60 research outputs found

    The Atapuerca sites and the Ibeas hominids

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    The Atapuerca railway Trench and Ibeas sites near Burgos, Spain, are cave fillings that include a series of deposits ranging from below the Matuyama/Bruhnes reversal up to the end of Middle Pleistocene. The lowest fossil-bearing bed in the Trench contains an assemblage of large and small Mammals including Mimomys savini, Pitymys gregaloides, Pliomys episcopalis, Crocuta crocuta, Dama sp. and Megacerini; the uppermost assemblage includes Canis lupus, Lynx spelaea, Panthera (Leo) fossilis, Felis sylvestris, Equus caballus steinheimensis, E.c. germanicus, Pitymys subtenaneus, Microtus arvalis agrestis, Pliomys lenki, and also Panthera toscana, Dicerorhinus bemitoechus, Bison schoetensacki, which are equally present in the lowest level. The biostratigraphic correlation and dates of the sites are briefly discussed, as are the paleoclimatic interpretation of the Trench sequences. Stone artifacts are found in several layers; the earliest occurrences correspond to the upper beds containing Mimomys savini. A set of preserved human occupation floors has been excavated in the top fossil-bearing beds. The stone-tool assemblages of the upper levels are of upper-medial Acheulean to Charentian tradition. The rich bone breccia SH, in the Cueva Mayor-Cueva del Silo, Ibeas de Juarros, is a derived deposit, due to a mud flow that dispersed and carried the skeletons of many carnivores and humans. The taxa represented are: Vrsus deningeri (largely dominant), Panthera (Leo) fossilis, Vulpes vulpes, Homo sapiens var. Several traits of both mandibular and cranial remains are summarized. Preliminary attempts at dating suggest that the Ibeas fossil man is older than the Last Interglacial, or oxygen-isotope stage 5

    Early Pleistocene enamel proteome from Dmanisi resolves Stephanorhinus phylogeny

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    The sequencing of ancient DNA has enabled the reconstruction of speciation, migration and admixture events for extinct taxa. However, the irreversible post-mortem degradation2 of ancient DNA has so far limited its recovery—outside permafrost areas—to specimens that are not older than approximately 0.5 million years (Myr). By contrast, tandem mass spectrometry has enabled the sequencing of approximately 1.5-Myr-old collagen type I, and suggested the presence of protein residues in fossils of the Cretaceous period—although with limited phylogenetic use. In the absence of molecular evidence, the speciation of several extinct species of the Early and Middle Pleistocene epoch remains contentious. Here we address the phylogenetic relationships of the Eurasian Rhinocerotidae of the Pleistocene epoch, using the proteome of dental enamel from a Stephanorhinus tooth that is approximately 1.77-Myr old, recovered from the archaeological site of Dmanisi (South Caucasus, Georgia). Molecular phylogenetic analyses place this Stephanorhinus as a sister group to the clade formed by the woolly rhinoceros (Coelodonta antiquitatis) and Merck’s rhinoceros (Stephanorhinus kirchbergensis). We show that Coelodonta evolved from an early Stephanorhinus lineage, and that this latter genus includes at least two distinct evolutionary lines. The genus Stephanorhinus is therefore currently paraphyletic, and its systematic revision is needed. We demonstrate that sequencing the proteome of Early Pleistocene dental enamel overcomes the limitations of phylogenetic inference based on ancient collagen or DNA. Our approach also provides additional information about the sex and taxonomic assignment of other specimens from Dmanisi. Our findings reveal that proteomic investigation of ancient dental enamel—which is the hardest tissue in vertebrates, and is highly abundant in the fossil record—can push the reconstruction of molecular evolution further back into the Early Pleistocene epoch, beyond the currently known limits of ancient DNA preservation

    Supraorbital morphology and social dynamics in human evolution

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    Uniquely, with respect to Middle Pleistocene hominins, anatomically modern humans do not possess marked browridges, and have a more vertical forehead with mobile eyebrows that play a key role in social signalling and communication. The presence and variability of browridges in archaic Homo and their absence in ourselves have led to debate concerning their morphogenesis and function, with two main hypotheses being put forward; that browridge morphology is the result of the spatial relationship between the orbits and the braincase, and that browridge morphology is significantly impacted by biting mechanics. Here we virtually manipulate browridge morphology of an archaic hominin (Kabwe 1), showing that it is much larger than the minimum required to fulfil spatial demands and that browridge size has little impact on mechanical performance during biting. Since browridge morphology in this fossil is not driven by spatial and mechanical requirements alone, the role of the supraorbital region in social communication is a potentially significant factor. We propose that conversion of the large browridges of our immediate ancestors to a more vertical frontal in modern humans allowed highly mobile eyebrows to display subtle affiliative emotions

    The Logistic Random Field - A Convenient Graphical Model For Learning Parameters For Mrf-Based Labeling

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    Graphical models are fundamental tools for modeling images and other applications. In this paper, we propose the Logistic Random Field (LRF) model for representing a discrete-valued graphical model. The LRF model is based on an underlying quadratic model and a logistic function. The chief advantages of the LRF are its convenience and flexibility. The quadratic model makes inference easy to implement using standard numerical linear algebra routines. This quadratic model also allows the log-likelihood of the training data to be differentiated with respect to any parameter in the model, enhancing the flexibility of the LRF model. To demonstrate the usefulness of this model we use it to learn how to segment objects, specifically roads, horses, and cows. In addition, we demonstrate the flexibility of the LRF model by incorporating super-pixels. We then show that the LRF segmentation model produces segmentations that are competitive with recently published results. ©2008 IEEE
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