Technical note: A geostatistical fixed-lag Kalman smoother for atmospheric inversions

International audienceInverse modeling methods are now commonly used for estimating surface fluxes of carbon dioxide, using atmospheric mass fraction measurements combined with a numerical atmospheric transport model. The geostatistical approach to flux estimation takes advantage of the spatial and/or temporal correlation in fluxes and does not require prior flux estimates. In this work, a geostatistical implementation of a fixed-lag Kalman smoother is developed to improve the computational efficiency of the inverse problem. This method makes it feasible to perform multi-year inversions, at fine resolutions, and with large amounts of data. The new method is applied to the recovery of global gridscale carbon dioxide fluxes for 1997 to 2001 using pseudodata representative of a subset of the NOAA-ESRL Cooperative Air Sampling Network

Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions

Many inverse problems in the atmospheric sciences involve parameters with known physical constraints. Examples include nonnegativity (e.g., emissions of some urban air pollutants) or upward limits implied by reaction or solubility constants. However, probabilistic inverse modeling approaches based on Gaussian assumptions cannot incorporate such bounds and thus often produce unrealistic results. The atmospheric literature lacks consensus on the best means to overcome this problem, and existing atmospheric studies rely on a limited number of the possible methods with little examination of the relative merits of each. <br><br> This paper investigates the applicability of several approaches to bounded inverse problems. A common method of data transformations is found to unrealistically skew estimates for the examined example application. The method of Lagrange multipliers and two Markov chain Monte Carlo (MCMC) methods yield more realistic and accurate results. In general, the examined MCMC approaches produce the most realistic result but can require substantial computational time. Lagrange multipliers offer an appealing option for large, computationally intensive problems when exact uncertainty bounds are less central to the analysis. A synthetic data inversion of US anthropogenic methane emissions illustrates the strengths and weaknesses of each approach

Spatial and temporal resolution of carbon flux estimates for 1983?2002

International audienceWe discuss the spatial and temporal resolution of monthly carbon flux estimates for the period 1983?2002 using a fixed-lag Kalman Smoother technique with a global chemical transport model, and the GLOBALVIEW data product. The observational network has expanded substantially over this period, and we the improvement in the constraints provided flux estimates by observations for the 1990's in comparison to the 1980's. The estimated uncertainties also decrease as observational coverage expands. In this study, we use the Globalview data product for a network that changes every 5 y, rather than using a small number of continually-operating sites (fewer observational constraints) or a large number of sites, some of which may consist almost entirely of extrapolated data. We show that the discontinuities resulting from network changes reflect uncertainty due to a sparse and variable network. This uncertainty effectively limits the resolution of trends in carbon fluxes. The ability of the inversion to distinguish, or resolve, carbon fluxes at various spatial scales is examined using a diagnostic known as the resolution kernel. We find that the global partition between land and ocean fluxes is well-resolved even for the very sparse network of the 1980's, although prior information makes a significant contribution to the resolution. The ability to distinguish zonal average fluxes has improved significantly since the 1980's, especially for the tropics, where the zonal ocean and land biosphere fluxes can be distinguished. Care must be taken when interpreting zonal average fluxes, however, since the lack of air samples for some regions in a zone may result in a large influence from prior flux estimates for these regions. We show that many of the TransCom 3 source regions are distinguishable throughout the period over which estimates are produced. Examples are Boreal and Temperate North America. The resolution of fluxes from Europe and Australia has greatly improved since the 1990's. Other regions, notably Tropical South America and the Equatorial Atlantic remain practically unresolved. Comparisons of the average seasonal cycle of the estimated carbon fluxes with the seasonal cycle of the prior flux estimates reveals a large adjustment of the summertime uptake of carbon for Boreal Eurasia, and an earlier onset of springtime uptake for Temperate North America. In addition, significantly larger seasonal cycles are obtained for some ocean regions, such as the Northern Ocean, North Pacific, North Atlantic and Western Equatorial Pacific, regions that appear to be well-resolved by the inversion

Using CO 2 spatial variability to quantify representation errors of satellite CO 2 retrievals

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95625/1/grl24793.pd

Evidence for mTOR pathway activation in a spectrum of epilepsy-associated pathologies

Introduction Activation of the mTOR pathway has been linked to the cytopathology and epileptogenicity of malformations, specifically Focal Cortical Dysplasia (FCD) and Tuberous Sclerosis (TSC). Experimental and clinical trials have shown than mTOR inhibitors have anti-epileptogenic effects in TS. Dysmorphic neurones and balloon cells are hallmarks of FCDIIb and TSC, but similar cells are also occasionally observed in other acquired epileptogenic pathologies, including hippocampal sclerosis (HS) and Rasmussen’s encephalitis (RE). Our aim was to explore mTOR pathway activation in a range of epilepsy-associated pathologies and in lesion-negative cases. Results 50 epilepsy surgical pathologies were selected including HS ILAE type 1 with (5) and without dysmorphic neurones (4), FCDIIa (1), FCDIIb (5), FCDIIIa (5), FCDIIIb (3), FCDIIId (3), RE (5) and cortex adjacent to cavernoma (1). We also included pathology-negative epilepsy cases; temporal cortex (7), frontal cortex (2), paired frontal cortical samples with different ictal activity according to intracranial EEG recordings (4), cortex with acute injuries from electrode tracks (5) and additionally non-epilepsy surgical controls (3). Immunohistochemistry for phospho-S6 (pS6) ser240/244 and ser235/236 and double-labelling for Iba1, neurofilament, GFAP, GFAPdelta, doublecortin, and nestin were performed. Predominant neuronal labelling was observed with pS6 ser240/244 and glial labelling with pS6 ser235/236 in all pathology types but with evidence for co-expression in a proportion of cells in all pathologies. Intense labelling of dysmorphic neurones and balloon cells was observed in FCDIIb, but dysmorphic neurones were also labelled in RE and HS. There was no difference in pS6 labelling in paired samples according to ictal activity. Double-labelling immunofluorescent studies further demonstrated the co-localisation of pS6 with nestin, doublecortin, GFAPdelta in populations of small, immature neuroglial cells in a range of epilepsy pathologies. Conclusions Although mTOR activation has been more studied in the FCDIIb and TSC, our observations suggest this pathway is activated in a variety of epilepsy-associated pathologies, and in varied cell types including dysmorphic neurones, microglia and immature cell types. There was no definite evidence from our studies to suggest that pS6 expression is directly related to disease activity

Improving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimation

Addressing a variety of questions within Earth science disciplines entails the inference of the spatiotemporal distribution of parameters of interest based on observations of related quantities. Such estimation problems often represent inverse problems that are formulated as linear optimization problems. Computational limitations arise when the number of observations and/or the size of the discretized state space becomes large, especially if the inverse problem is formulated in a probabilistic framework and therefore aims to assess the uncertainty associated with the estimates. This work proposes two approaches to lower the computational costs and memory requirements for large linear space–time inverse problems, taking the Bayesian approach for estimating carbon dioxide (CO2) emissions and uptake (a.k.a. fluxes) as a prototypical example. The first algorithm can be used to efficiently multiply two matrices, as long as one can be expressed as a Kronecker product of two smaller matrices, a condition that is typical when multiplying a sensitivity matrix by a covariance matrix in the solution of inverse problems. The second algorithm can be used to compute a posteriori uncertainties directly at aggregated spatiotemporal scales, which are the scales of most interest in many inverse problems. Both algorithms have significantly lower memory requirements and computational complexity relative to direct computation of the same quantities (O(n2.5) vs. O(n3)). For an examined benchmark problem, the two algorithms yielded massive savings in floating point operations relative to direct computation of the same quantities. Sample computer codes are provided for assessing the computational and memory efficiency of the proposed algorithms for matrices of different dimensions

The roles of DNA, RNA and histone methylation in ageing and cancer.

Chromatin is a macromolecular complex predominantly comprising DNA, histone proteins and RNA. The methylation of chromatin components is highly conserved as it helps coordinate the regulation of gene expression, DNA repair and DNA replication. Dynamic changes in chromatin methylation are essential for cell-fate determination and development. Consequently, inherited or acquired mutations in the major factors that regulate the methylation of DNA, RNA and/or histones are commonly observed in developmental disorders, ageing and cancer. This has provided the impetus for the clinical development of epigenetic therapies aimed at resetting the methylation imbalance observed in these disorders. In this Review, we discuss the cellular functions of chromatin methylation and focus on how this fundamental biological process is corrupted in cancer. We discuss methylation-based cancer therapies and provide a perspective on the emerging data from early-phase clinical trial therapies that target regulators of DNA and histone methylation. We also highlight promising therapeutic strategies, including monitoring chromatin methylation for diagnostic purposes and combination epigenetic therapy strategies that may improve immune surveillance in cancer and increase the efficacy of conventional and targeted anticancer drugs