With mucosal healing now entrenched as a clinical trial end point

With mucosal healing now entrenched as a clinical trial end point and significant evidence demonstrating that mucosal healing modifies the course of the disease, including potentially C59 wnt price reducing the risk of cancer via primary and secondary prevention, one question that remains is how is this new paradigm

best applied in the clinic? Key issues include how patients in clinical remission should be monitored, and what a clinician should do when active inflammation is encountered on surveillance endoscopy. Assessment of the mucosa and success at achieving healing requires interval evaluation of the bowel, and current evidence further favors histology. This approach implies the need for repeat endoscopic assessment, which has limitations in cost and patient acceptance. Although endoscopy for dysplasia detection Dabrafenib is effective and continually improving with technology, the invasiveness, lack of resources, and, probably, cost-ineffectiveness precludes the performance of endoscopy (and biopsies) every 3 to 6 months from the time of diagnosis. Therefore, surrogate markers of mucosal healing, including blood-based and stool-based biomarkers and noninvasive, nonradiation imaging techniques will remain a focus of continued investigation. For example, the use of neutrophil-derived fecal markers, including calprotectin and lactoferrin, has been positively correlated with

endoscopic and histologic activity.43 The key clinical consideration is that baseline determinations of these noninvasive assessments must be obtained and correlated with endoscopic findings to provide Epothilone B (EPO906, Patupilone) meaning to changes over time. In addition, the timing intervals for monitoring remain unclear. Extrapolating from primary clinical trials evaluating mucosal healing, it is known that in the case of anti–TNF-α agents by week 6 to 8, mucosal healing rates (Mayo endoscopic subscore or equivalent

score 0–1) were 42.3% to 62.0% in UC,41, 44, 45 and 46 and by weeks 10 to 12 were 27% to 31% in Crohn’s disease.47 and 48 An important point is that in all of the UC trials, the maintenance rates of mucosal healing were all similar to or lower than that at the induction time point, suggesting that surrogate evaluation as frequently as every 8 weeks could indicate a change in mucosal healing. For now, the most frequent question that arises is related to the performance of routine (guideline-based) surveillance in the asymptomatic patient and the unanticipated inflammation. First, it is important to determine whether the findings are due to an alternative cause such as infection with Clostridium difficile or cytomegalovirus. In the setting of true active inflammation, the clinician should reassess the patient’s symptoms (or lack thereof) and adherence to the existing regimen of therapy, as often patients will self-discontinue or self-reduce a dose without a discussion with their provider; this is especially true when the patient is feeling well.

For quantification of staining, 800 μL of 10% acetic acid (Merck,

For quantification of staining, 800 μL of 10% acetic acid (Merck, Darmstadt, Germany) was added to each well, and the plate was incubated at room temperature for 30 min with shaking. The monolayer, now loosely attached to the plate, was then scraped from the plate with a cell scraper (Corning Incorporated, NY, USA) and transferred to a 1.5 mL microcentrifuge tube with a wide-mouth pipette. After vortexing

for 30 s, the slurry was overlaid with 500 μL of mineral oil (Sigma–Aldrich, St. Louis, MO, USA), heated to exactly 85 °C Bleomycin for 10 min, and transferred to ice for 5 min. The slurry was then centrifuged at 20,000 × g for 15 min and 500 μL of the supernatant was removed to a new 1.5 mL

microcentrifuge tube. Then 200 μL of 10% ammonium hydroxide (Sigma–Aldrich, St. Louis, MO, USA) was added to neutralize the acid. Aliquots (150 μL) of the supernatant were read in duplicate in 96-wells format at 405 nm by software VersaMax in an ELISA reader. Cells cultured without OSI-906 purchase osteogenic medium were used as staining negative control. Reverse transcription followed by qPR was utilized in order to evaluate the effect of PTH administration on the expression of ALP, COL1, MMP-2, BGN and DSPP genes in MDPC-23 cells. The total RNA was harvested from cells in 6-well plates (n = 3) and extracted using the TRIzol® reagent (Invitrogen, Carlsbad, CA, USA), following the manufacturer’s recommendation. The RNA quantification and purity were measured by photometric measurement using a Nanodrop 2000 Spectrophotometer

(Thermo Fisher Scientific, Wilmington, DE, USA), and the RNA quality was assessed by electrophoresis on a denaturing 2% agarosis gel. One microgram of total highly purified RNA was treated with DNase (Invitrogen, Carlsbad, CA, USA) and 500 ng was used for cDNA synthesis. The reaction was carried out using the SuperScript III First-strand Synthesis of the Oligo (dT) primer (Invitrogen, Carlsbad, CA, DNA ligase USA), following the manufacturer’s recommendations. Real-time PCR was conducted in the LightCycler® 480 II (Roche Diagnostics GmbH, Indianapolis, IN, USA) using the Jump Start SYBR Green Taq Ready Mix™ (Sigma–Aldrich, St. Louis, MO, USA). In the amplification it was used the TaqMan® Hydrolysis Probe (Applied Biosystems, Life Technologies, Carlsbad, CA, USA) for ALP (Assay ID: Mm00475834_m1) and DSPP (Assay ID: Mm00515666_m1) and primers sequences (IDT®, Integrated DNA Technologies, Coralville, IA, USA) for COL1, MMP-2 and BGN, designed with Primer3 software (http://biotools.umassmed.edu/bioapps/primer3_www.cgi). The sequences of the primers used were: COL1 (Col1a1, Gene ID: 12842) (forward 5′-GTCAGCAGATTGAGAACATCC-3′; reverse 5′-TGAGTAGGGAACACACAGGTC-3′, amplicon: 196 pb, GenBank NM_007742.

Similar results were obtained with the poisons from other Brazili

Similar results were obtained with the poisons from other Brazilian fish such as the stingrays Potamotrygon cf. scobina and Potamotrygon gr. orbygnyi ( Magalhães et al., 2006). Ku-0059436 nmr Furthermore, toxins present in snake venoms that induce systemic and local effects are substances with known inflammatory activity. Another important

finding was that only peptide fractions obtained from venom or skin mucus provoked changes in blood flow or in the caliber of the vessels participating in microcirculation. Fractions Fv1, Fv2 and Fv3 induced a venular stasis; moreover, Fv2 induced constriction of arteriolar vessels. Regarding the fractions obtained from skin mucus, Fm1 and Fm5 induced hemorrhage, and Fm2 and Fm6 enlargement of arteriolar wall diameter. These results demonstrate differences regarding the action of peptides and proteins present in sting venom and skin mucus of C. spixii. While the protein fractions produced a typical inflammatory process in post-capillary venules, the peptide fractions caused more harmful effects, such as venular stasis, hemorrhage and changes in the arteriolar

wall diameter. These circulatory alterations can explain the clinical manifestations observed in human catfish envenomations, which are based in ischemia, blanching and necrosis ( Haddad Jr. and Martins, 2006). Since the 1960s, the family of bioactive bradykinin-potentiating peptides found in animal venoms (BPPs) has received special attention. The history of this family of hypotensive SB203580 datasheet peptides is well known, but no such peptides with similar sequences have been found in aquatic animals such as fish. Recently, our group identified in the venom of the stingray P. gr. orbignyi a peptide called Orpotrin (HGGYKPTDK) which had constrictor activity on the arterioles in mice ( Conceição et al., 2006) but had no similarity to any other peptide or BPP. From the fractionation of sting venom and skin mucus of C. spixii, we have 3 fractions capable of inducing changes in arteriolar wall diameter.

Interestingly, studies conducted check details by Junqueira et al. (2007) did not describe any alterations in the arteriolar wall diameter when total venom or skin mucus was applied to the microcirculatory net. However studies with other species of catfish showed a hypotensive response ( Datta et al., 1982) or even a hypertensive response in vivo ( Auddy et al., 1994) produced by venom. Moreover, it was confirmed that the skin mucus from the catfish Arius thalassinus has a vasoconstrictor effect ( Al-Hassan et al., 1986). Despite these works demonstrating that catfish venoms cause changes in vessel diameter, none of them isolated or characterized the molecules responsible for these effects. Finally, we described for the first time a Warm Temperature Acclimation-Related Protein 65-kDa (Wap65) in sting venom and skin mucus of C.

PBMCs were incubated with magnetic microbeads (130-091-153, Monoc

PBMCs were incubated with magnetic microbeads (130-091-153, Monocyte Isolation Kit II, Miltenyi Biotec, Bergisch Gladbach, Germany) in accordance with the

manufacturer’s protocol, and final isolation of monocytes was achieved using a magnetic cell sorter (AutoMACS, Miltenyi Biotec, Germany). PBMCs were differentiated into dendritic cells using an established protocol (Rogler et al., 1998); monocytes were cultivated in flasks for 1 week under optimal conditions (37 °C, 5% CO2, 95% relative humidity [RH]) with 5 ng/mL IL-4 and 50 ng/mL granulocyte–macrophage colony-stimulating factor (GM-CSF). As described above for ivDCs, peripheral blood monocytes were differentiated into macrophages based on the PLX3397 established protocol cited, with monocytes being cultivated in Teflon bags for 1 week under optimal conditions (37 °C, 5% CO2, 95% RH). Differentiated macrophages were detached from the Teflon bags by incubation at 4 °C. The monocytic/macrophage-like THP-1 cell line was cultivated in Roswell Park Memorial Institute medium containing 10% fetal calf serum (FCS), supplemented with penicillin (100 U/mL) Selleckchem Carfilzomib and streptomycin (100 μg/mL) under standard conditions (37 °C, 5% CO2, 95% RH). Human epithelial colorectal adenocarcinoma (Caco-2) cells were

grown in high glucose Dulbecco’s Modified Eagle’s Medium containing 10% FCS, supplemented with penicillin (100 U/mL) and streptomycin (100 μg/mL) under recommended conditions (37 °C, 10% CO2, 95% RH). For both cell lines, passaging was carried out according to the guidelines

of the American Type Culture Collection (ATCC, 2012). The influence of retinoids on the LPS-induced cytokine response of ivDCs, ivMACs and THP-1 cells was evaluated in each cell type using the same experimental methodology. Primary cells were adjusted to a density of 1 × 106 cells/mL and plated onto 96-well plates (100 μL/well). THP-1 Farnesyltransferase cells were incubated in six-well plates at a density of 7 × 105 cells/mL. Retinoids were added to the medium (0.01, 0.1, 1.0 and 5.0 μg/mL) for ivDCs, ivMACs and THP-1 cells, and pre-incubated for 1 h prior to stimulation with LPS (to a final concentration of 100 ng/ml) for a further 48 h at 37 °C. Incubation medium was collected and processed for cytokine analysis (Rules-Based Medicine, Austin, Texas, USA) using Human Cytokine MAP A 1.0® array. Levels of IL-15 were below the detection limit of the assay and were excluded from the analysis. For studies in ivDCs, cytokine response data shown are based on at least six (LPS-induced) and at least four (no LPS) independently performed experiments, each corresponding to a different donor. In ivMACs, these data (both LPS-induced and no LPS) were each based on at least four independently performed experiments, each corresponding to a different donor. Data shown for cytokine response in THP-1 cells are based on three independent experiments.

, 2007) At present, bridging the organism-population gap seems o

, 2007). At present, bridging the organism-population gap seems only feasible through use of population models as demonstrated for Arctic cod, capelin (Mallotus villosus), and herring (Clupea harengus) by Hjermann et al. (2007) and for northern shrimp (Pandalus borealis) by Ravagnan et al. (2010), or by employing a risk assessment approach. Beyer et al. (2012) performed a risk assessment for effects of C4–C7 APs in PW on three economically important fish populations on the NCS: Atlantic cod, haddock,

and saithe (Pollacius virens), based on fish distribution data, hazard information of APs in PW, data on PW discharges, and plume dispersion described by the exposure and risk model DREAM ( Reed and Hetland, 2002 and Reed et al., 2001). Their conclusion was that the environmental exposure to C4–C7 APs from Venetoclax molecular weight PW is too low to have any significant effect on the reproduction of fish stocks. Neff et al. (2006) and Durell et al. (2006) came to the same conclusion regarding the risk from PAHs in PW to the wider pelagic ecosystem in the NS when combining dispersion modeling by DREAM and PAH

measurements in passive samplers (SPMDs) and caged mussels. Smit et al. (2009) described a systematic relationship between sub-individual and individual sensitivity to oil from SSDs for DNA damage and oxidative stress biomarkers in 6 marine species and similar SSDs for whole-organism chronic fitness in 26 marine species. On average the selected biomarkers were a factor 35–50 more sensitive than the whole-organism response. The results implied that the 95% safety level (the lower 5 Epigenetics inhibitor percentile or HC5, commonly used as PNEC in risk assessments), for whole-organism exposure to total hydrocarbons would safeguarded only 86% of the species from genotoxic damage and Vitamin B12 79% from oxidative stress. The authors stress that their data were insufficient to support this as a general

relationship, but data from Bechmann and Taban, 2004, Bechmann et al., 2004, Buffagni et al., 2010 and Carls et al., 1999, (Hansen et al., 2011), Heintz et al., 2000, Jonsson and Björkblom, 2011, Pinturier et al., 2008 and Sanni et al., 2005, and Stien et al. (1998) provide supporting evidence from a wider range of sub-tropical to high-arctic species of fish and invertebrates that the whole organism responses are less sensitive to oil than biomarker responses. Smit et al. (2009) present a conceptual model suggesting further reduction in sensitivity as one moves up to the population level. This would concur with the idea that environmental factors governing the health and performance of a population, may override toxic effects on parts of the population. The studies above cover sensitivity to oil, but the authors suggest that the relationship may be valid for PW as well. If that is the case, it is even more unlikely that wide scale population effects should occur when individual effects are only seen locally.

Concerning the signaling process of the hormone, it has been demo

Concerning the signaling process of the hormone, it has been demonstrated that modulates the activation of AKT, protein involved in postnatal cardiac growth and coronary angiogenesis [19], time-dependently and is associated to PI3K and AMPK phosphorylation, protein kinases that have central role in the signaling processes for energy metabolism and cell growth [2], [3], [6] and [23]. In this paper we hypothesized that obesity and heart hypertrophy caused by early life overnutrition could learn more be related to changes in ghrelin signaling in heart, mainly in ghrelin-associated proteins (AKT, PI3K and AMPK), inducing a new pattern of heart growth or remodeling

and heart energetic availability. Virgin female Swiss mice were time crossed at 3 months of age. During pregnancy and lactation, they were singly housed in

individual cages and had ad libitum water and a standard pellet diet. After birth, litters were adjusted to nine pups per dam. At postnatal day 3, to induce early postnatal overnutrition, the number of pups per dam was adjusted to three male mice per litter to form the small litter (SL) [35], whereas litters containing nine pups per mother served as controls (normal litter (NL). To complete sample size only one male mice of each litter was used in order to discard pups of the same litter [48]. After weaning at postnatal day 21, mice were housed with three mice per cage with free access to water and standard chow in a temperature-controlled crotamiton http://www.selleckchem.com/products/BIBW2992.html room with a 12 h light:12 h darkness cycle. They were weighed weekly and were killed at 180 days of age. Before the sacrifice mice were fasted overnight, injected with heparin (5000 U/kg), and then anesthetized with Avertin (0.3 g/kg body weight, via i.p. injection). They were cared for in accordance with the Animal Care and Use Committee of the Biology Institute of the State University of Rio de Janeiro, which based its analysis on the principles described

in the Guide for Care and Use of Laboratory Animals [5]. After a 12-h fast, blood glucose concentration was measured from blood droplets removed from the tail vein of 180 day old SL and age-matched NL mice with a glucometer (Accu-Chek, Roche, Sao Paulo, Brazil). Acylated ghrelin levels were determined in plasma using a commercial assay kit (Millipore, ELISA Kit, Rat/Mouse Ghrelin active). Blood sample was obtained under anesthesia by heart puncture, collected into a centrifuge tube containing K3 EDTA to achieve a final concentration of 1.735 mg/mL and treated with Pefabloc followed by immediate centrifugation (3000 rpm for 10 min at 4 °C). Plasma samples were acidified with HCl to a final concentration of 0.05 N and stored at −20 °C until assayed.

Although not the primary focus of this effort, the intercompariso

Although not the primary focus of this effort, the intercomparison of simulated fluxes and pCO2 from four different reanalysis products provides an opportunity to gain insights into inherent model and data ocean carbon issues. First we note that the reanalysis products are largely not capable of rectifying the major discrepancies between the model and data. Second we note that as we descend from coarser to finer resolution, the issues become more important. For both air–sea fluxes and pCO2, global model

agreement with in situ data is strong, with maximum deviations of 19% for FCO2 and 0.6% for pCO2 among all the reanalysis forcing products (Fig. 5 and Fig. 7). Deviations for pCO2 are much smaller than fluxes. Basin correlations are statistically significant at P < 0.05 for all forcings for both FCO2 and pCO2, and correlation coefficients range from 0.73 to 0.80. On regional scales, more model-data deviations Buparlisib datasheet are apparent and they can be large at times. We note particularly the South Atlantic and to a lesser extent the North Atlantic (Fig. 5 and Fig. 7). For Everolimus in vivo air–sea fluxes, additional problems are seen in the Pacific basins (except the Equatorial Pacific) and the Equatorial Atlantic. pCO2 estimates exhibit much smaller discrepancies in the above basins but not in the North and South Atlantic (Fig. 7). Since the results from the different

forcings only partially alleviate the model-data differences, we suggest that here the problems arise in the model formulation and/or the comparison with in situ data. On smaller scales the discrepancies between model and data are larger still (Fig. 11 and Fig. 12). For the full model domain and interpolated in situ climatology (top panels in Fig. 11), noteworthy Paclitaxel datasheet deviations are the high source regions in the model in the Southern Ocean along the 60oS band, high sources along the US/Canada East and West coasts

in the North Atlantic and Pacific, and model sinks in the southern sub-tropical Atlantic and Pacific. The 60°S Southern Ocean band of high atmospheric source is common to all the reanalysis versions, and the discrepancy is partially the result of sampling biases in the in situ data. Public data sets of pCO2 and FCO2 (Takahashi et al., 2009) are taken from point measurements in the ocean, gridded to 5° longitude by 4° latitude, binned to an annual mean climatology, and with residual gaps filled. Each of these steps potentially introduces a bias in the final result, and is especially important when comparing to model annual means, which have no sampling issues. Binning to a coarse grid reduces variability and over-represents the influences of observation points closest to gaps. Constructing annual means where data exist for only a few months creates an unbalanced representation, with the sampled months over-represented. If the sampled months occur at a low or high point in the seasonal cycle, the problem is exacerbated.

The friction at the bottom is calculated using the quadratic rela

The friction at the bottom is calculated using the quadratic relationship from the flow speed equation(3) EPZ015666 Fbx=CD|u→|u,Fby=CD|u→|v, where CD   (= 2.5 × 10−3) is the bottom friction coefficient, and u→ is the current velocity. The bottom friction coefficient is taken to be constant, since reliable data on sea bottom irregularities are lacking. The wave-induced force per unit surface area is the gradient of radiation stresses. It reads: equation(4) Fwavex=1ρ0(−∂Sxx∂x−∂Sxy∂y),Fwavey=1ρ0(−∂Syx∂x−∂Syy∂y),

where ρ0 is the reference density and S is the radiation stress tensor as given by equation(5) Sxx=ρ0g∫ncos2θ+n12Edσdθ,Sxy=Syx=ρ0g∫nsinθcosθEdσdθ,Syy=ρ0g∫[nsin2θ+n−12]Edσdθ, where n is the ratio of the group velocity to the phase velocity. E(σ, θ) denotes the two-dimensional wave spectrum in frequency and directional space respectively. The terms of horizontal turbulence are calculated using the constant eddy viscosity coefficient AH: equation(6) Gx=AH(∂2u∂x2+∂2u∂y2),Gy=AH(∂2v∂x2+∂2v∂y2). The eddy viscosity coefficient for all grids is 50 m2 s−1. The kinematic wind stress components Selleckchem LDE225 are calculated as: equation(7) Fxw=τxwρ0=ρaρ0cduw|u→w|,Fyw=τywρ0=ρaρ0cdvw|u→w|, where u→w is the wind velocity vector, uw and vw are wind components, τwx and τwy are wind stress components, cd(= 1.3 × 10−3)

is the drag coefficient, and ρa is the air density. Thus, the numerical model takes into account bottom topography, the Earth’s rotation, friction at the sea bottom and horizontal eddy viscosity. Temperature and salinity fields are not calculated in the model. Consequently, the baroclinic component of currents is not taken into account; in the Väinameri region this is of minor importance compared to wind forcing and sea level changes (Otsmann et al. 2001). The model did not include the river runoff into the Gulf of Riga because of its minor role in the water exchange through the Suur Strait. According to previous modelling studies, the river inflow affects mainly the flows in the Irbe Strait because the Suur Strait has a smaller cross-section and Sirolimus cell line a higher resistance (Otsmann et al., 1997, Otsmann et al., 2001 and Suursaar

et al., 2002: Figure 3f). A triple-nested circulation model was used for the simulation of currents and water exchange in the Suur Strait. The coarse grid model covered the whole Baltic Sea with a spatially constant grid size of 2×2 km. Digital topography was taken from Seifert et al. (2001). No open boundary conditions were implemented for this grid. The model for the Väinameri region had a grid size of 400×400 m (Figure 1b), whereas the boundary conditions for water transport were obtained from the whole Baltic Sea model. The high resolution model for the Suur Strait area had a grid step of 100×100 m (Figure 1c), and boundary conditions were obtained from the Väinameri model. One-way grid nesting was used for both model domains.

ThermoML covers a wide variety of properties (≈125) and deals wit

ThermoML covers a wide variety of properties (≈125) and deals with pure chemical compounds, multicomponent mixtures, and chemical reactions. Biochemical substances and reactions are explicitly Everolimus cell line covered in ThermoML (Chirico et al., 2010). The intent is that the developed dictionary and corresponding XML schema will become an internationally accepted standard for thermodynamic data storage and exchange (Frenkel et al., 2011). Thermodynamics provides a formal structure or framework by which one can calculate values for many properties of substances and reactions. However, to be made useful, this framework must

be filled with values of properties that can be obtained either by direct measurement or which can be calculated from other measured property values by means of thermodynamic DAPT datasheet relations. A recent publication ( Goldberg, 2009) contains a brief description of how thermodynamic networks

can be used to calculate values of standard molar Gibbs energies of formation ΔfG°, standard molar enthalpies of formation ΔfH°, and standard molar entropies S°. Once one has a table of these property values, one can calculate values of equilibrium constants K and standard molar enthalpies of reaction ΔrH° for any reaction in which the appropriate values of the properties of the reactants and products are listed in the table. It is important to appreciate that serious errors can result if values of standard formation properties from different tables are combined to calculate property values for a given reaction. Also, pertinent to the construction of such tables are values of associated properties such as standard molar enthalpies of combustion ΔcH°, standard molar entropies S°, standard molar heat capacities Cp°, solubilities s, and standard molar enthalpies of solution ΔsolH°. Table 1 provides references to several tables of standard formation properties that are relevant to biochemical substances and reactions and to several other sources that contain tabulations of the aforementioned properties. However, if the

desired property values are not found Cisplatin nmr in these sources, one must either search for the desired property values in the literature or determine if the desired values can be calculated by using thermodynamic relations. In the absence of any directly measured values or values that can be obtained by means of a thermodynamic calculation, one can turn to estimation methods ( Goldberg, 2009) to obtain possibly the desired property value(s). The author has no conflict of interest. “
“Enzymes represent the largest and most diverse group of all proteins, catalysing all chemical reactions in the metabolism of all organisms. In addition to metabolism they also play a key role in the regulation of metabolic steps within the cell.

This information will contribute to enhance water management and

This information will contribute to enhance water management and improve the design of adaptive measures. In the following section, we introduce the precipitation data and the methodology that includes SPI estimation, PCA and SSA. In Section 3, we present the results for the spatiotemporal behavior of dry and wet EPE and the spatial extent of extreme drought and wetness. Finally, some implications of the findings are discussed in Section 4 and the concluding remarks are presented in

Section 5. We used monthly observed precipitation data from 23 meteorological stations from the National Weather Service and National Institute of Agricultural Technology in Argentina. The stations were chosen considering

this website their record length and completeness, 17-AAG clinical trial the absence of gaps and the data quality. Stations in the NEA are not homogeneously distributed in space (see Fig. 1b), and therefore we used the following high-resolution (0.5° × 0.5°) gridded precipitation datasets: the Climatic Research Unit time-series dataset version 3.2 (CRU TS 3.2, Jones and Harris, 2012), spanning 1901–2011 and the Global Precipitation Climatology Centre dataset version 6 (GPCC v6, Schneider et al., 2011) from 1901 to 2010. The process of selection of the precipitation series used in this paper is based on the stability of the meteorological stations and in the

confidence in the measurements as evidenced by tests of coherence and consistency: Kolmogorov–Smirnov (Von Storch and Zwiers, 1999) and double mass curves of doubly accumulated precipitation (Remenieras, 1974). Furthermore, the degree of randomness in the time series was assessed by the accumulated periodogram method (Anderson, 1977). Moreover, the selection process of the time series satisfies Bay 11-7085 the requirements of quality control stated in Chapter 9 of the WMO Guide to Hydrological Practices (WMO, 2008). The gridded datasets were validated with observed precipitation by creating average spatial time series (Fig. 2). The mean values of the series are 941 mm in the observed series, 925 mm in GPCC v6 dataset and 868 mm in CRU TS 3.2. We also calculated the Pearson correlation coefficients between average spatial time series of observed data and the gridded datasets. For the total period of our paper (1901–2010), the correlation coefficient between observed data and GPCC v6 (r = 0.946) was quite similar to the correlation with CRU TS 3.2 (r = 0.943). Both Pearson correlation coefficients are statistically significant at the 0.01 confidence level. The 99% confidence interval for r is computed from the probability points of the standard normal distribution ( Chatfield, 2004). Fig.