Abstract

This article examines differences in subjective culture among three societies that vary in their extent of urbanization and differe,Uiation and within these societies between females and males. David Bakan's agency-communion and Talcott Parsons' instrumental-expressive distinc lions are used to capture both these ruralurban and male-female differences using data collected with Harry Triandis' antecedent-consequent method of studying subjective culture. Both betweensociety and within-society differences in subjective culture are found, although they occur independently of each other, Cross-cultural differences are stronger for concepts dealing with group life, and sex differences are stronger for concepts regarding individual actions and selforientations. Specifications and extensions of existing theory, as well as directions for future research, are suggested.Social theorists have long noted the importance of understanding Zeitgeist (how people organize and perceive their social world), which involves a subjective understanding of the world, or what has been called "subjective culture." This subjective culture may refer to people's common understandings of tenns and colloquialisms, the way ''they attend to cues from the environment, the way they think about 'what goes with what,' and the way they feel about different aspects of the environment" (Triandis, 1976, p. 3; see also Triandis, Vassilou, Vassilou, Tanaka, & Shanmugam, 1972).The classic sociological work of Durkheim, Tonnies, Weber, and Simmel posited basic differences in world view or subjective culture between rural and urban societies; contemporary rural sociologists have continued an interest in

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Protein structure predictions have broad impact on several science disciplines such as biology, bioengineering, and medical science. AlphaFold2 and RoseTTAFold are the current state-of-the-art AI methods to predict the structures of proteins with an accuracy comparable to lower-resolution experimental methods. In its 2021 year review, both these methods were recognized as breakthrough of the year by Science magazine and method of the year by Nature magazine. It is timely and important to provide training and support on these emerging methods. Our crash course Enabling Protein Structure Prediction with Artificial Intelligence was conducted in collaboration with domain experts and research computing professionals. The crash course was well received by the community as there were 750 registrants from all over the world. Here we provide the summary of the crash course, describe our findings in organizing the crash course, and explain what preparation steps helped us with the hands-on training.