Bayesian regression versus machine learning for rapid age estimation of archaeological features identified with lidar at Angkor.

Lidar (light-detection and ranging) has revolutionized archaeology. We are now able to produce high-resolution maps of archaeological surface features over vast areas, allowing us to see ancient land-use and anthropogenic landscape modification at previously un-imagined scales. In the tropics, this has enabled documentation of previously archaeologically unrecorded cities in various tropical regions, igniting scientific and popular interest in ancient tropical urbanism. An emerging challenge, however, is to add temporal depth to this torrent of new spatial data because traditional archaeological investigations are time consuming and inherently destructive. So far, we are aware of only one attempt to apply statistics and machine learning to remotely-sensed data in order to add time-depth to spatial data. Using temples at the well-known massive urban complex of Angkor in Cambodia as a case study, a predictive model was developed combining standard regression with novel machine learning methods to estimate temple foundation dates for undated Angkorian temples identified with remote sensing, including lidar. The model's predictions were used to produce an historical population curve for Angkor and study urban expansion at this important ancient tropical urban centre. The approach, however, has certain limitations. Importantly, its handling of uncertainties leaves room for improvement, and like many machine learning approaches it is opaque regarding which predictor variables are most relevant. Here we describe a new study in which we investigated an alternative Bayesian regression approach applied to the same case study. We compare the two models in terms of their inner workings, results, and interpretive utility. We also use an updated database of Angkorian temples as the training dataset, allowing us to produce the most current estimate for temple foundations and historic spatiotemporal urban growth patterns at Angkor. Our results demonstrate that, in principle, predictive statistical and machine learning methods could be used to rapidly add chronological information to large lidar datasets and a Bayesian paradigm makes it possible to incorporate important uncertainties-especially chronological-into modelled temporal estimates.

Provisioning an Early City: Spatial Equilibrium in the Agricultural Economy at Angkor, Cambodia.

A dominant view in economic anthropology is that farmers must overcome decreasing marginal returns in the process of intensification. However, it is difficult to reconcile this view with the emergence of urban systems, which require substantial increases in labor productivity to support a growing non-farming population. This quandary is starkly posed by the rise of Angkor (Cambodia, 9th-fourteenth centuries CE), one of the most extensive preindustrial cities yet documented through archaeology. Here, we leverage extensive documentation of the Greater Angkor Region to illustrate how the social and spatial organization of agricultural production contributed to its food system. First, we find evidence for supra-household-level organization that generated increasing returns to farming labor. Second, we find spatial patterns which indicate that land-use choices took transportation costs to the urban core into account. These patterns suggest agricultural production at Angkor was organized in ways that are more similar to other forms of urban production than to a smallholder system. Supplementary Information: The online version contains supplementary material available at 10.1007/s10816-021-09535-5.

Evaluation of the BD Phoenix CPO detect panel for prediction of Ambler class carbapenemases.

Rapid detection of carbapenemases as a cause of resistance is beneficial for infection control and antimicrobial therapy. The BD Phoenix NMIC-502 panel and CPO detect test identifies presence of carbapenemases in Enterobacterales such as Klebsiella pneumoniae and assigns them to Ambler classes. To evaluate the performance of the CPO detect panel, we employed a European collection of 1222 K. pneumoniae including carbapenem non-susceptible and susceptible clinical isolates from 26 countries, for which draft genomes were available after Illumina sequencing and the presence of carbapenemase genes had been identified by ARIBA gene calling. The CPO panel detected 488 out of 494 carbapenemase-encoding isolates as positive and six as negative. One-hundred and two isolates were tested positive for carbapenemase in the absence of any carbapenemase gene. The CPO panel identified 229 out of 230 KPC-positive isolates as carbapenemase producing and classified 62 of these as class A enzyme. Similarly, the CPO panel correctly specified 167 of 182 as class D. Regarding metallo-beta-lactamases, the CPO panel assigned 78 of 90 MBL positive isolates to class B enzymes. The sensitivity of the CPO panel in detecting carbapenemase activity was 99.5%, 97.7% and 98.3% for class A, B and D enzymes, respectively. The sensitivity in assignation to Ambler class A, B and D was 27%, 86% and 91%, respectively. An overall sensitivity of 98.8% and specificity of 86% in unclassified detection of carbapenemases was observed, with frequent false positive detection of carbapenemase producing organisms, thus rendering further confirmatory tests necessary.

Diachronic modeling of the population within the medieval Greater Angkor Region settlement complex.

Angkor is one of the world's largest premodern settlement complexes (9th to 15th centuries CE), but to date, no comprehensive demographic study has been completed, and key aspects of its population and demographic history remain unknown. Here, we combine lidar, archaeological excavation data, radiocarbon dates, and machine learning algorithms to create maps that model the development of the city and its population growth through time. We conclude that the Greater Angkor Region was home to approximately 700,000 to 900,000 inhabitants at its apogee in the 13th century CE. This granular, diachronic, paleodemographic model of the Angkor complex can be applied to any ancient civilization.

Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia.

Archaeologists often need to date and group artifact types to discern typologies, chronologies, and classifications. For over a century, statisticians have been using classification and clustering techniques to infer patterns in data that can be defined by algorithms. In the case of archaeology, linear regression algorithms are often used to chronologically date features and sites, and pattern recognition is used to develop typologies and classifications. However, archaeological data is often expensive to collect, and analyses are often limited by poor sample sizes and datasets. Here we show that recent advances in computation allow archaeologists to use machine learning based on much of the same statistical theory to address more complex problems using increased computing power and larger and incomplete datasets. This paper approaches the problem of predicting the chronology of archaeological sites through a case study of medieval temples in Angkor, Cambodia. For this study, we have a large dataset of temples with known architectural elements and artifacts; however, less than ten percent of the sample of temples have known dates, and much of the attribute data is incomplete. Our results suggest that the algorithms can predict dates for temples from 821-1150 CE with a 49-66-year average absolute error. We find that this method surpasses traditional supervised and unsupervised statistical approaches for under-specified portions of the dataset and is a promising new method for anthropological inquiry.

A new cytotoxic steroid from co-fermentation of two marine alga-derived micro-organisms.

Bioactivity-guided chemical investigation of a co-culture of marine-derived micro-organisms has yielded one new steroid, 7ß-hydroxycholesterol-1ß-carboxylic acid (1) with an unprecedented carboxylic acid group at C-1, together with three known steroidal metabolites (2-4). The chemical structures and stereochemistry of the isolated compounds were unambiguously determined based on extensive 1D, 2D NMR and HR-ESI-MS measurements. The isolated compounds were assessed for their cytotoxic activity against four different human tumour cell lines K562 (leukaemia), HCT116 (colon), A2780 (ovary) and its cisplatin-resistant mutant (A2780 CisR), and they revealed moderate activities with IC50 values ranging from 10.0 to 100.0 µM.