Darcy‐scale multiphase flow in geological formations is significantly influenced by the wettability of the fluid‐solid system. So far it has not been understood how wettability impacts the pore‐scale flow regimes within rocks, which were in most cases regarded as an alteration from the base case of strongly water‐wet conditions by adjustment of contact angles. In this study, we directly image the pore‐scale flow regime in a carbonate altered to a mixed‐wet condition by aging with crude oil to represent the natural configuration in an oil reservoir with fast synchrotron‐based X‐ray computed tomography. We find that the pore‐scale flow regime is dominated by ganglion dynamics in which the pore space is intermittently filled with oil and brine. The frequency and size of these fluctuations are greater than in water‐wet rock such that their impact on the overall flow and relative permeability cannot be neglected in modeling approaches.

Until recently, our understanding of the evolution of human growth and development derived from studies of fossil juveniles that employed extant populations for both age determination and comparison. This circular approach has led to considerable debate about the human-like and ape-like affinities of fossil hominins. Teeth are invaluable for understanding maturation as age at death can be directly assessed from dental microstructure, and dental development has been shown to correlate with life history across primates broadly. We employ non-destructive synchrotron imaging to characterize incremental development, molar emergence, and age at death in more than 20 Australopithecus anamensis, Australopithecus africanus, Paranthropus robustus and South African early Homo juveniles. Long-period line periodicities range from at least 6–12 days (possibly 5–13 days), and do not support the hypothesis that australopiths have lower mean values than extant or fossil Homo. Crown formation times of australopith and early Homo postcanine teeth fall below or at the low end of extant human values; Paranthropus robustus dentitions have the shortest formation times. Pliocene and early Pleistocene hominins show remarkable variation, and previous reports of age at death that employ a narrow range of estimated long-period line periodicities, cuspal enamel thicknesses, or initiation ages are likely to be in error. New chronological ages for SK 62 and StW 151 are several months younger than previous histological estimates, while Sts 24 is more than one year older. Extant human standards overestimate age at death in hominins predating Homo sapiens, and should not be applied to other fossil taxa. We urge caution when inferring life history as aspects of dental development in Pliocene and early Pleistocene fossils are distinct from modern humans and African apes, and recent work has challenged the predictive power of primate-wide associations between hominoid first molar emergence and certain life history variables.

Summary: In modern microscopy, the field of view is often increased by obtaining an image mosaic, where multiple sub-images are taken side-by-side and combined post-acquisition. Mosaic imaging often leads to long imaging times that can increase the probability of sample deformation during the acquisition due to, e.g. changes in the environment, damage caused by the radiation used to probe the sample or biologically induced deterioration. Here we propose a technique, based on local phase correlation, to detect the deformations and construct an artifact-free image mosaic from deformed sub-images. The implementation of the method supports distributed computing and can be used to generate teravoxel-size mosaics. We demonstrate its capabilities by assembling a 5.6 teravoxel tomographic image mosaic of microvasculature in whole mouse brain. The method is compared to existing rigid stitching implementations designed for very large datasets, and observed to create artifact-free image mosaics in comparable runtime with the same hardware resources. Availability and implementation: The stitching software and C++/Python source code are available at GitHub (https://github.com/arttumiettinen/pi2) along with an example dataset and user instructions.