Measures of maturation in early fossil hominins: Events at the first transition from australopiths to early Homo

An important question in palaeoanthropology is whether, among the australopiths and the first fossil hominins attributed to early Homo, there was a shift towards a more prolonged period of growth that can be distinguished from that of the living great apes and whether between the end of weaning and the beginning of puberty there was a slow period of growth as there is in modern humans. Evidence for the pace of growth in early fossil hominins comes from preserved tooth microstructure. A record of incremental growth in enamel and dentine persists that allows us to reconstruct tooth growth and compare key measures of dental maturation with modern humans and living great apes. Despite their diverse diets and way of life it is currently difficult to identify any clear differences in the timing of dental development among living great apes, australopiths and the earliest hominins attributed to the genus Homo. There is, however, limited evidence that some early hominins may have attained a greater proportion of their body mass and stature relatively earlier in the growth period than is typical of modern humans today

Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration

Neural networks have been proposed for medical image registration by learning, with a substantial amount of training data, the optimal transformations between image pairs. These trained networks can further be optimized on a single pair of test images - known as test-time optimization. This work formulates image registration as a meta-learning algorithm. Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes. The proposed meta-registration is hypothesized to maximize the efficiency and effectiveness of the test-time optimization in the "outer" meta-optimization of the networks. For image guidance applications that often are time-critical yet limited in training data, the potentially gained speed and accuracy are compared with classical registration algorithms, registration networks without meta-learning, and single-pair optimization without test-time optimization data. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients. These experiments demonstrate the effectiveness of a meta-registration protocol, which yields significantly improved performance relative to existing learning-based methods. Furthermore, the meta-registration achieves comparable results to classical iterative methods in a fraction of the time, owing to its rapid test-time optimization process.Comment: Accepted to ASMUS 2022 Workshop at MICCA

Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data

Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid point set registration approach using deep neural networks. As FPT does not assume constraints based on point vicinity or correspondence, it may be trained simply and in a flexible manner by minimizing an unsupervised loss based on the Chamfer Distance. This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available. To test the limit of the correspondence finding ability of FPT and its dependency on training data sets, this work explores the generalizability of the FPT from well-curated non-medical data sets to medical imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate its effectiveness and the superior registration performance of FPT over iterative and learning-based point set registration methods. Second, we demonstrate superior performance in rigid and non-rigid registration and robustness to missing data. Last, we highlight the interesting generalizability of the ModelNet-trained FPT by registering reconstructed freehand ultrasound scans of the spine and generic spine models without additional training, whereby the average difference to the ground truth curvatures is 1.3 degrees, across 13 patients.Comment: Accepted to ASMUS 2022 Workshop at MICCA

Dental development and age at death of the holotype of Anapithecus hernyaki (RUD 9) using synchrotron virtual histology

The chronology of dental development and life history of primitive catarrhines provides a crucial comparative framework for understanding the evolution of hominoids and Old World monkeys. Among the extinct groups of catarrhines are the pliopithecoids, with no known descendants. Anapithecus hernyaki is a medium-size stem catarrhine known from Austria, Hungary and Germany around 10 Ma, and represents a terminal lineage of a clade predating the divergence of hominoids and cercopithecoids, probably more than 30 Ma. In a previous study, Anapithecus was characterized as having fast dental development. Here, we used non-destructive propagation phase contrast synchrotron micro-tomography to image several dental microstructural features in the mixed mandibular dentition of RUD 9, the holotype of A. hernyaki. We estimate its age at death to be 1.9 years and describe the pattern, sequence and timing of tooth mineralization. Our results do not support any simplistic correlation between body mass and striae periodicity, since RUD 9 has a 3-day periodicity, which was previously thought unlikely based on body mass estimates in Anapithecus. We demonstrate that the teeth in RUD 9 grew even faster and initiated even earlier in development than suggested previously. Permanent first molars and the canine initiated 49 and 38 days prenatally, respectively. These results contribute to a better understanding of dental development in Anapithecus and may provide a window into the dental development of the last common ancestor of hominoids and cercopithecoids

Terahertz emission mechanism and laser excitation position dependence of nano-grating electrode photomixers

The emission mechanism of continuous wave (CW) terahertz (THz) photomixers that make use of nanostructured gratings (NSGs) is studied. Two different photomixer designs, based on a single-sided NSG and a double-sided NSG, embedded in the same antenna design and fabricated on an Fe doped InGaAsP substrate, are characterized with ∼1550 nm excitation. They are shown to exhibit similar performance in terms of spectral bandwidth and emitted power. The emission is mapped in terms of the laser excitation position, from which the emission mechanism is assigned to an enhanced optical electric field at the tips of the NSGs

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Cloud computing offers massive scalability and elasticity required by many scientific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new oppor-tunities for application developers. This paper investigates how workflow sys-tems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to integrate Big Data processing with workflows and science gateways work well in different cloud infrastructures and application scenarios, and can be used to create massively parallel applications for scientific analysis of Big Data

Volunteering in the care of people with severe mental illness: a systematic review

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