Increased Mast Cell Density and Airway Responses to Allergic and Non-Allergic Stimuli in a Sheep Model of Chronic Asthma

BACKGROUND: Increased mast cell (MC) density and changes in their distribution in airway tissues is thought to contribute significantly to the pathophysiology of asthma. However, the time sequence for these changes and how they impact small airway function in asthma is not fully understood. The aim of the current study was to characterise temporal changes in airway MC density and correlate these changes with functional airway responses in sheep chronically challenged with house dust mite (HDM) allergen. METHODOLOGY/PRINCIPAL FINDINGS: MC density was examined on lung tissue from four spatially separate lung segments of allergic sheep which received weekly challenges with HDM allergen for 0, 8, 16 or 24 weeks. Lung tissue was collected from each segment 7 days following the final challenge. The density of tryptase-positive and chymase-positive MCs (MC(T) and MC(TC) respectively) was assessed by morphometric analysis of airway sections immunohistochemically stained with antibodies against MC tryptase and chymase. MC(T) and MC(TC) density was increased in small bronchi following 24 weeks of HDM challenges compared with controls (P<0.05). The MC(TC)/MC(T) ratio was significantly increased in HDM challenged sheep compared to controls (P<0.05). MC(T) and MC(TC) density was inversely correlated with allergen-induced increases in peripheral airway resistance after 24 weeks of allergen exposure (P<0.05). MC(T) density was also negatively correlated with airway responsiveness after 24 challenges (P<0.01). CONCLUSIONS: MC(T) and MC(TC) density in the small airways correlates with better lung function in this sheep model of chronic asthma. Whether this finding indicates that under some conditions mast cells have protective activities in asthma, or that other explanations are to be considered requires further investigation

Snapshot Provisioning of Cloud Application Stacks to Face Traffic Surges

Traffic surges, like the Slashdot effect, occur when a web application is overloaded by a huge number of requests, potentially leading to unavailability. Unfortunately, such traffic variations are generally totally unplanned, of great amplitude, within a very short period, and a variable delay to return to a normal regime. In this report, we introduce PeakForecast as an elastic middleware solution to detect and absorb a traffic surge. In particular, PeakForecast can, from a trace of queries received in the last seconds, minutes or hours, to detect if the underlying system is facing a traffic surge or not, and then estimate the future traffic using a forecast model with an acceptable precision, thereby calculating the number of resources required to absorb the remaining traffic to come. We validate our solution by experimental results demonstrating that it can provide instantaneous elasticity of resources for traffic surges observed on the Japanese version of Wikipedia during the Fukushima Daiichi nuclear disaster in March 2011.Les pics de trafic, tels que l'effet Slashdot, apparaissent lorsqu'une application web doit faire face un nombre important de requêtes qui peut potentiellement entraîner une mise hors service de l'application. Malheureusement, de telles variations de traffic sont en général totalement imprévues et d'une grande amplitude, arrivent pendant une très courte période de temps et le retour à un régime normal prend un délai variable. Dans ce rapport, nous présentons PeakForecast qui est une solution intergicielle élastique pour détecter et absorber les pics de trafic. En particulier, PeakForecast peut, à partir des traces de requêtes reçues dans les dernières secondes, minutes ou heures, détecter si le système sous-jacent fait face ou non à un pic de trafic, estimer le trafic futur en utilisant un modèle de prédiction suffisamment précis, et calculer le nombre de ressources nécessaires à l'absorption du trafic restant à venir. Nous validons notre solution avec des résultats expérimentaux qui démontrent qu'elle fournit une élasticité instantanée des ressources pour des pics de trafic qui ont été observés sur la version japonaise de Wikipedia lors de l'accident nucléaire de Fukushima Daiichi en mars 2011

Risk factors for suicide in Hungary: a case-control study

<p>Abstract</p> <p>Background</p> <p>Hungary previously had one of the highest suicide rates in the world, but experienced major social and economic changes from 1990 onwards. We aimed to investigate the antecedents of suicide in Hungary. We hypothesised that suicide in Hungary would be associated with both risk factors for suicide as identified in Western studies, and experiences related to social and economic restructuring.</p> <p>Methods</p> <p>We carried out a controlled psychological autopsy study. Informants for 194 cases (suicide deaths in Budapest and Pest County 2002–2004) and 194 controls were interviewed by clinicians using a detailed schedule.</p> <p>Results</p> <p>Many of the demographic and clinical risk factors associated with suicide in other settings were also associated with suicide in Hungary; for example, being unmarried or having no current relationship, lack of other social contacts, low educational attainment, history of self-harm, current diagnosis of affective disorder (including bipolar disorder) or personality disorder, and experiencing a recent major adverse life event. A number of variables reflecting experiences since economic restructuring were also associated with suicide; for example, unemployment, concern over work propects, changes in living standards, practising religion. Just 20% of cases with evidence of depression at the time of death had received antidepressants.</p> <p>Conclusion</p> <p>Suicide rates in Hungary are falling. Our study identified a number of risk factors related to individual-level demographic and clinical characteristics, and possibly recent societal change. Improved management of psychiatric disorder and self-harm may result in further reductions in suicide rates.</p

A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease

Late-onset Alzheimer’s disease is a prevalent age-related polygenic disease that accounts for 50–70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer’s disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer’s disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer’s disease to identify further genetic variants that contribute to Alzheimer’s pathology