, 2013)). Antibodies from two IFNγ-specific clones, AF10 and EH9, were purified from high density culture (miniPERM, Sarstedt) with Hi Trap Protein G HP columns (Amersham-Pharmacia, UK) according to the manufacturer’s instructions. After dialysis against PBS, the concentration of these antibodies was estimated by measurement of the absorbance at 280 nm.

CKC were infected with A/Turkey/England/1977/H7N7 for use in co-culture as previously described (Singh et al., 2010a). Briefly, confluent monolayers of CKC (after a minimum of MAPK inhibitor 8 passages) were infected with AIVs for 1 h at a Multiplicity of Infection (MOI) of 3–5, washed with PBS, and incubated for 4 h with CKC growth media without FCS, supplemented with TPCK trypsin (Sigma). Cells were then washed, dispersed with trypsin, washed again, counted, resuspended in leukocyte culture media and then irradiated with 3000 rad using a Gammacell 1000 Elite caesium 137 gamma irradiator (Nordion, Canada). For infection with recombinant MVA, CKCs were infected by incubation for 1 h at 37 °C at an MOI of 5. We optimized these conditions through analysis of GFP transgene expression by confocal microscopy (Supplementary Fig. 1). Following incubation, cells were washed, counted, irradiated as described, and resuspended in leukocyte culture media. The

irradiated CKC were used at a ratio of 1:10 (CKC:splenocyte) in co-culture ELISpot. For confocal imaging 5×104 primary CKC in growth media per chamber of an 8 chamber slide (Lab-TekII, Nunc)

were incubated at 41 °C, 5% CO2, Selleck CH5424802 for 1 day. Any non-adherent cells were discarded and the adherent cell population was infected with MVA-GFP constructs as described above. After incubation, cells were fixed with a solution of 4% paraformaldehyde for 20 min, and then washed in PBS. Nuclei were stained by incubation with 2 μg/ml DAPI (Sigma) for 10 min. Sections were mounted in Vectashield Wilson disease protein (Vector Laboratories) and analyzed using a confocal microscope (Leica SP2 with 405-, 488-, and 568-nm lasers). Spleens were macerated in cold sterile PBS and passed through a 100 μm cell strainer (Fisher, UK). Cell suspensions were centrifuged at 220 × g for 10 min at 4 °C and resuspended in culture media (RPMI 1640 medium with Glutamax supplemented with 10% FCS, 100 U/ml penicillin, and 100 μg/ml streptomycin) (all from Life Technologies, UK) before under-laying Histopaque 1119 (Sigma, UK) and centrifuged at 2000 rpm (492 ×g) for 20 min at 4 °C. Cells harvested from the interphase were washed twice, counted using a Countess™ automated cell counter (Life Technologies) and resuspended at 5 × 106/ml. ChIFNγ ELISpot was carried out as described previously ( Ariaans et al., 2008), using either antibodies from a commercially available kit for detection of chicken IFNγ protein (chicken IFNγ ELISA kit, Life Technologies ®) or EH9/AF10 antibodies produced as described.

The resulting genome sequences therefore contain intermixed sequences from different tumour clones, as well as from admixed normal cells. Computational methods can determine

which mutations are clonal (present in all tumour cells) and which are subclonal [15]. In addition, by analyzing point mutation and copy number data further with bioinformatics algorithms, phylogenetic trees of different tumour subclones can be inferred [12]. Although these methods Cabozantinib mw provide important information on the genomes of distinct cell populations within the tumour, the number of tumour cell populations they can disentangle is limited, and inferring rare subclonal populations remains difficult. Recent advances have made it possible to profile the genomes of single cells. The isolation

of single cancer cells, followed by amplification of the DNA and array profiling or next-generation sequencing (Figure 1), opens avenues to study tumour subclonal architecture and tumour evolution in unprecedented depth. Here, we provide an overview of current methods to profile genomes of single cells. We discuss their strengths and limitations and the perspectives they offer for cancer research and therapy monitoring. To isolate single cells from solid tumours, two main approaches have been developed. The first method exploits the precision of modern flow cytometry to sort nuclei from single cells [16 and 17••]. Tissue-cubes of ∼1 mm3, cut off a (frozen) solid tumour, are teased apart in cell lysis buffer, containing DAPI, a fluorescent DNA-intercalator, and the resulting single Enzalutamide cell line nuclei are flow-sorted based on DNA content. This technique provides the advantage of allowing identification and isolation of tumour subpopulations on the basis of ploidy [16 and 17••]. Although the cytoplasm is lost, extensions to analyses of the transcriptome per se are possible [ 18]. However, this approach also entails limitations. Loperamide In particular, micronuclei may be lost. Micronuclei are not merely by-products from genomic instability but are likely prone to DNA-replication stress and further

DNA-mutational processes [ 19] and therefore may be important players in tumour evolution. A second method disperses the tissue from fresh solid tumour biopsies in a single-cell suspension, using enzymatic treatments, including, for example, collagenases [20•]. Intact individual cells can subsequently be isolated using (mouth-controlled) pipetting, modern cell-sorting or microfluidics systems with or without applying immunocytochemistry. Microfluidics devices provide the advantage that in addition to capturing individual cells, they also provide nanoliter reaction chambers to further process the nucleic acids of multiple individual cells in parallel under highly standardized conditions at significantly reduced reagent costs.

Blood glucose and body weight data were analyzed using repeated measures analysis of variance (ANOVA), and differences between the groups were assessed using the Bonferroni post-hoc test. Data obtained from motor skills tests, as well as optical densitometry of TH-ir were analyzed using one-way ANOVA and Bonferroni post-hoc test. Statistical significance was set at P < 0.05. Data were

run on Statistica 6.0 software package (StatSoft, Inc., USA). All data are represented by the mean ± standard error of mean (SEM). We thank Antônio Generoso Severino for his technical assistance. This study was supported by grants from CNPq and CAPES. P.S. do Nascimento was supported by a Ph.D. scholarship from CNPq, M. Achaval and B.D. Schaan are CNPq investigators. We are in debt Sunitinib with Roche, who donated us the test strips.

“
“The prefrontal cortex (PFC) is a set of neocortical areas involved in a variety of cognitive functions that are instrumental in working memory (WM) processing (Baddeley, 1992, D’Esposito et al., 2000 and de Saint Blanquat et al., 2010). Damage to the PFC selleck of rodents, nonhuman primates, and humans produces profound deficits in performance on WM tasks (Passingham, 1985, Funahashi et al., 1993, Miller, 2000 and Tsuchida and Vasopressin Receptor Fellows, 2009). Working memory has been described as a multi-component system (Baddeley, 2003 and Repovs and Baddeley, 2006) or a collection of distinct cognitive processes (Floresco and Phillips, 2001, Bunting and Cowan, 2005 and Cowan, 2008) that provides active maintenance of trial-unique information in temporary

storage. In both laboratory tasks and in normal cognition, WM enables manipulation, processing, and retrieval of memories, which are converted efficiently into long-term memory after both short (seconds) and long (minutes to hours) delays (Fuster, 1997, Floresco and Phillips, 2001, Phillips et al., 2004, Funahashi, 2006 and Rios Valentim et al., 2009). During the delay period of WM tasks, brain imaging studies in humans using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have shown increased blood flow within the PFC (Jonides et al., 1993, Petrides et al., 1993 and Badre and D’Esposito, 2007). Consistent with the increased perfusion, imaging studies have also shown higher activity of the PFC during the delay period of WM tasks (Wagner et al., 2001, Rypma, 2006 and Motes and Rypma, 2010).

In other words, these high values could simply indicate that a much larger catchment is producing much more flow. To verify if this scale issue actually magnifies the performance of the models, we re-performed the multiple regression analyses using specific runoff (in m3 s−1 km−2) as dependent variables and computed NSE based on volumetric runoff for the two sets of power-law models predicting either specific or volumetric runoff. According to this efficiency coefficient, the models predicting

specific runoff were found not to outperform those described in this paper and are therefore not reported here. Except for the model predicting maximum daily flow which has one of the lowest values for Rpred2, the models predicting the higher half of the FDC (0.05 ≤ flow percentiles ≤ 0.60), HSP assay have a mean Rpred2 (92.97%) higher than that (90.41%) of the models predicting the lower half of the FDC (0.70 ≤ flow percentiles ≤ 0.95 and “Min”). This comparison only considers the best model (highest Rpred2) for each flow metric (Table 3). The better prediction of high flow, compared to low flow, suggests that the explanatory

variables tested in this analysis (mainly geomorphological and climate characteristics) do not correspond to the catchment characteristics that predominantly control low flows. Similar contrast between the predictive power of high-flow and low-flow models has been observed Ruxolitinib under various hydrological conditions (Thomas and Benson, 1970), suggesting that more efforts are needed to generate catchment characteristics suitable for multivariate low flow predictions. Fig. 2 illustrates this contrast in performance by comparing

observed (Qj,obs) and predicted (Qj,pred) flow in each studied catchment j for mean annual flow ( Fig. 2a and c) and for the model predicting the 0.95 flow percentile with the best performance ( Fig. 2b and d). Runoff values are volumetric (m3 s−1) in Fig. 2a and b and specific (m3 s−1 km−2) in Fig. 2c and d. The NSE values calculated with volumetric runoff ( Fig. 2a and b) are greater than those obtained with specific runoff ( Fig. 2c and d), reflecting the mass balance effect Paclitaxel (i.e. larger catchments produce more flow) explained above. Although the scatter plots in Fig. 2a and b align well along the first bisector, 30% and 50% of the catchments, respectively, have an absolute normalized error (ANEj for catchment j, Eq. (8)) greater than 40%. These errors result from the assumptions of the modeling method and from possible inaccuracies in the original flow values used in the model parameterization. Even though cross-validation has been performed, extrapolation to ungauged catchments still adds non-measurable uncertainty. Therefore, we encourage users of these models to cross check predicted flow with other flow prediction methods, if they are available. equation(8) ANEj=Qj,pred−Qj,obsQj,obs Fig.

The goal of this article is to discuss common benign and malignant pediatric hepatic lesions and their key MR imaging findings. Particular emphasis is placed on the utility of new hepatocyte-specific contrast agents to narrow the differential diagnosis. Alexander J. Towbin, Suraj D. Serai, and

Daniel J. Podberesky Traditionally, many diffuse diseases of the liver could only be diagnosed by liver biopsy. Although still considered the gold standard, liver biopsy is limited by its small sample size, invasive nature, and subjectivity of interpretation. There have been significant advances in functional magnetic resonance (MR) imaging of the liver. These advances now provide radiologists with High Content Screening the tools to evaluate the liver at the molecular level, allowing quantification of hepatic fat and iron, and enabling the identification of liver fibrosis at its earliest stages. These methods provide objective measures of diffuse liver processes and aid hepatologists in the diagnosis and management of liver disease. Nathan D. Egbert, David A. Bloom, and Jonathan R. Dillman Magnetic resonance cholangiopancreatography (MRCP) is an extremely useful tool for evaluating a wide

variety of disorders affecting the pancreaticobiliary system in neonates/infants, children, and adolescents. This imaging technique has numerous distinct advantages over http://www.selleckchem.com/products/KU-60019.html alternative diagnostic modalities, such as endoscopic retrograde cholangiopancreatography and percutaneous transhepatic cholangiography, including its noninvasive nature and lack of ionizing radiation. Such advantages make MRCP the preferred first-line method for advanced imaging the pediatric pancreaticobiliary tree, after ultrasonography. This article presents a contemporary review

of the use of MRCP in the pediatric population, including techniques, indications, and the imaging appearances of common and uncommon pediatric disorders. Michael S. Gee, Mark Bittman, Monica Epelman, Sara O. Vargas, and Edward Y. Lee The differential diagnosis of renal masses in pediatric patients includes benign and malignant tumors, as well as nonneoplastic mass-like lesions mimicking tumors. Although the spectrum of renal masses in children has some overlap with that of adults, it is important to understand the renal pathologic processes specific next to the pediatric population, as well as their characteristic imaging appearances and clinical presentations. This article reviews benign and malignant renal masses in children, with an emphasis on magnetic resonance imaging and clinical features that are specific to each lesion type. Melkamu Adeb, Kassa Darge, Jonathan R. Dillman, Michael Carr, and Monica Epelman Duplex renal collecting systems are common congenital anomalies of the upper urinary tract. In most cases they are incidental findings and not associated with additional pathologies. They demonstrate, however, higher incidences of hydroureteronephrosis, ureteroceles, and ectopic ureters.

Variation in the APOE, lipoprotein lipase (LPL) and cholesterol ester transfer protein (CETP) genes has been consistently associated with variation in lipid levels in adults [6] and [7]. However, genetic variation in these gene loci explain only a modest proportion of inter-individual variability in fasting lipid levels [8]. We

genotyped the GENDAI cohort for ten variants in the APOE, LPL, CETP genes and the APOA5/A4/C3 cluster, to examine if the reported Screening Library in vitro effects could be replicated in children and assess if these associations could be further modulated by body mass index (BMI). Participants were drawn from the children recruited in the GENDAI study. Briefly, a random sample of 2492 children attending school in the Attica region in Greece were invited to join the study. A total of 1138 children were recruited from November 2005 to June 2006. Due to the heterogeneity in allele frequencies between Greek and non-Greek Caucasians, only children of Greek nationality, mean age: 11.2 ± 0.7 years (n = 882; 418 males and 464 females), were included in the present study. Details of recruitment and data collection have been previously described [3].

All persons gave their informed consent prior to inclusion in the study. The study was approved by the Institutional Review Board of Harokopio University and the Greek Ministry learn more of Education [3]. A salting-out procedure [9] was used to isolate DNA samples from whole blood. Ten single nucleotide polymorphisms (SNPs) in six candidate genes; LPL S447X (rs328), CETP Taq1B (rs708272), APOE (rs7412, rs429358), APOA5 −1131C > T (rs662799) and S19W (rs3135506), APOA4 S347T (rs675) and APOC3 −482C > T (rs2854117), 1100C > T (rs4520) and 3238C > G (rs5128) were genotyped using TaqMan technology

(Applied Biosciences, ABI, Warrington UK). Reactions were performed on 384-well microplates and analysed using ABI TaqMan 7900HT software. Primers and MGB probes are available upon request. Hardy–Weinberg equilibrium (HWE) CYTH4 was examined by chi-square goodness of fit test. A p value of <0.05 was taken as deviation from HWE. Plasma levels of insulin, TG, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) and all anthropometric measures were natural log-transformed. For association studies a p value of <0.01 was taken as statistically significant. Setting a threshold of significance was the chosen method above Bonferroni corrections, since the candidate genes studied had been selected for based on a priori hypothesis and biological plausibility. A p value of <0.05 was taken as statistically significant in Principal Component Analysis (PCA). The majority of statistical analyses were performed using Intercooled Stata 8.2 for Windows (StataCorp LP, Texas, USA). Haplotype association analysis was carried out using Thesias [10]. PCA was carried out using SAS (SAS Institute Inc., Cary, NC).

As optimal task performance requires focusing on the task-relevant numerical dimension, larger facilitation from physical size information reflects the intrusion of the task-irrelevant stimulus dimension into processing. Hence, this effect is a marker of failure to inhibit the task-irrelevant stimulus dimension. Second, there was a larger distance effect in DD than in controls in the physical size decision Stroop task ( Supplementary Fig. 2H). This means that task-irrelevant numerical information had a larger effect on RT in

DD than in controls. Third and fourth, trail-making A (Mean/SE: DD = 58.3 ± 5.4 sec; Control = 41.3 ± 2.0 sec) and mental rotation (DD = 66.7 ± 4.4 sec; Control = 56.0 ± 3.5 sec) solution times were longer in DD than in controls. Further, this website there was a marginally larger congruency effect in the animal size decision Stroop task in DD than in controls ( Supplementary Fig. 2B). This means that task-irrelevant physical size information had marginally larger effect on RT in DD than in controls. Again, both permutation testing and confidence interval estimation showed that symbolic and non-symbolic slope was a highly non-discriminative parameter between groups. There were no effects

in coefficient of variation (see Supplementary Fig. 3). Regression analysis was used to study the relative weight of variables which significantly discriminated between DD and control and correlated with maths performance. The three visuo-spatial memory measures ERK inhibitor datasheet (Dot Matrix, OOO Recall and Processing) were averaged to form a single ‘Visuo-spatial memory’ measure. Molecular motor The RT facilitation effect from the numerical Stroop task and the RT distance effect from the physical size decision Stroop task were averaged to form an ‘Inhibition’ score because only these measures showed

a significant correlation with maths performance (see correlations in Figs. 2 and 3). The counting-range slope from accuracy data was also used because this also showed a significant correlation with maths performance. Correlations between the above variables and maths scores are shown in Table 4. The above three variables were entered into the analysis simultaneously. The regression had a significant fit [R2 = .583, F(20,3) = 9.30, p < .0001]. Visuo-spatial WM [Standardized Beta (β) = .48, t(20) = 3.2, p = .0045] was a significant predictor and Inhibition [β = .36, t(20) = 2.06, p = .0522] was a marginally significant predictor. Subitizing slope was a non-significant predictor [β = −.17, t(20) = −1.02, p = .31]. When only Visuo-spatial WM and Inhibition were entered into the regression the overall fit remained unchanged: [R2 = .561, F(21,2) = 13.39, p < .0001]. Visuo-spatial WM: β = .48, t(21) = 3.24, p = .0039. Inhibition: β = .45, t(21) = 3.00, p = .0068. When verbal IQ (WISC Vocabulary), Raven score and processing speed were added to the regression, the overall fit increased [R2 = .633, F(20,3) = 9.30, p < .

When 80% confluent, the cells were infected with a predetermined dilution of O. tsutsugamushi (isolate UT76) inoculum and incubated at 35 °C with 5% CO2 using maintenance media (5% FBS + RPMI 1640, (Gibco, Carlsbad, CA, USA)) for 8 hours. Following incubation, the infected cells were fixed and permeabilized in acetone for 10 min at −20 °C and allowed to air dry. Indirect immunofluorescence (IFA) was performed to visualize the intracellular O.

tsutsugamushi organisms. The coverslips were incubated with pooled human serum (diluted 1:320 in PBS) from O. tsutsugamushi confirmed-patients at 37 °C for 30 min, washed twice with PBS, then further incubated with FITC-conjugated goat antihuman IgG (Gibco) diluted ERK inhibitor mouse 1:40 in PBS for 30 min at 37 °C. The monolayer was then washed twice with PBS and the cells were counterstained with 0.00125% (w/v) Evans blue. The infected cells were visualized by epifluorescence microscopy (Nikon Eclipse 80i, Nikon Corp., Chiyoda-ku, Tokyo, Japan). Images of O. tsutsugamushi infected in cell culture were captured by digital camera (Nikon Digital Sight DS-5M-L1, Japan) at a 400× magnification. The method for enumeration of O. tsutsugamushi using ImageJ required the

image file to be converted from RGB color to 8-bit grayscale. The manual counting of the O. tsutsugamushi particles was performed using the built-in cell-counter plugin of the ImageJ program. After opening the image to be counted, the cell-counter plugin was opened (commands used: Plugins > Analyze > Cell Counter), ‘internalize’ and

‘Type 1’ selected. The Orientia particles Ruxolitinib mw were manually counted by the operator by moving the crosshairs over the particle and confirming the identity of Casein kinase 1 the particle by clicking the mouse button. The number of Orientia particles selected was then displayed within the plugin. Automated counting of the O. tsutsugamushi particles uses threshold algorithms to discriminate the features of interest from background. The threshold level is dependent on the algorithm selected and in this study Minimum, MaxEntropy, RenyiEntropy and Yen threshold algorithms 4, 5 and 6 were used however another twelve algorithms were assessed and found to be unsuitable for this application. To set the counting threshold following opening the selected image, the following commands Image > Adjust > Threshold > select algorithm to be applied > Apply were used and the image converted to a binary image by selecting Process > Binary > Make binary. O. tsutsugamushi particles were counted using the commands Analyze > Analyze Particles, with the the upper and lower limits for the particle size set at 0–infinity, selected to ‘Show outlines’ and checked box to ‘Summarize’. Each counted particle was outlined and numbered in a new window. Twenty-five IFA image fields were digitally photographed and the images processed as described above. O.

05% Surfactant P20 at pH 7.4 with 1 mg/ml BSA (Sigma)), and filtered through 0.22 μ multiscreen GV filter plates (Millipore). Filtered periplasmic extracts were injected over immobilized ligand for 3 min at 30 μl/min. Dissociation was followed for 10 min. The surface was regenerated following each analyte injection with 10 mM glycine at pH 1.7. Data was double referenced by subtracting the reference spot within the flow cell which was an activated and deactivated blank surface,

as well as subtracting out blank injections. Following referencing, the data were fit to a 1:1 dissociation model using Biacore 4000 evaluation software. To express Skp and FkpA in the E. coli cytoplasm, DNAs encoding these chaperones lacking their signal sequences and containing V5 and FLAG tags, respectively, were amplified by PCR from the Selleck BMS-936558 XL1-Blue E. coli chromosome. The gene products, designated

as cytSkp-V5 and cytFkpA-FLAG, were cloned into the l-arabinose-inducible expression vector, pAR3 ( Perez-Perez and Gutierrez, 1995) either separately ( Fig. 1a), or as a bicistronic gene sequence cyt[Skp + FkpA] encoding both cytFkpA and cytSkp http://www.selleckchem.com/products/Adriamycin.html ( Fig. 1b) for expression in the E. coli cytoplasm. Vectors were also constructed containing Skp and FkpA with their native signal sequences for expression in the E. coli periplasm ( Fig. 1a). Plasmids containing cytSkp-V5 and/or cytFkpA-FLAG, were transformed into E. coli TG1 cell cultures, grown to log phase, induced with l-arabinose, and periplasmic and cytoplasmic

extracts prepared. Western blot analysis using Inositol monophosphatase 1 anti-V5 and anti-FLAG tag antibodies verified that cytSkp and cytFkpA expressed on the same or separate plasmids were produced in the cytoplasm of TG1 cells ( Fig. 2, Lanes 3 and 5). Lower amounts of cytSkp and cytFkpA also were observed in an E. coli periplasmic extract ( Fig. 2, Lanes 2 and 4) which may be due to escape of the chaperones through the inner membrane during the generation of the extracts. The two bands that appear upon overexpression of cytSkp in E. coli ( Fig. 2b) could be attributed to an incomplete processing of Skp corresponding to the precursor and mature forms of Skp. Other scientists have previously demonstrated similar results when probing Skp using anti-Skp antisera ( Volokhina et al., 2011). We first tested the effect of co-expressing FkpA and Skp on secretion into the bacterial periplasm of Fabs containing kappa light chains. Initially, two human kappa Fabs, ING1 (anti-EpCAM) and XPA23 (anti-IL1β) and a murine anti-human insulin receptor kappa Fab, 83-7 (Soos et al., 1986) were expressed in TG1 cells in the presence or absence of cytoplasmically or periplasmically-expressed FkpA or Skp, either alone or in combination. The level of Fab in the periplasm capable of binding to EpCAM and IL1β was assessed by ELISA.

, 2007). In most climate change studies, GCMs have been used to project future climatic variables. However, due to a limitation of GCMs to incorporate local topography (spatial and temporal scales), the direct use of their outputs in impact studies on the local scale of e.g. hydrological catchments is

limited. To bridge the gaps between the climate model and local scales, downscaling is commonly used in practice. Dynamic downscaling and statistical downscaling are the most commonly used methods (Bergstrom, 2001, Fowler et al., 2007, Pinto et al., Protein Tyrosine Kinase inhibitor 2010, Schoof et al., 2009 and Wilby et al., 1999). Dynamic downscaling by Regional Climate Models (RCMs) ensures consistency between climatological variables, however they are computationally expensive. Statistical downscaling models, on the other

hand, are based on statistical relationships and hence require less computational time. Extensive research has been carried out with both approaches (e.g., Chen et al., 2012, Maraun et al., 2010, Teutschbein et al., 2011 and Willems and Vrac, 2011). Besides the scale issue, there is often a clear bias in the statistics of variables produced by GCMs such as rainfall and temperature (Kay et al., 2006 and Kotlarski, 2005). Therefore hydrologically important variables need to be adjusted to obtain realistic time series for use in local impact studies (Graham et BGJ398 al., 2007). A conventional way to adjust future time series is referred to as bias correction (Lenderink, 2007) where correction factors are derived by comparing the GCM output with observed weather variables in the reference period, and then applied to GCM output for future climate. While bias-correction generally reproduces the variability described by different climatic conditions simulated by GCM projections, one disadvantage is the assumption of stationarity, i.e. that the correction Exoribonuclease factors do not change with time. As indicated by Rana et al. (2012), the rainfall intensity and frequency

for Mumbai is related to certain global climate indices such as the Indian Ocean Dipole, the El Niño-Southern Oscillation and the East Atlantic Pattern. These established connections between local rainfall and large-scale climate features suggest the possibility to statistically downscale GCM data directly to the local scale. The objective of this paper is to apply a statistical approach termed Distribution-based Scaling (DBS) technique, which has been tested and applied to RCM data, to scale GCM data. This includes the application of the DBS model to GCM projections for the area, an analysis of the scaling methodology and its applicability to GCM data, and finally assessment of the future impacts on the city of Mumbai due to climate change as projected by nine different GCM projections. The study is carried out for the city of Mumbai, (18°58′30″ N, 72°49′33″ E; formerly Bombay) the capital of Maharashtra state, located in the south-western part of India.