Evolutionary ecomorphology of the Falkland Islands wolf Dusicyon australis

The Falkland Islands wolf Dusicyon australis is an extinct canid that was once the only endemic terrestrial mammal to inhabit the Falkland Islands. There is still a puzzling picture of the morphological adaptations of this wolf that quickly evolved from its mainland fossil ancestor: Dusicyon avus. We employ a geometric morphometric approach to identify patterns of skull shape variation in extant canids and Dusicyon spp. The Falkland Islands wolf and its fossil ancestor show a more carnivorous feeding morphology than other South American foxes, and they cluster morphologically with jackals. This supports convergence in skull shape between Dusicyon and Old World canids, although the convergence is not as strong as that exhibited by their sister hyper- and hypocarnivorous taxa

Tools for the identification of victims of domestic abuse and modern slavery in remote services: A systematic review

Objective: To explore the technology-based tools available for supporting the identification of victims of domestic abuse and modern slavery in remote services and consider the benefits and challenges posed by the existing tools. Methods: We searched six academic databases. Studies were considered for inclusion if they were published in English between 2000 and 2023. The QuADS quality appraisal tool was used to assess the methodological quality of included studies. A narrative synthesis was conducted using the convergent integrated approach. Results: Twenty-four studies were included, of which two were professional guidelines; each reported on a distinct technology-based tool for remote services. All tools related to domestic abuse and 21 focused on screening for intimate partner violence among young and mid-life women (18–65) in high-income countries. The review did not identify tools that support the identification of victims of modern slavery. We identified eight common themes of tool strengths, highlighting that the remote approach to screening was practical, acceptable to victims, and, in some circumstances, elicited better outcomes than face-to-face approaches. Five themes pointed to tool challenges, such as concerns around privacy and safety, and the inability of computerised tools to provide empathy and emotional support. Conclusions: Available technology-based tools may support the identification of victims of domestic abuse by health and social care practitioners in remote services. However, it is important to be mindful of the limitations of such tools and the effects individuals’ screening preferences can have on outcomes. Future research should focus on developing tools to support the identification of victims of modern slavery, as well as empirically validating tools for screening during remote consultations

Coalitions of things: supporting ISR tasks via Internet of Things approaches

In the wake of rapid maturing of Internet of Things (IoT) approaches and technologies in the commercial sector, the IoT is increasingly seen as a key ‘disruptive’ technology in military environments. Future operational environments are expected to be characterized by a lower proportion of human participants and a higher proportion of autonomous and semi-autonomous devices. This view is reflected in both US ‘third offset’ and UK ‘information age’ thinking and is likely to have a profound effect on how multinational coalition operations are conducted in the future. Much of the initial consideration of IoT adoption in the military domain has rightly focused on security concerns, reflecting similar cautions in the early era of electronic commerce. As IoT approaches mature, this initial technical focus is likely to shift to considerations of interactivity and policy. In this paper, rather than considering the broader range of IoT applications in the military context, we focus on roles for IoT concepts and devices in future intelligence, surveillance and reconnaissance (ISR) tasks, drawing on experience in sensor-mission resourcing and human-computer collaboration (HCC) for ISR. We highlight the importance of low training overheads in the adoption of IoT approaches, and the need to balance proactivity and interactivity (push vs pull modes). As with sensing systems over the last decade, we emphasize that, to be valuable in ISR tasks, IoT devices will need a degree of mission-awareness in addition to an ability to self-manage their limited resources (power, memory, bandwidth, computation, etc). In coalition operations, the management and potential sharing of IoT devices and systems among partners (e.g., in cross-coalition tactical-edge ISR teams) becomes a key issue due heterogeneous factors such as language, policy, procedure and doctrine. Finally, we briefly outline a platform that we have developed in order to experiment with human-IoT teaming on ISR tasks, in both physical and virtual settings

A geometric network model of intrinsic grey-matter connectivity of the human brain

Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuro- science is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct ‘shortcuts’ through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections

Quantitative proteomics of a B12 -dependent alga grown in coculture with bacteria reveals metabolic tradeoffs required for mutualism

The unicellular green alga Lobomonas rostrata requires an external supply of vitamin B12 (cobalamin) for growth, which it can obtain in stable laboratory cultures from the soil bacterium Mesorhizobium loti in exchange for photosynthate. We investigated changes in protein expression in the alga that allow it to engage in this mutualism. We used quantitative isobaric tagging (iTRAQ) proteomics to determine the L. rostrata proteome grown axenically with B12 supplementation or in coculture with M. loti. Data are available via ProteomeXchange (PXD005046). Using the related Chlamydomonas reinhardtii as a reference genome, 588 algal proteins could be identified. Enzymes of amino acid biosynthesis were higher in coculture than in axenic culture, and this was reflected in increased amounts of total cellular protein and several free amino acids. A number of heat shock proteins were also elevated. Conversely, photosynthetic proteins and those of chloroplast protein synthesis were significantly lower in L. rostrata cells in coculture. These observations were confirmed by measurement of electron transfer rates in cells grown under the two conditions. The results indicate that, despite the stability of the mutualism, L. rostrata experiences stress in coculture with M. loti, and must adjust its metabolism accordingly.The work in this manuscript was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK (grant BB/I013164/1, a CASE studentship for M.B.C. joint with PML Applications Ltd, Plymouth, UK, and a DTP studentship for F.B.); an EU FP7 Marie Curie ITN Photo.Comm, no. 317184 for U.J.K.; Engineering and Physical Science Research Council (EPSRC) grant EP/E036252/1; and the Natural Environment Research Council (NERC) grant NE/J024767/1

Uncertainty-Aware situational understanding

Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. probabilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore consider the case of subjective (uncertain) Bayesian networks. In previous work we notice that when observations are out of the ordinary, confidence decreases because the relevant training data, effective instantiations, to determine the probabilities for unobserved variables, on the basis of the observed variables, is significantly smaller than the size of the training data, the total number of instantiations. It is therefore of primary importance for the ultimate goal of situational understanding to be able to efficiently determine the reasoning paths that lead to low confidence whenever and wherever it occurs: This can guide specific data collection exercises to reduce such an uncertainty. We propose three methods to this end, and we evaluate them on the basis of a case-study developed in collaboration with professional intelligence analysts