory CSR information disclosure and a firm's total factor productivity (TFP), using data from A-share listed companies in China over the period 2009–2020. The results show that: (1) Improving the quality of mandatory CSR disclosure leads to a significant increase in a firm's TFP. (2) By alleviating a firm's financing constraints and agency costs, the improved quality of mandatory CSR disclosure effectively enhances the firm's investment efficiency and innovation capacity, leading to higher TFP. This research extends the influence of CSR disclosure on the economic development outcomes of Chinese firms, and provides theoretical guidance for the development and improvement of CSR disclosure mechanisms in emerging economies. [ABSTRACT FROM AUTHOR]

The Chinese government has implemented a series of corporate social responsibility (CSR)-related policies in recent years, aiming to achieve high-quality economic development and transformation. To assess the effectiveness of these CSR policies, this study empirically explores the relationship between the quality of CSR information disclosure and corporate innovation performance using data from A-share listed companies in Shanghai and Shenzhen, China, from 2009 to 2020. The results show that: (1) Improvement in the quality of CSR information can significantly improve corporate innovation performance. (2) Further analysis reveals that the improvement of CSR information quality not only helps to reduce the agency costs and alleviate the financing constraints of enterprises but also reduces the cost of external interpretation of corporate innovation projects and conveys a good corporate image by attracting the attention of analysts and the media, which all contribute to the improvement of corporate innovation performance. This study further refines the mechanism of the impact of CSR disclosure on firms' innovation performance, which may provide guidance to governments in developing and improving CSR disclosure systems in the future. [ABSTRACT FROM AUTHOR]

Purpose: Rapid diagnosis of acute ischemic stroke (AIS) is critical to achieve positive outcomes and prognosis. This study aimed to construct a model to automatically identify the infarct core based on non-contrast-enhanced CT images, especially for small infarcts., Methods: The baseline CT scans of AIS patients, who had DWI scans obtained within less than 2 h apart, were included in this retrospective study. A modified Target-based deep learning model of YOLOv5 was developed to detect infarctions on CT. Randomly selected CT images were used for testing and evaluated by neuroradiologists and the model, using the DWI as a reference standard. Intraclass correlation coefficient (ICC) and weighted kappa were calculated to assess the agreement. The paired chi-square test was used to compare the diagnostic efficacy of physician groups and automated models in subregions. p  < 0.05 was considered statistically significant., Results: Five hundred and eighty four AIS patients were enrolled in total, finally 275 cases were eligible. Modified YOLOv5 perform better with increased precision (0.82), recall (0.81) and mean average precision (0.79) than original YOLOv5. Model showed higher consistency to the DWI-ASPECTS scores (ICC = 0.669, κ  = 0.447) than neuroradiologists (ICC = 0.452, κ = 0.247). The sensitivity (75.86% vs. 63.79%), specificity (98.87% vs. 95.02%), and accuracy (96.20% vs. 91.40%) were better than neuroradiologists. Automatic model had better diagnostic efficacy than physician diagnosis in the M6 region ( p  = 0.039)., Conclusion: The deep learning model was able to detect small infarct core on CT images more accurately. It provided the infarct portion and extent, which is valuable in assessing the severity of disease and guiding treatment procedures., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Qu, Tang, Gao, Li, Zhao, Ban, Chen, Lu and Wang.)

Background and Purpose: Delayed cerebral ischemia is hard to diagnose early due to gradual, symptomless development. This study aimed to develop an automated model for predicting delayed cerebral ischemia following aneurysmal SAH on NCCT., Materials and Methods: This retrospective study included 400 patients with aneurysmal SAH (156 with delayed cerebral ischemia) who underwent NCCT. The study used ATT-Deeplabv3+ for automatically segmenting hemorrhagic regions using semisupervised learning. Principal component analysis was used for reducing the dimensionality of deep learning features extracted from the average pooling layer of ATT-DeepLabv3+. The classification model integrated clinical data, radiomics, and deep learning features to predict delayed cerebral ischemia. Feature selection involved Pearson correlation coefficients, least absolute shrinkage, and selection operator regression. We developed models based on clinical features, clinical-radiomics, and a combination of clinical, radiomics, and deep learning. The study selected logistic regression, Naive Bayes, Adaptive Boosting (AdaBoost), and multilayer perceptron as classifiers. The performance of segmentation and classification models was evaluated on their testing sets using the Dice similarity coefficient for segmentation, and the area under the receiver operating characteristic curve (AUC) and calibration curves for classification., Results: The segmentation process achieved a Dice similarity coefficient of 0.91 and the average time of 0.037 s/image. Seventeen features were selected to calculate the radiomics score. The clinical-radiomics-deep learning model with multilayer perceptron achieved the highest AUC of 0.84 (95% CI, 0.72-0.97), which outperformed the clinical-radiomics model ( P  = .002) and the clinical features model ( P  = .001) with multilayer perceptron. The performance of clinical-radiomics-deep learning model using AdaBoost was significantly superior to its clinical-radiomics model ( P  = .027). The performance of the clinical-radiomics-deep learning model and the clinical-radiomics model with logistic regression notably exceeded that of the model based solely on clinical features ( P  = .028; P  = .046). The AUC of the clinical-radiomics-deep learning model with multilayer perceptron ( P  < .001) and the clinical-radiomics model with logistic regression ( P  = .046) were significantly higher than the clinical model with logistic regression. Of all models, the clinical-radiomics-deep learning model with multilayer perceptron showed best calibration., Conclusions: The proposed 2-stage end-to-end model not only achieves rapid and accurate segmentation but also demonstrates superior diagnostic performance with high AUC values and good calibration in the clinical-radiomics-deep learning model, suggesting its potential to enhance delayed cerebral ischemia detection and treatment strategies., (© 2024 by American Journal of Neuroradiology.)