Agricultural productivity in past societies: toward an empirically informed model for testing cultural evolutionary hypotheses

Agricultural productivity, and its variation in space and time, plays a fundamental role in many theories of human social evolution. However, we often lack systematic information about the productivity of past agricultural systems on a scale large enough to test these theories properly. The effect of climate on crop yields has received a great deal of attention resulting in a range of empirical and process-based models, yet the focus has primarily been on current or future conditions. In this paper, we argue for a “bottom-up” approach that estimates potential productivity based on information about the agricultural practices and technologies used in past societies. Of key theoretical interest is using this information to estimate the carrying high quality historical and archaeological information about past societies in order to infer the temporal and geographic patterns of change in agricultural productivity and potential. We discuss information we need to collect about past agricultural techniques and practices, and introduce a new databank initiative that we have developed for collating the best available historical and archaeological evidence. A key benefit of our approach lies in making explicit the steps in the estimation of past productivities and carrying capacities, and in being able to assess the effects of different modelling assumptions. This is undoubtedly an ambitious task, yet promises to provide important insights into fundamental aspects of past societies, enabling us to test more rigorously key hypotheses about human socio-cultural evolution

E2F1 Regulates Cellular Growth by mTORC1 Signaling

During cell proliferation, growth must occur to maintain homeostatic cell size. Here we show that E2F1 is capable of inducing growth by regulating mTORC1 activity. The activation of cell growth and mTORC1 by E2F1 is dependent on both E2F1's ability to bind DNA and to regulate gene transcription, demonstrating that a gene induction expression program is required in this process. Unlike E2F1, E2F3 is unable to activate mTORC1, suggesting that growth activity could be restricted to individual E2F members. The effect of E2F1 on the activation of mTORC1 does not depend on Akt. Furthermore, over-expression of TSC2 does not interfere with the effect of E2F1, indicating that the E2F1-induced signal pathway can compensate for the inhibitory effect of TSC2 on Rheb. Immunolocalization studies demonstrate that E2F1 induces the translocation of mTORC1 to the late endosome vesicles, in a mechanism dependent of leucine. E2F1 and leucine, or insulin, together affect the activation of S6K stronger than alone suggesting that they are complementary in activating the signal pathway. From these studies, E2F1 emerges as a key protein that integrates cell division and growth, both of which are essential for cell proliferation

Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization

Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as "Seshat: Global History Databank." We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history

An Introduction to Seshat: Global History Databank

This article introduces the Seshat: Global History Databank, its potential, and its methodology. Seshat is a databank containing vast amounts of quantitative data buttressed by qualitative nuance for a large sample of historical and archaeological polities. Thee sample is global in scope and covers the period from the Neolithic Revolution to the Industrial Revolution. Seshat allows scholars to capture dynamic processes and to test theories about the co-evolution (or not) of social scale and complexity, agriculture, warfare, religion, and any number of such Big Questions. Seshat is rapidly becoming a massive resource for innovative cross-cultural and cross-disciplinary research. Seshat is part of a growing trend to use comparative historical data on a large scale and contributes as such to a growing consilience between the humanities and social sciences. Seshat is underpinned by a robust and transparent workflow to ensure the ever growing dataset is of high quality

Clusters of alcohol abstainers and drinkers incorporating motives against drinking: a random survey of 18 to 34 year olds in four cities in four different continents

Objective: The aim of this analysis was to identify alcohol consumption clusters for adolescents and early adults according to attitudes to drinking, motivations against drinking and perceptions associated with alcohol. Method: Interviews were undertaken with people aged 18–34 years old living in four cities in different regions of the world. Multistage random sampling was consistent across the four cities (Ilorin (Nigeria), Wuhan (China), Montevideo (Uruguay) and Moscow (Russia)). The questionnaire was forward and back translated into relevant languages and face-to-face interviewing undertaken. The data were weighted to the population of each city. In total 6235 structured interviews were undertaken (1391 in Ilorin, 1600 in Montevideo, 1604 in Moscow and 1640 in Wuhan). Questions regarding motivation against alcohol consumption (14 items), assessing perceptions (3 items) and attitudes to drinking in certain situations (8 items) were asked of all respondents including abstainers. Factor analysis was initially undertaken to identify highly related correlated variables. Results: Cluster analysis provided a variety of clusters (Ilorin (3 clusters), Montevideo (5), Moscow (4) and Wuhan (4)). At least one cluster in each city was dominated by abstainers and another by heavy episodic drinkers. Variations by city and alcohol consumption patterns existed in regards to variables included. Conclusion: This analysis detailed the city specific motivations against drinking alcohol, and the attitudes towards alcohol consumption. Differences highlight the influence of country/city specific culture, customs, laws, societal norms and traditions