Frameworks for peace in Northern Ireland: Analysis of the 1998 Belfast Agreement

The 1998 Belfast Agreement brought to an end over three decades of armed conflict in Northern Ireland. This paper summarizes the role of actors within and outside Northern Ireland, and the processes and mechanics of the Agreement itself. The Agreement is placed in the context of previous unsuccessful peace initiatives in the region, and elements within the political and economic environment at the time that facilitated agreement are identified. The consociational nature of the Agreement is set alongside concern about continuing sectarian division. It is argued that the Agreement was as much a product of previous failed attempts and the changed economic and political environment as it was a product of the negotiations. The Belfast Agreement is evaluated and tentative lessons for the Arab–Israeli and other peace processes are delineated

Hierarchies of Pain and Responsibility: Victims and War by Other Means in Northern Ireland

This article develops an earlier analysis of definitions and disqualifications of victimhood during armed conflict, claims of responsibility and apologies for harm, based on the Northern Ireland case. The significance of political structures is considered by considering the consociational nature of the 1998 Belfast/Good Friday Agreement, which established two parallel political dynasties, allowing the parties to the Northern Ireland conflict to ‘agree to disagree’. The nature of this agreement makes a ‘reconciliation’ between the parties optional and therefore unlikely without some intervention to address the grievances of the past, proposals for which were the responsibility of the Committee on Managing the Past whose report caused controversy

Financial considerations in the conduct of multi-centre randomised controlled trials: evidence from a qualitative study.

National Coordinating Centre for Research Methodology; Medical Research Council, UK Department of Health; Chief Scientist OfficeNot peer reviewedPublisher PD

Identifying potential terrorists: visuality, security and the channel project

This article analyses how British counter-radicalization policy in general, and the Channel project in particular, constitute individuals who are vulnerable to radicalization as visible, producing them as subjects of intervention. It thus asks, how can potential terrorists be identified and made knowable? The article first argues that to understand Channel, it is crucial to develop a conceptual account of the security politics of (in)visibilization that draws attention to the ways in which security regimes can, at times, function primarily through the production of regimes of (in)visibility. Using this approach, the article focusses on the role of ‘indicators’ as a technology of (in)visibilization. This role is central to the functioning of Channel, visibilizing certain subjects as threatening. Yet such a production is political. In bringing together a politics of care and a politics of identity, it is a regime of (in)visibility that produces new sites of intervention, contains significant potential consequences for the expression of certain identities, and raises new and troubling possibilities for how contemporary life may be secured

Interpretation, judgement and dialogue: a hermeneutical recollection of causal analysis in critical terrorism studies

This article problematises Critical Terrorism Studies’s (CTS) seem- ing reluctance to engage in causal explanation. An analysis of the meta-theoretical assumptions on causation in both orthodox and critical terrorism studies reveals that the latter’s refusal to incor- porate causal analysis in its broader research agenda reproduces – despite its commitment to epistemological pluralism – the for- mer’s understanding of causation as the only sustainable one. Elemental to this understanding is the idea that causation refers to the regular observation of constant conjunction. Due to the positivist leanings of such a conception, CTS is quick to dismiss it as consolidating Orthodox Terrorism Studies’s lack of critical self- reflexivity, responsibility of the researcher, and dedication towards informing state-led policies of counterterrorism. Drawing on recent work in the philosophy of science and International Relations, this article advances an alternative understanding of causation that emphasises its interpretative, normative and dialo- gical fabric. It is therefore argued that CTS should reclaim causal analysis as an essential element of its research agenda. This not only facilitates a more robust challenge against Orthodox Terrorism Studies’ conventional understanding of causation but also consolidates CTS’s endeavour of deepening and broadening our understanding that (re)embeds terrorist violence in its histor- ical and social context

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.</p

sPlot - a new tool for global vegetation analyses

Aims: Vegetation-plot records provide information on the presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community-weighted means of key traits. Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707