All RNA samples had an RNA integrity number of

at least six. Transcriptome analysis was performed following the manufacturer’s recommendation in the Affymetrix Gene Chip® Expression Analysis Technical Manual (Santa Clara, CA) as previously specified (Gebel et al., 2010). The quality of Affymetrix CEL files was checked by utilizing the R packages affy, gcrma, and affyPLM (Bolstad et al., 2005, Gautier et al., 2004 and Wu et al., this website 2005). Normalized Unscaled standard error (NUSE) box plots and relative log expression (RLE) box plots were generated to identify the potential outliers. A CEL file was identified as an outlier if the median of its NUSE was beyond 1.05 or the median of its RLE was beyond 0.1. Potential spatial artifacts on arrays were checked by plotting the image and pseudo image for all the arrays. Microarray expression values were generated from the CEL files using background correction, quantile normalization, and median polish summarization. A probe set was filtered out when the 95% quantile of the

log 2 expression value was less than 7. To extract a specific and robust gene signature which can discriminate the tumors of differently exposed mice, supervised machine-learning approaches including SAM (Tusher et al., 2001) and support vector machine (Cortes and Vapnik, 1995) were applied in a 10-fold cross-validation procedure. A preliminary pathway analysis was performed using DAVID (Huang et al., 2008 and Huang et al., 2009). For continuous data, in general the arithmetic mean and the standard PLX4032 error (SE) are given as descriptive statistics, but for chemical-analytical data describing the test atmosphere the SD was calculated. Continuous data, such as organ weights, were statistically evaluated using a 1-way analysis of variance (ANOVA) followed by a Tukey test (Zar, 1984). Statistical evaluation of all neoplastic findings was carried out using an exact Trend Test (Peto et al., 1980). All calculations were performed using the Pathdata-System statistical program (Rotkreuz, Switzerland). old Non-neoplastic findings were statistically analyzed with

a non-survival adjusted Trend Test (Armitage, 1955). For all neoplastic (except lung tumors) and non-neoplastic findings a one-sided Fisher’s exact test (pairwise comparisons of control groups vs. concentration groups) was performed. For the lung tumor incidence, the Fisher Exact Test was applied for overall analysis followed by pairwise comparison. For the lung tumor multiplicity, the 1-way ANOVA was applied followed by pairwise comparison using the Tukey test (Zar, 1984). For the calculation of the discriminatory power (β = 0.2) of various study designs, the minimal detectable difference (MDD) was calculated by comparing the slopes of two linear regression lines. A t-distributed test statistic was calculated ( Sachs, 1978) by using the same variance components of actual study data for both regression lines.

Quantile functions were used to derive new IDF curves for the 5–100 year RP events,

which were compared to the originally derived IDF curves. Gaps in short duration events, less than 24 h, were filled with IDF scaling relationships. Both the Chowdhury model (Rashid et al., 2012) and Nhat model (Nhat et al., 2006) were used (see Eqs. (1) and (2) below). The basis of the Chowdhury model GDC-0449 in vitro is the 24-h event rainfall depth, P24 (see Eq. (1)). Optimizing functions are used to derive the best fit values for its exponent (E) and constant (C). The Nhat model is based on the simple scaling of time and scale invariance of daily rainfall to derive intensities for shorter durations (Eq. (2)). GDC-0068 nmr The process relies on equating the probability of distribution of the parent duration (typically the 24-h) and any other duration (d). Parameter values for the exponent in the Nhat model are optimized to fit a training set, i.e. after the data sets for NMA and SIA are split into a training set and a verification set. The goodness of fit (CC  i) for predicted (P′d,iP′d,i) versus observed (Pd,i) rainfall depth for both models for each duration for the ‘ith’ year was optimized until both had approximately the same root mean square of errors (RMSE). The performance

of both models was compared to the AMS data, for the period 1957–1991, for variations in performance for each duration, and the optimal relationships used to fill the gaps. Modified Chowdhury (Rashid et al., 2012) of the Indian Meteorological Department (IMD) empirical reduction formula for estimation of rainfall depths, P (mm), for various durations (d) from Annual Maxima values. Where E and C are constants to be determined equation(1) Pd=P24d24E+C Simple scaling factor for derivation of shorter duration events intensities (id) by equating the frequency distributions, after Nhat et al. (2006) equation(2) iddist¯¯λd−Hd⋅iλd Gaps in the long duration events (2 days and longer) were filled using an artificial neural network (ANN) (see Appendix A) driven by National Centers for Environmental

Predictions and National Center for Atmospheric Research (NCEP/NCAR) re-analysis data (Kalnay et al., 1996 and NOAA, 2012). ANN is a statistical downscaling method that develops non-linear relationships between input Cytidine deaminase global gridded data and output at-station precipitation predictions. ANNs are described by Rumelhart et al. (1986) and Gegout et al. (1995). The method represents a good downscaling option for this study since previous studies suggest that it performs credibly and comparably to other downscaling methods (Goodess, 2007 and Abebe et al., 2000). Once calibrated, it can be deployed to determine future climates using projections from Global Climate Models (GCMs). No previous studies were found in, which a feed-forward ANN was used in a Caribbean extreme precipitation study.

, 2004). Much less attention, however, has been paid to the association between anxiety and the metabolic syndrome, with the few existing cross-sectional studies reporting mixed

findings (Carroll et al., 2009 and Dunbar et al., 2008). So far, the biological mechanisms underlying the association between depression and anxiety and later metabolic syndrome remain poorly understood. One biological factor receiving increased attention as a potential mechanism for this association is inflammation. C-reactive protein (CRP) is a non-specific marker of systemic Venetoclax cost inflammation. Its concentration rises as much as 2000-fold during the first 24–48 h after the onset of tissue injury or inflammation. Higher CRP plasma levels have been shown to be associated with both the metabolic syndrome (Devaraj Selleck Wortmannin et al., 2009) and depression

(Raison et al., 2006, Gimeno et al., 2009 and Howren et al., 2009) and anxiety (Bankier et al., 2009 and Pitsavos et al., 2006), although some studies failed to confirm these associations (Douglas et al., 2004, O’Donovan et al., 2010 and Whooley et al., 2007). Among the possible reasons for these inconsistent findings is the potential confounding of the relationship by factors such as lifestyle and socioeconomic conditions. Genetic studies have the potential to shed light on the role of CRP in the relationship

between depression and anxiety and the metabolic syndrome, since genes influencing CRP levels will not be influenced by these potential confounding factors. It has been estimated that the interindividual variability in blood CRP level is 35–52% heritable (Pankow et al., 2001 and MacGregor et al., 2004) and certain single nucleotide polymorphisms (SNPs) of the Resminostat CRP gene have been found to strongly influence the blood level of CRP ( Kolz et al., 2008, Almeida et al., 2009 and Lee et al., 2009). Two CRP SNPs, rs3093068 and rs1205, have been associated with variation in CRP level, with the C allele of rs3093068 and the C allele of rs1205 being associated with higher level of plasma CRP ( Kolz et al., 2008 and Halder et al., 2010). To date, only a few studies have investigated CRP variations in relation to the metabolic syndrome, with some providing null results ( Evrim et al., 2009 and Timpson et al., 2005), and the most recent reporting a significant association ( Hsu et al., 2010). Similarly, only two studies have evaluated associations between CRP polymorphisms and depression: one study reported a significant association between CRP rs1205 polymorphism and clinically significant depression in men ( Almeida et al., 2009), while another found no effect of three CRP polymorphisms or haplotypes on depressive scores ( Halder et al., 2010).

2010). Our results were consistent with those of Gantar et al. (2008), who found that the interactive effects between strains of Cyanobacteria and green algae depended both on the concentration of allelopathic compounds and on the time of exposure. We also observed the effects of enriched cell-free filtrates from one microalgal species on the growth of the other microalgal species at the initial cell densities 1.0 × 104 and 1.0 × 105 cells mL− 1 (Figure 2). It was evident that the growth selleck of P. donghaiense with initial cell densities of 1.0 × 104 cells mL− 1 was significantly inhibited by the filtrates from P. tricornutum cultures from LGS onwards (P < 0.0001). In contrast, when the initial cell

density of P. donghaiense was 1.0 × 105 cells mL− 1, the enriched filtrates of P. tricornutum promoted the growth of P. donghaiense at LGS and EGS (P < 0.05), after which a significant inhibitory effect manifested itself at SGS (P < 0.05). Meanwhile, the growth of P. tricornutum at both 1.0 × 104 and 1.0 × 105 cells mL− 1 was inhibited in the presence of cell-free filtrates from P. donghaiense (P < 0.0001). In the present study, besides the co-culture method, we also applied the cell-free filtrate method to assess the allelopathic interactions between P.

donghaiense and the diatom P. tricornutum. These methods, as expected, produced some identical results. In general, growth inhibition of one species was recorded in both the co-culture experiment and the enriched filtrate experiment, indicating PD0332991 ic50 that the allelopathic effects

of one species were acting on the other one. However, the extent of interference in the coculture experiment was not quite identical with that in the enriched filtrate experiment. The degree of growth inhibition and promotion response of P. donghaiense and P. tricornutum cells was different in the coculture experiment and enriched filtrate experiment. This indicated that the allelopathic substances of P. tricornutum acting on P. donghaiense were probably different in their chemical nature or could have reacted antagonistically/synergistically in the co-culture ( Yamasaki et al. 2007). An et al. (1996) assumed that the effect of the allelochemical pool of a plant might be characterised by two processes: the release and degradation of the allelochemicals. Note Carnitine palmitoyltransferase II that in our co-culture and filtrate experiments, the mode of allelopathy was different. Microalgal cells could rapidly and continuously release biologically-active allelochemicals into the culture medium in the co-culture, and this was also a result of the synergistic interaction of two or more compounds, some of which could have been degraded or lost in the filtrate experiment. Moreover, cell-to-cell contact in the co-culture was also responsible for the non-identical growth response of microalgal cells in the two methods. Nagasoe et al. (2006) found that the growth inhibition of Gyrodinium instriatum by Skeletonema costatum might require cell contact, but that G.

“
“Monocyte activation, triggering their adhesion to the endothelium PD-0332991 order and subsequent migration into the arterial intima, is an early event in atherogenesis [1], [2], [3] and [4]. Transformation into lipid-engorged macrophage foam cells follows, and leads to the appearance of fatty streaks, the first visible lesions in the vessel wall. Uptake of oxLDL by monocyte/macrophages is known to play a significant role in atherogenesis by stimulation of the secretion of pro-inflammatory cytokines, chemokines and other factors [5], but there is now considerable evidence to indicate that chylomicron remnants (CMR), the lipoproteins which transport fat of dietary

origin from the gut to the liver, are also strongly atherogenic [6]. Lipids from food are absorbed in the gut and secreted into lymph in large, triacylglycerol (TG)-rich lipoproteins called chylomicrons which then pass into the blood via the thoracic duct. Here they undergo rapid lipolysis, a process that removes some of their TG and forms the smaller CMR which deliver the remaining TG, cholesterol and other lipids to the liver [7]. Chylomicron remnants are taken up and retained in the artery wall [8] and [9], and remnant-like particles have been

isolated from the neointima of human atherosclerotic plaque and in animal models of atherosclerosis [10] and [11]. Delayed clearance of CMR correlates with the development of atherosclerotic lesions, and is associated with consumption of Western diets, obesity and type 2 diabetes [12] and [13]. Data from this laboratory and others has demonstrated that Vemurafenib in vitro CMR are taken up by human macrophages derived from the human monocyte cell line THP-1 or from macrophages derived from freshly isolated monocytes [14] and [15] inducing foam cell formation [16], expression of genes involved in lipid metabolism [17] and modulation of pro-inflammatory cytokine expression [18] and [19]. Furthermore, CMR inhibit endothelium-dependent relaxation of isolated arteries [8], [20] and [21], Dolutegravir ic50 and trigger pro-inflammatory signal transduction in human endothelial cells (EC; [22]). Monocytes are the precursors of macrophage foam cells and thus have a crucial

role in atherogenesis. Under inflammatory conditions, activation of both monocytes and EC triggers expression of adhesion molecules, cytokines and vasoactive mediators and promotes monocyte adhesion to the endothelium and subsequent migration into the arterial wall [1], [2] and [4]. The potential role of dietary fats in pro-inflammatory activation of circulating monocytes has not been explored experimentally, but TG-mediated expression of CD11b/Mac-1 has been reported after oral fat loading in normal healthy human volunteers [23] and [24]. Oxidative burst or reactive oxygen species (ROS) formation is a hallmark of monocyte activation and uptake of oxLDL by monocytes or monocyte-derived macrophages is known to be accompanied by ROS production [25].

g. Ban et al., 2014 and Cheung see more et al., 2013) but also performing a modeling study based on a subregion of Southeast Asia (Raja Ampat, Papua, in the Indonesian archipelago – see Box 1). Specifically, we used an Ecopath with Ecosim model parameterized for the Raja Ampat

reefs (Ainsworth et al., 2008), which we extended to include responses of space-limited algae. Then we modeled the effect a progressive 0–100% reduction in extent of coral cover will have on reef community structure, and the effect of these changes on fishery production (see Box 1). This study demonstrates how reef degradation will affect reef fishery production, and thus local livelihoods and the national economy. As a first approximation for identifying priorities for immediate management response, we constructed a simple model that ranks areas according selleckchem to cumulative pressures and potential user conflicts. To approximate the intensity of human impacts on tropical coastal seas around the world we used the ‘focalmean’ tool in ArcCatalog to extrapolate a population proximity index for each of the grid cells in the continental shelf region of the tropics. ‘Focalmean’ calculates a new value for each grid cell

in an existing grid, based on the value of surrounding grid cells. For our analyses, we used a circular Terminal deoxynucleotidyl transferase region around each grid cell, which extended out to a radius of 100 grid cells. This approximated a focal mean radius of about 93 km at the equator. We created a source grid for our focal mean calculations by combining the LandScan grid with the continental shelf grid. Each of the grid cells in the shelf region of the source grid had a value of 0, and all of the terrestrial grid cells had the corresponding population count information from LandScan. We masked out all land grid cells in the resulting focal mean grid. The shelf region greater than 100 km from a coast received

a population proximity index score of zero, since those areas were assumed to receive negligible direct impacts from urbanization. We acknowledge that certain ocean-based activities (e.g. offshore mineral extraction) will have impacts not captured by our approach. The 100 km wide coastal strip comprises 21% of all land, and is occupied by over 2.6 billion people (Fig. 1) at densities from <20 km−2 to >15,000 km−2, and an average density (97 km−2) over twice that of inland regions (41 km−2). Over half these people (1.36 billion) live on tropical coasts (just 7% of all land) at even higher densities (145 km−2). Tropical coasts hold 9 of 19 coastal megacities (>10 million people each), and are most densely populated (mean of 198 km−2) in South and Southeast Asia (Balk, 2011 and von Glasow et al., 2013).

7B, F(3,17) = 7.885, p = 0.0025), were completely inhibited by pre-treatment with piroxicam (p < 0.05). Selective COX-2 inhibition had no effect on circulating PGE2 levels. Next, we measured cytokine mRNA levels Nutlin-3a solubility dmso in the brain. TNF-α mRNA was significantly increased 3 h after LPS challenge ( Fig. 7C, F(5,25) = 3.723, p = 0.0035). Pre-treatment with piroxicam did not change the mRNA levels of TNF-α in the brain, while, pre-treatment with nimesulide significantly inhibited TNF-α mRNA expression. IL-6 mRNA levels were also increased after LPS challenge ( Fig. 7D, F(3,17) = 6.263, p = 0.0064), and like TNF-α, only inhibited by nimesulide pre-treatment.

Finally, we measured COX-2 mRNA levels, which were significantly up-regulated 3 h post LPS challenge ( Fig. 7E, F(3,18) = 4.674, p = 0.0017). Both piroxicam and nimesulide equally reduced COX-2 mRNA

CX-5461 order expression and were no longer different from saline-treated mice. The mechanism to explain these unexpected changes in COX-2 remain unknown, but it is possible that measurement at 3 h is too early to detect effects of the anti-inflammatory drugs tested. These data suggest that LPS-induced behavioural changes arise independent of cytokine production, and depend on COX-1 mediated peripheral and/or central PGE2 production. Furthermore, it suggests that cytokine synthesis in the brain, after intra-peritoneal challenge with LPS, largely depend on COX-2 signalling, and not on COX-1. Communication between the peripheral immune system and the brain is a well described phenomenon and underpins the metabolic and behavioural consequences of systemic infection and inflammatory diseases

(Dantzer et al., 1999, Dantzer et al., 1998 and Hart, 1988). Despite numerous studies, the biological mechanism(s) underlying these behavioural changes are still not fully understood. Previously, we showed a key role for PGs, and not the blood-borne cytokines IL-1β, IL-6 or TNF-α, in generating LPS-induced behavioural changes (Teeling et al., 2007). To further study the mechanisms underlying these observations, we pre-treated mice with a selection of widely-used anti-inflammatory drugs and assayed the behavioural changes and inflammatory mediator production following a systemic challenge with LPS. Pharmacological MycoClean Mycoplasma Removal Kit inhibition of cyclo-oxygenase enzymes COX-1 and COX-2, using indomethacin or ibuprofen, effectively attenuated the burrowing and open field response to systemic LPS-induced inflammation, while acetaminophen (paracetamol) or dexamethasone had no effect. Selective COX-1 inhibitors, piroxicam or sulindac, showed similar effects to indomethacin and ibuprofen and inhibited LPS-induced changes in burrowing and open-field activity. This effect was independent of IL-1β, IL-6 and TNF-α, generated either in the periphery or in the brain. Our findings therefore suggest a key role for COX-1, and not COX-2, in selected LPS-induced behavioural changes in normal, healthy mice.

In a certain way this behavior was already expected, since guar gum does not form a gel in solution, being used as a thickener and stabilizer (Dziezak, 1991). On the other hand, as the concentration of the polyols increased

in the solution, the dependence of the G′ moduli on the frequency decreased, indicating greater structuring of the systems. The addition of polyols decreased the values for the phase angle as compared to the values obtained with the pure gum (G05 and G1), suggesting an increase in system elasticity, which behavior became less similar to that of a liquid and closer to that of a gel. The increase in system structuring was not proportional to the gum/polyol concentrations in the system. The solutions containing 0.5 g/100 g guar gum, pure or with 10 g/100 g of any of the polyols, presented δ > 1, which is characteristic of a dilute solution. With the addition of 40 g/100 g of any of the polyols, there

was a change to δ < 1, although http://www.selleckchem.com/products/gsk1120212-jtp-74057.html the curves corresponding to the G05 systems were less dependent on frequency than those obtained with samples of G1. This is further evidence that the addition of 40 g/100 g polyol to solutions that already contain 1 g/100 g hydrocolloid creates a competitive effect for the water available in the system, resulting in less structured systems. The systems containing G1, pure and with polyols, showed liquid-like behavior at low frequencies (G″ > G′) and solid-liked behavior (G′ > G″) at higher frequencies, passing through a cross-over (G′ = G″). www.selleckchem.com/products/BIBW2992.html The cross-over moves to lower frequencies with increasing system concentration, indicating the behavior of a highly concentrated solution, as shown in Fig. 3 for solutions of guar gum added with maltitol. Chenlo et al. (2010) reported similar results to guar gum. Dynamic rheological measurements

were made by Evageliou, Kasapis, and Hember (1998), in systems composed of 0.5 g/100 g k-carrageen and high glucose syrup concentrations at a temperature of 5 °C, and the addition of 60 g/100 g glucose syrup resulted in an increase in system firmness. Doyle, Giannouli, Martin, Brooks, and Morris (2006), investigated the effect of high sorbitol concentrations (40–60 g/100 g) in the cryo-gelatinization of galactomannan (1 g/100 g). The gel strength showed an increase and subsequent reduction OSBPL9 with increasing polyol concentration, the maximum strength being attained with 50 g/100 g sorbitol. Comparing Fig. 2a and b, it can be seen that the values reached for G′ were slightly higher for maltitol than for sorbitol. The systems containing xylitol presented results very similar to those obtained with sorbitol, the corresponding data being shown in Fig. 4, which also shows the effect of freezing/thawing on the solutions. The dependence of G′ and G″ on the frequency can be described by a power law-type equation, as shown in equations (3) and (4) ( Kim & Yoo, 2006; Rao, 1999; Wang et al.

1±6.3 h (n=4) ( Fig. 6B). Thiazovivin order These results support the possibility that the excess Kir2.1 channels are readily degraded. If Kir current shortens the half-life of the channel, we expect that current blockade should increase the functional channels. To test this physiologically, we cultivated 293T cells, transfected

them with CMV promoter SNAP-Kir2.1 plasmid, in the presence or absence of Ba2+ and measured the whole cell conductance 24 and 48 h after transfection (Fig. 7). Expectedly, the whole cell conductance was significantly higher in the Ba2+treated cells, suggesting that the blockade increased the functional Kir2.1 channels. These findings raised the question of whether the degradation of Kir2.1 is accelerated specifically by Kir current or not. To test this, we prepared a 293T cell line,

142-3, which stably expresses SNAP-Kir2.1, using a lentiviral vector as described previously (Okada and Matsuda, 2008). Then we transfected plasmids which express GFP, Kv2.1, or Kv4.2 (Fig. 1C). In the GFP coexpressing 142-3 cells, the half-life of the SNAP-Kir2.1 is 54.8±7.7 h, which was longer than that of transient expression with plasmids. This is probably due to the low expression level of SNAP-Kir2.1 in this cell line. The coexpression of Kv1.4 not-significantly shortened the half-lives of SNAP-Kir2.1 compared with that of only GFP expressing cells (Fig. 5G). Coexpression of Kv2.1 significantly shortened the half-life to 32.6±2.6 h (p<0.05, n=4). Thus, there might be a heterologous acceleration of degradation among K+ channels. The spontaneous conversion of FT fluorescence

OSI-744 should allow us to monitor the changes in the rate of degradation of FT-Kir2.1. We established a 293T cell line, 116-5, which stably expresses Galeterone FT-Kir2.1, using a viral vector as described previously (Okada and Matsuda, 2008). The green fluorescence, i.e. from young FT-Kir2.1 proteins, was diffusely located at the plasma membrane in the control (Fig. 8A). Contrastingly, the yellow and red fluorescence, from old proteins, was punctuated, and some of them were internalized, indicating the temporal mobilization of FT-Kir2.1. We next examined the effect of CHX on the fluorescence in this line. Expectedly, no green fluorescence was observed in the CHX-treated cells, and most red fluorescence was still localized to the plasma membrane 24 h after. The CHX-treatment gradually decreased the green/red ratio (Fig. 8B), confirming the spontaneous conversion of the fluorescence of FT-Kir2.1. The control cells showed no change in the green/red ratio 24 and 48 h later, suggesting that the FT-Kir2.1 proteins were stably synthesized and degraded in the 116-5 cell line. To verify that the FT-fusion method can monitor the changes in the half-life, we added BaCl2, which slowed SNAP-Kir2.1′s degradation, to the medium of 116-5 cells. As shown in Fig. 8A and C, Ba2+ significantly decreased the green/red ratio 24 and 48 h after its addition.

Previous studies have also indicated that myosin-Va is found in synaptic vesicle preparations and forms stable complexes between synaptic vesicle membrane proteins (Mani et al., 1994 and Prekeris and Terrian, 1997). In the vertebrate brain, 5–15% of the total zinc is concentrated in synaptic vesicles

(Frederickson, 1989 and Frederickson and Moncrieff, 1994), which has been studied using the Neo-Timm method (Babb et al., 1991). Moreover, zinc serves as an endogenous neuromodulator of several important receptors, including N-methyl-d-aspartate (NMDA) ( Smart et al., 1994). Functional studies of honey bee myosin-Va have not been carried out until now. In this study, we addressed the effects of intracerebral injections of melittin PI3K inhibitor and NMDA on the honey bee. Melittin is a polypeptide present in bee venom (Habermann, 1972) and a potent calmodulin

antagonist (Steiner et al., 1986). Calmodulin is the most extensively studied member of the intracellular calcium-binding proteins, which includes myosin-Va. Additionally, NMDA is a glutamate-gated ion channel agonist present in both mammals and insects (Paoletti and Neyton, 2007). The selleck screening library NMDA receptor is involved in delayed neuronal death (Choi, 1988) and excitatory synaptic transmission in the central nervous system, which results in learning and memory (Albensi, 2007). A critical role of the NMDA receptor was recently demonstrated in olfactory learning and memory in Drosophila melanogaster ( Xia et al.,

2005) and A. mellifera ( Locatelli et al., 2005 and Si et al., 2004). The aims of this study were to elucidate some of the biochemical properties and the distribution of myosin-Va and to describe the expression patterns of molecular motors and SNARE proteins in the honey bee (A. mellifera L.) brain. Moreover, we evaluated the alterations in myosin-Va expression after intracerebral injections of melittin and NMDA. Rabbit affinity-purified polyclonal antibodies were used in this study. Anti-chicken brain myosin-Va (α-myosin-Va) head domain recombinant protein (Espreafico et al., 1992 and Suter et al., 2000), anti-pig myosin-VI (α-myosin-VI) tail fusion protein (Hasson and Mooseker, 1994) and anti-myosin-IXb Glutathione peroxidase heavy chain tail domain recombinant protein (Post et al., 1998) were all from the Mooseker Laboratory (Yale University, New Haven, CT, USA). Anti-rabbit myosin-IIb (α-myosin-IIb) was produced in the Larsons Laboratory (USP, Ribeirão Preto, SP, Brazil). The dynein light chain (α-DYNLL1/LC8) antibody was generated against the Chlamydomonas LC8 recombinant protein ( King et al., 1996). Mouse monoclonal antibodies used included anti-cytoplasmic dynein intermediate chain IC74 (α-DIC; Chemicon International Inc.