Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation during the late phase of synaptic plasticity. We present a mathematical model that describes these different phases of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency. The stabilization of potentiated synapses during the transition from early to late LTP occurs by protein synthesis dynamics that are shared by groups of synapses. The functional consequence of this shared process is that previously stabilized patterns of strong or weak synapses onto the same postsynaptic neuron are well protected against later changes induced by LTP/LTD protocols at individual synapses

Peanut‐induced anaphylaxis in children and adolescents: Data from the European Anaphylaxis Registry

Background Peanut allergy has a rising prevalence in high-income countries, affecting 0.5%-1.4% of children. This study aimed to better understand peanut anaphylaxis in comparison to anaphylaxis to other food triggers in European children and adolescents. Methods Data was sourced from the European Anaphylaxis Registry via an online questionnaire, after in-depth review of food-induced anaphylaxis cases in a tertiary paediatric allergy centre. Results 3514 cases of food anaphylaxis were reported between July 2007 - March 2018, 56% in patients younger than 18 years. Peanut anaphylaxis was recorded in 459 children and adolescents (85% of all peanut anaphylaxis cases). Previous reactions (42% vs. 38%; p = .001), asthma comorbidity (47% vs. 35%; p < .001), relevant cofactors (29% vs. 22%; p = .004) and biphasic reactions (10% vs. 4%; p = .001) were more commonly reported in peanut anaphylaxis. Most cases were labelled as severe anaphylaxis (Ring&Messmer grade III 65% vs. 56% and grade IV 1.1% vs. 0.9%; p = .001). Self-administration of intramuscular adrenaline was low (17% vs. 15%), professional adrenaline administration was higher in non-peanut food anaphylaxis (34% vs. 26%; p = .003). Hospitalization was higher for peanut anaphylaxis (67% vs. 54%; p = .004). Conclusions The European Anaphylaxis Registry data confirmed peanut as one of the major causes of severe, potentially life-threatening allergic reactions in European children, with some characteristic features e.g., presence of asthma comorbidity and increased rate of biphasic reactions. Usage of intramuscular adrenaline as first-line treatment is low and needs to be improved. The Registry, designed as the largest database on anaphylaxis, allows continuous assessment of this condition

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject