Synchronisation of egg hatching of brown hairstreak (Thecla betulae) and budburst of blackthorn (Prunus spinosa) in a warmer future

Synchronisation of the phenology of insect herbivores and their larval food plant is essential for the herbivores’ fitness. The monophagous brown hairstreak (Thecla betulae) lays its eggs during summer, hibernates as an egg, and hatches in April or May in the Netherlands. Its main larval food plant blackthorn (Prunus spinosa) flowers in early spring, just before the leaves appear. As soon as the Blackthorn opens its buds, and this varies with spring temperatures, food becomes available for the brown hairstreak. However, the suitability of the leaves as food for the young caterpillars is expected to decrease rapidly. Therefore, the timing of egg hatch is an important factor for larval growth. This study evaluates food availability for brown hairstreak at different temperatures. Egg hatch and budburst were monitored from 2004 to 2008 at different sites in the Netherlands. Results showed ample food availability at all monitored temperatures and sites but the degree of synchrony varied strongly with spring temperatures. To further study the effect of temperature on synchronisation, an experiment using normal temperatures of a reference year (T) and temperatures of T + 5°C was carried out in climate chambers. At T + 5°C, both budburst and egg hatch took place about 20 days earlier and thus, on average, elevated temperature did not affect synchrony. However, the total period of budburst was 11 days longer, whereas the period of egg hatching was 3 days shorter. The implications for larval growth by the brown hairstreak under a warmer climate are considered.

Confidence-based Optimization for the Newsvendor Problem

We introduce a novel strategy to address the issue of demand estimation in single-item single-period stochastic inventory optimisation problems. Our strategy analytically combines confidence interval analysis and inventory optimisation. We assume that the decision maker is given a set of past demand samples and we employ confidence interval analysis in order to identify a range of candidate order quantities that, with prescribed confidence probability, includes the real optimal order quantity for the underlying stochastic demand process with unknown stationary parameter(s). In addition, for each candidate order quantity that is identified, our approach can produce an upper and a lower bound for the associated cost. We apply our novel approach to three demand distribution in the exponential family: binomial, Poisson, and exponential. For two of these distributions we also discuss the extension to the case of unobserved lost sales. Numerical examples are presented in which we show how our approach complements existing frequentist - e.g. based on maximum likelihood estimators - or Bayesian strategies.Comment: Working draf

Incremental value of cardiovascular magnetic resonance over echocardiography in the detection of acute and chronic myocardial infarction

BACKGROUND: Although echocardiography is used as a first line imaging modality, its accuracy to detect acute and chronic myocardial infarction (MI) in relation to infarct characteristics as assessed with late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) is not well described. METHODS: One-hundred-forty-one echocardiograms performed in 88 first acute ST-elevation MI (STEMI) patients, 2 (IQR1-4) days (n = 61) and 102 (IQR92-112) days post-MI (n = 80), were pooled with echocardiograms of 36 healthy controls. 61 acute and 80 chronic echocardiograms were available for analysis (53 patients had both acute and chronic echocardiograms). Two experienced echocardiographers, blinded to clinical and CMR data, randomly evaluated all 177 echocardiograms for segmental wall motion abnormalities (SWMA). This was compared with LGE-CMR determined infarct characteristics, performed 104 ± 11 days post-MI. Enhancement on LGE-CMR matched the infarct-related artery territory in all patients (LAD 31%, LCx 12% and RCA 57%). RESULTS: The sensitivity of echocardiography to detect acute MI was 78.7% and 61.3% for chronic MI; specificity was 80.6%. Undetected MI were smaller, less transmural, and less extensive (6% [IQR3-12] vs. 15% [IQR9-24], 50 ± 14% vs. 61 ± 15%, 7 ± 3 vs. 9 ± 3 segments, p < 0.001 for all) and associated with higher left ventricular ejection fraction (LVEF) and non-anterior location as compared to detected MI (58 ± 5% vs. 46 ± 7%, p < 0.001 and 82% vs. 63%, p = 0.03). After multivariate analysis, LVEF and infarct size were the strongest independent predictors of detecting chronic MI (OR 0.78 [95%CI 0.68-0.88], p < 0.001 and OR 1.22 [95%CI0.99-1.51], p = 0.06, respectively). Increasing infarct transmurality was associated with increasing SWMA (p < 0.001). CONCLUSIONS: In patients presenting with STEMI, and thus a high likelihood of SWMA, the sensitivity of echocardiography to detect SWMA was higher in the acute than the chronic phase. Undetected MI were smaller, less extensive and less transmural, and associated with non-anterior localization and higher LVEF. Further work is needed to assess the diagnostic accuracy in patients with non-STEMI