The history of human inbreeding is controversial.1 In particular, how the development of sedentary and/or agricultural societies may have influenced overall inbreeding levels, relative to those of ...hunter-gatherer communities, is unclear.2–5 Here, we present an approach for reliable estimation of runs of homozygosity (ROHs) in genomes with ≥3× mean sequence coverage across >1 million SNPs and apply this to 411 ancient Eurasian genomes from the last 15,000 years.5–34 We show that the frequency of inbreeding, as measured by ROHs, has decreased over time. The strongest effect is associated with the Neolithic transition, but the trend has since continued, indicating a population size effect on inbreeding prevalence. We further show that most inbreeding in our historical sample can be attributed to small population size instead of consanguinity. Cases of high consanguinity were rare and only observed among members of farming societies in our sample. Despite the lack of evidence for common consanguinity in our ancient sample, consanguineous traditions are today prevalent in various modern-day Eurasian societies,1,35–37 suggesting that such practices may have become widespread within the last few millennia.
•A study of 411 ancient genomes shows inbreeding decreased over time•The decrease appears linked with population size increase enabled by agriculture•Extreme consanguineous matings did occur among agriculturalists but were rare
Ceballos et al. study 411 ancient genomes from west and central Eurasia to show that overall inbreeding levels have decreased over time, most likely owing to population size increases with agriculture. The sample contains highly consanguineous ancient individuals, but these are rare, and all come from agriculturalist backgrounds.
There is growing interest in uncovering genetic kinship patterns in past societies using low‐coverage palaeogenomes. Here, we benchmark four tools for kinship estimation with such data: lcMLkin, ...NgsRelate, KIN, and READ, which differ in their input, IBD estimation methods, and statistical approaches. We used pedigree and ancient genome sequence simulations to evaluate these tools when only a limited number (1 to 50 K, with minor allele frequency ≥0.01) of shared SNPs are available. The performance of all four tools was comparable using ≥20 K SNPs. We found that first‐degree related pairs can be accurately classified even with 1 K SNPs, with 85% F1 scores using READ and 96% using NgsRelate or lcMLkin. Distinguishing third‐degree relatives from unrelated pairs or second‐degree relatives was also possible with high accuracy (F1 > 90%) with 5 K SNPs using NgsRelate and lcMLkin, while READ and KIN showed lower success (69 and 79% respectively). Meanwhile, noise in population allele frequencies and inbreeding (first‐cousin mating) led to deviations in kinship coefficients, with different sensitivities across tools. We conclude that using multiple tools in parallel might be an effective approach to achieve robust estimates on ultra‐low‐coverage genomes.
Virtual reality (VR) is an emerging technology that is being used in a wide range of fields such as medicine, gaming, psychology and sociology. The use of VR is promising in the field of education ...and requires investigation, but research on the use of VR in education is still limited. This enables the exploration of new territories, and design education is one of them. Design education, an important part of the curriculum of architecture students who aim to conceptualize problem-solving, is still taught using traditional methodologies with touches of digital technologies. Thus, there is limited research into the implementation of VR. This study proposes using VR in basic design education and focuses on the usability of VR, especially for problem-solving activities. It presents the literature on basic design education of digital approaches, VR technologies, usability criteria and the technology acceptance model. In order to analyse the usability of VR, we conducted an experimental study with 20 first-year interior architecture and architecture students. We found that, statistically, there is a significant difference in terms of ‘the intention to use’ and ‘the perceived enjoyment’ between the VR group and the paper-based group. Moreover, there is, statistically, a difference in effectiveness within the paper-based group and the VR-based group when one compares the success of two types of design problems in the same group. Thus, one can summarize that using VR can strongly enhance problem-solving activities in interior architecture and for architecture students and that one can consider it to be a promising and complementary tool in basic design education.
Clickbait is a strategy that aims to attract people’s attention and direct them to specific content. Clickbait titles, created by the information that is not included in the main content or using ...intriguing expressions with various text-related features, have become very popular, especially in social media. This study expands the Turkish clickbait dataset that we had constructed for clickbait detection in our proof-of-concept study, written in Turkish. We achieve a 48,060 sample size by adding 8859 tweets and release a publicly available dataset – ClickbaitTR – with its open-source data analysis library. We apply machine learning algorithms such as Artificial Neural Network (ANN), Logistic Regression, Random Forest, Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Ensemble Classifier on 48,060 news headlines extracted from Twitter. The results show that the Logistic Regression algorithm has 85% accuracy; the Random Forest algorithm has a performance of 86% accuracy; the LSTM has 93% accuracy; the ANN has 93% accuracy; the Ensemble Classifier has 93% accuracy; and finally, the BiLSTM has 97% accuracy. A thorough discussion is provided for the psychological aspects of clickbait strategy focusing on curiosity and interest arousal. In addition to a successful clickbait detection performance and the detailed analysis of clickbait sentences in terms of language and psychological aspects, this study also contributes to clickbait detection studies with the largest clickbait dataset in Turkish.
Upper Mesopotamia played a key role in the Neolithic Transition in Southwest Asia through marked innovations in symbolism, technology, and diet. We present 13 ancient genomes (c. 8500 to 7500 cal ...BCE) from Pre-Pottery Neolithic Çayönü in the Tigris basin together with bioarchaeological and material culture data. Our findings reveal that Çayönü was a genetically diverse population, carrying mixed ancestry from western and eastern Fertile Crescent, and that the community received immigrants. Our results further suggest that the community was organized along biological family lines. We document bodily interventions such as head shaping and cauterization among the individuals examined, reflecting Çayönü’s cultural ingenuity. Last, we identify Upper Mesopotamia as the likely source of eastern gene flow into Neolithic Anatolia, in line with material culture evidence. We hypothesize that Upper Mesopotamia’s cultural dynamism during the Neolithic Transition was the product not only of its fertile lands but also of its interregional demographic connections.
Neolithic Upper Mesopotamia was a center of cultural innovation; ancient genomes now reveal that it was also a migration hub.
The social organization of the first fully sedentary societies that emerged during the Neolithic period in Southwest Asia remains enigmatic,1 mainly because material culture studies provide limited ...insight into this issue. However, because Neolithic Anatolian communities often buried their dead beneath domestic buildings,2 household composition and social structure can be studied through these human remains. Here, we describe genetic relatedness among co-burials associated with domestic buildings in Neolithic Anatolia using 59 ancient genomes, including 22 new genomes from Aşıklı Höyük and Çatalhöyük. We infer pedigree relationships by simultaneously analyzing multiple types of information, including autosomal and X chromosome kinship coefficients, maternal markers, and radiocarbon dating. In two early Neolithic villages dating to the 9th and 8th millennia BCE, Aşıklı Höyük and Boncuklu, we discover that siblings and parent-offspring pairings were frequent within domestic structures, which provides the first direct indication of close genetic relationships among co-burials. In contrast, in the 7th millennium BCE sites of Çatalhöyük and Barcın, where we study subadults interred within and around houses, we find close genetic relatives to be rare. Hence, genetic relatedness may not have played a major role in the choice of burial location at these latter two sites, at least for subadults. This supports the hypothesis that in Çatalhöyük,3–5 and possibly in some other Neolithic communities, domestic structures may have served as burial location for social units incorporating biologically unrelated individuals. Our results underscore the diversity of kin structures in Neolithic communities during this important phase of sociocultural development.
•Genetic kinship estimated from co-buried individuals’ genomes in Neolithic Anatolia•Close relatives are common among co-burials in Aşıklı and Boncuklu•Many unrelated infants found buried in the same building in Çatalhöyük and Barcın•Neolithic societies in Southwest Asia may have held diverse concepts of kinship
Yaka et al. use ancient genomes from Neolithic Anatolia and present evidence for diverse concepts of social kinship in Neolithic societies. In some communities, like Çatalhöyük, many genetically unrelated infants were buried together inside the same buildings, whereas in other sites, people buried together were frequently close biological kin.
Crowd behavior is the collective act and gathering of a group of individuals to achieve a shared purpose. Swarm intelligence-based optimization algorithms are usually used to solve complex problems ...for crowd behavior. Crowd simulations are often used for the analyses that require precision in different domains such as complex structural analysis, image recognition, creating nature-inspired non-player character movements in video games, and more. In this study, a generic crowd simulation framework that can be used to simulate already-available crowd simulation algorithms and design new ones was developed. The test environment layout was generated with the use of a generate-and-test algorithm combined with the crowd simulation algorithms to make sure that the generated content is meeting the requirements of a crowd simulation environment. Within the framework, three different crowd simulation algorithms —firefly algorithm, particle swarm optimization, and artificial bee colony— are generated and also implemented as puzzle-like video games. The results show that all fireflies achieved to gather at the global minimum of the generated layout faster and in a more precise way than the artificial bee colony algorithm and particle swarm optimization algorithm. The developed framework enables a generic and parametric testbed to design and compare different algorithms and to generate video games.
The increasing use of deep learning (DL) in safety-critical applications highlights the critical need for systematic and effective testing to ensure system reliability and quality. In this context, ...researchers have conducted various DL testing studies to identify weaknesses in Deep Neural Network (DNN) models, including exploring test coverage, generating challenging test inputs, and test selection. In this study, we propose a generic DNN testing framework that takes into consideration the distribution of test data and prioritizes them based on their potential to cause incorrect predictions by the tested DNN model. We evaluated the proposed framework using the image classification as a use case. We conducted empirical evaluations by implementing each phase with carefully chosen methods. We employed Variational Autoencoders to identify and eliminate out-of-distribution data from the test datasets. Additionally, we prioritize test data that increase uncertainty in the model, as these cases are more likely to reveal potential faults. The elimination of out-of-distribution data enables a more focused analysis to uncover the sources of DNN failures while using prioritized test data reduces the cost of test data labeling. Furthermore, we explored the use of post-hoc explainability methods to identify the cause of incorrect predictions, a process similar to debugging. This study can be a prelude to incorporating explainability methods into the model development process after testing.
Procedural content generation (PCG) has been an essential catalyzer in the last decade with its efficiency in creating game elements such as textures, game levels, and maps. Despite being ...successfully applied in various studies, new reliable evaluation tools are still needed to assess the quality of the generated game content. One example limitation of procedurally generated game worlds is the lacking spatial configuration. To address this issue, in this study, an assessment method was developed to evaluate the spatial quality of procedurally generated game worlds. For this purpose, Space Syntax was used, which incorporates a set of methods to analyze spatial configurations and movement. The analyses were applied to a new game developed by the authors —the Haunted House— and the performance was evaluated in terms of integration, connectivity, and depth distance. Results show that changing the room dimensions (i.e., 15x15, 25x25, and 35x35 units) modifies the performance measures as well as game design parameters —number of the spawning points (ranging from 1 to 4), critical axes (1 to 5), to name a few. The proposed approach is a first attempt to create various improved spatial configurations and provide an evaluation tool to analyze the PCG algorithms in level design.
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