The variance of 0.81% (nine amino acids) between the protein sequences of BGIOSGA035032

and SasRGA5 was in the HMA domain (amino acids 1001–1070), C′-end (amino acids 1071–1116) and the NBS domain (amino acid 416: V → M) ( Fig. 3). The slight variance of the two Pia/PiCO39 alleles in cv. 93-11 may not affect the function of Pia/PiCO39 because cultivar 104 (Peh-kuh-tsao-tu) with the same two Pia alleles as 93-11 was earlier deduced to harbor NU7441 in vivo just the Pia gene [37]. In addition, the Pi60(t)-differential isolate 001-99-1 was avirulent to all four Pia/PiCO39-harboring lines, namely, IRBLa-A, IRBLa-C, Aichi Asahi and CO39 ( Table 7). These results indicated that Pi60(t) could be Pia/PiCO39 or its allele. Differences in amino acids are marked in rectangular blocks. Eleven blast R genes, namely, Pita, Pita-2, Pi6(t), IWR-1 Pi12(t), Pi19(t), Pi20(t), Pi21(t), Pi39(t), Pi42(t), Pi58(t) and Pi157(t), are reported in the vicinity of Pi61(t) (9,924,675–10,124,186). Their target regions were roughly 5.6 kb (10,603,772–10,609,330), 3.1 Mb (10,078,620–13,211,331), 14.8 Mb (4,053,339–18,867,450), 8.1 Mb (6,988,220–15,120,464), 4.6 Mb (8,826,555–13,417,087), 3.6 Mb (6,988,220–10,603,823), 9.4 Mb (6,988,220–16,395,622),

38 kb (10,614,346–10,652,094), 4.2 Mb (8,073,819–12,248,913), 3.4 Mb (7,461,555–10,900,056) and 9.2 Mb (8,826,555–18,050,447), respectively [11], [57], [68], [69], [70] and [71]. To distinguish Pi61(t) from neighboring R genes, eight monogenic lines for Pita, Pita-2, Pi12(t), Pi19(t) and Pi20(t), i.e., IRBLta-CT2, C104PKT, IRBLta2-Pi, IRBLta2-Re, F128-1, IRBL12-M, IRBL19-A and IRBL20-IR24, were tested with five differential isolates, 001-99-1, P-2b, RB17, GZ26 and 99-26-2, and compared with the donor 93-11 ( Table 7). Differential reactions were clearly observed among the eight lines, except for IRBL12-M and IRBL20-IR24 ( Table 7), suggesting that Pi61(t) was different from Pita, Pita-2 and Pi19(t). Pi39(t) was at least 490 kb (10,124,186–10,614,346) away from Pi61(t) according to the Endonuclease distances between markers most tightly linked to the two genes. In addition, Pi39(t)

was mapped using the same differential isolate CHL724 as was Pi41, which also originated from cv. 93-11 and delimited to 16,534,669–16,588,406 bp on chromosome 12 [47] and [69]. This indicated that Pi39(t) could not be present in 93-11 together with Pi41. Therefore, we concluded that Pi61(t) was different from Pi39(t). As for Pi42(t), its co-segregating markers, including RRS63, were at least 0.19 cM from Pi61(t) ( Fig. 2-b). The target region of Pi42(t) contained six candidate NBS-LRR genes, and among them LOC_Os12g18374 was short-listed as a potential candidate of Pi42(t) based on restriction analysis of 11 candidate R gene-derived sequence tagged sequence (CRG-STS) markers [70]. However, LOC_Os12g18374 (10,621,450–10,630,781) was at least 497 kb (10124186–10621450) from Pi61(t).

For the oil spill predictions in the sea area around Crete, sea currents and sea surface temperatures have been acquired from the ALERMO (Aegean Levantine Regional Model) (Korres and Lascaratos, 2003 and Sofianos

et al., 2006). The ALERMO is downscaling from MyOcean (www.myocean.eu) regional MFS (Mediterranean Forecasting System) (Pinardi et al., 2007, Tonani et al., 2008 and Oddo et al., 2010) and covers the Eastern Mediterranean Ipatasertib price with forecast data every 6 h, with a horizontal resolution of 3 km. Both the MyOcean regional MFS and the downscaled ALERMO model use satellite-derived sea surface altimetry and available in-situ data. Wind data were obtained from SKIRON (Kallos and SKIRON group, 1998a, Kallos and SKIRON group, 1998b, Kallos and SKIRON group, 1998c, Kallos and SKIRON group, 1998d, Kallos and SKIRON group, 1998e and Kallos and SKIRON group, 1998f) as high frequency weather forecasts (every hour with a 5-km horizontal resolution), while wave data were obtained from CYCOFOS every 3 h, with a 10-km horizontal resolution (Galanis et al., 2012,

Zodiatis et al., 2014a and Zodiatis et al., 2014b). The three-step method proposed in this paper can be summarised as follows: (1) Bathymetric, geomorphological, geological and oceanographic data for the area of interest are initially acquired and analysed, considering these parameters buy INCB024360 as key to the dispersion of oil slicks in offshore areas. In this initial step, the morphological structure of onshore and offshore areas in Crete (Panagiotakis and Kokinou, in press) was analysed using bathymetric, elevation data, and their derivatives

(slope and aspect). Our aim was to select the areas of the possible oil spill accidents near to: (a) major sea-bottom features, (b) urban areas with important infrastructures and tourism sites, and (c) coastal regions showing high sensitivity to oil pollution due to their morphology and structure. Slope and aspect features are calculated for each point p of a bathymetric/topographic surface Z using the plane tangent vector u(p): equation(1) u(p)=∂Z(p)∂x,∂Z(p)∂yT Slope S  (p  ) is defined as the maximum rate of change in bathymetry or altitude. Thus, the (-)-p-Bromotetramisole Oxalate rates of surface change in the horizontal ∂Z(p)∂x and vertical ∂Z(p)∂y directions from the point p   can be used to determine the slope angle S  (p  ): equation(2) S(p)=tan-1(|u(p)|2)S(p)=tan-1u(p)2where tan−1 is the arctangent function and |u(p)|2u(p)2is the Euclidean norm of the vector u(p). Aspect identifies the downslope direction of the maximum rate of change in the value from each point to its neighbours. Therefore, it holds that Aspect can be defined as the slope direction on horizontal plane: equation(3) A(p)=atan2∂Z(p)∂y,-∂Z(p)∂xwhere a   tan 2 is the arctangent function with two arguments. The parameter a   tan 2(y  , x  ) is the angle between the positive x  -axis of a plane and the point given by the coordinates (x  , y  ) on this same plane.

ABA caused an increase in the concentration of the enzyme aspartate aminotransferase (AST) in serum in vivo and an increase in the concentration of AST and alanine aminotransferase (ALT) in vitro, which are used as indicators of damage to the hepatic parenchymal cells ( Klaassen and Eaton, 1991). We previously demonstrated that ABA inhibits the activity of FoF1-ATPase and adenine nucleotide translocator (ANT) when added at micromolar concentrations to isolated rat liver mitochondria, an effect associated with significantly reduced ATP synthesis ( Castanha Zanoli et al., 2012). FoF1-ATPase is an enzyme present in the inner

mitochondrial membrane that is responsible by ATP synthesis driven by the proton electrochemical gradient generated in the respiratory chain. The main components of the enzyme are Fo, an integral membrane protein that works as a proton channel, and F1, a hydrophilic moiety which Ribociclib contains the catalytic and

regulatory sites (Hatefi, 1993 and Pedersen, selleck compound 1996). ANT is other important component of the mitochondrial machinery of ATP synthesis because of its intrinsic adenine nucleotide translocase activity. ANT has been involved in both pathological (mitochondrial permeability transition formation/regulation and cell death) and physiological (adenine nucleotide exchange) mitochondrial events, making it a prime target for drug-induced toxicity (Oliveira and Wallace, 2006). The xenobiotic metabolism in the liver is accomplished by cytochrome P450 and its main C1GALT1 function is to increase the polarity of these substances, so excretion occurs more easily (Oga, 2008). However, this process is responsible for the toxic effects of numerous chemical compounds. The metabolites may cause adverse effects in the animal (Ioannides and Lewis, 2004, Mingatto et al., 2007 and Maioli et al., 2011) by changing a fundamental cellular component (mitochondria, for example) at the cellular and molecular level, thus modulating its function (Meyer and Kulkarni, 2001). Due to the important functions of the liver in animals and previous studies that indicated the occurrence of liver damage after the use of ABA, this study aims to characterize the mechanisms of

ABA toxicity on parameters related to bioenergetics and cell death and determine whether the toxicity induced by the compound is due to a possible activation following its metabolism in the liver. Abamectin, containing 92% avermectin B1a and 8% avermectin B1b, was kindly supplied by the company Ourofino Agribusiness (Cravinhos, SP, Brazil), proadifen was purchased from Sigma–Aldrich (St. Louis, MO, USA), and sodium pentobarbital was a gift from Cristália (Itapira, SP, Brazil). All other reagents were of the highest commercially available grade. Abamectin and proadifen were dissolved in anhydrous dimethyl sulfoxide (DMSO). All stock solutions were prepared using glass-distilled deionized water. Male Wistar rats aged 7–8 weeks and weighing approximately 200 g, were used in this study.

This might have driven the different responses to both types of targets, namely the slower responses and delayed ERPs to initially stressed target words. Crucially, however, type of target did not interact with ERP priming effects. Due to this, stress match and stress mismatch included the very same primes and target words, though in different combinations: Stress Match included stressed primes followed by initially stressed targets AND unstressed primes followed by initially unstressed targets. Stress Mismatch included unstressed primes followed Protease Inhibitor Library cell line by initially

stressed targets AND stressed primes followed by initially unstressed targets (see Table 1B). Thus, ERP stress priming cannot be deduced to inherent timing or linguistic differences between initially stressed and initially unstressed target words. We used unimodal auditory word onset priming to characterize

the function of prosody-relevant information in spoken word processing. Selumetinib In line with our former studies (Friedrich et al., 2004 and Schild et al., 2014), ERPs are indicative for processing of syllable stress that is independent from the processing of phoneme-relevant information. We found independent ERP stress priming and ERP phoneme priming. This is strong evidence for phoneme-free prosodic processing across the complex stream of spoken word recognition. Differential ERP stress priming effects across our studies suggest that phoneme-free prosodic processing serves several functions in the complex speech recognition stream. In the light of absent stress priming in the reaction time data, the ERPs reveal that lexical decision latencies obtained in word onset priming do not track those aspects of spoken word processing. The present ERP stress priming effect is partly comparable with that obtained why in our previous cross-modal auditory–visual study (Friedrich et al., 2004 and Friedrich et al., 2004). We found enhanced posterior negativity for stress mismatch compared to stress match, though in addition to this effect we found frontal stress priming with opposite polarity to the posterior one. Thus it appears that spoken primes modulate

more aspects of the processing of spoken targets (present study) than they modulate aspects of the processing of written targets (previous cross-modal study). However, based on comparably enhanced posterior negativity for stress mismatch in the present unimodal study and the former cross-modal study, we conclude that target modality does not alter the polarity of the posterior negativity related to stress priming. Thus the unique stress priming effect obtained in our previous unimodal auditory study (Schild et al., 2014) has to be linked to other differences between studies. We might conclude that the unbalanced sequence of stressed and unstressed syllables has driven the stress priming effect in our former unimodal auditory study.

, 2010). For the latter possibility,

Na-Cl water could have been present in shallow groundwater as a result of natural hydraulic connections to underlying strata and the idea of such connections is supported by the documentation of natural fractures (Jacobi, 2002), particularly J1 and J2 joint sets, in the Geneseo Shale (of the Genesee Group) which underlies the western portion of the county (Fig. 1) (Engelder et al., 2009). The lack of differences in methane concentrations across Pifithrin-�� mw different bedrock formations in which water wells were finished also supports the possibility that methane-rich Na-Cl water is migrating from deeper formations. In either case, this water chemistry is indicative of increased interaction with bedrock and less contribution of meteoric (precipitation-derived) water that would have infiltrated through overlying calcareous sediments (Fleisher, 1993). This extended residence time and potential interaction

with methane-rich strata (e.g. black shale) could have led to relatively higher methane concentrations (Molofsky et al., 2013). The Na-HCO3 groundwater and its associated dissolved methane likely resulted from groundwater residence time and rock-water interaction as well as redox processes. Longer residence times typically lead to increased concentrations of Na and HCO3 due to cation exchange between calcium and sodium Decitabine mw clonidine and oxidation of organic matter, and can also promote biological methane production as oxygen is used up and methanogenesis is thermodynamically favored (Kresse et al., 2012 and Thorstenson and Fisher, 1979). The methane isotopic signatures also support the presence of some microbial methane, with the majority of δ13C-CH4 values falling between −40 and −60‰, indicating likely mixing of biogenic and thermogenic methane (Whiticar, 1999). To better predict patterns in dissolved methane, it is useful to model the relationship between methane and readily

measurable environmental parameters. Such parameters could be GIS-derived characteristics described in previous sections or water quality and geochemical characteristics like specific conductance or sodium concentration. It is also important that such parameters be continuous, rather than classifications like ‘valley’ vs. ‘upslope’. Table 2 displays the results of the best multivariate regression models using selected variables from the full suite of landscape and chemical parameters. An initial model was developed using nine variables that were selected based on their Pearson correlation with methane. Using the six variables found to be significant (p < 0.05) – hardness, barium, chloride, sodium, sulfate and distance from active gas wells – a regression model was created that could explain 82% of variation in observed methane patterns (Fig. S3).

A diagnostic scoring cutoff set at 3 standard deviations above the mean for the normal patient selleck chemicals llc cohort yielded 11% sensitivity for colorectal cancer detection at 100% specificity with these samples. This method of setting cutoffs is commonly used for autoantibody immunoassays (e.g. Liu et al., 2009). Next, to technically validate the VeraCode™ bead assay using the p53 TAA,

we evaluated the data obtained from screening the same patient cohort against beads to which either purified recombinant p53 or cell-free produced p53 was attached (Fig. 2, middle and bottom panels, respectively). The cutoff and scoring were done as with the ELISA. The error bars represent the intra-assay bead-to-bead variance in fluorescence intensity within CDK inhibitor each sample-protein pair (i.e. variance of replicate beads). Results from ELISA were compared to results obtained from VeraCode™ beads. All 5 colorectal cancer samples which scored positive in the ELISA also score positive on both VeraCode™ bead assays (with both recombinant and cell-free p53 protein). In addition, two additional hits in the CRC cohort were detected by the VeraCode™ assay (same two patients detected with both recombinant

and cell-free proteins) but 100% specificity versus the normal patients was maintained. In order to establish intra-assay precision, we performed the multiplex bead assay on triplicate samples of four CRC and four normal patient sera/plasma in a 96-well plate. Two TAAs were used in this multiplexed experiment: The p53 control (discussed earlier) and Cyclin B1 (Koziol et al., 2003, Chen et al., 2007 and Reuschenbach et al., 2009). Each of the three replicate wells of each sample contained approximately 50 beads per TAA. Two previously known p53-positive sera (based on ELISA and VeraCode™ data

in Fig. 2) were chosen for this experiment, whereas their sero-reactivity against CyclinB1 was not known a priori (i.e. positives not necessarily expected based on low diagnostic sensitivity of individual ID-8 TAAs). Results are shown in Supplementary Fig. 2. An average intra-assay CV of 10% across all samples and proteins was achieved (see error bars in Supplementary Fig. 2 for more detail). The diagnostic scoring cutoff for p53 was calculated based on the normal samples as discussed earlier, however, for maximum stringency, the calculations were done before averaging the MFI values of the replicate samples (MFI = Mean Fluorescence Intensity; i.e. mean of all beads within one sample per TAA). With this, the scoring cutoff accounts for variance across the sample replicates. Of note, using this cutoff, previously known p53-positive samples were correctly detected in this VeraCode™ bead experiment, with no false positives (neither in CRC nor normal samples).

The ecosystem model ERGOM-MOM is an integrated biogeochemical

model linked to a 3D circulation model covering the entire Baltic Sea. A horizontal resolution of 1 nautical mile (nm) is applied in the western Baltic Sea and in inner and outer coastal waters. The vertical water column is sub-divided into layers with a thickness of 2 m. The biogeochemical model consists of nine state variables. This model is coupled with the circulation model via advection diffusion equations for the state variables. The nutrient variables are dissolved ammonium, nitrate and phosphate. Primary production is represented by three functional phytoplankton groups: large cells, CB-839 order small cells and nitrogen fixers. A dynamically developing bulk zooplankton variable provides grazing pressure on

the phytoplankton. Accumulated dead particles are represented in a detritus state variable. During the process of sedimentation a portion of the detritus is mineralized into dissolved ammonium and phosphate. Another portion reaches the sea bottom where it accumulates as sedimentary detritus and is subsequently buried, mineralized or resuspended in the water column. Under oxic conditions parts of phosphate are bound to iron oxides in the sediment, but can be mobilized under anoxic conditions. Oxygen concentrations are calculated from biogeochemical processes via stoichiometric ratios and control processes such as denitrification and nitrification. Neumann Methocarbamol [35], Neumann et al. [36] and Neumann and Schernewski JAK/stat pathway [37] provide detailed model descriptions and validations.

Recent comparative studies [19], [30] and [20] proved that the biogeochemical model ERGOM is sufficiently reliable in the western Baltic Sea and suitable for scenario simulations. Weather data for the present time were taken from the Rossby Center Atmosphere model RCA3.0 on the basis of ERA-40 [28]. For the historical simulations the weather reconstruction of Schenk and Zorita [43] was used. Riverine nutrient input for 1970–2000 was provided by the Baltic Nest Institute (BNI) including 80 catchment areas around the Baltic Sea. After 2000 the official HELCOM Pollution Load Compilation (PLC-5) data [23] for riverine nutrient input was used. Since PLC provides only aggregated country-wise data for the nine Baltic Sea basins, the country loads were allocated according to the share of each river in BNI data. The historic nutrient loads of 16 main Baltic rivers, outside Germany, were reconstructed by following the approach of Gustafsson et al. [21] and all loads attributed to these rivers. The atmospheric nutrient input was computed by distributing the loads taken from Ruoho-Airola et al. [40] for every sub-region including a decline towards the open sea.

, 2008). These

different tissue responses have been investigated under different in vitro and in vivo models in order to understand the local cytotoxicity and the systemic effects of the complex mixture of snake venoms ( Gutierrez et al., 1986; Sanchez et al., 1992; Melo et al., 1993; Melo and Ownby, 1999; Murakami et al., 2005; Teixeira et al., 2009; Escalante et al., 2011). The recommended therapy to snakebite envenomation has been based on the administration of animal-derived antivenom that can ameliorate and stop many of the venom effects (da Silva et al., 2007; Gutierrez et al., 2007, Gutierrez et al., 2011a and Gutierrez et al., 2011b). However, the local response induced by Bothrops snake venoms is described as being only partially neutralized by either the specific or the polyvalent antivenom even if the antivenom IOX1 is locally injected ( Chaves et al., 2003; da Silva et al., 2007; Gutierrez et al., 2007 and Gutierrez et al.,

2011a). The problem is bigger when the therapy is delayed for many different reasons, such as geographical problems or lack of accessibility to the antivenom ( Chippaux, 1998; Pardal et al., 2004; Gutierrez et al., 2007). In many rural areas in Brazil or elsewhere in the world where the antivenom is not easily available, local people use folk medicine such herbal preparations in the snakebite treatment, trying to interrupt the venom effects ( Martz, 1992; Mors et al., 2000; Coe and Anderson, 2005). When it is available, the use of antivenom can still elicit different reactions once they are animal-derived products. The local venom effects are poorly understood, and although many studies have been trying to develop new substances Lumacaftor able to stop or antagonize the powerful local inflammatory response induced by Bothrops venoms, which involves cytokines and white blood cells, it is still a challenge ( Lomonte et al., 1993; Olivo et al., 2007; Gutierrez et al., 2007; Melo et al., 2010). It has been difficult to develop new drugs for snakebite envenoming treatment, either from plants or from new planed synthetic molecules, because they are

not attractive to developed countries nor to big companies once they will not return the investment Parvulin and the endeavor ( Gutierrez et al., 2007; Lomonte et al., 2009). The local myonecrosis and inflammatory response are critical to late disabilities (Gutierrez et al., 1986; Rucavado and Lomonte, 1996; Teixeira et al., 2009), but even the well-known substances used for the treatment of allergic reactions induced by antivenom treatment are not frequently investigated for their anti-inflammatory activities (Chen et al., 2007; Olivo et al., 2007; Thiansookon and Rojnuckarin, 2008; Nascimento et al., 2010). Nascimento et al. (2010) described that dexamethasone decreased the acute inflammatory response induced by Bothrops moojeni in mice, and this observation is ascribed to the ability of dexamethasone to decrease the formation of eicosanoids in the presence of the venom.

Therefore, currently, many structural variants are still missed by single-cell genome sequencing. Nevertheless, filters have been designed to permit the detection of the structural architecture of copy number alterations following mapping of paired-end sequences selleck chemicals llc ( Figure 3c) [ 27••] and approaches to detect L1-retrotransposition have been developed [ 45•]. In a recent study, we were able to discover and fine-map intra-chromosomal as well as inter-chromosomal rearrangements in single cells. Furthermore,

we performed single-cell genome sequencing of individual breast cancer cells related by one cell cycle, and detected large de novo structural DNA imbalances acquired over one cell division [ 27••], providing proof of principle that single-cell sequencing can track tumour evolution in real time. Sequencing

allows discovering single nucleotide mutations (Figure 3d). However, genuine base substitutions in the cell have to be discriminated from WGA-polymerase base infidelities and sequencing errors [20•, 26••, 42••, 46••, 47, 48 and 49]. Therefore, reliable single-nucleotide substitution detection in non-haploid loci currently necessitates sequencing of multiple single cells [20•, Doxorubicin cell line 26••, 46••, 47 and 48], or confirmation by deep-sequencing of matched bulk tissue [42••], thus posing problems for the characterization of rare cell populations. Targeted sequencing of single-cell WGA products was recently applied to investigate single-nucleotide mutations in the exome, to hunt for heterogeneity in a renal carcinoma [20•], a myeloproliferative neoplasm [46••] and a bladder cancer [47]. Although see more variant calls of at least three cells had to be considered to filter WGA and sequencing artefacts from genuine base alterations, subclonal population structure could be profiled at high accuracy,

providing insight into progression and selection processes, and understanding of the difficulty of treating cancer. Single nucleotide and indel mutational landscapes in CTCs in patients with lung cancer [44•• and 50] and colorectal cancer [42•• and 51] were recently also determined by single-cell exome and cancer gene panel re-sequencing, respectively. These studies are signalling the promise of CTC sequencing for identifying therapeutic targets and regimens for personalized treatment. Using short in vitro cultures, mutation rates have been tracked over a limited amount of cell divisions. Whole-genome sequencing of multiple WGAed cells revealed a base mutation rate in a colorectal adenocarcinoma cell line that was 10-fold higher when compared to estimates of germline studies [ 26••].

Sorgente et al. (2003) used the Princeton Ocean Model (POM) to study the flow through the Sicily Channel. This modelling identified two main AW veins, one in the south along the African coast and the other in the north along the Sicilian coast. Based on geostrophic calculations using CTD data from April 2003–October 2003, Ferjani & Gana (2010) indicated that the mean inflow and outflow through the western side of the Sicily Channel were 0.5 and 0.4 × 106 m3 s− 1 respectively.

Stanev et al. (2000) characterized the water exchange through the Bosphorus-Marmara-Dardanelles system as a two-layer flow, in which see more Black Sea water occupied the surface layer (average flow of 0.019 × 106 m3 s− 1) and Mediterranean water occupied the deep layer (average flow of 0.009 × 106 m3 s− 1). Recent estimates indicate a reduction in inflow of approximately 0.003 × 106 m3 s− 1, which affects the North Aegean Sea circulation (Stanev & Peneva 2002). Nixon (2003) and Ludwig et al. (2009) estimated that the average discharge of the River Nile to the Mediterranean basin after the construction of the Aswan High Dam decreased by a factor of more than two. The paper aims to: (1) examine the water exchange through the Sicily Channel, (2) calculate the long-term change in vertical temperature and salinity distribution in the Eastern Mediterranean Basin, and (3) examine the heat and water balances of the Eastern Mediterranean Basin. The study

uses a simple ocean model to analyse a large set of meteorological and hydrological data used for forcing. The model simulations are validated and the main conclusions are drawn using independent selleck chemical oceanographic observations. The paper is structured as follows: section 2 presents the data and models used; section 3 presents the results, while section 4 discusses them; finally, the appendices provide a full description of the model. The study relies on the numerical modelling of the heat and water balances of the Eastern

Mediterranean Basin and the water exchange through the Sicily Channel. The present version of the model is vertically resolved and time-dependent, based on horizontally-averaged either input data over the study area and with in- and outflows controlling the vertical circulation. The meteorological data were horizontally averaged using linear interpolation over the EMB to describe the general features of the forcing data. Exchange through the Sicily Channel was modelled using: (1) current speeds across the Sicily Channel calculated from satellite recordings, (2) evaporation rates calculated from the model, (3) observed precipitation rates, and (4) observed river data. The period studied was 1958–2009. Several data sources have been used in this study, as follows: 1. Mediterranean Sea absolute dynamic topography data from May 2006 to October 2009. These data were extracted from the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) database available at http://www.aviso.