Two Late Pleistocene human femora from Trinil, Indonesia: Implications for body size and behavior in Southeast Asia.

Late Pleistocene hominin postcranial specimens from Southeast Asia are relatively rare. Here we describe and place into temporal and geographic context two partial femora from the site of Trinil, Indonesia, which are dated stratigraphically and via Uranium-series direct dating to ca. 37-32 ka. The specimens, designated Trinil 9 and 10, include most of the diaphysis, with Trinil 9 being much better preserved. Microcomputed tomography is used to determine cross-sectional diaphyseal properties, with an emphasis on midshaft anteroposterior to mediolateral bending rigidity (Ix/Iy), which has been shown to relate to both body shape and activity level in modern humans. The body mass of Trinil 9 is estimated from cortical area and reconstructed length using new equations based on a Pleistocene reference sample. Comparisons are carried out with a large sample of Pleistocene and Holocene East Asian, African, and European/West Asian femora. Our results show that Trinil 9 has a high Ix/Iy ratio, most consistent with a relatively narrow-bodied male from a mobile hunting-gathering population. It has an estimated body mass of 55.4 kg and a stature of 156 cm, which are small relative to Late Pleistocene males worldwide, but larger than the penecontemporaneous Deep Skull femur from Niah Cave, Malaysia, which is very likely female. This suggests the presence of small-bodied active hunter-gatherers in Southeast Asia during the later Late Pleistocene. Trinil 9 also contrasts strongly in morphology with earlier partial femora from Trinil dating to the late Early-early Middle Pleistocene (Femora II-V), and to a lesser extent with the well-known complete Femur I, most likely dating to the terminal Middle-early Late Pleistocene. Temporal changes in morphology among femoral specimens from Trinil parallel those observed in Homo throughout the Old World during the Pleistocene and document these differences within a single site.

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning.

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5,000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.