Irwin P, Damert W, Doner L: Curve fitting in nuclear magnetic resonance: illustrative examples using a spreadsheet and microcomputer.

LBH589 concentration Concepts Magn Reson 1994, 6:57–67.CrossRef 21. Balagadde F, You L, Hansen C, Arnold F, Quake S: Long-Term Monitoring of Bacteria Undergoing Programmed Population Control in a Microchemostat. Science 2005, 309:137–140.PubMedCrossRef Authors’ contributions PI designed all of the experiments, performed all calculations and statistical analyses, participated in running most of the experiments and drafting the manuscript. LN carried out all the TAPC and O2 electrode experiments and participated in drafting the manuscript. GP and CC assisted in the experiments using conditioned media, MM, and LB with disrupted cells and participated in O2 electrode experiments as well as drafting the manuscript. All authors read and approved the final manuscript.”
“Background Arsenic’s toxic and medicinal properties have been appreciated for more than two millennia [1]. Its two soluble inorganic forms, arsenite (+3) and arsenate (+5), entering drinking water from natural sources, have caused poisoning in Taiwan, Chile, Argentina, Bangladesh and West Bengal, and most recently arsenicosis (arsenic poisoning) has been Vistusertib manufacturer detected in people from Cambodia, Vietnam, Nepal, China, Inner Mongolia, Bolivia and Mexico [2, 3]. In CYT387 nmr addition, arsenic contamination

due to anthropogenic activity (e.g. mining) is increasing in importance in parts of the USA, Canada, Australia, Argentina and Mexico [4]. Although arsenic is toxic to most organisms, some prokaryotes have evolved mechanisms to gain energy by either oxidising or reducing it [5, 6]. Prokaryotic arsenic metabolism has been detected in hydrothermal and temperate environments Sitaxentan and has been shown to be involved in the redox cycling of arsenic [7–10]. The arsenite-oxidising bacteria isolated so far are phylogenetically diverse. The oxidation of arsenite may yield useable energy or may merely form part of a detoxification

process [6]. To date, all aerobic arsenite oxidation involves the arsenite oxidase that contains two heterologous subunits: AroA (also known as AoxB) and AroB (also known as AoxA) [6]. AroA is the large catalytic subunit that contains the molybdenum cofactor and a 3Fe-4S cluster and AroB contains a Rieske 2Fe-2S cluster [6]. Although arsenic metabolism has been detected in both moderate and high-temperature environments, and mesophilic and thermophilic arsenite oxidisers have been isolated, no arsenic metabolism (either dissimilatory arsenate reduction or arsenite oxidation) has ever been detected in cold environments (i.e. < 10°C). One such environment with high concentrations of arsenic is the Giant Mine, one of Canada’s oldest and largest gold mines. It is located a few kilometres north of Yellowknife, Northwest Territories, 62° north of the equator and 512 kilometres south of the Arctic Circle.

BioMetals 2010, 23:431–439.PubMedCrossRef 43. Schägger H: Tricine–SDS-PAGE. Nat Protoc 2006, 1:16–22.PubMedCrossRef 44. Iwatani S, Zendo T, Yoneyama F, Nakayama J, Sonomoto K: Characterization and structure analysis of a novel bacteriocin, lacticin Z, produced by Lactococcus lactis QU 14. Biosci Biotechnol Biochem 2007, 71:1984–1992.PubMedCrossRef Competing interests The authors declare that

they have no competing interests. Authors’ contributions XH carried out the reference collection and analysis, most experimental running of whole expermental work; RM participated partial experimental design, method improvement and partial paper writing; YZ was charge of expression and mainly fermentor INK1197 ic50 running; DT was charge of codon optimization and all materials preparation; XW was charge of partial DNA cloning and PCR techinque, and partial result analysis; DX participated partial peptide purification; JH corrected partial techincal design on microbiological methods; JW participated selleck chemicals llc and coordinated all sections of this work, design and running, results analysis and disscussion, paper writing and correction. All authors read and approved the final manuscript.”
“Background The Gram positive bacterium Streptococcus

pneumoniae frequently colonizes the nasopharynx but can also invade the host causing serious illnesses such as pneumonia, meningitis or bacteraemia [1]. A principal virulence factor of S. pneumoniae is the polysaccharide capsule protecting it from host immune defences by interfering with the deposition of complement and therefore opsonophagocytosis [2-4]. The capsule is the target of all currently available pneumococcal vaccines including the 13-valent pneumococcal

conjugate vaccine (PCV13) for children. The biochemical structure and linkage of repeating polysaccharide subunits determines the serotype of encapsulated strains. So far, more than 90 different serotypes have been identified [5-11] which differ in the type and number of genes encoding the proteins responsible for transcription, Glutathione peroxidase polymerization, elongation and export of the capsule. For almost all serotypes the capsule-encoding operon is located between non-capsule genes dexB and aliA [6,12,13]. The first four genes cpsA, cpsB, cpsC and cpsD are thought to play a role in regulation of capsular production and are largely conserved between serotypes [14,15]. Despite the importance of the capsule as a virulence factor, nonencapsulated pneumococci occur and in the nasopharynx may represent around 15% of pneumococcal isolates [16]. Nonencapsulated pneumococci are generally considered not to be ICG-001 ic50 virulent but are associated with outbreaks of conjunctivitis [17-19]. Although lacking the protection from opsonophagocytosis which a capsule affords, the absence of capsule may confer advantages.

10 We have also investigated the case β ≪ 1 with all other parameters \(\cal O(1)\) to verify that this case does indeed approach the racemic state at large times (that is, θ, ϕ, ζ → 0 as t → ∞). However, once again the difference in timescales can be observed, with the concentrations reaching equilibration on a faster timescale than the chiralities, due to the different magnitudes

of eigenvalues (Eq. 4.28). New Simplifications of the System We return to the Eqs. 2.35–2.39 in the case δ = 0, now writing x 2 = x and y = y 2 to obtain $$ \frac\rm d c\rm d t = – 2 \mu c + \mu\nu (x+y) – \alpha c(N_x+N_y) , $$ (5.1) $$ \frac\rm d x\rm d t = \mu c – \mu\nu x – \alpha x c + \beta (N_x-x + x_4) – \xi x^2 – \xi x N_x , $$ (5.2) $$ \frac\rm d y\rm d t = \mu c – \mu\nu y – \alpha y c + \beta (N_y-y + y_4) – \xi y^2

MK-4827 cost – \xi y N_y , $$ (5.3) $$ \frac\rm d N_x\rm d t = \mu c – \mu\nu x + \beta (N_x-x) – \xi x N_x , $$ (5.4) $$ \frac\rm d N_y\rm d t MK-1775 cost = \mu c – \mu\nu y + \beta (N_y-y) – \xi y N_y , $$ (5.5)which are not closed, since x 4, y 4 appear on the rhs’s of Eqs. 5.2 and 5.3, hence we need to find formulae to determine x 4 and y 4 in terms of x, y, N x , N y . One way of achieving this is to expand the system to include other click here properties of the distribution of cluster sizes. For example, equations governing the mass of crystals in each chirality can be derived as $$ \frac\rm d \varrho_x\rm d t=2\mu c-2\mu\nu x+2\alpha c N_x , \quad \frac\rm d \varrho_y\rm d t=2\mu c-2\mu\nu y+2\alpha c N_y . $$ (5.6)These introduce no more new new quantities into the macroscopic system of equations, and do not rely on knowing x 4 or y 4, (although they do require knowledge of x and y). In the remainder of this section we consider various potential formulae for x 4, y 4 in terms of macroscopic quantities so that a macroscopic system can be constructed. We then analyse such macroscopic systems in two specific limits to show that predictions

relating to symmetry-breaking can be made. Reductions Lonafarnib mouse The equations governing the larger cluster sizes x k , y k , are $$ \frac\rm d x_2k\rm d t = \beta( x_2k+2 – x_2k ) – (x_2k-x_2k-2)(\alpha c + \xi x) ; $$ (5.7)in general this has solutions of the form \(x_2k = \sum_j A_j(t) \Lambda_j^k-1\), where Λ j are parameters (typically taking values between unity (corresponding to a steady-state in which mass is being added to the distribution) and \(\frac\alpha c+\xi x\beta\) (the equilibrium value); and A j (t) are time-dependent; for some Λ j , A j will be constant. We assume that the distribution of each chirality of cluster is given by $$ x_2k = x \left( 1 – \frac1\lambda_x \right)^k-1 ,\qquad\qquad y_2k = y \left( 1 – \frac1\lambda_y \right)^k-1 , $$ (5.8)since solutions of this form may be steady-states of the governing Eq. 5.7.

Control

strain 81–176 exhibited about 28-fold greater invasion than 00–2426 (unadjusted P = 0.0000149). Isolate 00–2538, which carries the prophage, was 21-fold more invasive than 00–2426, a statistically significant result in pairwise comparisons using the Holm-Sidak method (unadjusted P = 0.000769). Prophage-carrying isolates 00–2544 and 00–2425 were 16-fold and 17-fold more invasive than isolate 00–2426 lacking the prophage. These results were not statistically significant in pairwise comparisons in One-way ANOVA using the Holm-Sidak Test. E. coli Top 10 was used as a negative control for invasion; the levels of invasion of isolate Selinexor datasheet 00–2426 and Top 10 were very similar throughout the experiments. Once again, the observation that isolate 00–2426 was much less selleckchem invasive than the other C. jejuni strains was observed consistently in experiments in which all isolates were tested within a Selleck Entospletinib single experiment, on the same day (Table 2). Association of prophage with patient symptoms and source Data on patient symptoms and the associated C. jejuni recovered from the patients

was obtained through a collaboration between the National Microbiology Laboratory and the Centre for Foodborne, Environmental, and Zoonotic Infectious Diseases in Guelph, ON, which administers the C-EnterNet sentinel site surveillance program in the Region of Waterloo, ON [7]. This has allowed comparisons of the CJIE1 prophage genotype with patient symptoms. The PCR method developed for single-step detection of CJIE1 also assesses the presence or absence of an indel or moron carrying the unique coding sequence ORF11 [6]. Results are summarized in Table 3 and can be interpreted as in the following example. Of all 204 patients answering the question of whether they had abdominal pain, for instance, 169 answered “yes” and the remainder answered “no”. Among the 153 patients from whom C. jejuni without CJIE1 was isolated and who also answered the question Rho on the questionnaire, 127 had abdominal pain and 26 did not. Similar interpretation can be applied

throughout the table. As a whole these analyses suggested that the presence of ORF11 may be responsible for higher rates of bloody diarrhea and hospitalization and lower rates of headache, while the presence of the CJIE1 prophage was associated with lower rates of vomiting and longer duration of illness. None of these differences were statistically significant. Differences in the rates of abdominal pain and fever were significant, with higher rates observed from isolates lacking CJIE1 (P = 0.037 and P < 0.001, respectively). In both cases the difference in rates remained significant when rates of each symptom were compared pairwise between isolates without CJIE1 and those with CJIE1 alone (abdominal pain P < 0.025, fever P < 0.

Membrane and DNA dyes were used concomitantly to visualise the cell periphery and the nucleoid (Figure 1B and

1C). Cells were classified into populations defined according to their number of foci, and the positioning of foci along the length of cells was evaluated for each population (Figures 1C and 2). The distances of the foci to the closest cell pole were scored on a five points scale along the long axis of the cell from the pole to mid-cell (Additional file 1, Figure S1). The ori, right and NS-right loci selleck inhibitor displayed 2 to 4 foci that mostly found at or near the quarter positions, whereas the ter locus displayed 1 or 2 foci, which were mostly located at mid-cell (Additional file 1, Figure S1). The proportion of mid-cell-located ter foci was lower for cells harbouring a single focus than for cells with two foci, consistent with a progressive migration of the ter region from the new cell RGFP966 datasheet pole to the learn more mid-cell during the cell cycle [7, 8, 21]. These findings are

consistent with previous observations using similar [9, 20] or different detection systems and growth conditions [6, 10]. Positioning of chromosome loci along the cell diameter The position of a fluorescent focus along the width of the cell cannot be directly determined using 2-D wide-field microscopy. Indeed, a focus located near the cell periphery may appear at the centre of the cell diameter or at the edge according to the orientation of the cell cylinder with respect to the focal plan. Nevertheless, since the orientations of the cell cylinder are expected to be random for a population of rod-shaped bacteria deposited on a plane surface, the mean position of particular foci can be calculated from the apparent distributions of foci along the cell diameter. We therefore measured the apparent distance along the cell diameter between foci and the membrane (Figure 1C). The datasets obtained were then compared with distributions calculated for different models of positioning across

the width of the cell (Methods). We defined five slices of equivalent surface in a quarter of Cisplatin purchase the cell section and calculated the expected distributions of foci according to the various models of positioning (the 2-D apparent foci distributions for various 3-D localisation patterns are shown in Figures 2, 3 and 4). Figure 2 Distributions of foci along the cell diameter. (A) Drawing showing the measurement of the apparent positions of foci along the cell diameter. Distances along the cell diameter between the centres of foci and the nearest membrane were measured. (B) Distributions of foci along the cell diameter for the ori, right and NS-rigth loci in the various cell classes. Distributions are plotted as the percentage of total foci in each cell class (Y-axis). The sample size of the cell classes is given on each graph.

Feliciano DV, Mattox KL, Burch JM, Bitondo CG, Jordan GL Jr: Packing for control of hepatic hemorrhage. J Trauma 1986,26(8):738–43.CrossRefPubMed 3. Rotondo MF, Schwab CW, McGonigal MD, Phillips GR, Fruchterman TM, Kauder DR, Latenser BA, Angood PA: ‘Damage control’: an approach

for improved survival in exsanguinating penetrating abdominal injury. J Trauma 1993,35(3):375–82.CrossRefPubMed 4. Rotondo MF, Zonies DH: The damage control sequence and underlying logic. Surg Clin North Am 1997,77(4):761–77.CrossRefPubMed 5. Burch JM, Denton JR, Noble RD: Physiologic rationale for abbreviated laparotomy. Surg Clin North Am 1997,77(4):779–82.CrossRefPubMed 6. Stawicki SP, Brooks A, Bilski T, Scaff D, Gupta R, Schwab CW, Gracias VH: The concept of damage control: extending

selleck the paradigm to emergency general surgery. Injury 2008,39(1):93–101.CrossRefPubMed 7. Miller PR, Chang MC, Hoth JJ, Holmes JH, Meredith JW: Colonic resection in the setting of damage control laparotomy: is delayed anastomosis safe? Am Surg 2007,73(6):606–9.PubMed 8. Jansen JO, Loudon MA: Damage control surgery in a non-trauma setting. Br J Surg 2007,94(7):789–90.CrossRefPubMed 9. Van Goor H: Interventional management of abdominal sepsis: when and how. Langenbecks Arch Surg 2002,387(5–6):191–200.CrossRefPubMed 10. Banieghbal B, Davies MR: Damage control laparotomy for generalized necrotizing enterocolitis. World J Surg 2004,28(2):183–6.CrossRefPubMed 11. Freeman AJ, Graham JC: Damage control surgery and angiography in cases of acute mesenteric Tipifarnib ic50 ischaemia. ANZ J Surg 2005,75(5):308–14.CrossRefPubMed 12. Levy MM, Fink MP, Marshall JC, Abraham E, Angus D, Cook D, Cohen Dimethyl sulfoxide J, Opal SM, Vincent JL, Ramsay G, SCCM/ESICM/ACCP/ATS/SIS: 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med 2003,31(4):1250–6.CrossRefPubMed 13. van Ruler O, Mahler CW, Boer KR, Reuland EA, Gooszen HG, Opmeer BC, de Graaf PW, Lamme B, Gerhards MF, Steller EP, van Till JW, de Borgie CJ, Gouma DJ, Reitsma JB, Boermeester MA, Dutch Peritonitis Study Group:

Comparison of on-demand vs. planned relaparotomy strategy in patients with severe peritonitis: a randomized trial. JAMA 2007,298(8):865–72.CrossRefPubMed 14. Hau T, Ohmann C, Wolmershäuser A, Wacha H, Yang Q: Planned relaparotomy vs. relaparotomy on demand in the treatment of intra-abdominal infections. The Peritonitis Study Group of the Surgical Infection Society-Europe. Arch Surg 1995,130(11):1193–6.PubMed 15. Lamme B, Boermeester MA, Reitsma JB, Mahler CW, Obertop H, Gouma DJ: Meta-analysis of relaparotomy for secondary peritonitis. Br J Surg 2002,89(12):1516–24.CrossRefPubMed Competing interests The authors declare that they have no competing interests. Authors’ contributions All authors read and approved the final manuscript. BP participated in the design of the study, performed the statistical analysis, drafted the manuscript and provided NU7441 ic50 critical revisions of all versions of the manuscript.

Infect Immun 1998, 66:1822–1826.PubMed 9. Kansau I, Raymond J, Bingen E, Courcoux P, Kalach N, Bergeret M, Braimi N, Dupont C, A L: Genotyping

of Helicobacter pylori isolates by sequencing of PCR products and comparison with the RAPD technique. Res Microbiol 1996, 147:661–669.CrossRefPubMed 10. Achtman M, Azuma T, Berg DE, Ito Y, Selleck Pitavastatin Morelli G, Pan Z-J, Suerbaum S, Thompson SA, Ende A, van Doorn L-J: Recombination and clonal groupings within Helicobacter pylori from different geographical regions. Mol Microbiol 1999, 32:459–470.CrossRefPubMed 11. Falush D, Kraft C, Taylor NS, Correa P, Fox JG, Achtman M, Suerbaum S: Recombination and mutation during long-term gastric colonization by Helicobacter pylori : estimates of clock rates, recombination size, and minimal age. Proc Natl Acad Sci USA 2001, 98:15056–15061.CrossRefPubMed 12. Ruboxistaurin manufacturer Falush D, Wirth T, Linz B, Pritchard JK, Stephens M, Kidd M, Blaser MJ, Graham DY, Vacher S, Perez-Perez GI, et al.: Traces of human migrations in Helicobacter pylori populations. Science 2003, 299:1582–1585.CrossRefPubMed 13. Lawrence JG, H O: Amelioration of bacterial genomes: rates of change and exchange. J Mol Evol 1997, 44:383–397.CrossRefPubMed 14. Vlieland CA: The Population of the Malay Peninsula: A Study in Human Migration.

Geogr Rev 1934, 24:61–78.CrossRef 15. Andaya LY: The search MRT67307 purchase for the ‘origins’ of Melayu. J Southeast Asian Stud 2001, 32:315–330.CrossRef 16. Hirschman C: The meaning and measurement of EthniCity in Malaysia:

an analysis of census classifications. J Asian Stud 1987, 46:555–582.CrossRef 17. Hill C, Soares P, Mormina M, Macaulay V, Meehan W, Blackburn J, Clarke D, Raja JM, Ismail P, Bulbeck D, Oppenheimer S, Richards M: Phylogeography and ethnogenesis of aboriginal Southeast Asians. Mol Biol Evol 2006, 23:2480–2491.CrossRefPubMed 18. Blaser MJ: Science, medicine, and the future – Helicobacter pylori and gastric diseases. Br Med J 1998, 316:1507–1510. 19. Devi SM, Ahmed I, Francalacci P, Hussain MA, Akhter Y, Alvi A, Sechi LA, Megraud F, Ahmed N: Ancestral European roots of Helicobacter pylori in India. BMC Genomics 2007, 8:184.CrossRefPubMed 20. Parsonnet J:Helicobacter pylori – the size of the problem. Gut 1998, 43:S6-S9.CrossRefPubMed Exoribonuclease 21. Goh KL, Parasakthi N: The racial cohort phenomenon: seroepidemiology of Helicobacter pylori infection in a multiracial South-East Asian Country. Eur J Gastroenterol Hepatol 2001, 13:177–183.CrossRefPubMed 22. Kaur G, Naing NN: Prevalence and ethnic distribution of Helicobacter pylori infection among endoscoped patients in north eastern peninsular Malaysia. Malaysian J Med Sci 2003, 10:66–70. 23. Boey CC, Goh KL, Lee WS, Parasakthi N: Seroprevalence of Helicobacter pylori infection in Malaysian children: evidence for ethnic differences in childhood. J Paediatr Child Health 1999, 35:151–152.CrossRefPubMed 24.

11 and × 1.04 and became 32.2 and 143.4 nm. Likewise, the AD was down by × 1.11 and became 9.9 × 109 cm−2 as shown in Table 1. The HDH in Figure 3 (d-4) now became clearly over ±20 nm wide along with the increased height of Au droplets. The self-assembled Au droplets on GaAs (111)A with the T a variation between 400°C and 550°C showed quite excellent uniformity as witnessed in the symmetric round FFT power spectra of MK0683 purchase Figure 3 (a-3) to (d-3) and showed an overall increased size with decreased

density as a function of the T a. The size and density evolution induced by the variation of the T a can be simply explained with the following equation [36]. The diffusion length (l D) can be expressed as where D is the surface diffusion coefficient and τ is the residence time of atoms. D can be written as  D ∝ T sub where T sub is the substrate temperature, namely T a in this case. With the increased T a, the D proportionally increases and it results

in an increased l D. With the increased l D, the density of the Au droplets can be decreased, given the stronger bonding energy between Au atoms (E a > E i). In this thermodynamic GSI-IX concentration equilibrium system, in order to keep the energy of the whole system in the lowest state, bigger droplets tend to absorb nearby adatoms to lower the surface energy, and thus, the size can grow larger and the density can be reduced until reaching the equilibrium.

Thus, this type of size and density evolution was witnessed in Ga and In metal droplets SN-38 supplier [35, 37, 38] and nanostructures [39–41] on various semiconductor substrates. Figure 4 Summary plots. Plots of the (a) average height, (b) average lateral diameter, and (c) average density of self-assembled Au droplets on various GaAs surfaces at the corresponding annealing temperature between 400°C and 550°C. Table 1 Summary of AH, LD, and AD of self-assembled Au droplets   I T a (°C) 400 450 500 550 Average height (AH) [nm] (111)A 23.4 25.4 28.9 32.2 (110) 22.6 24.7 28.2 31.2 (100) 21.7 24.0 26.7 29.7 (111)B 19.9 22.3 25.2 27.8 Average lateral diameter (LD) [nm] (111)A 128.6 133.8 138.5 143.4 3-oxoacyl-(acyl-carrier-protein) reductase (110) 122.5 128 133.8 141 (100) 115 124.5 130.8 139.1 (111)B 106.2 115.5 123.5 133.1 Average density (AD) [×108 cm−2] (111)A 139 123 110 99 (110) 148 131 118 107 (100) 160 141 129 119 (111)B 173 150 140 132 The Au droplets were fabricated by annealing between 400°C and 550°C on GaAs (111)A, (110), (100), and (111)B. I, index of substrates; T a, annealing temperature. Figure 5 summarizes the evolution process of the self-assembled Au droplets on GaAs (110) induced by the variation of the T a between 250°C and 550°C, and similarly, Figures 6 and 7 show that on GaAs (100) and (111)B.

Plant Cell Physiol 50:684–697PubMed Tóth SZ, Schansker G, Strasser RJ (2005a) In intact leaves, the maximum fluorescence level (F M) is independent of the redox state of the plastoquinone pool: a DCMU-inhibition study. Biochim Biophys Acta 1708:275–282PubMed Tóth SZ, Schansker G, Kissimon J, Kovács L, Garab G, Strasser RJ (2005b) Biophysical studies of photosystem II-related recovery processes after a heat pulse in barley seedling (Hordeum vulgare L). J Plant Physiol 162:181–194PubMed

Tóth SZ, Schansker G, Strasser RJ (2007a) A non-invasive assay of the plastoquinone pool redox state based on the OJIP-transient. Photosynth Res 93:193–203PubMed Tóth SZ, Schansker G, Garab G, Strasser RJ (2007b) Photosynthetic TPCA-1 electron transport activity in heat-treated barley leaves: the role RO4929097 price of internal alternative electron donors to photosystem II. Biochim Biophys Acta 1767:295–305PubMed Trissl HW, Wilhelm C (1993) Why do thylakoid membranes from higher plants form grana stacks? Trends Biochem Sci 18:415–419PubMed Tuba Z, Saxena DK, Srivastava K, Singh S, Sz Czebol, Kalaji MH (2010) Chlorophyll a fluorescence measurements

for validating the tolerant bryophytes for heavy metal (Pb) biomapping. Curr Sci C188-9 mw 98:1505–1508 Tyystjärvi E, Aro EM (1996) The rate constant of photoinhibition, measured in lincomycin-treated leaves, is directly proportional to light intensity. Proc Natl Acad Sci USA 93:2213–2218PubMedCentralPubMed Adenosine Tyystjärvi E, Koski A, Keränen M, Nevalainen O (1999) The Kautsky curve is a built-in bar code. Biophys J 77:1159–1167PubMedCentralPubMed van der Weij-de

Wit CD, Ihalainen JA, van Grondelle R, Dekker JP (2007) Excitation energy transfer in native and unstacked thylakoid membranes studied by low temperature and ultrafast fluorescence spectroscopy. Photosynth Res 93:173–182PubMed van Dorssen RJ, Breton J, Plijter JJ, Satoh K, van Gorkom HJ, Amesz J (1987) Spectroscopic properties of the reaction center and of the 47 kDa chlorophyll protein of photosystem II. Biochim Biophys Acta 893:267–274 van Heerden PDR, Swanepoel JW, Krüger GHJ (2007) Modulation of photosynthesis by drought in two desert scrub species exhibiting C3-mode CO2 assimilation. Environ Exp Bot 61:124–136 van Kooten O, Snel JF (1990) The use of chlorophyll fluorescence nomenclature in plant stress physiology. Photosynth Res 25:147–150PubMed van Wijk KJ, Krause GH (1991) Oxygen dependence of photoinhibition at low temperature in intact protoplasts of Valerianella locusta L. Planta 186:135–142PubMed Vass I, Govindjee (1996) Thermoluminescence from the photosynthetic apparatus. Photosynth Res 48:117–126PubMed Vass I, Sass L, Spetea C, Bakou A, Ghanotakis DF, Petrouleas V (1996) UV-B-induced inhibition of photosystem II electron transport studied by EPR and chlorophyll fluorescence: impairment of donor and acceptor side components.

Table 3 Relationship Temsirolimus supplier between the p73 (rs6695978 G > A) polymorphism and known clinicopathological variables of ovarian cancer Clinicopathological Variables All mTOR signaling pathway Genotype(%) A allele frequency Adjusteda GG GA+AA P OR (95 % CI) Age 308   0.948   < 52 118 88 (74.6) 30 (25.4) 0.136 1.00

(ref) ≥52 190 146 (76.8) 44 (23.2) 0.137 2.87 (0.93-5.84) Clinical stage 300       0.474   I-II 92 69 (75.0) 23 (25.0) 0.131 1.00 (ref) III-IV 208 158 (76.0) 50 (24.0) 0.142 1.30 (0.89-1.93) Tumor histology 308   0.003   Serous 196 150 (76.5) 46 (23.5) 0.128   1.00 (ref) Mucinous 24 15 (62.5) 9 (37.5) 0.250 0.001 3.48 (1.15-6.83) Endometrioid 22 17 (77.3) 5 (22.7) 0.114 0.337 2.25 (0.96-4.44) Mixed/other 66 52 (78.8) 14 (21.2) 0.136 0.597 0.93 (0.76-1.19) Degree of differentiation 246   0.005   High 28 22 (78.6) 6 (21.4) 0.107   1.00 (ref) Medium 82 65 (79.3) 17 (20.7) 0.104 0.827 1.15 (0.86-1.69) Low 136 98 (72.1) 38 (27.9) 0.162 0.003 1.87 (1.03-3.47) Tumor behavior 294   0.838   Borderline 48 37 (77.1) 11 (22.9) 0.125 1.00 (ref) Invasive 246 191 (77.6) 55 (22.4) 0.122 0.91 (0.79-1.03) Lymph check details node statusb 176   0.010   Negative 62 50 (80.6) 12 (19.4) 0.105 1.00 (ref) Positive

114 83 (72.8) 31 (27.2) 0.154 1.69 (1.14-2.75) ERc 183   0.002   Negative 42 36 (85.7) 6 (14.3) 0.095 1.00 (ref) Positive 141 100 (70.9) 41 (29.1) 0.163 2.72 (1.38-4.81) PRc 171   0.329   Negative 66 49 (74.2) 17 (25.8) 0.144   1.00 (ref) Positive 105 81 (77.1) 24 (22.9) 0.129   1.43 (0.76-2.32) a Logistic regression model adjusted for age, BMI, Thalidomide number liveborn, oral contraceptive use, cigarette smoking, ovarian cancer family history. b For advanced ovarian cancer patients, in terms of primary cytoreductive surgery, whether to simultaneously apply pelvic and para-aortic lymph node dissection is controversial. The general consensus that pelvic and para-aortic lymph node dissection does not increase the 5-year

survival rate and improve prognosis has been widely accepted. Thus, some patients involved in our study only underwent primary cytoreductive surgery without pelvic and para-aortic lymph node dissection. The data regarding lymph node status in patients were partially missing. c Unlike breast cancer and endometrial cancer, the significance of ER and PR in the clinical treatment and prognosis of ovarian cancer is also valuable and disputed. Meanwhile, combined with the economic condition of the patients, some cases did not undergo ER and PR immunohistochemical analyses. All statistical tests were two-sided with a significance level of P ≤ 0.05. Discussion Recent studies have revealed that several genetic polymorphisms may play important roles in the pathogenesis of ovarian cancer [14, 15], and women who carried the gene mutation (BRCA1 mutation) had an increased risk (by up to 50%) of developing ovarian cancer in a lifetime [16].