Nonthermal Emission from Star-Forming Galaxies

The detections of high-energy gamma-ray emission from the nearby starburst galaxies M82 & NGC253, and other local group galaxies, broaden our knowledge of star-driven nonthermal processes and phenomena in non-AGN star-forming galaxies. We review basic aspects of the related processes and their modeling in starburst galaxies. Since these processes involve both energetic electrons and protons accelerated by SN shocks, their respective radiative yields can be used to explore the SN-particle-radiation connection. Specifically, the relation between SN activity, energetic particles, and their radiative yields, is assessed through respective measures of the particle energy density in several star-forming galaxies. The deduced energy densities range from O(0.1) eV/cm^3 in very quiet environments to O(100) eV/cm^3 in regions with very high star-formation rates.Comment: 17 pages, 5 figures, to be published in Astrophysics and Space Science Proceeding

Biology, Methodology or Chance? The Degree Distributions of Bipartite Ecological Networks

The distribution of the number of links per species, or degree distribution, is widely used as a summary of the topology of complex networks. Degree distributions have been studied in a range of ecological networks, including both mutualistic bipartite networks of plants and pollinators or seed dispersers and antagonistic bipartite networks of plants and their consumers. The shape of a degree distribution, for example whether it follows an exponential or power-law form, is typically taken to be indicative of the processes structuring the network. The skewed degree distributions of bipartite mutualistic and antagonistic networks are usually assumed to show that ecological or co-evolutionary processes constrain the relative numbers of specialists and generalists in the network. I show that a simple null model based on the principle of maximum entropy cannot be rejected as a model for the degree distributions in most of the 115 bipartite ecological networks tested here. The model requires knowledge of the number of nodes and links in the network, but needs no other ecological information. The model cannot be rejected for 159 (69%) of the 230 degree distributions of the 115 networks tested. It performed equally well on the plant and animal degree distributions, and cannot be rejected for 81 (70%) of the 115 plant distributions and 78 (68%) of the animal distributions. There are consistent differences between the degree distributions of mutualistic and antagonistic networks, suggesting that different processes are constraining these two classes of networks. Fit to the MaxEnt null model is consistently poor among the largest mutualistic networks. Potential ecological and methodological explanations for deviations from the model suggest that spatial and temporal heterogeneity are important drivers of the structure of these large networks

Integrating ecology and evolutionary theory. A game changer for biodiversity conservation?

Currently, one of the central arguments in favour of biodiversity conservation is that it is essential for the maintenance of ecosystem services, that is, the benefits that people receive from ecosystems. However, the relationship between ecosystem services and biodiversity is contested and needs clarification. The goal of this chapter is to spell out the interaction and reciprocal influences between conservation science, evolutionary biology, and ecology, in order to understand whether a stronger integration of evolutionary and ecological studies might help clarify the interaction between biodiversity and ecosystem functioning as well as influence biodiversity conservation practices. To this end, the eco-evolutionary feedback theory proposed by David Post and Eric Palkovacs is analysed, arguing that it helps operationalise niche construction theory and develop a more sophisticated understanding of the relationship between ecosystem functioning and biodiversity. Finally, it is proposed that by deepening the integration of ecological and evolutionary factors in our understanding of ecosystem functioning, the eco-evolutionary feedback theory is supportive of an “evolutionary-enlightened management” of biodiversity within the ecosystem services approach.info:eu-repo/semantics/publishedVersio

Exploring new physics frontiers through numerical relativity

The demand to obtain answers to highly complex problems within strong-field gravity has been met with significant progress in the numerical solution of Einstein's equations - along with some spectacular results - in various setups. We review techniques for solving Einstein's equations in generic spacetimes, focusing on fully nonlinear evolutions but also on how to benchmark those results with perturbative approaches. The results address problems in high-energy physics, holography, mathematical physics, fundamental physics, astrophysics and cosmology

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Published versio

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multi-national data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar was found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-negligible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Minimal intervention dentistry for managing carious lesions into dentine in primary teeth: an umbrella review

Abstract Purpose This umbrella review systematically appraised published systematic reviews on Minimal Intervention Dentistry interventions carried out to manage dentine carious primary teeth to determine how best to translate the available evidence into practice, and to provide recommendations for what requires further research. Method An experienced information specialist searched MEDLINE, Embase, Cochrane Database of Systematic Reviews, Epistemonikos, Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, and the NIHR Journals Library. In addition, the PROSPERO database was searched to identify forthcoming systematic reviews. Searches were built around the following four concepts: primary teeth AND caries/carious lesion AND Minimal Intervention Dentistry AND systematic review/meta-analysis. Searches were restricted to English language, systematic reviews with/without meta-analyses published between January 2000 and August 2020. Two reviewers independently screened all titles and abstracts. Interventions included involved no dentine carious tissue removal (fissure sealants, resin infiltration, topical application of 38% Silver Diamine Fluoride, and Hall Technique), non-restorative caries control, and selective removal of carious tissue involving both stepwise excavation and atraumatic restorative treatment. Systematic reviews were selected, data extracted, and risk of bias assessed using ROBIS by two independent reviewers. Studies overlap was calculated using corrected covered area. Results Eighteen systematic reviews were included in total; 8 assessed the caries arresting effects of 38% Silver Demine Fluoride (SDF), 1 on the Hall Technique (HT), 1 on selective removal of carious tissue, and eight investigated interventions using atraumatic restorative treatment (ART). Included systematic reviews were published between 2006 and 2020, covering a defined time frame of included randomised controlled trials ranging from 1969 to 2018. Systematic reviews assessed the sealing efficacy of fissure sealants and resin infiltration in carious primary teeth were excluded due to pooled data reporting on caries arrest in both enamel and outer third of dentine with the majority of these carious lesions being limited to enamel. Therefore, fissure sealants and resin infiltration are not recommended for the management of dentinal caries lesions in primary teeth. Topical application of 38% SDF showed a significant caries arrest effect in primary teeth (p &lt; 0.05), and its success rate in arresting dental caries increased when it was applied twice (range between 53 and 91%) rather than once a year (range between 31 and 79%). Data on HT were limited and revealed that preformed metal crowns placed using the HT were likely to reduce discomfort at time of treatment, the risk of major failure (pulp treatment or extraction needed) and pain compared to conventional restorations. Selective removal of carious tissue particularly in deep carious lesions has significantly reduced the risk of pulp exposure (77% and 69% risk reduction with one-step selective caries removal and stepwise excavation, respectively). ART showed higher success rate when placed in single surface compared to multi-surface cavities (86% and 48.7–88%, respectively, over 3 years follow-up). Conclusion Minimal Intervention Dentistry techniques, namely 38% SDF, HT, selective removal of carious tissue, and ART for single surface cavity, appear to be effective in arresting the progress of dentinal caries in primary teeth when compared to no treatment, or conventional restorations. There is clear need to increase the emphasis on considering these techniques for managing carious primary teeth as a mainstream option rather than a compromise option in circumstances where the conventional approach is not possible due to cooperation or cost. </jats:sec