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Ancient texts and archaeological evidence indicate substantial lead exposure during antiquity that potentially impacted human health. Although lead exposure routes were many and included the use of glazed tablewares, paints, cosmetics, and even intentional ingestion, the most significant for the nonelite, rural majority of the population may have been through background air pollution from mining and smelting of silver and lead ores that underpinned the Roman economy. Here, we determined potential health effects of this air pollution using Arctic ice core measurements of Roman-era lead pollution, atmospheric modeling, and modern epidemiology-based relationships between air concentrations, blood lead levels (BLLs), and cognitive decline. Findings suggest air lead concentrations exceeded 150 ng/m 3 near metallurgical emission sources, with average enhancements of >1.0 ng/m 3 over Europe during the Pax Romana apogee of the Roman Empire. The result was blood lead enhancements in young children of about 2.4 µg/dl above an estimated Neolithic background of 1.0 µg/dl, leading to widespread cognitive decline including a 2.5-to-3 point reduction in intelligence quotient throughout the Roman Empire., Competing Interests: Competing interests statement:The authors declare no competing interest.

A new information processing method, an artificial neural net, was applied to characterise the variability of anthropological features of the human nasal skeleton. The aim was to find different types of nasal skeletons. A neural net with 15*15 nodes was trained by 17 standard anthropological parameters taken from 184 skulls of the Aachen collection. The trained neural net delivers its classification in a two-dimensional map. Different types of noses were locally separated within the map. Rare and frequent types may be distinguished after one passage of the complete collection through the net. Statistical descriptive analysis, hierarchical cluster analysis, and discriminant analysis were applied to the same data set. These parallel applications allowed comparison of the new approach to the more traditional ones. In general the classification by the neural net is in correspondence with cluster analysis and discriminant analysis. However, it goes beyond these classifications because of the possibility of differentiating the types in multi-dimensional dependencies. Furthermore, places in the map are kept blank for intermediate forms, which may be theoretically expected, but were not included in the training set. In conclusion, the application of a neural network is a suitable method for investigating large collections of biological material. The gained classification may be helpful in anatomy and anthropology as well as in forensic medicine. It may be used to characterise the peculiarity of a whole set as well as to find particular cases within the set.