The human microbiome has recently become a valuable source of information about host life and health. To date little is known about how it may have evolved during key phases along our history, such as the Neolithic transition towards agriculture. Here, we shed light on the evolution experienced by the oral microbiome during this transition, comparing Palaeolithic hunter-gatherers with Neolithic and Copper Age farmers that populated a same restricted area in Italy. We integrate the analysis of 76 dental calculus oral microbiomes with the dietary information derived from the identification of embedded plant remains. We detect a stronger deviation from the hunter-gatherer microbiome composition in the last part of the Neolithic, while to a lesser extent in the early phases of the transition. Our findings demonstrate that the introduction of agriculture affected host microbiome, supporting the hypothesis of a gradual transition within the investigated populations.

We present the geomorphological map of the northwestern part of the Kurdistan Region of Iraq, where the landscape expresses the tectonic activity associated with the Arabia-Eurasia convergence and Neogene climate change. These processes influenced the evolution of landforms and fluvial pathways, where major rivers Tigris, Khabur, and Great Zab incise the landscape of Northeastern Mesopotamia Anticlinal ridges and syncline trough compose the Zagros orogen. The development of water and wind gaps, slope, and karsts processes in the highlands and the tilting of fluvial terraces in the flat areas are the main evidence of the relationship between tectonics, climate variations and geomorphological processes. During the Quaternary, especially after the Last Glacial Maximum, fluctuating arid and wet periods also influenced local landforms and fluvial patterns of the area. Finally, the intensified Holocene human occupation and agricultural activities during the passage to more complex societies over time impacted the evolution of the landscape in this part of Mesopotamia.

This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution.

In the first millennia of the Holocene, human communities in the Fertile Crescent experienced drastic cultural and technological transformations that modified social and human-environments interactions, ultimately leading to the rise of complex societies. The potential influence of climate on this “Neolithic Revolution” has long been debated. Here we present a speleothem record from the Kurdistan Region of Iraq, covering from Early Neolithic to Early Chalcolithic periods (~ 11 to 7.3 ka, 9000–5300 BCE). The record reveals the influence of the Siberian High on regional precipitation, and shows large hydroclimatic variability at the multicentennial scale. In particular, it highlights wetter conditions between 9.7 and 9.0 ka, followed by an abrupt reduction of precipitation between 9.0 and 8.5 ka, and a wetter interval between 8.5 and 8.0 ka. A comparison with regional and local archaeological data demonstrates an influence of recorded hydroclimatic changes on settlement patterns (size, distribution, permanent vs. seasonal occupation) and on the exploitation of water resources by Neolithic to Chalcolithic populations. Our record does not show prominent hydroclimatic changes at 9.3 and 8.2 ka, thus not supporting direct influence of such rapid and widespread events on the process of Neolithization and its cultural dispersal.

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions.