Examining deterrence and backlash effects in counter-terrorism : the case of ETA

Scholars are increasingly drawing on models and theories from the field of Criminology to offer new insights on terrorist violence. A particularly useful framework by LaFree, Dugan, and Korte works from the assumption that illegal behaviour can be affected by the threat and/or imposition of punishment. It sees the results of the government's intervention in terms of deterrence (state's repressive action leads to a reduction in terrorism violence), and backlash (state's repressive action leads to defiance and retaliation, and to an upsurge of terrorism violence). This article applies this model to a case study of the government's responses to Euzkadi Ta Askatasuna (ETA). It uses a variation of survival analysis technique -Series Hazard- to assess the impact of six major initiatives on the risk of new ETA attacks in the period from 1977 to 2010. Mostly, the results provide support for both backlash interpretations, although important questions regarding interpretation are raised.PostprintPeer reviewe

Distributed Management of Application Layer Multicast Trees for IPTV Services

Este trabajo es una ponencia recogida en el IX Jornadas de Ingeniería Telemática (JITEL 2010), celebradas en la Universidad de Valladolid, los días 29 de septiembre y 1 de octubre de 2010 en Valladolid (España). La web del evento es http://jitel2010.tel.uva.es/IP multicast is an eff cient mechanism to distribute IPTV content towards multiple users, but nowadays Internet Providers f lter out this kind of traff c, mainly due to security and accounting issues. In order to overcome this f ltering, the same idea of IP multicast can be implemented at the end user terminals, but implementing multicast trees at the application layer, also known as Application Layer Multicast (ALM). ALM has some drawbacks, mainly due to dynamic behavior of users, joining and leaving the trees. In order to minimize the impact of this behavior, this paper proposes a distributed management for ALM in IPTV, instead of a centralized one, where some nodes connected to a tree will be on charge of the management of that tree. Furthermore, all nodes are dynamically conf gured with the substitute of its parent node in order to accelerate the reconnection process. Results presented in this article show that this proposal provides fast reconstructions and low management load per node, while keeping a balanced tree topology.Este artículo está financiado parcialmente por el proyecto MEDIANET (S-2009/TIC-1468) de la Comunidad de Madrid y por la Cátedra Telefónica en Internet del Futuro para la Productividad de la Universidad Carlos III de Madrid.Publicad

New Trends in Industrial Equipment for the Improvement of Asphalt Roofing Process

Roofing techniques are the key for weather resistance and energy efficiency of buildings. Installing asphalt roofing rolls is one of the most popular roof protection methods. This is usually carried out manually. Workers apply heat to the rolls by means of burners. Operators must follow quite a few steps to roof: place the rolls on the ground, unroll, apply energy, and secure. From 20 to 25 rolls per day can be installed by an operator using this manual procedure (200–250 m2). So, the fact that manual installation means such a reduced work capacity has pushed the development of industrial equipment. Besides, requirement for reduction of CO2 emissions is forcing to develop systems that optimize the fuel consumptions or even to replace fuel burners by other types of electric heating devices. In this chapter, a review of the state of the art and market of equipment for accelerating asphalt roofing process is presented. A detailed description of some systems and patents is given. The main geometric, physical, and performance parameters will be described and compared. Two new systems based on torches and infrared heating show double installation speed that is the usual manual roofing rate

Use of spatiotemporal analysis of laboratory submission data to identify potential outbreaks of new or emerging diseases in cattle in Great Britain

BACKGROUND: New and emerging diseases of livestock may impact animal welfare, trade and public health. Early detection of outbreaks can reduce the impact of these diseases by triggering control measures that limit the number of cases that occur. The aim of this study was to investigate whether prospective spatiotemporal methods could be used to identify outbreaks of new and emerging diseases in scanning surveillance data. SaTScan was used to identify clusters of unusually high levels of submissions where a diagnosis could not be reached (DNR) using different probability models and baselines. The clusters detected were subjected to a further selection process to reduce the number of false positives and a more detailed epidemiological analysis to ascertain whether they were likely to represent real outbreaks. RESULTS: 187,925 submissions of clinical material from cattle were made to the Regional Laboratory of the Veterinary Laboratories Agency (VLA) between 2002 and 2007, and the results were stored on the VLA FarmFile database. 16,925 of these were classified as DNRs and included in the analyses. Variation in the number and proportion of DNRs was found between syndromes and regions, so a spatiotemporal analysis for each DNR syndrome was done. Six clusters were identified using the Bernoulli model after applying selection criteria (e.g. size of cluster). The further epidemiological analysis revealed that one of the systemic clusters could plausibly have been due to Johne's disease. The remainder were either due to misclassification or not consistent with a single diagnosis. CONCLUSIONS: Our analyses have demonstrated that spatiotemporal methods can be used to detect clusters of new or emerging diseases, identify clusters of known diseases that may not have been diagnosed and identify misclassification in the data, and highlighted the impact of data quality on the ability to detect outbreaks. Spatiotemporal methods should be used alongside current temporal methods for analysis of scanning surveillance data. These statistical analyses should be followed by further investigation of possible outbreaks to determine whether cases have common features suggesting that these are likely to represent real outbreaks, or whether issues with the collection or processing of information have resulted in false positives

Elective Open Suprarenal Aneurysm Repair in England from 2000 to 2010 an Observational Study of Hospital Episode Statistics

Background: Open surgery is widely used as a benchmark for the results of fenestrated endovascular repair of complex abdominal aortic aneurysms (AAA). However, the existing evidence stems from single-centre experiences, and may not be reproducible in wider practice. National outcomes provide valuable information regarding the safety of suprarenal aneurysm repair. Methods: Demographic and clinical data were extracted from English Hospital Episodes Statistics for patients undergoing elective suprarenal aneurysm repair from 1 April 2000 to 31 March 2010. Thirty-day mortality and five-year survival were analysed by logistic regression and Cox proportional hazards modeling. Results: 793 patients underwent surgery with 14% overall 30-day mortality, which did not improve over the study period. Independent predictors of 30-day mortality included age, renal disease and previous myocardial infarction. 5-year survival was independently reduced by age, renal disease, liver disease, chronic pulmonary disease, and known metastatic solid tumour. There was significant regional variation in both 30-day mortality and 5-year survival after risk-adjustment. Regional differences in outcome were eliminated in a sensitivity analysis for perioperative outcome, conducted by restricting analysis to survivors of the first 30 days after surgery. Conclusions: Elective suprarenal aneurysm repair was associated with considerable mortality and significant regional variation across England. These data provide a benchmark to assess the efficacy of complex endovascular repair of supra-renal aneurysms, though cautious interpretation is required due to the lack of information regarding aneurysm morphology. More detailed study is required, ideally through the mandatory submission of data to a national registry of suprarenal aneurysm repair

Disease-specific changes in Reelin protein and mRNA in neurodegenerative diseases

Reelin is an extracellular glycoprotein that modulates neuronal function and synaptic plasticity in the adult brain. Decreased levels of Reelin activity have been postulated as a key factor during neurodegeneration in Alzheimer's disease (AD) and in aging. Thus, changes in levels of full-length Reelin and Reelin fragments have been revealed in cerebrospinal fluid (CSF) and in post-mortem brains samples of AD patients with respect to non-AD patients. However, conflicting studies have reported decreased or unchanged levels of full-length Reelin in AD patients compared to control (nND) cases in post-mortem brains and CSF samples. In addition, a compelling analysis of Reelin levels in neurodegenerative diseases other than AD is missing. In this study, we analyzed brain levels of RELN mRNA and Reelin protein in post-mortem frontal cortex samples from different sporadic AD stages, Parkinson's disease with dementia (PDD), and Creutzfeldt-Jakob disease (sCJD), obtained from five different Biobanks. In addition, we measured Reelin protein levels in CSF samples of patients with mild cognitive impairment (MCI), dementia, or sCJD diagnosis and a group of neurologically healthy cases. The results indicate an increase in RELN mRNA in the frontal cortex of advanced stages of AD and in sCJD(I) compared to controls. This was not observed in PDD and early AD stages. However, Reelin protein levels in frontal cortex samples were unchanged between nND and advanced AD stages and PDD. Nevertheless, they decreased in the CSF of patients with dementia in comparison to those not suffering with dementia and patients with MCI. With respect to sCJD, there was a tendency to increase in brain samples in comparison to nND and to decrease in the CSF with respect to nND. In conclusion, Reelin levels in CSF cannot be considered as a diagnostic biomarker for AD or PDD. However, we feel that the CSF Reelin changes observed between MCI, patients with dementia, and sCJD might be helpful in generating a biomarker signature in prodromal studies of unidentified dementia and sCJD

Effects and mechanisms of mindfulness training and physical exercise on cognition, emotional wellbeing, and brain outcomes in chronic stroke patients : Study protocol of the MindFit project randomized controlled trial

Post-stroke cognitive and emotional complications are frequent in the chronic stages of stroke and have important implications for the functionality and quality of life of those affected and their caregivers. Strategies such as mindfulness meditation, physical exercise (PE), or computerized cognitive training (CCT) may benefit stroke patients by impacting neuroplasticity and brain health. One hundred and forty-one chronic stroke patients are randomly allocated to receive mindfulness-based stress reduction + CCT (n = 47), multicomponent PE program + CCT (n = 47), or CCT alone (n = 47). Interventions consist of 12-week home-based programs five days per week. Before and after the interventions, we collect data from cognitive, psychological, and physical tests, blood and stool samples, and structural and functional brain scans. The effects of the interventions on cognitive and emotional outcomes will be described in intention-to-treat and per-protocol analyses. We will also explore potential mediators and moderators, such as genetic, molecular, brain, demographic, and clinical factors in our per-protocol sample. The MindFit Project is a randomized clinical trial that aims to assess the impact of mindfulness and PE combined with CCT on chronic stroke patients' cognitive and emotional wellbeing. Furthermore, our design takes a multimodal biopsychosocial approach that will generate new knowledge at multiple levels of evidence, from molecular bases to behavioral changes. , identifier NCT04759950

Effects and mechanisms of mindfulness training and physical exercise on cognition, emotional wellbeing, and brain outcomes in chronic stroke patients: Study protocol of the MindFit project randomized controlled trial

Background: Post-stroke cognitive and emotional complications are frequent in the chronic stages of stroke and have important implications for the functionality and quality of life of those affected and their caregivers. Strategies such as mindfulness meditation, physical exercise (PE), or computerized cognitive training (CCT) may benefit stroke patients by impacting neuroplasticity and brain health. Materials and methods: One hundred and forty-one chronic stroke patients are randomly allocated to receive mindfulness-based stress reduction + CCT (n = 47), multicomponent PE program + CCT (n = 47), or CCT alone (n = 47). Interventions consist of 12-week home-based programs five days per week. Before and after the interventions, we collect data from cognitive, psychological, and physical tests, blood and stool samples, and structural and functional brain scans. Results: The effects of the interventions on cognitive and emotional outcomes will be described in intention-to-treat and per-protocol analyses. We will also explore potential mediators and moderators, such as genetic, molecular, brain, demographic, and clinical factors in our per-protocol sample. Discussion: The MindFit Project is a randomized clinical trial that aims to assess the impact of mindfulness and PE combined with CCT on chronic stroke patients' cognitive and emotional wellbeing. Furthermore, our design takes a multimodal biopsychosocial approach that will generate new knowledge at multiple levels of evidence, from molecular bases to behavioral changes. Clinical trial registration: www.ClinicalTrials.gov, identifier NCT04759950

An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data