Fe/GeTe(111) heterostructures as an avenue towards 'ferroelectric Rashba semiconductors'-based spintronics

By performing density functional theory (DFT) and Green's functions calculations, complemented by X-ray Photoemission Spectroscopy, we investigate the electronic structure of Fe/GeTe(111), a prototypical ferromagnetic/Rashba-ferroelectric interface. We reveal that such system exhibits several intriguing properties resulting from the complex interplay of exchange interaction, electric polarization and spin-orbit coupling. Despite a rather strong interfacial hybridization between Fe and GeTe bands, resulting in a complete suppression of the surface states of the latter, the bulk Rashba bands are hardly altered by the ferromagnetic overlayer. This could have a deep impact on spin dependent phenomena observed at this interface, such as spin-to-charge interconversion, which are likely to involve bulk rather than surface Rashba states.Comment: 8 pages, 4 figure

Thinking beyond organism energy use: A trait-based bioenergetic mechanistic approach for predictions of life history traits in marine organisms

The functional trait-based bioenergetic approach is emergent in many ecological spectra, from the conservation of natural resources to mitigation and adaptation strategies in a global climate change context. Such an approach relies on being able to exploit mechanistic rules to connect environmental human-induced variability to functional traits (i.e. all those specific traits defining species in terms of their ecological roles) and use these to provide estimates of species life history traits (LH; e.g. body size, fecundity per life span, number of reproductive events). LHs are species-specific and proximate determinants of population characteristics in a certain habitat. They represent the most valuable quantitative information to investigate how broad potential distributional boundaries of a species are, and to feed predictive population models. There is much to be found in the current literature that describes mechanistic functional trait-based bioenergetics models, using them to test ecological hypotheses, but a mathematical framework often renders interpretation and use complicated. Here, we wanted to present a simpler interpretation and description of one of the most important recent mechanistic bioenergetic theories: the dynamic energy budget theory by Kooijman (Dynamic Energy Budget Theory for Metabolic Organisation, 2010, Cambridge University Press, Cambridge). Our main aim was to disentangle those aspects that at first reading may seem too mathematically challenging to many marine biologists, ecologists and environmental scientists, and present them for use in mechanistic applications

Deeply Virtual Compton Scattering Off Helium Nuclei with Positron Beams

Positron initiated deeply virtual Compton scattering (DVCS) off 4He and 3He nuclei is described. The way the so-called d-term could be obtained from the real part of the relevant Compton form factor is summarized, and the importance and novelty of this measurement is discussed. The measurements addressed for 3He targets could be very useful even in a standard unpolarized target setup, measuring beam spin and beam charge asymmetries only. The unpolarized beam charge asymmetries for DVCS off 3He and 4He are also estimated, at JLab kinematics and, for 4He, also at a configuration typical at the future Electron-Ion Collider. Incoherent DVCS processes, in particular the ones with tagging the internal target by measuring slow recoiling nuclei, and the unique possibility offered by positron beams for the investigation of Compton form factors of higher twist, are also briefly addressed

Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs

Clustering (or community detection) on multilayer graphs poses several additional complications with respect to standard graphs as different layers may be characterized by different structures and types of information. One of the major challenges is to establish the extent to which each layer contributes to the cluster assignment in order to effectively take advantage of the multilayer structure and improve upon the classification obtained using the individual layers or their union. However, making an informed a-priori assessment about the clustering information content of the layers can be very complicated. In this work, we assume a semi-supervised learning setting, where the class of a small percentage of nodes is initially provided, and we propose a parameter-free Laplacian-regularized model that learns an optimal nonlinear combination of the different layers from the available input labels. The learning algorithm is based on a Frank-Wolfe optimization scheme with inexact gradient, combined with a modified Label Propagation iteration. We provide a detailed convergence analysis of the algorithm and extensive experiments on synthetic and real-world datasets, showing that the proposed method compares favourably with a variety of baselines and outperforms each individual layer when used in isolation

Louvain-like Methods for Community Detection in Multi-Layer Networks

In many complex systems, entities interact with each other through complicated patterns that embed different relationships, thus generating networks with multiple levels and/or multiple types of edges. When trying to improve our understanding of those complex networks, it is of paramount importance to explicitly take the multiple layers of connectivity into account in the analysis. In this paper, we focus on detecting community structures in multi-layer networks, i.e., detecting groups of well-connected nodes shared among the layers, a very popular task that poses a lot of interesting questions and challenges. Most of the available algorithms in this context either reduce multi-layer networks to a single-layer network or try to extend algorithms for single-layer networks by using consensus clustering. Those approaches have anyway been criticized lately. They indeed ignore the connections among the different layers, hence giving low accuracy. To overcome these issues, we propose new community detection methods based on tailored Louvain-like strategies that simultaneously handle the multiple layers. We consider the informative case, where all layers show a community structure, and the noisy case, where some layers only add noise to the system. We report experiments on both artificial and real-world networks showing the effectiveness of the proposed strategies.Comment: 16 pages, 4 figure

Role of infection and inflammation in a mouse model of preterm labour

Increasing evidence highlights that term labour is an inflammatory event associated with increased production of pro-­‐inflammatory mediators and leukocyte influx into the intrauterine tissues. Preterm labour (PTL), defined as labour before 37 weeks gestation, is a major clinical problem, and preterm birth is the leading cause of neonatal mortality and morbidity worldwide. The causes of PTL are poorly understood, but intrauterine infection and inflammation have been shown to be important factors. Therefore, there is growing interest in the hypothesis that preterm labour may occur as a result of the premature activation of the inflammatory pathways normally initiated with labour at term, either idiopathically, or in response to a pathological intrauterine infection. The aim of this thesis was to use a mouse model of infection-­induced PTL to: characterise the local inflammatory and immune response to an intrauterine infection; investigate the potential of anti‐inflammatory agents to delay delivery of pups and to improve their survival; and to investigate the role of specific immune cell populations in infection-­induced preterm labour. To characterise the inflammatory and immune response to intrauterine infection, CD1 mice received an intrauterine injection of PBS vehicle or increasing doses of bacterial-­derived lipopolysaccharide (LPS) on day 17 of gestation. Time to delivery, and the number of live born pups were determined. Intrauterine administration of increasing doses of LPS dose-­dependently induced preterm labour and reduced the proportion of live born pups. Analysis of tissues harvested six hours post-­surgery demonstrated that in response to intrauterine LPS administration, there was increased expression of inflammatory cytokines and chemokines within the utero-­placental tissues, amniotic fluid and maternal serum; and an influx of neutrophils into the decidua, compared to mice receiving PBS. Given these results, the potential of anti‐inflammatory agents to delay LPS-­induced preterm delivery and improve pup survival was then investigated using the same mouse model. Prior to intrauterine LPS administration, mice were pre-­‐treated with epi-­lipoxin, BML-­111 (a stable lipoxin analogue), or IL-­10. Time to delivery was unaffected by pre-­treatment with the anti-­inflammatory agents, however epi-­lipoxin significantly increased the proportion of live born pups in mice delivering preterm, compared to mice receiving only LPS. To further investigate the role of immune cells in infection-­induced PTL, antibody-­based depletion strategies were used to selectively deplete specific immune cell populations to determine whether they played a causative role in LPS­‐induced preterm delivery. Despite successful depletion of macrophages or neutrophils, it was not found to significantly affect LPS-­induced preterm delivery, suggesting these immune cells are not required for the induction of preterm labour in response to intrauterine infection. However, it is likely that they contribute to the intrauterine inflammatory response as depletion resulted in altered inflammatory signalling within the intrauterine tissues. Collectively, this work has demonstrated that the presence of intrauterine bacterial LPS, as a surrogate model of infection, induces a robust inflammatory and immune response within the utero‐placental tissues that involves the increased production of inflammatory mediators and the influx of immune cells into the decidua, which ultimately leads to PTL. Whilst the anti-­inflammatory treatments tested here did not delay LPS-­induced PTL, epi-­lipoxin attenuated LPS-­induced mortality in pups born preterm, suggesting this anti‐inflammatory agent may be useful in protecting the fetus from the adverse effects of infection-­induced preterm birth. Using models such as the one described here, are vital to improving our understanding of the events regulating the induction of PTL and will ultimately aid the search for novel therapeutic options for the treatment of PTL

Predicting biological invasions in marine habitats through eco-physiological mechanistic models: a case study with the bivalve Brachidontes pharaonis

Aim We used a coupled biophysical ecology (BE)-physiological mechanistic modelling approach based on the Dynamic Energy Budget theory (DEB, Dynamic energy budget theory for metabolic organisation, 2010, Cambridge University Press, Cambridge; DEB) to generate spatially explicit predictions of physiological performance (maximal size and reproductive output) for the invasive mussel, Brachidontes pharaonis. Location We examined 26 sites throughout the central Mediterranean Sea. Methods We ran models under subtidal and intertidal conditions; hourly weather and water temperature data were obtained from the Italian Buoy Network, and monthly CHL-a data were obtained from satellite imagery. Results Mechanistic analysis of the B. pharaonis fundamental niche shows that subtidal sites in the Central Mediterranean are generally suitable for this invasive bivalve but that intertidal habitats appear to serve as genetic sinks. Main conclusions A BE-DEB approach enabled an assessment of how the physical environment affects the potential distribution of B. pharaonis. Combined with models of larval dispersal, this approach can provide estimates of the likelihood that an invasive species will become established

Eco-physiological response of two marine bivalves to acute exposition to commercial Bt-based pesticide

Microbial products based on the entomopathogenic bacterium Bacillus thuringiensis (Bt) are among the most common biopesticides used worldwide to suppress insect pests in forests, horticulture and agricultural crops. Some of the effects of commercial Bt have been recorded for terrestrial and freshwater non-target organisms but little research is available on marine fauna. Nevertheless, due to the contiguity of agro-ecosystems and coastal habitats, marine fauna may be highly influenced by this control method. We studied the effect of a commercial Bt product on the physiological and ecological responses and the energy budget of two of the most frequent marine intertidal bivalves in the Mediterranean, the native Mytilaster minimus and the invasive Brachidontes pharaonis. To test the effects experimentally, we simulated the worst scenarios possible using the average dose applied to fields and a hypothetical accumulation dose. The results showed the feeding rates of both species were affected detrimentally by the different experimental conditions; higher concentrations led to higher respiration rates, however neither species showed any significant difference in excretion rates. The biopesticide had a significant effect on the energy budget, the values decreasing with doses. In addition, it led to high mortality for the worst treatments and, in both species, induced significantly higher cardiac activity than in the controls. These results indicate a measurable effect of Bt commercial products on marine organisms, and great attention should be paid to biopesticides composed by entomopathogenic bacteria and addictive compounds. In addition, the results highlight the urgent need to study not only the effects of anthropogenic pressures on target organisms but also to extend our view to other ecosystems not expected to be influenced. Gaining data at the organismal level should help increase the sustainability of pest control and reduce the consequences of side-effects