Search Results (4)

Chionea is classified within the Tipuloidea superfamily and predominantly inhabits cold regions. However, its phylogenetic relationships remain contentious. In this study, the first three mitogenomes of Chionea (Diptera: Limoniidae) sampled in northeastern China (Jilin region) were sequenced, and their phylogenetic relationships were reconstructed by integrating these sequences with 30 additional Tipuloidea mitogenomes retrieved from NCBI. Unlike other Tipuloidea species, which are predominantly distributed in relatively warmer regions, this research investigates whether positive selection has acted on the mitogenomes of these three Chionea species due to environmental pressures, thereby elucidating key evolutionary drivers for Chionea. The three mitogenomes of Chionea exhibit characteristic features typical of insect mitogenomes, comprising 13 protein-coding genes (PCGs), 2 ribosomal RNA genes (16S rRNA and 12S rRNA), 22 transfer RNA genes (tRNA), and a single non-coding control region (D-loop). Notably, the secondary structure of trnS1 lacks the DHU arm in all three samples, and UUA (Leu) emerges as the most frequently utilized codon. Furthermore, the COX2 and ND5 genes utilize incomplete stop codons “T”. Utilizing these 13 PCGs, we reconstructed the internal phylogenetic relationships within Tipuloidea, revealing that Chionea tianhuashana and C. sphaerae form sister branches, while (C. tianhuashana + C. sphaerae) constitutes a sister branch to C. crassipes. Moreover, our analysis confirms the monophyly of Tipulidae, Tipula, and Nephrotoma as well as the polyphyly of Tipulinae, Chioneinae, and Limoniidae. In the branch site model analysis, three positively selected sites were detected when Chionea was designated as the foreground branches: COX3 (at position 242), ND5 (at position 535), and ND6 (at position 138). Full article

This work describes a computational method for reconstructing clusters of social relationships among early modern printers and publishers, the most determinant agents for the process of transformation of scientific knowledge. The method is applied to a dataset retrieved from the Sphaera corpus, a collection of 359 editions of textbooks used at European universities and produced between the years 1472 and 1650. The method makes use of standard bibliographic data and fingerprints; social relationships are defined as “awareness relationships”. The historical background is constituted of the production and economic practices of early modern printers and publishers in the academic book market. The work concludes with empirically validating historical case studies, their historical interpretation, and suggestions for further improvements by utilizing machine learning technologies. Full article

Recent advances in object detection facilitated by deep learning have led to numerous solutions in a myriad of fields ranging from medical diagnosis to autonomous driving. However, historical research is yet to reap the benefits of such advances. This is generally due to the low number of large, coherent, and annotated datasets of historical documents, as well as the overwhelming focus on Optical Character Recognition to support the analysis of historical documents. In this paper, we highlight the importance of visual elements, in particular illustrations in historical documents, and offer a public multi-class historical visual element dataset based on the Sphaera corpus. Additionally, we train an image extraction model based on YOLO architecture and publish it through a publicly available web-service to detect and extract multi-class images from historical documents in an effort to bridge the gap between traditional and computational approaches in historical studies. Full article

The development of the field of digital humanities in recent years has led to the increased use of knowledge graphs within the community. Many digital humanities projects tend to model their data based on CIDOC-CRM ontology, which offers a wide array of classes appropriate for storing humanities and cultural heritage data. The CIDOC-CRM ontology model leads to a knowledge graph structure in which many entities are often linked to each other through chains of relations, which means that relevant information often lies many hops away from their entities. In this paper, we present a method based on graph walks and text processing to extract entity information and provide semantically relevant embeddings. In the process, we were able to generate similarity recommendations as well as explore their underlying data structure. This approach was then demonstrated on the Sphaera Dataset which was modeled according to the CIDOC-CRM data structure. Full article