Possible Brucellosis in an Early Hominin Skeleton from Sterkfontein,South Africa

We report on the paleopathological analysis of the partial skeleton of the late Pliocene hominin species Australopithecus africanus Stw 431 from Sterkfontein, South Africa. A previous study noted the presence of lesions on vertebral bodies diagnosed as spondylosis deformans due to trauma. Instead, we suggest that these lesions are pathological changes due to the initial phases of an infectious disease, brucellosis. The macroscopic, microscopic and radiological appearance of the lytic lesions of the lumbar vertebrae is consistent with brucellosis. The hypothesis of brucellosis (most often associated with the consumption of animal proteins) in a 2.4 to 2.8 million year old hominid has a host of important implications for human evolution. The consumption of meat has been regarded an important factor in supporting, directing or altering human evolution. Perhaps the earliest (up to 2.5 million years ago) paleontological evidence for meat eating consists of cut marks on animal remains and stone tools that could have made these marks. Now with the hypothesis of brucellosis in A. africanus, we may have evidence of occasional meat eating directly linked to a fossil hominin.     

A wavelet-based ECG delineation algorithm for 32-bit integer online processing

Background  

Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding.     

Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.