Dalgaard Tychsen (orangejoin3)

Penicyrone A's configurational analysis necessitated a revision of the previously documented absolute configuration at C-6 in verrucosidin derivatives. ) to (6 ). The 9 /9 A molecule's epimers are stereoisomers that are identical except for the orientation of a single substituent at a chiral center. The observed growth inhibition of some pathogenic bacteria by these compounds suggests their suitability as lead candidates for the creation of new antimicrobial agents. 101007/s42995-023-00173-2 provides access to supplementary material associated with the online version. The supplementary material referenced in the online version is accessible through the following URL: 101007/s42995-023-00173-2. Uncertain environments necessitate the application of both human and artificial reasoning. Ideally, the probability distribution is present. Yet, there are instances where probabilistic details are imprecise or completely absent. In such cases, our reasoning process is informed by higher-order uncertainties. In the sphere of artificial intelligence, formal argumentation, particularly within the context of Dung's abstract argumentation, is among the most prominent formal methods to represent defeasible reasoning. Cognition reveals reasoning as an argumentative and social activity, a view supported by Mercier and Sperber's work. Formal argumentation, in this paper, establishes a framework for reasoning about higher-order uncertainty. In developing our approach, we build upon Haenni's probabilistic argumentation, adding several key improvements to the existing system. We integrate it with deductive argumentation, encompassing both argument representation and attacks, and leveraging abstract argumentation semantics to select pertinent arguments from a collection of potentially conflicting ones. We exemplify how our system performs in accordance with the rationality postulates in formal argumentation. Secondarily, we explore a range of perspectives concerning argumentative persuasiveness, examined both abstractly and via real-world examples. Employing this methodology, the paper creates a formal framework for reasoning about higher-order uncertainty, which may find applications in both artificial intelligence and human cognition. Our investigation examines the individual profiles of those who posted hate speech on a multilingual Facebook reaction dataset, specifically concerning news related to migrants and the LGBT+ community. This collection contains English, Dutch, Slovenian, and Croatian as its languages. Manual annotation was applied to each utterance, classifying it as hateful or acceptable speech. Following that, we utilized binary logistic regression to analyze the correlation between author profiles (including age, gender, and language) and the creation of hateful comments. The results, uniform across four languages, validate prior research. Men are found to produce more hateful comments than women, and an age-related increase in hate speech is apparent. While our findings reinforce prior tendencies, they also introduce important subtleties. Age and gender dynamics exhibit minor differences across diverse linguistic and cultural landscapes, suggesting that distinct socio-political realities are operational. Finally, we scrutinize the importance of author demographics in the study of hate speech, where the profiles of prototypical hate-speakers can be used for identifying hate speech, raising public awareness, and for strategic counter-initiatives to the dissemination of (online) hatred. We conclude by examining the importance of author demographics within hate speech studies, demonstrating how the traits of typical hate speakers can inform strategies for detecting hate speech, raising awareness, and mitigating the spread of (online) animosity. Among the neurological sequelae of SARS-CoV-2 infection, olfactory dysfunction, brain inflammation, mal