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Landscapes are socially produced and reproduced spaces. This is easily recognizable for large-scale urban groups with built environments that dominate living places. But it also pertains to all types of societies and cultures, even small-scale hunter–gatherers, once the ontological beliefs structuring landscape perception and use are acknowledged. The foragers of south–central and southern California and the Great Basin illustrate this fact. They maintained a widely shared ontological perspective supported by a fundamental cognitive postulate. This is that supernatural power, the principle causative agent in the universe, was differentially distributed among individuals and places. The distribution of power, revealed by certain geomorphological features and natural events, structured their perceptions of landscape. These perceptions were expressed in ritual and symbolism, including petroglyphs and pictographs as durable manifestations of ceremonies on the landscape. The ontological relationship between power and landscape explains a longstanding question in hunter–gatherer archaeology: Why were rock writing sites created at specific locations? It also explains another equally significant but rarely considered and related problem: Why do some localities have massive quantities of rock writings that dwarf most other sites? The landscape symbolism of and the placement of sites by Native Californian and Great Basin tribes is explained by reference to their shared ontological beliefs, illustrating how they structured their ritual practices and archaeological record.

A sacred site that draws pilgrims from distant regions is a distinctive resource for studying religion. Research into a site’s relevance to pilgrims and how it came to be founded contributes to a better understanding of religious activity. To address these issues, a thorough historical analysis of a sacred site’s records is essential. Such an analysis endeavors to distinguish the historical facts of a sacred site from its narratives and further discusses the significance of each. With such intent, this study focuses on the Rujing Stupa, a sacred site of significant importance to transnational pilgrimages that has yet to receive sufficient scholarly attention. The stupa, which is located at the Jingci Monastery in Hangzhou, China, is believed to hold the relics of Tiantong Rujing 天童如浄 (1163–1228), a Song Dynasty monk. Although the modern stele inscription at this location indicates that the stupa was founded in the 13th century, shortly after the monk’s death, this paper examines the historical reinventions within the inscription and traces the influence of Japanese narratives on such a reinvention. This study demonstrates that the Rujing Stupa was established by, and for, the Japanese Sōtō Buddhists. The Chinese monk’s connection to the Sōtō pilgrims lies in Rujing’s role as the master who instructed Dōgen 道元 (1200–1253), the founder of the Sōtō tradition, making his stupa a sacred site for the Sōtō community. Concerns of commemoration and reifying doctrinal authenticity motivated two generations of Japanese pilgrims to construct the Rujing stupa in the late 19th and 20th centuries, respectively. On the other hand, Rujing’s significance and the presence of the Sōtō tradition were scarcely acknowledged in China until the early modern period. Only in the late 20th century did Chinese Buddhists begin to appreciate this stupa. Examining the site’s historical reinventions and identifying the factors that shape its narrative, this case study offers insights into the investigation of sacred sites and suggests a concern for narrative in the examination of a site’s history and significance.

Abstract Background With more clinical trials are offering optional participation in the collection of bio-specimens for biobanking comes the increasing complexity of requirements of informed consent forms. The aim of this study is to develop an automatic natural language processing (NLP) tool to annotate informed consent documents to promote biorepository data regulation, sharing, and decision support. We collected informed consent documents from several publicly available sources, then manually annotated them, covering sentences containing permission information about the sharing of either bio-specimens or donor data, or conducting genetic research or future research using bio-specimens or donor data. Results We evaluated a variety of machine learning algorithms including random forest (RF) and support vector machine (SVM) for the automatic identification of these sentences. 120 informed consent documents containing 29,204 sentences were annotated, of which 1250 sentences (4.28%) provide answers to a permission question. A support vector machine (SVM) model achieved a F-1 score of 0.95 on classifying the sentences when using a gold standard, which is a prefiltered corpus containing all relevant sentences. Conclusions This study provides the feasibility of using machine learning tools to classify permission-related sentences in informed consent documents.

Although John Chrysostom is critical of the theatre, delivering a homily was never a tiresome monologue of the preacher in Byzantium; it was a theatrical performance combining text-reading and multiple ceremonies, during which spaces, lights, and materials were manipulated to create marvellous spectacles and enslave the audience spiritually and emotionally. At times, orators described the physical features of the venues where they spoke, as did Leo VI the Wise for two newly founded churches and Constantine VII Porphyrogenitus for the second most important church of the Empire, the Holy Apostles. But in most cases, the performance aspect of their speeches could only be known indirectly from two ceremonial handbooks, Kletorologion and De Ceremoniis. It is also necessary to indicate that the spectacles in homilies were not always real and present; they sometimes came to exist in listeners’ minds through picturesque descriptions (ekphraseis) and fictional figures (ethopoiiai) composed by preachers.

According to Numbers 21:4–9, the Nehushtan was a copper/bronze snake effigy that functioned as a ‘magical’ healing tool to cure the early Israelites from venomous snakebites they incurred during their desert wanderings. What is unclear from the narrative is the symbolic significance of the event, the materials used, the technical skills required, and whether magic was at play. Firstly, when considering the magical effects of the Nehushtan, we must define which type of magic—apotropaic or sympathetic—was involved. Based upon existing scholarship on the topic, the general consensus is that the Nehushtan represented sympathetic magic, underpinned in this instance by homeopathic/imitative magic. To highlight this point, this study will provide selected examples of both types of magic so that the Nehushtan’s association with sympathetic magic can be illustrated. Secondly, and most importantly, we must consider why the image of a snake was chosen if the very affliction (envenomation) suffered by the people was caused by the creature now being posited as a symbol of divine healing. Did the ancient perceptions of snakes and healing play a role in this decision? Why did the early Israelites not question the logic behind the use of a magical snake effigy when both magic and effigies were technically prohibited by biblical laws? To answer these questions, the study will consider the historical background (the Exodus from Egypt), the set (geographical location), and the setting (cultural contact and influence) in which the narrative of the Nehushtan took place.

Abstract Background During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels. Methods We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC. Results This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences. Discussion and conclusion Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more “fit for purpose” NLP methods could be developed and used to facilitate the drug development process.

Abstract Background Extracting information from free texts using natural language processing (NLP) can save time and reduce the hassle of manually extracting large quantities of data from incredibly complex clinical notes of cancer patients. This study aimed to systematically review studies that used NLP methods to identify cancer concepts from clinical notes automatically. Methods PubMed, Scopus, Web of Science, and Embase were searched for English language papers using a combination of the terms concerning “Cancer”, “NLP”, “Coding”, and “Registries” until June 29, 2021. Two reviewers independently assessed the eligibility of papers for inclusion in the review. Results Most of the software programs used for concept extraction reported were developed by the researchers (n = 7). Rule-based algorithms were the most frequently used algorithms for developing these programs. In most articles, the criteria of accuracy (n = 14) and sensitivity (n = 12) were used to evaluate the algorithms. In addition, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and Unified Medical Language System (UMLS) were the most commonly used terminologies to identify concepts. Most studies focused on breast cancer (n = 4, 19%) and lung cancer (n = 4, 19%). Conclusion The use of NLP for extracting the concepts and symptoms of cancer has increased in recent years. The rule-based algorithms are well-liked algorithms by developers. Due to these algorithms' high accuracy and sensitivity in identifying and extracting cancer concepts, we suggested that future studies use these algorithms to extract the concepts of other diseases as well.

It is certainly possible that we might learn to better acknowledge the spirits of our ancestors who came before us, as well as to recognize them in such ways that we also learn to embrace the ‘woven density’ of our own lives, our histories and our communities. By doing so, we might begin to discover that the spirits we had thought were removed from our modern, secularized world never fully left us, just as the irrationality of our humanity cannot be fully tamed via a reductive, rational and scientific outlook on life. There are, as Bruno Latour had frequently argued, many modes of human existence that interact in complex networks of relations. To fall back on any one particular mode as if it could dominate over others would only grant us a mistaken impression of our own humanity, even though this is what has been practiced for centuries in order to legitimate a particular, sovereign configuration of power. It is to the credit of archaeologists and hauntologists alike that we are more able than ever to take account of the complexity of ourselves in ways that we had previously ignored or repressed.