Distribution de l’ARNm des récepteurs opioïdes delta et mu dans les afférences primaires

Les opioïdes, comme la morphine, sont des analgésiques fréquemment utilisés pour traiter les douleurs modérées à sévères. Ceux utilisés en clinique agissent en activant le récepteur opioïde mu (MOP) ce qui entraîne de l’analgésie, mais aussi plusieurs effets indésirables comme de la dépression respiratoire, de la constipation, de la tolérance et de la dépendance. Il existe deux autres récepteurs opioïdes : le delta (DOP) et le kappa (KOP). Le laboratoire s’intéresse au potentiel rôle du DOP dans la nociception, puisque son activation procure de l’analgésie dans plusieurs modèles de douleur. Dans cette étude, nous nous sommes intéressés à la distribution du DOP et du MOP dans les afférences primaires soit les ganglions de la racine dorsale (DRG) et les ganglions trijumeaux (TG) de rats, souris, singes et humains. Ces neurones sont la première étape dans la transmission de stimuli potentiellement douloureux, en transmettant ces signaux de la périphérie à la corne dorsale de la moelle épinière. Pour ce faire, le DOP et le MOP seront identifiés à l’aide de leur ARNm grâce à l’hybridation in situ couplée à la technologie RNAscope. L’identité des neurones sur lesquels sont exprimés les récepteurs sera déterminée à l’aide d’anticorps (NF200, IB4) et de sondes de RNAscope (P2RX3, TAC1) qui ciblent les neurones myélinisés (A, A et A), les neurones C non peptidergiques et peptidergiques. Nous avons découvert que dans toutes les espèces, le DOP et le MOP sont présents dans tous les types de neurones. L’ARNm du DOP est principalement exprimé dans les neurones myélinisés chez le rat (DRG et TG) et les TG de souris, alors qu’il est plus au niveau des fibres C non peptidergiques chez le singe et les DRG de souris. L’ARNm du MOP était plutôt trouvé dans les fibres C peptidergiques chez le singe et le rat, alors que chez la souris, il était dans les fibres C non peptidergiques (DRG et TG) et myélinisées (TG et TG de rat). Pour toutes les espèces et types d’afférences primaires, la coexpression entre le DOP et le MOP était principalement retrouvée dans les fibres myélinisées. De ce fait, nous concluons que même si des différences sont présentes entre les espèces, le DOP et le MOP sont impliqués dans la régulation de la nociception

Observing climate change trends in ocean biogeochemistry: when and where

Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change-driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long-term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long-term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (<~30 years) to detect trends than other areas. In addition, we quantify the ‘footprint’ of existing and planned time series stations, that is the area over which a station is representative of a broader region. Footprints are generally largest for pH and sea surface temperature, but nevertheless the existing network of observatories only represents 9–15% of the global ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change-driven responses in the marine ecosystem

Emerging negative Atlantic Multidecadal Oscillation index in spite of warm subtropics

Sea surface temperatures in the northern North Atlantic have shown a marked decrease over the past several years. The sea surface in the subpolar gyre is now as cold as it was during the last cold phase of the Atlantic Multidecadal Oscillation index in the 1990s. This climate index is associated with shifts in hurricane activity, rainfall patterns and intensity, and changes in fish populations. However, unlike the last cold period in the Atlantic, the spatial pattern of sea surface temperature anomalies in the Atlantic is not uniformly cool, but instead has anomalously cold temperatures in the subpolar gyre, warm temperatures in the subtropics and cool anomalies over the tropics. The tripole pattern of anomalies has increased the subpolar to subtropical meridional gradient in SSTs, which are not represented by the AMO index value, but which may lead to increased atmospheric baroclinicity and storminess. Here we show that the recent Atlantic cooling is likely to persist, as predicted by a statistical forecast of subsurface ocean temperatures and consistent with the irreversible nature of watermass changes involved in the recent cooling of the subpolar gyre

Directed transport in a classical lattice with a high-frequency driving

We analyze the dynamics of a classical particle in a spatially periodic potential under the influence of a periodic in time uniform force. It was shown in [S.Flach, O.Yevtushenko, Y. Zolotaryuk, Phys. Rev. Lett. 84, 2358 (2000)] that despite zero average force, directed transport is possible in the system. Asymptotic description of this phenomenon for the case of slow driving was developed in [X. Leoncini, A. Neishtadt, A. Vasiliev, Phys. Rev. E 79, 026213 (2009)]. Here we consider the case of fast driving using canonical perturbation theory. An asymptotic formula is derived for the average drift velocity as a function of the system parameters and the driving law. We show that directed transport arises in an effective Hamiltonian that does not possess chaotic dynamics, thereby clarifying the relation between chaos and transport in the system. Sufficient conditions for transport are derived.Comment: 5 page

Distinguishing trends and shifts from memory in climate data

The detection of climate change and its attribution to the corresponding underlying processes is challenging because signals such as trends and shifts are superposed on variability arising from the memory within the climate system. Statistical methods used to characterize change in time series must be flexible enough to distinguish these components. Here we propose an approach tailored to distinguish these different modes of change by fitting a series of models and selecting the most suitable one according to an information criterion. The models involve combinations of a constant mean or a trend superposed to a background of white noise with or without autocorrelation to characterize the memory, and are able to detect multiple changepoints in each model configuration. Through a simulation study on synthetic time series, the approach is shown to be effective in distinguishing abrupt changes from trends and memory by identifying the true number and timing of abrupt changes when they are present. Furthermore, the proposed method is better performing than two commonly used approaches for the detection of abrupt changes in climate time series. Using this approach, the so-called hiatus in recent global mean surface warming fails to be detected as a shift in the rate of temperature rise but is instead consistent with steady increase since the 1960s/1970s. Our method also supports the hypothesis that the Pacific decadal oscillation behaves as a short-memory process rather than forced mean shifts as previously suggested. These examples demonstrate the usefulness of the proposed approach for change detection and for avoiding the most pervasive types of mistake in the detection of climate change. © 2018 American Meteorological Society

Regional surface chlorophyll trends and uncertainties in the global ocean

Changes in marine primary productivity are key to determine how climate change might impact marine ecosystems and fisheries. Satellite ocean color sensors provide coverage of global ocean chlorophyll with a combined record length of ~ 20 years. Coupled physical–biogeochemical models can inform on expected changes and are used here to constrain observational trend estimates and their uncertainty. We produce estimates of ocean surface chlorophyll trends, by using Coupled Model Intercomparison Project (CMIP5) models to form priors as a “first guess”, which are then updated using satellite observations in a Bayesian spatio-temporal model. Regional chlorophyll trends are found to be significantly different from zero in 18/23 regions, in the range ± 1.8% year−1. A global average of these regional trends shows a net positive trend of 0.08 ± 0.35% year−1, highlighting the importance of considering chlorophyll changes at a regional level. We compare these results with estimates obtained with the commonly used “vague” prior, representing no independent knowledge; coupled model priors are shown to slightly reduce trend magnitude and uncertainties in most regions. The statistical model used here provides a robust framework for making best use of all available information and can be applied to improve understanding of global change

Changepoint Detection:An Analysis of the Central England Temperature Series

This paper presents a statistical analysis of structural changes in the Central England temperature series, one of the longest surface temperature records available. A changepoint analysis is performed to detect abrupt changes, which can be regarded as a preliminary step before further analysis is conducted to identify the causes of the changes (e.g., artificial, human-induced or natural variability). Regression models with structural breaks, including mean and trend shifts, are fitted to the series and compared via two commonly used multiple changepoint penalized likelihood criteria that balance model fit quality (as measured by likelihood) against parsimony considerations. Our changepoint model fits, with independent and short-memory errors, are also compared with a different class of models termed long-memory models that have been previously used by other authors to describe persistence features in temperature series. In the end, the optimal model is judged to be one containing a changepoint in the late 1980s, with a transition to an intensified warming regime. This timing and warming conclusion is consistent across changepoint models compared in this analysis. The variability of the series is not found to be significantly changing, and shift features are judged to be more plausible than either short- or long-memory autocorrelations. The final proposed model is one including trend-shifts (both intercept and slope parameters) with independent errors. The analysis serves as a walk-through tutorial of different changepoint techniques, illustrating what can be statistically inferred

Good Practices and Common Pitfalls in Climate Time Series Changepoint Techniques:A Review

Climate changepoint (homogenization) methods abound today, with a myriad of techniques existing in both the climate and statistics literature. Unfortunately, the appropriate changepoint technique to use remains unclear to many. Further complicating issues, changepoint conclusions are not robust to small perturbations in assumptions; for example, allowing for a trend or correlation in the series can drastically change conclusions. This paper is a review of the changepoint topic, with an emphasis on illuminating the models and techniques that allow the scientist to make reliable conclusions. Pitfalls to avoid are demonstrated via actual applications. The discourse begins by narrating the salient statistical features of most climate time series. Thereafter, single and multiple changepoint problems are considered. Several pitfalls are discussed en route and good practices are recommended. While the majority of our applications involve temperature series, other settings are mentioned

Rapid emergence of climate change in environmental drivers of marine ecosystems

Climate change is expected to modify ecological responses in the ocean, with the potential for important effects on the ecosystem services provided to humankind. Here we address the question of how rapidly multiple drivers of marine ecosystem change develop in the future ocean. By analysing an ensemble of models we find that, within the next 15 years, the climate change-driven trends in multiple ecosystem drivers emerge from the background of natural variability in 55% of the ocean and propagate rapidly to encompass 86% of the ocean by 2050 under a ‘business-as-usual’ scenario. However, we also demonstrate that the exposure of marine ecosystems to climate change-induced stress can be drastically reduced via climate mitigation measures; with mitigation, the proportion of ocean susceptible to multiple drivers within the next 15 years is reduced to 34%. Mitigation slows the pace at which multiple drivers emerge, allowing an additional 20 years for adaptation in marine ecological and socio-economic systems alike

Ocean colour signature of climate change

Monitoring changes in marine phytoplankton is important as they form the foundation of the marine food web and are crucial in the carbon cycle. Often Chlorophyll-a (Chl-a) is used to track changes in phytoplankton, since there are global, regular satellite-derived estimates. However, satellite sensors do not measure Chl-a directly. Instead, Chl-a is estimated from remote sensing reflectance (RRS): the ratio of upwelling radiance to the downwelling irradiance at the ocean’s surface. Using a model, we show that RRS in the blue-green spectrum is likely to have a stronger and earlier climate-change-driven signal than Chl-a. This is because RRS has lower natural variability and integrates not only changes to in-water Chl-a, but also alterations in other optically important constituents. Phytoplankton community structure, which strongly affects ocean optics, is likely to show one of the clearest and most rapid signatures of changes to the base of the marine ecosystem