Analysing and Applying Stakeholder Perceptions to Improve Protected Area Governance in Ugandan Conservation Landscapes

Given the diversity of active institutions and stakeholders in a landscape, and the difficulties in ensuring inclusive decision-making, evaluating landscape governance can help surface and address underlying issues. In the context of two protected area landscapes in Uganda, where landscape approaches are being implemented through a wider project on landscape governance, we analyse stakeholder perceptions of inclusive decision-making and then use this evaluation to stimulate dialogue amongst stakeholder groups in each landscape. We ask, how can capturing, analysing, and collaboratively applying people’s perceptions address inclusive decision-making in landscape governance? We collected and analysed perceptions using SenseMaker®, a software package that enables analysis of micronarratives (stories) from the field based on how respondents classify their own stories, using triads, dyads, stones, and multiple-choice questions. This self-categorisation by the respondent reduces bias in the analysis and allows the micronarrative to be cross-examined in a variety of ways when analysed using Sensemaker. This analysis created an integrated view of the stakeholder’s perceptions about inclusive decision-making in landscape governance. The results show large portions of the respondents feel their voices are neglected, and management of the landscape is poor in Mount Elgon, while in Agoro-Agu, it is the opposite trend. During a community feedback process, reasons for these trends were discussed and solutions proposed. Some of the underlying factors include historical relationships with park authorities and displacement during park creation. To more precisely answer our research question, one could have extended stays in the communities studied in these landscapes, using ethnographic methods including interviews and participant observation; nonetheless, our method, including the feedback process, was an innovative and important way to confront our findings with the informants directly and foster collaborative action. We conclude that understanding people’s perceptions, including through participatory feedback, can significantly inform and improve management decisions, help resolve conflicts, and facilitate dialogue between different stakeholders in the landscape

Power, Connected Coalitions, and Efficiency: Challenges to the Council of the European Union

This article is concerned with challenges to reforming the voting procedures of the Council of the European Union (EU). The next major waves of EU enlargement will cause the Union to increase to a membership of first twenty-one, and then twenty-six or possibly even more states. How does enlargement affect the Council's inherent "capacity to act" under the currently used qualified majority voting rule? It is demon strated here that the expected increase in EU membership will most likely induce a larger "status quo bias" as compared to the present situation in the Council if the crucial majority decision quota is not lowered. In addition, the article is responding to some criticism that has been applied against assessing the leverage of EU governments in one of the EU's most important institutions: the Council of the EU. By resorting to techniques that capture the influence of a priori coalitions on the one hand and "connected coalitions" among EU governments on the other—applying n- person cooperative game theory—the piece illustrates how the assessment of relative voting leverage in the framework of weighted voting systems may be extended and applied to situations in which the specific distribu tion of members' preferences is known. These calculations are again relevant in the face of the upcoming rounds of EU enlargement and projects for institutional reform.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68064/2/10.1177_019251219902000404.pd

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Interacting humans use forces in specific frequencies to exchange information by touch

Object-mediated joint action is believed to be enabled by implicit information exchange between interacting individuals using subtle haptic signals within their interaction forces. The characteristics of these haptic signals have, however, remained unclear. Here we analyzed the interaction forces during an empirical dyadic interaction task using Granger-Geweke causality analysis, which allowed us to quantify the causal influence of each individual's forces on their partner's. We observed that the inter-partner influence was not the same at every frequency. Specifically, in the frequency band of [2.15-7] Hz, we observed inter-partner differences of causal influence that were invariant of the movement frequencies in the task and present only when information exchange was indispensable for task performance. Moreover, the interpartner difference in this frequency band was observed to be correlated with the task performance by the dyad. Our results suggest that forces in the [2.15-7] Hz band constitute task related information exchange between individuals during physical interactions

Social paediatrics

Social paediatrics is an approach to child health that focuses on the child, in illness and in health, within the context of their society, environment, school, and family. The glossary clarifies the range of terms used to describe aspects of paediatric practice that overlap or are subsumed under social paediatrics and defines key social paediatric concepts. The glossary was compiled by a process of consultation and consensus building among the authors who are all members of the European Society for Social Paediatrics. Social paediatricians from outside Europe were included giving a more international perspective

Plasma Markers of Neutrophil Extracellular Trap Are Linked to Survival but Not to Pulmonary Embolism in COVID-19-Related ARDS Patients

International audiencentroduction: Coronavirus disease 2019 (COVID-19) can cause life-threatening acute respiratory distress syndrome (ARDS). Recent data suggest a role for neutrophil extracellular traps (NETs) in COVID-19-related lung damage partly due to microthrombus formation. Besides, pulmonary embolism (PE) is frequent in severe COVID-19 patients, suggesting that immunothrombosis could also be responsible for increased PE occurrence in these patients. Here, we evaluate whether plasma levels of NET markers measured shorty after admission of hospitalized COVID-19 patients are associated with clinical outcomes in terms of clinical worsening, survival, and PE occurrence.Patients and Methods: Ninety-six hospitalized COVID-19 patients were included, 50 with ARDS (severe disease) and 46 with moderate disease. We collected plasma early after admission and measured 3 NET markers: total DNA, myeloperoxidase (MPO)–DNA complexes, and citrullinated histone H3. Comparisons between survivors and non-survivors and patients developing PE and those not developing PE were assessed by Mann–Whitney test.Results: Analysis in the whole population of hospitalized COVID-19 patients revealed increased circulating biomarkers of NETs in patients who will die from COVID-19 and in patients who will subsequently develop PE. Restriction of our analysis in the most severe patients, i.e., the ones who enter the hospital for COVID-19-related ARDS, confirmed the link between NET biomarker levels and survival but not PE occurrence.Conclusion: Our results strongly reinforce the hypothesis that NETosis is an attractive therapeutic target to prevent COVID-19 progression but that it does not seem to be linked to PE occurrence in patients hospitalized with COVID-19