Solving this fraction, we obtained (13) However, it should be not

Solving this fraction, we obtained (13) However, it Selleckchem Captisol should be noted that Z-average should only be employed to provide the characteristic size of the particles if the suspension is monomodal (only one peak), spherical, and monodisperse. As shown

in Figure 3, for a mixture of particles with obvious size difference (bimodal distribution), the calculated Z-average carries irrelevant size information. Figure 3 Z -average (cumulant) size for particle TPCA-1 suspension with bimodal distribution. DLS measurement of MNPs The underlying challenges of measuring the size of MNPs by DLS lay in the facts that (1) for engineering applications, these particles are typically coated with macromolecules to enhance their colloidal stability (see Figure 4) and (2) there present dipole-dipole

learn more magnetic interactions between the none superparamagnetic nanoparticles. Adsorbing macromolecules onto the surface of particles tends to increase the apparent R H of particles. This increase in R H is a convenient measure of the thickness of the adsorbed macromolecules [65]. This section is dedicated to the scrutiny of these two phenomena and also suspension concentration effect in dictating the DLS measurement of MNPs. All DLS measurements were performed with a Malvern Instrument Zetasizer Nano Series (Malvern Instruments, Westborough, MA, USA) equipped with a He-Ne laser (λ = 633 nm, max 5 mW) and operated Tau-protein kinase at a scattering angle of 173°. In all measurements, 1 mL of particle suspensions was employed and placed in a 10 mm × 10 mm quartz cuvette. The iron oxide MNP used in this study was synthesized by a high-temperature decomposition method [17]. Figure 4 Pictorial representation of two MNPs and major interactions. The image shows two MNPs coated with macromolecules with repeated segments and the major interactions involved between them in dictating the colloidal stability of MNP suspension. Size dependency of MNP in DLS measurement In order to demonstrate the sizing capability of DLS, measurements were conducted on three species of Fe3O4

MNPs produced by high-temperature decomposition method which are surface modified with oleic acid/oleylamine in toluene (Figure 5). The TEM image analyses performed on micrographs shown in Figure 5 (from top to bottom) indicate that the diameter of each particle species is 7.2 ± 0.9 nm, 14.5 ± 1.8 nm, and 20.1 ± 4.3 nm, respectively. The diameters of these particles obtained from TEM and DLS are tabulated in Table 3. It is very likely that the main differences between the measured diameters from these two techniques are due to the presence of an adsorbing layer, which is composed of oleic acid (OA) and oleylamine (OY), on the surface of the particle. Small molecular size organic compounds, such as OA and OY, are electron transparent, and therefore, they did not show up in the TEM micrograph (Figure 5).

Insect Molecular Biology 2002, 11 (1) : 97–103 PubMedCrossRef 4

Insect Molecular Biology 2002, 11 (1) : 97–103.PubMedCrossRef 4. Salehi M, Izadpanah K, Siampour M, Bagheri A, Faghihi SM: Transmission of ‘Candidatus Phytoplasma aurantifolia’ to Bakraee (Citrus reticulata

hybrid) by feral Hishimonus phycitis selleckchem leafhoppers in Iran. Plant Disease 2007, 91 (4) : 466–466.CrossRef 5. Lee IM, Davis RE, Gundersen-Rindal DE: Phytoplasma: Phytopathogenic mollicutes. Annual Review of Microbiology 2000, 54: 221–255.PubMedCrossRef 6. Matteoni JA, Sinclair WA: Stomatal Closure in Plants Infected with Mycoplasmalike Organisms. Phytopathology 1983, 73 (3) : 398–402.CrossRef 7. Garnier M, Foissac X, Gaurivaud P, Laigret F, Renaudin J, Saillard C, Bove JM: Mycoplasmas, plants, insect vectors: a matrimonial triangle. Comptes Rendus De L Academie Des Sciences Serie Iii-Sciences De La Vie-Life Sciences 2001, 324 (10) : 923–928.CrossRef 8. Seemu¨ ller E, Garnier M, Schneider B: Mycoplasmas of plants and insects. In Molecular Biology and Pathogenicity of Mycoplasmas. Edited by: Razin S, Herrmann R. New York: Kluwer Academic/Plenum; 2002:91–115.CrossRef 9. Bai XD, Zhang JH, Ewing A, Miller SA, Radek AJ, Shevchenko DV, Ipatasertib Tsukerman

K, Walunas T, Lapidus A, Campbell JW, et al.: Living with genome instability: the adaptation selleck chemicals of phytoplasmas to diverse environments of their insect and plant hosts. Journal of Bacteriology 2006, 188 (10) : 3682–3696.PubMedCrossRef 10. Lepka P, Stitt M, Moll E, Seemuller E: Effect of phytoplasmal infection on concentration and translocation of carbohydrates and amino acids in periwinkle and tobacco. Physiological and Molecular Plant Pathology 1999, 55 (1) : 59–68.CrossRef 11. Jagoueix-Eveillard S, Tarendeau F, Guolter K, Danet JL, Bove JM, Garnier M: Catharanthus roseus genes regulated

differentially by mollicute infections. Molecular Plant-Microbe Interactions 2001, 14 (2) : 225–233.PubMedCrossRef 12. Carlos EF: Transcriptional profiling on trees affected by citrus blight and identification of an etiological contrast potentially associated with the disease. University of Florida; 2004. 13. Christensen NM, Axelsen KB, Nicolaisen M, Schulz A: Phytoplasmas and their interactions with hosts. Trends in Plant Science 2005, 10 (11) : 526–535.PubMedCrossRef 14. RVX-208 Kwon SI, Park OK: Autophagy in Plants. Journal of Plant Biology 2008, 51: 313–320.CrossRef 15. Rose TL, Bonneau L, Der C, Marty-Mazars D, Marty F: Starvation-induced expression of autophagy-related genes in Arabidopsis. Biology of the Cell 2006, 98: 53–67.PubMedCrossRef 16. Maust BE, Espadas F, Talavera C, Aguilar M, Santamaria JM, Oropeza C: Changes in carbohydrate metabolism in coconut palms infected with the lethal yellowing phytoplasma. Phytopathology 2003, 93 (8) : 976–981.PubMedCrossRef 17. Lamb CJ, Lawton MA, Dron M, Dixon RA: Signals and Transduction Mechanisms for Activation of Plant Defenses against Microbial Attack. Cell 1989, 56 (2) : 215–224.PubMedCrossRef 18. Bateman A, Bycroft M: The structure of a LysM domain from E.

2002) In contrast, short grasses

2002). In contrast, short grasses selleck kinase inhibitor maintained by heavy livestock

grazing, such as those in the pastoral areas of the Mara in the wet season (Ogutu et al. 2005), have higher digestibility and nutritional quality. Heavy livestock grazing on the ranches, furthermore, tends to promote production of more net grass biomass, which in turn attracts more herbivores than in the reserve with no livestock. Consequently, sustained livestock grazing in the ranches, by keeping grass stem biomass low, renders grasses more digestible and enhances their nutritional quality (McNaughton 1976). This enables herbivores to realize greater protein consumption on the ranches than selleck products they do in the reserve in the wet season. As well, Adriamycin price nutrient-rich pastoral settlement (boma) sites

in the ranches represent key sources of nutritionally sufficient forage, especially for lactating females in the wet season (Muchiru et al. 2008; Augustine et al. 2010). In addition, during the wet season, it is likely that lions are more abundant in the reserve (Reid et al. 2003), with taller grass cover, than in the ranches (Ogutu et al. 2005). Predator densities are also higher in the reserve than in the ranches in the dry season (Reid et al. 2003), reflecting not only their preference for high grass cover, but also avoidance of human and livestock activities on the ranches (Ogutu et al. 2005). Since predation risk increases with grass height in the Serengeti (Hopcraft et al. 2005) and Mara Region (Kanga et al. 2011) and since grass

cover is shorter and predator density is lower on the ranches than in the reserve, small and medium herbivores likely experience lower predation risk on the ranches than in the reserve (Sinclair why et al. 2003). In the dry season, when surface water and forage availability are reduced, heavy livestock grazing in the pastoral ranches forces wildlife to disperse to the reserve, where the migratory wildebeest and zebra and fires have removed the taller grasses and improved visibility. Thus, heavy livestock grazing in the pastoral ranches facilitates small and medium-sized herbivores in the wet season, but competition with livestock in the dry season for food and water, pushes them into the reserve where they are facilitated by migratory herds, which also absorb most of the predation pressure (Ogutu et al. 2008). Accordingly, we formulated the following four initial expectations based on herbivore body size. (1) The densities of the small-sized herbivores (15–50 kg), would be higher in the Koyiaki pastoral ranch in both seasons due to the higher prevalence of short grass that is safer year round.

References 1 Graham DY, Lew GM, Evans DG, Evans DJ Jr, Klein PD:

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Worku M, Moore SA, Penn CW, O’Toole PW: HP0958 is an essential Nintedanib (BIBF 1120) motility gene in Helicobacter pylori . FEMS Microbiol Lett 2005, 248:47–55.PubMedCrossRef 12. Brahmachary P, Dashti MG, Olson JW, Hoover TR: Helicobacter pylori FlgR is an enhancer-independent activator of sigma 54-RNA polymerase holoenzyme. J Bacteriol 2004, 186:4535–4542.PubMedCrossRef 13. GDC-0449 clinical trial Colland F, Rain J-C, Gounon P, Labigne A, Legrain P, De Reuse H: Identification of the Helicobacter pylori anti-sigma 28 factor. Mol Microbiol 2001, 41:477–487.PubMedCrossRef 14. Josenhans C, Niehus E, Amersbach S, Horster A, Betz C, Drescher B, Hughes KT, Suerbaum S: Functional characterization of the antagonistic flagellar late regulators FliA and FlgM of Helicobacter pylori and their effects on the H. pylori transcriptome. Mol Microbiol 2002, 43:307–322.PubMedCrossRef 15. Macnab RM: How bacteria assemble flagella. Ann Rev Microbiol 2003, 57:77–100.CrossRef 16.

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H Yu and R Liu

H. Yu and R. Liu Trichostatin A chemical structure 1461 [HMAS 29851 (M)]; Qiongzhong County, Limu Mt., 6 July 1960, J. H. Yu and R. Liu 1761 [HMAS 28817 (S)]; Selonsertib Lingshui County, Diaoluo Mt., 28 Oct. 1987, GDGM 14161 [as Macrolepiota procera (Scop.: Fr.) Singer in Bi et al. 1997]; Lingshui County, Diaoluo Mt., 27 Mar. 1989,

GDGM 15514 (as M. procera in Bi et al. 1997). Sichuan Province: Xichang City, 4 July1971, X. L. Mao and Q. M. Ma 129 [HMAS 36880 (S), as M. gracilenta (Krombh.) Wasser in Ying et al. 1994, as Lepiota gracilenta (Krombh.) Quél. in Ying and Zang 1994 ]; Kangding County, Gongga Mt., alt. 2800 m, under Picea and Betula, 17 July 1982, Y. Xuan (HKAS 9751); Miyi County, 27 July 1986, M. S. Yuan 1186 (HKAS 18396, as M. procera in Yuan and Sun 2007). Tibet (Xizang Autonomous Region): Mêdog (Motuo) County, alt. 850 m, 2 Aug. 1983, X. L. Mao LCZ696 order M1160 [HMAS 52719 (S), as M. procera in Mao 1995]; Mêdog (Motuo), 3 Aug. 1983, X. L. Mao M1166 [HMAS 54142, as Leucoagaricus excoriatus (Schaeff.) Singer in Li et al 1995]. Yunnan Province: Dongshan, alt. 2000 m., Sept. 1982, W. K. Zheng 828 (HKAS 10342);

Kunming City, 29 June 1942, W. F. Chiu [HMAS 12189 (S)]; Kunming Institute of Botany, Oct. 2000, X. H. Wang 1201 (HKAS 38171); Kunming City, Heilongtan, 15 Aug. 1974, M. Zang 954 (HKAS 954); Kunming City, Heilongtang, 18 Aug. 1975, X. J. Li 2608 (HKAS 40470); Kunming City, Heilongtan, 14 July 1976, M. Zang 2716 (HKAS 40455); Kunming City, Changchong Mt., 12 July 1984, L. S. Wang 1 (HKAS 13115); Kunming City, Heilongtan, 11 July 1986, L. S. Wang 31594 (HKAS 3365); Kunming City, Heilongtan, 20 Aug. 1987, Y. Xuan 1375 (HKAS 18311); Kunming City, Kunming Institute of Botany, 25 July 1990, Z. L. Yang 1019 (HKAS 22693); Kunming City, 20 June 1973, L. W. Xu and Y. C. Zong and Q. M. Ma 209 [HMAS 36287 (S), as Lepiota excoriata (Schaeff.) P. Kumm. in Ying et al. 1994]; Kunming next City, Heilongtan, alt. 1980 m., 15 Oct. 2001, Z. L.

Yang 3214 (HKAS 38718); Kunming City, Heilongtan, 17 Sept. 2001, Z. L. Yang 3203 (HKAS 38462); Fuming County, under Pinus yunnanensis, 27 July 1998, Z. J. Li and M. Zang 12977 (HKAS 34016); Songming County, Liangwang Mt., 17 Sept. 1979, G. M. Feng 1 (HKAS 4632); Songming County, Baiyi Xiang, 22 July 1998, X. H. Wang 412 (HKAS 35957); Songming County, Aziying, 29 July 1998, M. Zang 12979 (HKAS 34018); Yiliang County, 1 Sept. 1999, Z. L. Yang 2622 (HKAS 34066); Yuxi City, 20 July 1991, X. X. Liu 3a (HKAS 23404a); Gejiu City, Datun, 15 Sept. 1986, K. K. Chen 157 (HKAS 18200); Lüchun County, 11 Oct. 1973, M. Zang 325 (HKAS 325); Lufeng County, Yipinglang, alt. 1800 m, 27 June 1978, 86048 (HKAS 4493); Guangnan County, 29 June 1959, Q. Z. Wang 747 [HMAS 25146 (M)]; Qiubei County, 15 July 1959, Q. Z. Wang 787 [HMAS 25143 (M), as M. gracilenta in Ying et al. 1994]; Jinghong City, 30 Oct. 1958, S. J. Han and L. Y. Chen 5327 [HMAS 26225 (M)], Menglun County, 14 Sept. 1974, M.

coli

(Gmet_3169, 48% identical) that has no homolog in G

coli

(Gmet_3169, 48% identical) that has no homolog in G. sulfurreducens. In the catabolic direction, in addition to pyruvate kinase (Gmet_0122 = GSU3331) that converts phosphoenolpyruvate to pyruvate plus ATP, G. metallireducens has a homolog of E. coli phosphoenolpyruvate carboxylase (Gmet_0304, 30% identical, also found in Geobacter FRC-32) that may convert phosphoenolpyruvate to oxaloacetate irreversibly (Figure 3b) and contribute to the observed futile cycling of pyruvate/oxaloacetate/phosphoenolpyruvate [34] if not tightly regulated. Thus, control of the fate of pyruvate appears to be more complex in G. metallireducens than in G. sulfurreducens. Figure 3 Potential futile cycling of pyruvate/oxaloacetate selleck inhibitor and phosphoenolpyruvate in G. metallireducens. (a) Conversion of pyruvate to phosphoenolpyruvate. (b) Conversion of phosphoenolpyruvate to pyruvate or oxaloacetate. Evidence of recent fumarate respiration in G. metallireducens The succinate dehydrogenase complex of G. sulfurreducens also functions as a respiratory fumarate reductase, possibly in association with a co-transcribed b-type cytochrome [35]. G. metallireducens has homologous genes (Gmet_2397-Gmet_2395 = GSU1176-GSU1178), but is unable to grow

with fumarate as the terminal GDC 941 electron acceptor unless transformed with a plasmid that expresses the dicarboxylic acid exchange transporter gene dcuB of G. sulfurreducens [35], which has homologues in Geobacter FRC-32, G. bemidjiensis, G. lovleyi, and G. uraniireducens. Surprisingly, G. metallireducens has acquired another putative succinate dehydrogenase or fumarate reductase complex (Gmet_0308-Gmet_0310), not found in other Geobacteraceae, by lateral gene transfer from a PLX3397 manufacturer relative of the Chlorobiaceae (phylogenetic trees not shown), and evolved it into a gene cluster that includes enzymes of central metabolism acquired from other sources (Figure 4). Thus, G. metallireducens may have actually enhanced its ability Molecular motor to respire fumarate before recently losing the requisite transporter.

Figure 4 Acquisition of a second fumarate reductase/succinate dehydrogenase by G. metallireducens. (a) The ancestral gene cluster. (b) The gene cluster acquired from a relative of the Chlorobiaceae, located near other acquired genes relevant to central metabolism: an uncharacterized enzyme related to succinyl-CoA synthetase and citrate synthase (Gmet_0305-Gmet_0306) and phosphoenolpyruvate carboxylase (Gmet_0304). Conserved nucleotide sequences (black stripes) were also identified in the two regions. Nitrate respiration and loss of the modE regulon from G. metallireducens G. metallireducens is able to respire nitrate [4], whereas G. sulfurreducens cannot [24]. The nitrate reductase activity of G.

Fifty-seven to 65% of the endemic species sampled in these commun

Fifty-seven to 65% of the endemic species sampled in these communities had population

densities that fall below this threshold, placing them at high risk. For introduced species, the trend HDAC inhibitor mechanism between population density category and probability of drastic decline was weaker. Introduced species that occurred at relatively low population densities appeared to be much less vulnerable than corresponding endemic species, but vulnerability was fairly similar for higher density introduced and endemic species. Fig. 1 Relationship between arthropod population density and likelihood of drastic population decline (defined as having at least 90% of all individuals captured in uninvaded plots). Species are grouped by density GANT61 cell line categories; numbers in parentheses indicate number of species in each category. Gray bars show the observed percentage of species exhibiting

patterns of drastic decline. Horizontal lines within gray bars show the percentage of species expected to exhibit patterns of drastic decline purely by chance. Above population densities of about 9–14 individuals, this latter percentage essentially drops to zero. Black dots connected by lines show the chance-corrected likelihood of drastic decline for each category (calculated as the observed percentage minus the percentage expected by chance) Taxonomic trends and variability Several taxonomic orders in these arthropod communities stand out as being particularly vulnerable to invasive ants, when accounting for provenance. Endemic beetles selleck inhibitor (Coleoptera) and spiders (Araneae), both rare and non-rare species, were strongly reduced in invaded areas with high consistency (Tables 3, 4). In addition, endemic barklice (Psocoptera) and non-rare endemic moths (Lepidoptera) were more likely than not to be strongly reduced in invaded areas. Several additional orders had high rates of negative

impact, but these were represented second by single species, making it difficult to draw conclusions. Overall, at least one endemic species in each order was strongly impacted at one or more sites. Among introduced species, only Hymenoptera (bees, wasps and a pair of relatively uncommon ant species) were consistently impacted by ants. The remaining orders were much more variable among species in the inferred responses to ant invasion. Table 3 Responses of non-rare species to ant invasion, grouped by taxonomic ordera Class Order Impact scoreb Rate of pop variability (%)c % negative % weak % positive % variable (a) endemic species  Arachnida Araneae 100(5) 0(0) 0(0) 0(0) 0  Diplopoda Cambalida 100(1) 0(0) 0(0) 0(0) na  Entognatha Collembola 42.8(3) 28.6(2) 0(0) 28.6(2) 100  Insecta Coleoptera 100(3) 0(0) 0(0) 0(0) na  Insecta Diptera 20.0(1) 20.0(1) 20.0(1) 40.0(2) 100  Insecta Hemiptera 47.6(10) 19.0(4) 14.3(3) 19.

Lancet 1992,340(8818):507–10 PubMedCrossRef 459 Pauly DF, Pepine

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V, Ramaekers M, Van Schuylenberg R, Verbessem P, Wijnen H, Hespel P: No effects of oral ribose supplementation on repeated maximal exercise and de novo ATP resynthesis. J Appl Physiol 2001,91(5):2275–81.PubMed 461. Berardi JM, Ziegenfuss TN: Effects of ribose supplementation on repeated sprint performance in men. J Strength Cond Res 2003,17(1):47–52.PubMed Selleckchem VRT752271 462. Kreider RB, Melton C, Greenwood M, Rasmussen C, Lundberg J, Earnest C, Almada A: Effects of oral D-ribose supplementation on anaerobic capacity and selected metabolic markers in healthy males. Int J Sport Nutr Exerc Metab 2003,13(1):76–86.PubMed 463. Dunne L, Worley S, Macknin

M: Ribose versus dextrose supplementation, association with rowing performance: a double-blind study. Clin J Sport Med 2006,16(1):68–71.PubMedCrossRef 464. Kerksick C, Rasmussen C, Bowden R, Leutholtz B, Harvey T, Earnest C, Greenwood M, Almada A, Kreider R: Effects of ribose supplementation prior to and during intense exercise on anaerobic capacity and metabolic markers. Int J Sport Nutr Exerc Metab 2005,15(6):653–64.PubMed 465. Hargreaves M, McKenna MJ, Jenkins DG, Warmington SA, Li JL, Snow RJ, Febbraio MA: Muscle metabolites and performance www.selleckchem.com/products/mk-5108-vx-689.html during high-intensity, intermittent exercise. J Appl Physiol 1998,84(5):1687–91.PubMed 466. Starling RD, Trappe TA, Short KR, Sheffield-Moore M, Jozsi AC, Fink WJ, Costill DL: Effect of inosine supplementation on aerobic and anaerobic cycling performance. Med Sci Sotrastaurin chemical structure Sports Exerc 1996,28(9):1193–8.PubMedCrossRef 467. Williams MH, Kreider RB, Hunter DW, Somma CT, Shall LM, Woodhouse ML, Rokitski L: Effect of inosine supplementation (-)-p-Bromotetramisole Oxalate on 3-mile treadmill run performance and VO2 peak.

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Blood analysis All blood samples were obtained in duplicate asept

Blood analysis All blood samples were obtained in duplicate aseptically from the fingertip via lancet (Accu-Chek Safe-T-Pro Plus single-use sterile lancets, Roche Diagnostics, Mannheim, Germany) and collected in 100 μL electrolyte balanced heparin coated capillary tubes (Radiometer, West Sussex, UK). Samples were immediately analyzed (95 μL) for whole blood glucose and lactate

using a clinical blood gas and electrolyte analyzer (ABL 800 basic, blood gas and electrolyte analyzer, Radiometer, West Sussex, UK). Nutritional intervention Participants consumed three different beverages all matched for energy content: CHO only (67 g.hr-1 of maltodextrin derived from corn starch); CHO-PRO (53.1 g.hr-1 of maltodextrin, 13.6 g.hr-1 of whey protein concentrate); or CHO-PRO-PEP (53.1 g.hr-1 of maltodextrin, 11.0 g.hr-1 of whey protein Selleck Mocetinostat selleck kinase inhibitor concentrate, 2.4 g.hr-1 of peptides (fish meat hydrolysate extracted from salmon)). Treatment beverages were blinded by the manufacturer and provided in powder form (Nutrimarine Life Science, Bergen, Norway). Prior to each trial the powder was weighed (Kern EW 120-4NM electronic bench-top scales, Kern & Sohn GmBH, Belingen, Germany) and subsequently mixed with water (magnetic stirrer HI-200 M, Hanna Instruments,

Bedfordshire, UK) in accordance with the manufacturer’s recommendations, with the addition of 5 ml of lemon food flavoring added to each total dose (1080 ml) to enhance blinding and palatability. All solutions were administered via an opaque drinks bottle. Participants consumed 180 ml of each respective beverage every 15 min of the 90 min cycle starting at the onset of exercise. Statistical analysis All statistical analyses were conducted using IBM SPSS Statistics 19 (SPSS Inc., Chicago, IL). Central tendency

and dispersion of the sample data are reported as the mean and standard MI-503 deviation for normally distributed Histamine H2 receptor data and the median and interquartile range otherwise. Comparisons of means across the three experimental conditions and time (where applicable) for all outcome variables were performed using the MIXED procedure. The factors Condition and Time were both included in the model as categorical variables for body mass, urine osmolality, time trial time, mean and peak power output and VO2. Time was treated as a continuous variable for heart rate, RER, blood glucose concentration, blood lactate concentration and RPE. The residuals for the urine osmolality model were positively skewed, which was corrected with natural log transformation of the observed data. Two-tailed statistical significance was accepted as p < 0.05. Results Body mass and urine osmolality There were no significant differences between experimental conditions for body mass, (F = 0.001, p > 0.99) or urine osmolality (F = 0.03, p = 0.97) before exercise.