Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland

The coarse spacing of automatic rain gauges complicates near-real- time spatial analyses of precipitation. We test the possibility of improving such analyses by considering, in addition to the in situ measurements, the spatial covariance structure inferred from past observations with a denser network. To this end, a statistical reconstruction technique, reduced space optimal interpolation (RSOI), is applied over Switzerland, a region of complex topography. RSOI consists of two main parts. First, principal component analysis (PCA) is applied to obtain a reduced space representation of gridded high- resolution precipitation fields available for a multiyear calibration period in the past. Second, sparse real-time rain gauge observations are used to estimate the principal component scores and to reconstruct the precipitation field. In this way, climatological information at higher resolution than the near-real-time measurements is incorporated into the spatial analysis. PCA is found to efficiently reduce the dimensionality of the calibration fields, and RSOI is successful despite the difficulties associated with the statistical distribution of daily precipitation (skewness, dry days). Examples and a systematic evaluation show substantial added value over a simple interpolation technique that uses near-real-time observations only. The benefit is particularly strong for larger- scale precipitation and prominent topographic effects. Small-scale precipitation features are reconstructed at a skill comparable to that of the simple technique. Stratifying the reconstruction method by the types of weather type classifications yields little added skill. Apart from application in near real time, RSOI may also be valuable for enhancing instrumental precipitation analyses for the historic past when direct observations were sparse.

A search for invariant relative satellite motion

This report presents the canonical Hamiltonian formulation of relative satellite motion. The unperturbed Hamiltonian model is shown to be equivalent to the well known Hill-Clohessy-Wilshire (HCW) linear formulation. The in°uence of perturbations of the nonlinear Gravitational potential and the oblateness of the Earth; J2 perturbations are also modelled within the Hamiltonian formulation. The modelling incorporates eccentricity of the reference orbit. The corresponding Hamiltonian vector ¯elds are computed and implemented in Simulink. A numerical method is presented aimed at locating periodic or quasi-periodic relative satellite motion. The numerical method outlined in this paper is applied to the Hamiltonian system. Although the orbits considered here are weakly unstable at best, in the case of eccentricity only, the method ¯nds exact periodic orbits. When other perturbations such as nonlinear gravitational terms are added, drift is signicantly reduced and in the case of the J2 perturbation with and without the nonlinear gravitational potential term, bounded quasi-periodic solutions are found. Advantages of using Newton's method to search for periodic or quasi-periodic relative satellite motion include simplicity of implementation, repeatability of solutions due to its non-random nature, and fast convergence. Given that the use of bounded or drifting trajectories as control references carries practical di±culties over long-term missions, Principal Component Analysis (PCA) is applied to the quasi-periodic or slowly drifting trajectories to help provide a closed reference trajectory for the implementation of closed loop control. In order to evaluate the e®ect of the quality of the model used to generate the periodic reference trajectory, a study involving closed loop control of a simulated master/follower formation was performed. 2 The results of the closed loop control study indicate that the quality of the model employed for generating the reference trajectory used for control purposes has an important in°uence on the resulting amount of fuel required to track the reference trajectory. The model used to generate LQR controller gains also has an e®ect on the e±ciency of the controller.

Gut microbial catabolism of grape seed flavan-3-ols by human faecal microbiota. Targetted analysis of precursor compounds, intermediate metabolites and end-products.

In vitro batch culture fermentations were conducted with grape seed polyphenols and human faecal microbiota, in order to monitor both changes in precursor flavan-3-ols and the formation of microbial-derived metabolites. By the application of UPLC-DAD-ESI-TQ MS, monomers, and dimeric and trimeric procyanidins were shown to be degraded during the first 10 h of fermentation, with notable inter-individual differences being observed between fermentations. This period (10 h) also coincided with the maximum formation of intermediate metabolites, such as 5-(3′,4′-dihydroxyphenyl)-γ-valerolactone and 4-hydroxy-5-(3′,4′-dihydroxyphenyl)-valeric acid, and of several phenolic acids, including 3-(3,4-dihydroxyphenyl)-propionic acid, 3,4-dihydroxyphenylacetic acid, 4-hydroxymandelic acid, and gallic acid (5–10 h maximum formation). Later phases of the incubations (10–48 h) were characterised by the appearance of mono- and non-hydroxylated forms of previous metabolites by dehydroxylation reactions. Of particular interest was the detection of γ-valerolactone, which was seen for the first time as a metabolite from the microbial catabolism of flavan-3-ols. Changes registered during fermentation were finally summarised by a principal component analysis (PCA). Results revealed that 5-(3′,4′-dihydroxyphenyl)-γ-valerolactone was a key metabolite in explaining inter-individual differences and delineating the rate and extent of the microbial catabolism of flavan-3-ols, which could finally affect absorption and bioactivity of these compounds.

Extreme wind storms over Europe in present and future climate: a cluster analysis approach

Boreal winter wind storm situations over Central Europe are investigated by means of an objective cluster analysis. Surface data from the NCEP-Reanalysis and ECHAM4/OPYC3-climate change GHG simulation (IS92a) are considered. To achieve an optimum separation of clusters of extreme storm conditions, 55 clusters of weather patterns are differentiated. To reduce the computational effort, a PCA is initially performed, leading to a data reduction of about 98 %. The clustering itself was computed on 3-day periods constructed with the first six PCs using "k-means" clustering algorithm. The applied method enables an evaluation of the time evolution of the synoptic developments. The climate change signal is constructed by a projection of the GCM simulation on the EOFs attained from the NCEP-Reanalysis. Consequently, the same clusters are obtained and frequency distributions can be compared. For Central Europe, four primary storm clusters are identified. These clusters feature almost 72 % of the historical extreme storms events and add only to 5 % of the total relative frequency. Moreover, they show a statistically significant signature in the associated wind fields over Europe. An increased frequency of Central European storm clusters is detected with enhanced GHG conditions, associated with an enhancement of the pressure gradient over Central Europe. Consequently, more intense wind events over Central Europe are expected. The presented algorithm will be highly valuable for the analysis of huge data amounts as is required for e.g. multi-model ensemble analysis, particularly because of the enormous data reduction.

Differentiation of neotropical ecosystems by modern soil phytolith assemblages and its implications for palaeoenvironmental and archaeological reconstructions

The interpretation of Neotropical fossil phytolith assemblages for palaeoenvironmental and archaeological reconstructions relies on the development of appropriate modern analogues. We analyzed modern phytolith assemblages from the soils of ten distinctive tropical vegetation communities in eastern lowland Bolivia, ranging from terra firme humid evergreen forest to seasonally-inundated savannah. Results show that broad ecosystems – evergreen tropical forest, semi-deciduous dry tropical forest, and savannah – can be clearly differentiated by examination of their phytolith spectra and the application of Principal Component Analysis (PCA). Differences in phytolith assemblages between particular vegetation communities within each of these ecosystems are more subtle, but can still be identified. Comparison of phytolith assemblages with pollen rain data and stable carbon isotope analyses from the same vegetation plots show that these proxies are not only complementary, but significantly improve taxonomic and ecosystem resolution, and therefore our ability to interpret palaeoenvironmental and archaeological records. Our data underline the utility of phytolith analyses for reconstructing Amazon Holocene vegetation histories and pre-Columbian land use, particularly the high spatial resolution possible with terrestrial soil-based phytolith studies.

The bile acid receptor TGR5 does not interact with β-arrestins or traffic to endosomes but transmits sustained signals from plasma membrane rafts.

TGR5 is a G protein-coupled receptor that mediates bile acid (BA) effects on energy balance, inflammation, digestion and sensation. The mechanisms and spatiotemporal control of TGR5 signaling are poorly understood. We investigated TGR5 signaling and trafficking in transfected HEK293 cells and colonocytes (NCM460) that endogenously express TGR5. BAs (deoxycholic acid, DCA, taurolithocholic acid, TLCA) and the selective agonists oleanolic acid (OA) and 3-(2-chlorophenyl)-N-(4-chlorophenyl)-N, 5-dimethylisoxazole-4-carboxamide (CCDC) stimulated cAMP formation but did not induce TGR5 endocytosis or recruitment of β-arrestins, assessed by confocal microscopy. DCA, TLCA and OA did not stimulate TGR5 association with β-arrestin 1/2 or G protein-coupled receptor kinase (GRK) 2/5/6, determined by bioluminescence resonance energy transfer. CCDC stimulated a low level of TGR5 interaction with β-arrestin2 and GRK2. DCA induced cAMP formation at the plasma membrane and cytosol, determined using exchange factor directly regulated by cAMP (Epac2)-based reporters, but cAMP signals did not desensitize. AG1478, an inhibitor of epidermal growth factor receptor (EGFR) tyrosine kinase, the metalloprotease inhibitor batimastat, and methyl-β-cyclodextrin and filipin, which block lipid raft formation, prevented DCA stimulation of extracellular signal regulated kinase (ERK1/2). BRET analysis revealed TGR5 and EGFR interactions that were blocked by disruption of lipid rafts. DCA stimulated TGR5 redistribution to plasma membrane microdomains, localized by immunogold electron microscopy. Thus, TGR5 does not interact with β-arrestins, desensitize or traffic to endosomes. TGR5 signals from plasma membrane rafts that facilitate EGFR interaction and transactivation. An understanding of the spatiotemporal control of TGR5 signaling provides insights into the actions of BAs and therapeutic TGR5 agonists/antagonists.

Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.

Phonological therapy in jargon aphasia: effects on naming and neologisms

Background: Jargon aphasia is one of the most intractable forms of aphasia with limited recommendation on amelioration of associated naming difficulties and neologisms. The few naming therapy studies that exist in jargon aphasia have utilized either semantic or phonological approaches but the results have been equivocal. Moreover, the effect of therapy on characteristics of neologisms is less explored. Aims: This study investigates the effectiveness of a phonological naming therapy (i.e., phonological component analysis, PCA) on picture naming abilities and on quantitative and qualitative changes in neologisms for an individual with jargon aphasia (FF). Methods: FF showed evidence of jargon aphasia with severe naming difficulties and produced a very high proportion of neologisms. A single-subject multiple probe design across behaviors was employed to evaluate the effects of PCA therapy on the accuracy for three sets of words. In therapy, a phonological components analysis chart was used to identify five phonological components (i.e., rhymes, first sound, first sound associate, final sound, number of syllables) for each target word. Generalization effects—change in percent accuracy and error pattern—were examined comparing pre-and post-therapy responses on the Philadelphia Naming Test and these responses were analyzed to explore the characteristics of the neologisms. The quantitative change in neologisms was measured by change in the proportion of neologisms from pre- to post-therapy and the qualitative change was indexed by the phonological overlap between target and neologism. Results: As a consequence of PCA therapy, FF showed a significant improvement in his ability to name the treated items. His performance in maintenance and follow-up phases remained comparable to his performance during the therapy phases. Generalization to other naming tasks did not show a change in accuracy but distinct differences in error pattern (an increase in proportion of real word responses and a decrease in proportion of neologisms) were observed. Notably, the decrease in neologisms occurred with a corresponding trend for increase in the phonological similarity between the neologisms and the targets. Conclusions: This study demonstrated the effectiveness of a phonological therapy for improving naming abilities and reducing the amount of neologisms in an individual with severe jargon aphasia. The positive outcome of this research is encouraging, as it provides evidence for effective therapies for jargon aphasia and also emphasizes that use of the quality and quantity of errors may provide a sensitive outcome measure to determine therapy effectiveness, in particular for client groups who are difficult to treat.

An infrared desert dust index for the Along-Track Scanning Radiometers

A new aerosol index for the Along-Track Scanning Radiometers (ATSRs) is presented that provides a means to detect desert dust contamination in infrared SST retrievals. The ATSR Saharan dust index (ASDI) utilises only the thermal infrared channels and may therefore be applied consistently to the entire ATSR data record (1991 to present), for both day time and night time observations. The derivation of the ASDI is based on a principal component (PC) analysis (PCA) of two unique pairs of channel brightness temperature differences (BTDs). In 2-D space (i.e. BTD vs BTD), it is found that the loci of data unaffected by aerosol are confined to a single axis of variability. In contrast, the loci of aerosol-contaminated data fall off-axis, shifting in a direction that is approximately orthogonal to the clear-sky axis. The ASDI is therefore defined to be the second PC, where the first PC accounts for the clear-sky variability. The primary ASDI utilises the ATSR nadir and forward-view observations at 11 and 12 μm (ASDI2). A secondary, three-channel nadir-only ASDI (ASDI3) is also defined for situations where data from the forward view are not available. Empirical and theoretical analyses suggest that ASDI is well correlated with aerosol optical depth (AOD: correlation r is typically > 0.7) and provides an effective tool for detecting desert mineral dust. Overall, ASDI2 is found to be more effective than ASDI3, with the latter being sensitive only to very high dust loading. In addition, use of ASDI3 is confined to night time observations as it relies on data from the 3.7 μm channel, which is sensitive to reflected solar radiation. This highlights the benefits of having data from both a nadir- and a forward-view for this particular approach to aerosol detection.