Proximal versus distant suffering in TV news discourses on COVID-19 pandemic

News is central to human communication and has an important signifying power as a particular subsystem within language. This study sets out to comprehensively examine how four major TV global news providers – CNN, BBC, DW and RT – have covered the COVID-19 pandemic from outbreak to mid-crisis. We apply a multi-level content analysis approach that rests on theories of proximization and representation of distant suffering, following a computer-assisted analysis that aids in identifying concepts occurrence and the semantic relationship among the highly frequent clusters. We explore the news representation during 2020 of COVID-19 as proximal versus distant discourses of suffering, safety and compassion conceptualized in light of theories on distant suffering. A total number of 12 dataset reports consisting of 2,017,875 words were analyzed. The results suggest that the COVID-19 pandemic news formulates a particular type of discourse on suffering that individualizes the sufferer, sets out the course of action and turns the fast-approaching pandemic into a global cause for action.

Computer-assisted Analysis of Zhu Fonian’s Original Mahayana Sutras

In her 2010 study of the Shi zhu duan jie jing T309, Jan Nattier found that several passages in T309 were copied from earlier Chinese Buddhist texts. She thus proposed that T309 is not a translation from an Indian text, but a “forgery” by Zhu Fonian. Extending Nattier’s analysis with the help of TACL, a tool for computational textual analysis, we conducted a more thorough analysis of Zhu Fonian’s four Mahayana texts, namely, T309, the Pusa chu tai jing T384, the Zhongyin jin T385, and the Pusa yingluo jing T656, and found in T309 and T656 additional content deriving from earlier Chinese texts. On the basis of this analysis of these features of the texts, we propose that all four were likely compiled by Zhu Fonian himself.

ACD/Structure Elucidator: 20 Years in the History of Development

The first methods associated with the Computer-Assisted Structure Elucidation (CASE) of small molecules were published over fifty years ago when spectroscopy and computer science were both in their infancy. The incredible leaps in both areas of technology could not have been envisaged at that time, but both have enabled CASE expert systems to achieve performance levels that in their present state can outperform many scientists in terms of speed to solution. The computer-assisted analysis of enormous matrices of data exemplified 1D and 2D high-resolution NMR spectroscopy datasets can easily solve what just a few years ago would have been deemed to be complex structures. While not a panacea, the application of such tools can provide support to even the most skilled spectroscopist. By this point the structures of a great number of molecular skeletons, including hundreds of complex natural products, have been elucidated using such programs. At this juncture, the expert system ACD/Structure Elucidator is likely the most advanced CASE system available and, being a commercial software product, is installed and used in many organizations. This article will provide an overview of the research and development required to pursue the lofty goals set almost two decades ago to facilitate highly automated approaches to solving complex structures from analytical spectroscopy data, using NMR as the primary data-type.