Olive phenology as a sensitive indicator of future climatic warming in the Mediterranean

Experimental and modelling work suggests a strong dependence of olive flowering date on spring temperatures. Since airborne pollen concentrations reflect the flowering phenology of olive populations within a radius of 50 km, they may be a sensitive regional indicator of climatic warming. We assessed this potential sensitivity with phenology models fitted to flowering dates inferred from maximum airborne pollen data. Of four models tested, a thermal time model gave the best fit for Montpellier, France, and was the most effective at the regional scale, providing reasonable predictions for 10 sites in the western Mediterranean. This model was forced with replicated future temperature simulations for the western Mediterranean from a coupled ocean-atmosphere general circulation model (GCM). The GCM temperatures rose by 4·5 °C between 1990 and 2099 with a 1% per year increase in greenhouse gases, and modelled flowering date advanced at a rate of 6·2 d per °C. The results indicated that this long-term regional trend in phenology might be statistically significant as early as 2030, but with marked spatial variation in magnitude, with the calculated flowering date between the 1990s and 2030s advancing by 3–23 d. Future monitoring of airborne olive pollen may therefore provide an early biological indicator of climatic warming in the Mediterranean

Gaviscon® vs. omeprazole in symptomatic treatment of moderate gastroesophageal reflux. a direct comparative randomised trial

<p>Abstract</p> <p>Background</p> <p>Medical management of GERD mainly uses proton pump inhibitors. Alginates also have proven efficacy. The aim of this trial was to compare short-term efficacy of an alginate (Gaviscon^®, 4 × 10 mL/day) and omeprazole (20 mg/day) on GERD symptoms in general practice.</p> <p>Methods</p> <p>A 14-day multicentre randomised double-blind double-dummy non-inferiority trial compared Gaviscon^®(4 × 10 mL/day) and omeprazole (20 mg/day) in patients with 2-6 day heartburn episodes weekly without alarm signals. The primary outcome was the mean time to onset of the first 24-h heartburn-free period after initial dosing. Secondary outcomes were the proportion of patients without heartburn by D7, pain relief by D7, and reduction in pain intensity by D7 and D14.</p> <p>Results</p> <p>278 patients were recruited; 120 were included in the Gaviscon^®group and 121 in the omeprazole group for the per protocol non-inferiority analysis. The mean time to onset of the first 24-h heartburn-free period after initial dosing was 2.0 (± 2.2) days for Gaviscon^®and 2.0 (± 2.3) days for omeprazole (<it>p </it>= 0.93); mean intergroup difference was 0.01 ± 1.55 days (95% CI = -0.41 to 0.43): i.e., less than the lower limit of the 95% CI of -0.5 days predetermined to demonstrate non-inferiority. The mean number of heartburn-free days by D7 was significantly greater in the omeprazole group: 3.7 ± 2.3 days vs. 3.1 ± 2.1 (<it>p </it>= 0.02). On D7, overall quality of pain relief was slightly in favour of omeprazole (<it>p </it>= 0.049). There was no significant difference in the reduction in pain intensity between groups by D7 (<it>p = </it>0.11) or D14 (<it>p = </it>0.08). Tolerance and safety were good and comparable in both groups.</p> <p>Conclusion</p> <p>Gaviscon^®was non-inferior to omeprazole in achieving a 24-h heartburn-free period in moderate episodic heartburn, and is a relevant effective alternative treatment in moderate GERD in primary care.</p> <p>Trial registration</p> <p><a href="http://www.controlled-trials.com/ISRCTN62203233">ISRCTN62203233</a>.</p

Model-Derived Dispersal Pathways from Multiple Source Populations Explain Variability of Invertebrate Larval Supply

Background: Predicting the spatial and temporal patterns of marine larval dispersal and supply is a challenging task due to the small size of the larvae and the variability of oceanographic processes. Addressing this problem requires the use of novel approaches capable of capturing the inherent variability in the mechanisms involved. Methodology/Principal Findings: In this study we test whether dispersal and connectivity patterns generated from a biophysical model of larval dispersal of the crab Carcinus maenas, along the west coast of the Iberian Peninsula, can predict the highly variable daily pattern of wind-driven larval supply to an estuary observed during the peak reproductive season (March–June) in 2006 and 2007. Cross-correlations between observed and predicted supply were significant (p,0.05) and strong, ranging from 0.34 to 0.81 at time lags of 26 to+5 d. Importantly, the model correctly predicted observed cross-shelf distributions (Pearson r = 0.82, p,0.001, and r = 0.79, p,0.01, in 2006 and 2007) and indicated that all supply events were comprised of larvae that had been retained within the inner shelf; larvae transported to the outer shelf and beyond never recruited. Estimated average dispersal distances ranged from 57 to 198 km and were only marginally affected by mortality. Conclusions/Significance: The high degree of predicted demographic connectivity over relatively large geographic scales is consistent with the lack of genetic structuring in C. maenas along the Iberian Peninsula. These findings indicate that the dynamic nature of larval dispersal can be captured by mechanistic biophysical models, which can be used to provid

Post-acute blood biomarkers and disease progression in traumatic brain injury

There is substantial interest in the potential for traumatic brain injury to result in progressive neurological deterioration. While blood biomarkers such as glial fibrillary acid protein (GFAP) and neurofilament light have been widely explored in characterizing acute traumatic brain injury (TBI), their use in the chronic phase is limited. Given increasing evidence that these proteins may be markers of ongoing neurodegeneration in a range of diseases, we examined their relationship to imaging changes and functional outcome in the months to years following TBI.Two-hundred and three patients were recruited in two separate cohorts; 6 months post-injury (n = 165); and >5 years post-injury (n = 38; 12 of whom also provided data ∼8 months post-TBI). Subjects underwent blood biomarker sampling (n = 199) and MRI (n = 172; including diffusion tensor imaging). Data from patient cohorts were compared to 59 healthy volunteers and 21 non-brain injury trauma controls. Mean diffusivity and fractional anisotropy were calculated in cortical grey matter, deep grey matter and whole brain white matter. Accelerated brain ageing was calculated at a whole brain level as the predicted age difference defined using T1-weighted images, and at a voxel-based level as the annualized Jacobian determinants in white matter and grey matter, referenced to a population of 652 healthy control subjects.Serum neurofilament light concentrations were elevated in the early chronic phase. While GFAP values were within the normal range at ∼8 months, many patients showed a secondary and temporally distinct elevations up to >5 years after injury. Biomarker elevation at 6 months was significantly related to metrics of microstructural injury on diffusion tensor imaging. Biomarker levels at ∼8 months predicted white matter volume loss at >5 years, and annualized brain volume loss between ∼8 months and 5 years. Patients who worsened functionally between ∼8 months and >5 years showed higher than predicted brain age and elevated neurofilament light levels.GFAP and neurofilament light levels can remain elevated months to years after TBI, and show distinct temporal profiles. These elevations correlate closely with microstructural injury in both grey and white matter on contemporaneous quantitative diffusion tensor imaging. Neurofilament light elevations at ∼8 months may predict ongoing white matter and brain volume loss over >5 years of follow-up. If confirmed, these findings suggest that blood biomarker levels at late time points could be used to identify TBI survivors who are at high risk of progressive neurological damage.</p

The anti-vaccination movement and resistance to allergen-immunotherapy: a guide for clinical allergists

Despite over a century of clinical use and a well-documented record of efficacy and safety, a growing minority in society questions the validity of vaccination and fear that this common public health intervention is the root-cause of severe health problems. This article questions whether growing public anti-vaccine sentiments might have the potential to spill-over into other therapies distinct from vaccination, namely allergen-immunotherapy. Allergen-immunotherapy shares certain medical vernacular with vaccination (e.g., allergy shots, allergy vaccines), and thus may become "guilty by association" due to these similarities. Indeed, this article demonstrates that anti-vaccine websites have begun unduly discrediting this allergy treatment regimen. Following an explanation of the anti-vaccine movement, the article aims to provide guidance on how clinicians can respond to patient fears towards allergen-immunotherapy in the clinical setting. This guide focuses on the provision of reliable information to patients in order to dispel misconceived associations between vaccination and allergen-immunotherapy, and the discussion of the risks and benefits of both therapies in order to assist patients in making autonomous decisions about their choice of allergy treatment

Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time