Following their dispersal out of Africa, humans colonised all continents of the world save one, Antarctica. Whilst Europe was initially peopled soon after this exodus, paleoclimatic, archaeological, and historical evidence suggest that successive waves and migrations of people have contributed to the population resident in Europe today. I therefore examined the impact of past events on the European population through the analysis of DNA sampled both from contemporary Europeans, and from worldwide populations pertinent to its history. I genotyped and analysed data from the Y chromosomes of over 2,000 haplogroup R-M269 European men from over 30 different populations and, in combination with comparable datasets gathered from the literature, show that there it is not possible to assign a date to the origin of this lineage in Europe, and thus that any conclusion as to the ancient or recent spread of this lineage in Europe is unfounded. I also show that commonly used Y chromosome lineage dating techniques based on STR variation are biased by the markers used and conclusions based on such dates should be viewed with a large amount of caution. I next use genome-wide SNP data from 1,550 individuals from 95 worldwide populations to explore the population structure of Europe and present an analysis of the detailed structure of Europe in a novel analytical framework using ChromoPainter and fineSTRUCTURE. Admixture analysis based this data reveals distinct genomic inputs to peripheral European populations, from North Africa, Sub-Saharan Africa, the Middle East, and East Asia, and provides dates for this admixture within the last 1,000 years that correspond to the emergence and decline of empires and kingdoms in these regions of Europe. This novel analysis highlights the importance of recent historical events on European population structure, but also suggests a degree of ancient structure across European populations. Taken together, these analyses demonstrate the substantial effects of both ancient and recent migrations and mixture on the contemporary genetic structure of Europe.

Eastern part of the African Sahel, connecting sub-Saharan Africa with North and East Africa, play an important role as a bidirectional corridor for vertically and horizontally migrations of populations. It is the strategic region to study human genetic diversity due to the presence of ethnically, linguistically, culturally and geographically diversity. This work is focused on the analysis of HVS-I and HVS-II segments of mtDNA. The work provides new information about genetic structure and migration activity of this region by analysis twelve populations belonging to three African linguistic families and different subsistent strategies. Analysis of mtDNA revealed the higher diversity of the populations of east Sudan and Horn of Africa, which is connected with the spreading of populations along the Nile River. It seems, that in this region linguistic factors have bigger impact on genetic diversity then the geografic ones. The opposite situation is observed in populations of Chad, where populations with similiar geografic location and different linguistic affilation revealed low genetic differentiation. The intra-population analysis shows the significant influence of genetic drift on the pastoralists living on the Red Sea Coast - Beja and Rashaida. In Beja is probably due to decrease of size of...

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The purpose of this work is to rethink the process of learning in human evolutionary systems. We take a sober look at how game theory, network theory, and chaos theory pertain specifically to the modeling, data, and training components of generalization in human systems. The value of our research is three-fold. First, our work is a direct approach to align machine learning generalization with core behavioral theories. We made our best effort to directly reconcile the axioms of these heretofore incompatible disciplines -- rather than moving from AI/ML towards the behavioral theories while building exclusively on AI/ML intuition. Second, this approach simplifies the learning process and makes it more intuitive for non-technical domain experts. We see increasing complexity in the models introduced in academic literature and, hence, increasing reliance on abstract hidden states learned by automatic feature engineering. The result is less understanding of how the models work and how they can be interpreted. However, these increasingly complex models are effective on the particular benchmark datasets they were designed for, but do not generalize. Our research highlights why these models are not generalizable and why behavioral theoretic intuition must have priority over the black box reliance on automatic feature engineering. Third, we introduce two novel methods that can be applied off-the-shelf: graph transformation for classification in human evolutionary systems (GT-CHES) and dynamic contrastive learning (DyCon). These models are most effective in mixed-motive human systems. While, GT-CHES is most suitable for tasks that involve event-based data, DyCon can be used on any temporal task.