85 and 86 Additionally, the evaluation of players’ physical performance can assist coaches in several

aspects, such as in the identification of individual physical strengths Autophagy inhibitor and weaknesses, evaluation of the effectiveness of a specific training program, setting individual and team physical fitness standards, talent identification and development.9 and 87 Recent publications have reported on commonly used measures of physiological and physical attributes of female football players of various groups (Table 2). The mean values shown in this table for maximal oxygen uptake (VO2max), performance in Yo–Yo Intermittent Recovery Test Level 1 (YYIR1), maximum heart rate (HRmax), 30 m sprint time, and counter-movement jump or vertical jump (CMJ/VJ) vary according to the players’ nationality, competitive level, and positional role. On average, these players achieved VO2max values that ranged from 45.1 to 55.5 mL/kg/min, YYIR1 scores of 780–1379 m, HRmax values of 189–202 bpm, 30 m sprint times of 4.34–4.96 s, and CMJ/VJ results of 28–50 cm (Table 2). The type of measurement methods used may also account for the discrepancies among the reported values. Due to the worldwide Cyclopamine order increased popularity and participation numbers in women’s football, many coaches that previously

only coached male players are now coaching female players as well. When coaching female players these coaches try to use the same physical training loads they used with the men without considering the specific characteristics of female players commonly due to lack of knowledge in this area. Therefore, experienced and novice coaches who are

now working in women’s football need to be aware of the main physical and physiological differences that exist between the genders. These differences start becoming more significant at the onset of puberty (∼12–14 years of age) depending on individual and sex-specific maturation rates.88 Before this time period the physical Parvulin differences between men and women are small and females may have a slight advantage for a short period of time because they usually experience their growth spurt and sexual maturation on average 2 years earlier than males.88 Once males enter into puberty and their testosterone levels start to increase, the gender physical differences lean to their favor. Thus, it is well known that in general females are lighter, shorter, have a lower muscle mass, and more essential sex-specific fat mass than their male counterparts due to inherent biological factors that result in lower absolute physical capacities (e.g., aerobic endurance, muscular strength, power, speed, and agility) for the average woman compared to the average man.

001). We

proposed a final model (Fig. 2) to predict PA based on the nested regression model analysis and mediation analysis. On the basis of this model, only the predisposing factors, able and worth, predicted MPAR directly. All reinforcing and enabling factors, except the hypothesized language barriers, predicted MPAR indirectly through able and worth. Sex and BMI were also predictors of MPAR. Over half of the participants in this study met the PA recommendation, indicating that they were more physically active than many of their contemporaries in China,33 as well as more active than what had previously been reported among those enrolled in American colleges ABT-888 and universities.3 This may be because the physical and social environment of American society has positively influenced their participation.34 For example, in the current study, social and physical Dabrafenib datasheet environment factors, such as social support, role modeling, and accessibility to PA resources, were found to have indirect effects on PA participation among Chinese

international students. This finding may result from efforts underway in America focused on reprioritizing healthy, active living and building environments that support such practices (e.g., Active Community Environments, Rails to Trails, Michelle Obama’s efforts as First Lady of the United States focused on childhood obesity). It would seem that such endeavors are positively influencing Chinese international students, although the

present study does not allow for causal inference. Although the acculturation effect on PA participation remains unclear,35 the current study does suggest a potential Mannose-binding protein-associated serine protease protective effect of American culture on Chinese international students’ PA behavior. Also, males in this sample were 1.49 times more likely than were the females to meet the PA recommendation. This is consistent with previous research suggesting that female college students are less active than are their male peers.13 BMI also significantly predicted MPAR. Contrary to previous findings, the current study showed that having a higher BMI was associated with greater odds of meeting the PA recommendation. Given that the majority of participants had a normal body weight, a higher BMI may indicate more muscle mass and a more physically active lifestyle. Or, it could be that the students with higher BMI values were using PA as a means of counteracting the situation. Consistent with Welk’s YPAP model,5 the factors that predicted MPAR were the predisposing, enabling, and reinforcing factors. However, the predisposing factors (i.e., perceived competence, self-efficacy, attitude, and enjoyment) were the only factors to predict MPAR directly. Others have also observed the importance of these predictors on PA participation among different college-aged population segments.

A plot of observed versus predicted log δ values ( Fig. 5a) as well as a plot of observed versus predicted β values ( Fig. 5b) including their corresponding correlation coefficients is presented in Fig. 5. Observed versus predicted Salmonella count values for all data are presented in Fig. 6. Additionally, Table 4 shows the correlation (R), % discrepancy (% Df) and % bias (% Bf) values for predicted

versus observed time required for GSI-IX first decimal reduction (δ), shape factor values (β) and Salmonella counts in the different food products used. Data presented in Fig. 5a and the results in Table 4 (all data) indicate that the secondary model (Eq. (19)) provides a high correlation between observed versus predicted times required for first decimal reductions (R = 0.97, p < 0.001). The correlation of observed versus predicted shape factor values was not as satisfactory (R = 0.03, p = 0.915), with Eq.  (20) both over and under predicting β values ( Fig. 5b). Still,

as seen in Fig. 6 and Table 4, a significant correlation (R = 0.94, p < 0.001) of observed versus predicted CFU values was obtained when using the developed secondary models selleck kinase inhibitor to predict the survival of Salmonella in all tested food types. The degrees of discrepancy and bias found between the secondary predictive models and the data used to develop these models was found to be 16% discrepancy and − 2% bias. A negative percent bias is indicative of a tendency of the models to underestimate

survival Sitaxentan numbers (even when using the data that derived the model). This underestimation followed from the degree to which the shape parameter (in Eq.  (20)) deviated from the observed values and was more prominent at the lower CFU values. The extent to which the models underestimated the survival of Salmonella in the validation data is illustrated in Fig. 6. Data points which appear below the equivalence line are CFU values that have been underestimated and are consistent with the shape factor results in Fig. 5b. As seen in Table 4, the % bias and % accuracy factors showed a discrepancy of 41% and a bias of − 7% for all validation data collected. These discrepancy and bias values differ from those inherent to the models (16% and − 2%). However, the data collected in non-fat products including wheat flour, non-fat dry milk and whey protein powder ( Table 4) gave 12% discrepancy and − 3% bias. The bias and accuracy percentage results in non-fat food are within the error margin inherent to the models, and are an example of the consistency of the models in predicting survival data in non-fat foods. The higher discrepancy and bias percentages obtained for the whole dataset are the result of the higher discrepancy and bias percentages found for data in low-fat food products (which contain 12% fat). Table 4 shows low-fat products to have 50% discrepancy and − 9% bias.

, 2010), Kavalali and coworkers (Chung et al., 2010) along with Fredj and Burrone (Fredj and Burrone, 2009) have concluded that both types of neurotransmission are fueled by distinct SV populations. Furthermore, the molecular interactions driving spontaneous versus evoked release seem to differ (Deák et al., 2006 and Xu et al., 2009). Based on the premise that distinct SV pools may differentially contribute to various forms of synaptic transmission, Ramirez et al. (2012) reasoned that spontaneously fusing SVs may in fact be distinguished by molecular composition from their counterparts involved in AP-driven exocytosis. SVs are known Selleck RG7204 to contain a number of SNARE proteins besides the canonical SNARE synaptobrevin 2 (Syb2) (Takamori

et al., 2006), a crucial E7080 order factor for evoked release that acts via complex formation with its cognate SNAREs syntaxin and SNAP-25 on the plasma membrane. Among the noncanonical SNAREs found on SVs are the endosomal proteins Vti1a and vesicle-associated membrane protein 7 (VAMP7, also called Ti-VAMP) (Takamori et al., 2006). Vti1a, by associating with VAMP4, syntaxin 6, and syntaxin 13, regulates fusion of early endosomes. An isoform of Vti1a is enriched on SVs and has been postulated to form a distinct SNARE complex with unclear function (Antonin et al., 2000). VAMP7 mediates exocytosis of lysosome-related organelles and

may regulate neurotransmission at hippocampal mossy fiber synapses (Chaineau et al., Mannose-binding protein-associated serine protease 2009). To investigate whether these noncanonical SNAREs differentially regulate evoked versus spontaneous release, Ramirez et al. (2012) first characterized the exo-endocytic trafficking of Vti1a and VAMP7 tagged with a pH-sensitive GFP moiety commonly referred to as pHluorin. In hippocampal neuronal cultures, pHluorin-tagged Vti1a and VAMP7 appear to be localized to synapses where they reside in acidic compartments, most likely SVs, although a fraction of them may be targeted to endosomes.

When Kavalali and coworkers compared the exocytic behavior of their various SNARE-pHluorin chimeras they noted surprising differences: AP stimulation at 20 Hz caused the exocytic fusion of a sizeable fraction of Syb2-pHluorin as expected, whereas Vti1a-pHluorin and VAMP7-pHluorin were only reluctantly mobilized. By contrast, when they probed spontaneous neurotransmission using the v-ATPase blocker folimycin to prevent postfusion reacidification of SVs, they observed that Vti1a-pHluorin underwent substantial fusion detectable by fluorescence dequenching. VAMP7-pHluorin showed only modest levels of exocytosis both in the presence or absence of electrical stimulation. These data suggest that Vti1a preferentially traffics at rest. As differential behavior of pHluorin-tagged SNAREs could originate from different synapses (Kavalali et al., 2011), the authors produced spectrally shifted pHluorin variants tagged with the DsRed derivative mOrange, which also exhibits pH-dependent changes in fluorescence.

When a significant main effect was detected with ANOVA tests, Bonferroni’s post hoc correction was used to determine significance

between pairwise comparisons. Normalized values are plotted as a percentage of the average value during the baseline period. Unless stated otherwise, reported values are mean ± SEM. For all statistical comparisons, asterisks indicate a significant effect at the following levels of significance: ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. To assess INCB018424 the distribution of all pyramidal neurons in multidimensional space, we performed a K-means cluster analysis in MATLAB (MathWorks). First, we performed Student’s t tests on each electrophysiological property and morphological parameter to compare bursting and regular-spiking neurons. Using only those parameters that were significantly different, we constructed a 15-dimension matrix for all 110 neurons (consisting of seven morphological properties: total basal dendritic length, total tuft dendritic length, average basal branching

order, average tuft branching order, distance to main apical bifurcation, and the number of branch points in the basal Selleck ABT263 and tuft regions; as well as eight electrophysiological properties: input resistance, sag ratio, subthreshold dV/dt, ADP amplitude, threshold of the second spike, maximal dV/dt during the rising and falling phases of the second spike, and the FWHM of the first spike). Initial spike frequency Linifanib (ABT-869) was not included in the cluster analysis, though these values were significantly different between firing types. Based on these values, the K-means test selected k random cells to seed k clusters (n = 2–10). For all 15 normalized parameters, the Euclidian distance from these k seeds was calculated for all remaining cells, and each cell was then assigned to the cluster it was closest to. The cluster centers were then recalculated, and the process was repeated iteratively until the distributions ceased to change. To determine whether the computed clusters represent a single population or arise from multiple cell types, we computed a cluster index from the 15-dimensional matrix, defined as the ratio

of the sum of the square distances from each multidimensional point to its cluster center and the sum of the square distances from each point to the overall mean. This index varies from zero to one, with values close to zero corresponding to very tight clusters. Assuming that the cells were defined by a single multivariate Gaussian (the null hypothesis, which we would expect if these neurons belonged to the same cell type), we calculated a million cluster index values by repeatedly drawing 110 random samples from that distribution. The p value represents the likelihood that the simulated data have a cluster index greater than the experimental data. To determine whether k clusters (2–10) were represented in the data, we applied the jump method of Sugar and James (2003).

We believe this first wave of activity is consistent with a combination of intra-area processing and feedforward inter-area processing of the visual image.

The only known means of rapidly conveying information through the ventral pathway is via the spiking activity that travels along axons. Thus, we consider the neuronal representation in a given cortical area (e.g., the “IT representation”) to be the spatiotemporal pattern of spikes produced by the set of pyramidal neurons that project out of that area (e.g., the spiking patterns traveling along the population of axons that project out of IT; see Figure 3B). How is the spiking activity of individual neurons thought to encode visual information? Most studies have investigated the response properties of neurons in the ventral pathway by assuming a firing rate (or, equivalently, a spike Tyrosine Kinase Inhibitor Library ic50 Ruxolitinib count) code, i.e., by counting how many spikes each neuron fires over several tens or hundreds of milliseconds following the presentation of a visual image, adjusted for latency (e.g., see Figures 4A and 4B). Historically, this temporal window (here called the “decoding” window) was justified by the observation that its resulting spike rate is typically well modulated by relevant parameters of the presented visual images (such as object identity, position, or size; Desimone et al., 1984, Kobatake and Tanaka, 1994b, Logothetis and Sheinberg,

1996 and Tanaka, 1996) (see examples of IT neuronal responses in Figures 4A–4C), analogous to the well-understood firing isothipendyl rate modulation in area V1 by “low level” stimulus properties such as bar orientation (reviewed by Lennie and Movshon, 2005). Like all cortical neurons, neuronal spiking throughout the ventral pathway is variable in the ms-scale timing of spikes, resulting in rate variability for repeated presentations of a nominally identical visual stimulus. This spike timing variability is consistent with a Poisson-like

stochastic spike generation process with an underlying rate determined by each particular image (e.g., Kara et al., 2000 and McAdams and Maunsell, 1999). Despite this variability, one can reliably infer what object, among a set of tested visual objects, was presented from the rates elicited across the IT population (e.g., Abbott et al., 1996, Aggelopoulos and Rolls, 2005, De Baene et al., 2007, Heller et al., 1995, Hung et al., 2005, Li et al., 2009, Op de Beeck et al., 2001 and Rust and DiCarlo, 2010). It remains unknown whether the ms-scale spike variability found in the ventral pathway is “noise” (in that it does not directly help stimulus encoding/decoding) or if it is somehow synchronized over populations of neurons to convey useful, perhaps “multiplexed” information (reviewed by Ermentrout et al., 2008). Empirically, taking into account the fine temporal structure of IT neuronal spiking patterns (e.g.

We characterized these tools in brain slices and used them to define

the spatiotemporal dynamics of opioid signaling with unprecedented resolution. Enkephalins and dynorphins are the most prominent opioid peptides in the brain (Khachaturian et al., 1985). We chose to work with LE and Dyn-8 (Figure 1A) because they are the smallest and most chemically stable endogenous opioids from these peptide families. LE activates delta and mu opioid receptors with nanomolar affinity but is inactive at kappa receptors (Toll et al., 1998). The three additional C-terminal amino acids found in Dyn-8 confer nanomolar potency at kappa receptors in addition to mu and delta receptors (Toll et al., 1998). To render these peptides inactive until exposed to light, we produced analogs modified www.selleckchem.com/products/Lapatinib-Ditosylate.html at the N-terminal tyrosine side chain with the carboxynitrobenzyl (CNB) chromophore,

which photoreleases tyrosine with high quantum yield (∼0.3) (Sreekumar et al., 1998) on the microsecond timescale (Tatsu et al., 1996) (see Supplemental Information, available online, for information on peptide production and handling). Extensive studies into the structure-activity relationships of enkephalins (Morley, 1980) and dynorphins (Chavkin and Goldstein, 1981) have revealed an essential role for their common N-terminal tyrosine (Y) in receptor activation. In particular, alkylation (Beddell et al., 1977) or removal (Terenius et al., 1976) of the phenolic OH group reduces the potency of enkephalin analogs, suggesting that modification of the tyrosine side chain of LE and Dyn-8 may be a viable caging strategy. check details Based on these considerations, we designed heptaminol CNB-Y-[Leu5]-enkephalin (CYLE) and CNB-Y-Dyn-8 (CYD8) (Figure 1A) to release LE and Dyn-8, respectively, in response to illumination with UV light. The chemical structure of CYLE is shown in Figure 1B. Reverse-phase high-pressure

liquid chromatography experiments confirmed that both peptides cleanly photorelease their parent peptides in pH 7.4 phosphate-buffered saline in response to 355 nm laser illumination (Figure S1) and that they are stable in the dark at room temperature for >48 hr (data not shown). To determine whether CYLE and CYD8 are inactive at opioid receptors prior to photolysis, we compared their activity on opioid receptors relative to that of LE and Dyn-8, respectively, using an in vitro functional cellular assay. To detect opioid receptor activation, we utilized HEK293 cells that stably express a Gαs-Gαi chimera (S.D. Liberles and L.B. Buck, personal communication). This chimeric protein allows GPCRs that normally do not signal through Gαs to stimulate adenylate cyclase and control the transcription of a cAMP-dependent reporter construct. Cells were cotransfected with the opioid receptor of interest and the reporter construct such that receptor activation leads to production of secreted alkaline phosphatase (SEAP).

Trunk center of mass was defined as the location of the center of mass of the trunk in space. Right and left center of pressure (COP) was the location of the COP of each foot on the surface of the force plates. The dependent variables included the average T_ANG, T_AVEL, T_COM, and average speeds of right and Panobinostat mouse left foot COP. T_ANG was calculated by determining the average differences between the minimum trunk angle and maximum trunk angle during trials. T_AVEL was calculated by dividing the sum of the changes in trunk angle during the trial by the total trial time. Similarly, T_COM and COP speeds were calculated by dividing trunk center of mass and foot COP trajectories by the

trial time. SPSS statistical analysis software v.19.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the data, with the aim of comparing the T_ANG, T_AVEL, T_COM, and right and left foot selleck COP speeds between the three sitting surfaces. A one-way repeated measures MANOVA was used to determine differences in the

dependent variables between the three sitting conditions. For significant main effects, post hoc pairwise comparisons were performed using a Bonferroni correction to locate the differences between conditions. A critical α probability level of 0.05 was used for all analyses. No significant main effects were found for the T_ANG around the ML axis (p = 0.331), AP axis (p = 0.513), or longitudinal axis (p = 0.108) ( Fig. 1). No significant main effects were found for the T_AVEL around the ML axis (p = 0.053) ( Fig. 2) and T_COM in the AP direction

(p = 0.121) ( Fig. 3). Significant main effects for T_AVEL around the AP axis (p = 0.037) and the longitudinal axis (p = 0.040) were found ( Fig. 2). In addition, L-NAME HCl T_COM in the ML (p < 0.001) and longitudinal directions (p < 0.001) were also significant ( Fig. 3). Post hoc pairwise comparisons revealed differences in T_AVEL and T_COM between sitting surfaces. The ball condition demonstrated greater T_AVEL around the AP axis than the chair condition (p = 0.005). In addition, the ball condition demonstrated greater T_AVEL around the longitudinal axis compared to the air-cushion (p = 0.050) and the chair conditions (p = 0.037). Furthermore, the ball condition had greater T_COM in the ML direction compared to the air-cushion (p = 0.001) and the chair (p = 0.001) conditions. In the longitudinal direction, the ball condition had greater T_COM compared to the air-cushion (p = 0.004) and the chair (p = 0.007) conditions. The air cushion also demonstrated greater T_COM in the ML direction than the chair condition (p = 0.008). Table 1 shows the means ± SD of the COP speeds for the three sitting conditions. No significant main effects were found for the average speeds of foot COP in the ML direction for the right (p = 0.458) and left (p = 0.489) feet. However, significant main effects were found in the AP direction for both the left (p = 0.006) and right (p = 0.004) feet.

, 2010; Eto et al., 2010). We have shown that the C-terminal aa 856–881 domain of DLK-1L can bind to the kinase domain of both human MAP3K13 and C. elegans DLK-1. Expression of human MAP3K13 in C. elegans neurons can functionally complement dlk-1. The DLK-1L hexapeptide is completely conserved in MAP3K13 but not in MAP3K12. Short isoforms for MAP3K12 are detected as ESTs; MAP3K12 and MAP3K13 can form homomers or heteromers ( Ikeda et al.,

2001; Nihalani et al., 2000). Thus far, most reported studies have focused on the MAP3K12/DLK, and different types of neurons lacking DLK show a range of phenotypes from neurite regeneration to axon degeneration and to neuronal death ( Ghosh et al., 2011; Itoh et al., 2009, 2011; Miller et al., 2009). At present, much less is known about MAP3K13/LZK, ISRIB clinical trial although it has been implicated in neurite outgrowth and may interact with the regrowth inhibitor Nogo ( Dickson et al., 2010). Our discovery of antagonistic C. elegans DLK-1

isoforms, together with our demonstration of functional conservation of DLK-1L and MAP3K13/LZK, suggest that the activation mechanisms of DLK family MAPKKKs in neurons may be conserved. Specifically, we speculate that MAP3K13 could also be kept in an inhibited state by an endogenous inhibitory isoform. As MAP3K12 and MAP3K13 are almost identical buy Screening Library in their kinase and LZ domains, MAP3K12 isoforms could provide this inhibitory function. Clearly, it will be informative to assess the role of MAP3K13 in synaptic development and axon regrowth and its possible crosstalk with MAP3K12. We maintained C. elegans strains on NGM plates at 20°C–22.5°C as described by Brenner Terminal deoxynucleotidyl transferase (1974). The dlk-1 mutations are listed in Table S1; mutations affecting the kinase domain are shown in Figure S1B. We used juIs1[Punc-25-SNB-1::GFP] for viewing GABA motor neuron synapses ( Hallam and Jin, 1998), and muIs32[Pmec-7-GFP] ( Ch’ng et al., 2003) for viewing touch

neuron morphology and axon regeneration studies. Other transgenes and strains are described in Table S2. We scored fluorescent reporters in live animals using a Zeiss Axioplan 2 microscope equipped with Chroma HQ filters. For quantification of touch neuron morphology using muIs32, 100–150 1-day-old adults were analyzed. For quantification of GABAergic motor neuron synapse morphology using juIs1, confocal images of dorsal cords in the midbody were collected on 1-day-old adults immobilized in 1% 1-phenoxy-2-propanol (TCI America) in M9 buffer. For GFP-DLK-1L, GFP-DLK-1S, and CFP-DLK-1L/YFP-DLK-1S, images were collected from 1-day-old adults using a Zeiss LSM510 confocal microscope. For synapse morphology and DLK-1L/S localization, z stack images (1 μm/section) were shown in the figures. We cut PLM axons in anesthetized L4 larvae using a near-infrared Ti-Sapphire laser (KMLabs) as described (Wu et al., 2007).

2 to 4.8 mm angled at 45° along the long axis to ensure targeting to the MC layer (Figure 1A). In this study, as reported previously by Kay and Laurent (1999) and Rinberg et al. (2006), no spikes were detected while the electrodes traversed the granule cell layer. Once the electrode reached the ventral MC, layer spikes with amplitudes ranging from 100 to 2000 μV were detected with spontaneous firing frequency characteristic of MCs (Figures S1A and S5, MCL). As shown in Figure S5, recording from the granule cell layer yielded significantly smaller voltage deviations. Recordings from electrodes

displaying only such small voltage deviations were infrequent and PI3K inhibitor were not analyzed to avoid contamination by granule-cell generated multiunit activity. Because granule cell signals were too small to be detected when thresholding based upon recordings in the MC layer, these cells almost certainly do not contribute to the multiunit activity detected in the MC layer. Once the MC layer was reached,

the arrays were fixed in place with titanium skull screws and nail acrylic with one of the titanium screws serving as the ground. Although the electrodes do not record spikes from the granule cells, we term the recorded units “suspected MCs” because our measurements may include some internal tufted cells. All animal procedures were performed under a protocol approved by the institutional animal care and use committee of the University of Colorado Anschutz Medical Campus. Surgical procedures for cannula implantation were based upon the work of Wesson et al. (2008). Briefly, animals were anesthetized as described above, and lidocaine click here was injected into the epidermis above the frontal nasal bone as a local anesthetic. An incision was made down the midline and the skull was cleaned with 3% H202. Next, a hole was drilled 1 mm anterior to the frontal/nasal fissure and 1 mm lateral from the

midline. A hollow cannula was then lowered into the hole and fixed in place with nail acrylic. Mice were anesthetized with nembutal (100 mg/kg) and perfused with 4% paraformaldehyde. Fixed heads were placed in PBS containing 5% Prohance Sodium butyrate (Bracco Diagnostics Inc, Princeton, NJ) and1% distilled H2O for 2 weeks prior to imaging. Imaging experiments were conducted on a Bruker Biospec 7-T horizontal-bore system (Bruker Inc, Billerica, MA) controlled with Paravision 4.0 software. The brain specimens were placed inside a sealed container filled with Fomblin liquid (Solvay Slexis, West Deptford, NJ) to minimize artifacts arising from air-tissue interface. A standard 3D Fast Spin Echo sequence was used to acquire the 256 images for each head (repetition time, 500 ms; echo time, 8.6 ms; echo train length, 4; number of averages, 4; scan time, 11 hr 22 min). The imaging resolution was 78 μm isotropic. Volumes were constructed using ImageJ 1.42q software and final images were contrast enhanced using Photoshop 6.0.