The apportionment of tooth size and its implications in Australopithecus sediba versus other Plio-pleistocene and recent African hominins

Objectives: Australopithecus sediba is characterized further by providing formerly unpublished and refined mesiodistal and buccolingual crown measurements in the MH1 and MH2 specimens. After size correction, these data were compared with those in other fossil and recent samples to facilitate additional insight into diachronic hominin affinities. Materials and Methods: Six comparative samples consist of fossil species: A. africanus, A. afarensis, Homo habilis, Paranthropus robustus, P. boisei, and H. erectus. Others comprise H. sapiens and Pan troglodytes. Re-estimates of “actual” dimensions in damaged A. sediba teeth were effected through repeated measurements by independent observers. X-ray synchrotron microtomography allowed measurement of crowns obscured by matrix and non-eruption. Tooth size apportionment analysis, an established technique for intraspecific comparisons, was then applied at this interspecific level to assess phenetic affinities using both within- and among-group data. Results: Comparison of these highly heritable dimensions identified a general trend for smaller posterior relative to larger anterior teeth (not including canines), contra Paranthropus, that allies A. sediba with other australopiths and Homo; however, specific reductions and/or shape variation in the species’ canines, third premolars, and anterior molars relative to the other teeth mirror the patterning characteristic of Homo. Discussion: Of all samples, including east African australopiths, A. sediba appears most like H. habilis, H. erectus and H. sapiens regarding how crown size is apportioned along the tooth rows. These findings parallel those in prior studies of dental and other skeletal data, including several that suggest A. sediba is a close relative of, if not ancestral to, Homo

Seeking legitimacy through CSR: Institutional Pressures and Corporate Responses of Multinationals in Sri Lanka

Arguably, the corporate social responsibility (CSR) practices of multinational enterprises (MNEs) are influenced by a wide range of both internal and external factors. Perhaps most critical among the exogenous forces operating on MNEs are those exerted by state and other key institutional actors in host countries. Crucially, academic research conducted to date offers little data about how MNEs use their CSR activities to strategically manage their relationship with those actors in order to gain legitimisation advantages in host countries. This paper addresses that gap by exploring interactions between external institutional pressures and firm-level CSR activities, which take the form of community initiatives, to examine how MNEs develop their legitimacy-seeking policies and practices. In focusing on a developing country, Sri Lanka, this paper provides valuable insights into how MNEs instrumentally utilise community initiatives in a country where relationship-building with governmental and other powerful non-governmental actors can be vitally important for the long-term viability of the business. Drawing on neo-institutional theory and CSR literature, this paper examines and contributes to the embryonic but emerging debate about the instrumental and political implications of CSR. The evidence presented and discussed here reveals the extent to which, and the reasons why, MNEs engage in complex legitimacy-seeking relationships with Sri Lankan institutions

Subtle genetic changes enhance virulence of methicillin resistant and sensitive Staphylococcus aureus

<p>Abstract</p> <p>Background</p> <p>Community acquired (CA) methicillin-resistant <it>Staphylococcus aureus </it>(MRSA) increasingly causes disease worldwide. USA300 has emerged as the predominant clone causing superficial and invasive infections in children and adults in the USA. Epidemiological studies suggest that USA300 is more virulent than other CA-MRSA. The genetic determinants that render virulence and dominance to USA300 remain unclear.</p> <p>Results</p> <p>We sequenced the genomes of two pediatric USA300 isolates: one CA-MRSA and one CA-methicillin susceptible (MSSA), isolated at Texas Children's Hospital in Houston. DNA sequencing was performed by Sanger dideoxy whole genome shotgun (WGS) and 454 Life Sciences pyrosequencing strategies. The sequence of the USA300 MRSA strain was rigorously annotated. In USA300-MRSA 2658 chromosomal open reading frames were predicted and 3.1 and 27 kilobase (kb) plasmids were identified. USA300-MSSA contained a 20 kb plasmid with some homology to the 27 kb plasmid found in USA300-MRSA. Two regions found in US300-MRSA were absent in USA300-MSSA. One of these carried the arginine deiminase operon that appears to have been acquired from <it>S. epidermidis</it>. The USA300 sequence was aligned with other sequenced <it>S. aureus </it>genomes and regions unique to USA300 MRSA were identified.</p> <p>Conclusion</p> <p>USA300-MRSA is highly similar to other MRSA strains based on whole genome alignments and gene content, indicating that the differences in pathogenesis are due to subtle changes rather than to large-scale acquisition of virulence factor genes. The USA300 Houston isolate differs from another sequenced USA300 strain isolate, derived from a patient in San Francisco, in plasmid content and a number of sequence polymorphisms. Such differences will provide new insights into the evolution of pathogens.</p

Brain haemorrhage detection through SVM classification of impedance measurements

Machine Learning is becoming increasingly important in interpreting biological signals. In this work, we examine the potential for classification in brain haemorrhage detection. Numerical head and brain models with and without haemorrhagic lesions are designed. Impedance measurements from an electrode array positioned on the exterior of the head are used to train and test linear support vector machine (SVM) classifiers. The results show that this emerging measurement technique may have promise for detection and diagnosis of brain haemorrhage when coupled with such classifiers.peer-reviewe

Green and golden seaweed tides on the rise

Sudden beaching of huge seaweed masses smother the coastline and form rotting piles on the shore. The number of reports of these events in previously unaffected areas has increased worldwide in recent years. These 'seaweed tides' can harm tourism-based economies, smother aquaculture operations or disrupt traditional artisanal fisheries. Coastal eutrophication is the obvious, ultimate explanation for the increase in seaweed biomass, but the proximate processes that are responsible for individual beaching events are complex and require dedicated study to develop effective mitigation strategies. Harvesting the macroalgae, a valuable raw material, before they beach could well be developed into an effective solution

Brain haemorrhage detection through SVM classification of electrical impedance tomography measurements

A brain haemorrhage constitutes a serious medical scenario with a need for rapid, accurate detection to facilitate treatment initiation. Machine learning (ML) techniques applied to such medical diagnostic problems can improve the rate and accuracy of bleed detection leading to improved patient outcomes. In this chapter we examine the potential role of support vector machine (SVM) type classifiers in detecting such haemorrhagic lesions (bleeds) using electrical impedance tomography (EIT) measurement frames as the source of training and test data. A two-layer computational model of the head is designed, with EIT frame generation simulated from electrodes placed on the surface of the head model. A wide variety of test scenarios are modelled, including variations in measurement noise, bleed size and location, electrode position, and anatomy. Initial results using a linear SVM classifier applied to test scenarios, with and without pre-processing of the EIT measurement frame, are summarised. The classifier returned detection accuracies >90% with signal-to-noise ratios of ≥60 dB; was independent of bleed location, capable of detecting bleeds as small as 10 ml; and was unaffected by slight variances of ±2 mm in electrode position. However, the performance was degraded with anatomical variations. Options for improvement of performance, including selection of a different kernel and pre-processing of the frames prior to implementing the classifier, are then examined. This analysis demonstrated that using the radial basis function as the kernel for the SVM classifier and principal component analysis (PCA) to select specific features leads to the most accurate and robust performance. The analysis and results indicate that the coupling of EIT with ML has potential for improvement in the detection of bleeds such as brain haemorrhages.The research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Programme/ERC Grant Agreement BioElecPro n.637780, Science Foundation Ireland (SFI) grant number 15/ERCS/3276, the Hardiman Research Scholarship from NUIG, the charity RESPECT, the Irish Research Council GOIPD/2017/854 fund, and the People Programme (Marie Curie Action) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728.Peer reviewed2021-08-2