Data Intelligence最新文献

Resampling approaches for the quantitative analysis of spatially distributed cells 对空间分布细胞进行定量分析的重采样方法

Data Intelligence Pub Date : 2024-03-15 DOI: 10.1162/dint_a_00249

G. Bertolazzi, Michele Tumminello, G. Morello, Beatrice Belmonte, Claudio Tripodo

{"title":"Resampling approaches for the quantitative analysis of spatially distributed cells","authors":"G. Bertolazzi, Michele Tumminello, G. Morello, Beatrice Belmonte, Claudio Tripodo","doi":"10.1162/dint_a_00249","DOIUrl":"https://doi.org/10.1162/dint_a_00249","url":null,"abstract":"\u0000 Image segmentation is a crucial step in various image analysis pipelines and constitutes one of the cutting-edge areas of digital pathology. The advent of quantitative analysis has enabled the evaluation of millions of individual cells in tissues, allowing for the combined assessment of morphological features, biomarker expression, and spatial context.\u0000 The recorded cells can be described as a point pattern process. However, the classical statistical approaches to point pattern processes prove unreliable in this context due to the presence of multiple irregularly-shaped interstitial cell-devoid spaces in the domain, which correspond to anatomical features (e.g. vessels, lipid vacuoles, glandular lumina) or tissue artefacts (e.g. tissue fractures), and whose coordinates are unknown. These interstitial spaces impede the accurate calculation of the domain area, resulting in biased clustering measurements. Moreover, the mistaken inclusion of empty regions of the domain can directly impact the results of hypothesis testing.\u0000 The literature currently lacks any introduced bias correction method to address interstitial cell-devoid spaces. To address this gap, we propose novel resampling methods for testing spatial randomness and evaluating relationships among different cell populations. Our methods obviate the need for domain area estimation and provide non-biased clustering measurements. We created the SpaceR software (https://github.com/GBertolazzi/SpaceR) to enhance the accessibility of our methodologies.","PeriodicalId":57117,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140237922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applying a Context-based Method to Build a Knowledge Graph for the Blue Amazon 应用基于上下文的方法为蓝色亚马逊构建知识图谱

Data Intelligence Pub Date : 2024-03-11 DOI: 10.1162/dint_a_00223

P. D. M. Ligabue, A. Brandão, S. M. Peres, F. G. Cozman, Paulo Pirozelli

{"title":"Applying a Context-based Method to Build a Knowledge Graph for the Blue Amazon","authors":"P. D. M. Ligabue, A. Brandão, S. M. Peres, F. G. Cozman, Paulo Pirozelli","doi":"10.1162/dint_a_00223","DOIUrl":"https://doi.org/10.1162/dint_a_00223","url":null,"abstract":"\u0000 Knowledge graphs are employed in several tasks, such as question answering and recommendation systems, due to their ability to represent relationships between concepts. Automatically constructing such a graphs, however, remains an unresolved challenge within knowledge representation. To tackle this challenge, we propose CtxKG, a method specifically aimed at extracting knowledge graphs in a context of limited resources in which the only input is a set of unstructured text documents. CtxKG is based on OpenIE (a relationship triple extraction method) and BERT (a language model) and contains four stages: the extraction of relationship triples directly from text; the identification of synonyms across triples; the merging of similar entities; and the building of bridges between knowledge graphs of different documents. Our method distinguishes itself from those in the current literature (i) through its use of the parse tree to avoid the overlapping entities produced by base implementations of OpenIE; and (ii) through its bridges, which create a connected network of graphs, overcoming a limitation similar methods have of one isolated graph per document. We compare our method to two others by generating graphs for movie articles from Wikipedia and contrasting them with benchmark graphs built from the OMDb movie database. Our results suggest that our method is able to improve multiple aspects of knowledge graph construction. They also highlight the critical role that triple identification and named-entity recognition have in improving the quality of automatically generated graphs, suggesting future paths for investigation. Finally, we apply CtxKG to build BlabKG, a knowledge graph for the Blue Amazon, and discuss possible improvements.","PeriodicalId":57117,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The stance and factors of international organizations towards countries from a Chinese perspective 从中国视角看国际组织对各国的立场和因素

Data Intelligence Pub Date : 2024-03-11 DOI: 10.1162/dint_a_00248

Zhang Qin, Xue Haili, Yaotian Wang, Wang Yao, Zhang Ziqin, Qinghua Qin, Haoguang Liang

{"title":"The stance and factors of international organizations towards countries from a Chinese perspective","authors":"Zhang Qin, Xue Haili, Yaotian Wang, Wang Yao, Zhang Ziqin, Qinghua Qin, Haoguang Liang","doi":"10.1162/dint_a_00248","DOIUrl":"https://doi.org/10.1162/dint_a_00248","url":null,"abstract":"\u0000 The degree and scope of constraints imposed by International Organizations (IOs) on States are increasing, and identifying the factors affecting the IOs' stances on States is helpful to enhance the state's discourse power and influence in the international community. First, by coding the records of Regular Press Conferences of the Speaking Office of the Chinese Ministry of Foreign Affairs during the period of 2018–2022, we obtained a dataset of IOs' stances on China-related events. Second, we constructed political relation, economic relation, and humanistic relation indicators to complement the influence factors, adopted the Bayesian logit model, and applied the Monte Carlo Markov chain algorithm and Gibbs sampling to analyze the probability of IOs' positive stances towards China. The result shows that IOs' category, length of establishment, functional position, and relationship with China are all related to their tendency of making a statement about China. In terms of the heterogeneity of event types, forum-type IOs are significantly inclined to give positive assessment compared to service-type IOs on events focusing on China's own development. Further analysis reveals that the model for analyzing and predicting the attitudes of IOs is more effective when the international situation is in a stable period.","PeriodicalId":57117,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Price Mechanism, Government Constraints and Carbon Trading Pilot Policy for Emission Reduction 价格机制、政府约束与碳交易减排试点政策

Data Intelligence Pub Date : 2024-03-11 DOI: 10.1162/dint_a_00247

Huang Wei, Xue Haili, Zhang Qin, Lan Xiao, Haoguang Liang

引用次数: 0