1 Epidemiology of Developmental Brain Disorders Department, New York State Psychiatric Institute, New York, NY 10032, 2 Gertrude H. Sergievsky Center and 3 Mailman School of Public Health, Columbia University, 4 Research Foundation for Mental Hygiene, New York State Psychiatric Institute, New York, NY and Graduate School of Arts and Sciences, Columbia University, 5 Department of Obstetrics and Gynecology, Columbia University, New York, NY 10032 and 6 Southwest Women's Sonography, Albuquerque, NM 87109, USA
7 To whom correspondence should be addressed at: Psychiatric Institute, Epidemiology, 722 West 168th Street, Room 1607, New York, NY 10032. Email: jkk3{at}columbia.edu
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Abstract |
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Key words: age/epidemiology/FSH/inhibin B/ovarian follicles
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Introduction |
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Studies of autopsy and surgical specimens show that the germ cell population, which is largest during fetal development (7x106), decreases approximately exponentially with chronological age (Block, 1952
; Baker, 1963
; Thomford et al., 1987
; Faddy et al., 1992
; Leidy et al., 1998
; Westhoff et al., 2000
). A similar decline is evident for antral follicles, the small fraction (<0.5%) of the total pool that develops and enlarges during each menstrual cycle. Sonographic studies confirm that antral follicle count declines with chronological age in women of reproductive age (Reuss et al., 1996
; Scheffer et al., 1999
; Broekmans et al., 2004
; Kline et al., 2004
). Moreover, a detectable decline in follicle count may precede detectable changes in hormone levels (Scheffer et al., 1999
; Kline et al., 2004
). It is reasonable to infer that the decline in fecundity with chronological age reflects, at least in part, the diminishing supply of oocytes. Among infertile women, indicators of the size of the oocyte pool supplement chronological age as predictors of the response to assisted reproductive technologies. While findings are not entirely consistent (Bukman and Heineman, 2001
; Bancsi et al., 2003
), antral follicle count, ovarian volume and pretreatment levels of FSH and inhibin B have each been associated with ovarian response to hormonal stimulation and clinical pregnancy rates.
In this paper we assume that antral follicle count is the best indicator of the size of the underlying follicle pool. Obtaining high quality data is, however, labour intense. While follicles can be counted with moderate to high reliability in research settings (Scheffer et al., 2002; Kline et al., 2004
), similar results are less likely in clinical practice (Hansen et al., 2003
), at least until specialized (potentially automated) three-dimensional applications are developed and widely available. We therefore sought to identify indicators other than antral follicle count which would be serviceable to clinicians seeking to estimate the size of the underlying follicle pool.
We draw on data from a sample of recently pregnant women to extend observations on the natural history of ovarian ageing. First, we describe the relations between chronological age and several potential indicators of ovarian age: the number of antral follicles, the diameter of the largest follicle, the surface area of the follicles, ovarian volume, and levels of FSH, inhibin B and estradiol (E2). We also describe associations between indicators. Next, from among the indicators that can be easily obtained (age, ovarian volume and levels of FSH, inhibin B and E2) we identify the combination that best predicts antral follicle count. We also consider the utility of inhibin B level since, in clinical practice, measurement of this hormone is less common than measurement of FSH and E2.
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Materials and methods |
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The protocol
Briefly, from September 1998 to April 2001, we identified women aged 18 years with singleton prefetal losses (developmental age <9 weeks) whose products of conception were submitted to the Pathology Department of a hospital in New York State. We asked for permission to karyotype the abortus. If a woman's loss was successfully karyotyped, we asked her to complete a short telephone interview to determine her eligibility. Eligible women who consented to the protocol completed a more extensive telephone interview and made two visits to the study hospital during the first week of their second or later menstrual cycle, the first on day 14 for a blood sample and the second on day 57 for transvaginal sonography.
To obtain valid ovarian age measures, we required: no pituitary disorder or hormonal disorder related to ovarian function, no oophorectomy, no hormonal medication, no pregnancy at the time of ultrasound, no breastfeeding or breastfeeding no more than once per day during the menstrual cycle preceding the study assessments. We required that any diagnosis be current, the report of the diagnostic work-up informative and the clinical symptoms and treatment consistent with the diagnosis. The study reproductive endocrinologist (A.C.K.) reviewed the interview data to determine whether or not a potential participant currently had a condition associated with altered hormone levels.
Women with spontaneous abortions
Of the 244 women with karyotyped losses, 127 (52%) completed the protocol (Table I). The principal reasons for not completing the protocol were: refusal (23%) and ineligibility (25%), primarily due to use of hormonal contraceptives or pregnancy soon after the index loss. Six women were excluded because of hormonal conditions and another six were excluded due to use of fertility drugs, although only two had experienced conception delay >1 year prior to the study pregnancy.
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Women with live births
For each woman with a trisomic loss who completed the study, we selected an age-matched control with a chromosomally and anatomically normal live birth 1800 g, no pregnancy loss since the index pregnancy and no known trisomic pregnancy. They were selected from the hospital delivery log of women who delivered during the 713 months preceding the date of selection. Live birth controls were matched to trisomy cases for projected age (±6 months) at the sonography visit. If a selected control was ineligible for the study or refused to participate, we replaced her. The protocol for women with live births was identical to the protocol for women with prefetal losses.
In total, we selected 219 women with live births, 65 of whom (30%) completed the protocol (Table I). The principal reasons for not completing the protocol were refusal (31%) and ineligibility (37%), primarily due to use of hormonal contraceptives or breastfeeding. Two women were excluded because of hormonal conditions.
Women who completed the protocol tended to be older, though not significantly so, than women who did not. Among the 144 women who completed the eligibility interview, the odds of completing the protocol did not differ with educational attainment, parity, number of prior induced abortions or number of prior spontaneous abortions.
Analytical sample
Analyses exclude repeat study entrances of four women (all with pregnancy losses) to maintain the independence of observations. They also exclude 12 women: eight women for whom we were able to scan only one ovaryeven though, based on history and transabdominal scans, each woman was known to have two ovariesand four women who had conditionsthree a cyst and one an endometriomawhich might have obscured the follicle count. Thus the analytical sample includes 176 women with both ovaries scanned and unobstructed.
Characteristics of the sample (Table II)
Among the 176 women, 117 had an index pregnancy ending in spontaneous abortion and 59 an index pregnancy ending in live birth. Average age at ultrasound was 34 years (range 2248). The majority were white and had completed high school. Ninety-five per cent completed the blood and sonography protocols after the second or third menstrual period following the index loss or, for women with live births, the introductory letter.
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We videotaped the sonography scan. The scans were counted in four randomized batches. The randomization procedure was unknown to the sonographer.
The sonographer (M.L.R.) identified the optimal scan, converted it to a digitized format and exported it to Matrox Inspector (Matrox Electronic Systems Ltd, Dorval, Quebec, Canada 2005), an interactive imaging software. We used the software to: (i) follow each sonolucency interpreted as a follicle through the scan, frame by frame, to identify its maximum diameter; and (ii) measure the maximum diameter of each follicle in the vertical plane by calibrating measurements to the centimetre scale generated by the ultrasound machine. Diameters ranged from 1.2 to 24.1 mm, with 99.3% of follicles <12 mm in diameter.
To assess the reliability of the counting procedure, we counted again the follicles in 40 ovaries (Kline et al., 2004). The intra-class correlation coefficient was 0.92. Analyses use the second count for the 20 women whose scans were counted twice. Counts ranged from 2 to 70 (median=15, mean=18.7, SD=12.8). The mean difference between counts in the left and right ovaries was 0.2 (SD=5.4, range=15 to 28); the correlation between them was 0.70 (P<0.0001).
We also examined associations with three alternative measures: (i) diameter of the largest follicle, because some data suggest that advancing age is associated with earlier emergence of the dominant follicle (Klein et al., 1996a, 2000
); (ii) the sum of the antral follicle surface areas, to examine whether surface area is more closely related to inhibin B level than to follicle count; (iii) total ovarian volume, as a possible indicator, easily obtained, of the number of antral follicles. We assumed that antral follicles are spherical, estimating the surface area of each by 4
r2, where r is the radius. We assumed that each ovary is ellipsoidal, estimating the volume of each by D1xD2xD3x
/6, where D1, D2 and D3 are the three dimensions of the ovary. For the sum of the surface areas, the mean difference between left and right ovaries was 55.1 mm2 (SD=511.2, range 1710.6 to 1315.2); the correlation between them was 0.40 (P<0.0001). For ovarian volume, the mean difference between left and right ovaries was 0.8 cm3 (SD=4.5, range=16.8 to 16.1); the correlation between them was 0.25 (P=0.25).
Serum hormone levels
Blood samples were processed in a refrigerated centrifuge and, after separation, sera were frozen at 25°C at the study hospital; they were shipped to New York City and stored at 20°C. FSH and E2 were measured by solid-phase chemiluminescent enzyme immunoassays (Diagnostic Products Co. Los Angeles, CA) (Immulite); dimeric inhibin B was measured by radioimmunoassay (Oxford Bio-Innovation Ltd, Upper Heyford, Oxfordshire, England). For FSH, sensitivity (the minimum detection limit) was 0.1 mIU/ml; intra- and inter-assay coefficients of variation (CV) were 9.3 and 10.5% respectively. For inhibin B, sensitivity was 20 pg/ml; intra- and inter-assay CV were 5.1 and 6.2%, respectively. For E2, sensitivity was 20 pg/ml; intra- and inter-assay CV were 1.9 and 5% respectively.
Table III shows summary statistics on each of the ovarian age indicators. Follicles were counted and hormones assayed without knowledge of any subject characteristics.
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We first describe the magnitude of associations: (i) between chronological age and each ovarian age indicator; and (ii) between the ovarian age indicators, adjusting for chronological age. For each indicator we illustrate the pattern of change with chronological age by showing medians and the least squares regression equation that describes change with age. We fit models with first-, second- and third-order terms for age, but retained a higher-order term only if it significantly improved the proportion of variance explained and visual inspection of the data suggested that the association with age was not log-linear. The descriptive regression equations are based on 168 women; they exclude eight women with one or two ovarian age indicators that were >3 SD from the value predicted, given age.
Second, drawing on the entire sample of 176, we used ordinary least squares regression analysis to predict follicle count using chronological age and readily obtained indicators of ovarian agenamely, ovarian volume and hormone levels.
We evaluated the accuracy of eight regression models to discriminate between women with low antral follicle count (older ovarian age) and high antral follicle count using Receiver Operating Characteristic (ROC) curves (McNeil and Hanley, 1984). We defined low count as
10 antral follicles. We chose
10 follicles for scientific and practical reasons: it corresponds to the median count at 39 years, an age at which infecundity rates increase steeply (Schwartz and Mayaux, 1982
; Menken et al., 1986
); it demarcates the lowest 30% of the sample, providing a sufficient number of women with low count for analysis.
The larger the area under the ROC curve, the better the discriminatory capability of the model. The two models with the best ROCs include measures taken at two different points during the menstrual cycle, on day 14 for hormones and on day 57 for ovarian volume. Five models, including the model with age only, which are based on data from a single time-point, might be more practicable for clinicians. For each model, we determined the cutpoint that corresponds to sensitivity of 80% and set out the corresponding specificity, positive predictive value and negative predictive value. Because predictive value is a function of the proportion with low count, we also describe the characteristics of five of the models separately for women aged <35 and
35 years.
We repeated all regression analyses adjusting for the outcome of the index pregnancy (spontaneous abortion versus live birth). Results were unchanged (data not shown). For our primary regression model predicting follicle count, we also examined whether the addition of other covariates (i.e. obstetric variables, menstrual characteristics, body mass index, education, smoking) added significantly to the proportion of variance in follicle count explained by the model; they did not.
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Results |
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Age-adjusted correlations between ovarian age indicators (Table IV)
Ln(1+antral follicle count) is most strongly related to Ln(1+follicle surface area) (r=0.76) and significantly associated with ovarian volume (r=0.48), Ln(FSH) (r=0.34) and Ln(inhibin B) (r=0.24). Among the remaining measures, the pattern of associations is, for the most part, consistent with expectation. For example, Ln(1+follicle surface area) is positively associated with Ln(inhibin B) (r=0.34) and with Ln(1+follicle maximum diameter) (r=0.47). Ln(FSH) is inversely related to Ln(E2) (r=0.23).
Ln(FSH) is not correlated with Ln(inhibin B) (r=0.03). Because inhibin B levels were lower on day 1 (median=33.0 pg/ml, n=16) than on days 24 (median=86.5 pg/ml, n=160), we repeated the analysis, limiting the sample to women with blood samples taken on days 24. Ln(FSH) and Ln(inhibin B) are weakly correlated (r=0.15, P=0.053). The correlations between inhibin B and other ovarian indicators are similar to those in the entire sample.
Predicting antral follicle count (Table V)
Using only age and four readily available measures (ovarian volume, FSH, inhibin B, E2), we sought the regression model which best predicts Ln(1+antral follicle count). Only E2 is not independently and significantly related to follicle count (R2=0.005, adjusting for chronological age, P=0.28). Thus, we did not consider E2 further in the regression analyses.
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Receiver operating characteristics
Fifty-three (30.1%) women had 10 antral follicles. We assessed the performance of all models to distinguish women with
10 follicles from women with >10 (Table VI). The areas under all ROC curves are good, especially for models A (age, ovarian volume, FSH, inhibin B) and B (age, ovarian volume, FSH). For comparison, we show the model which includes only a linear term for age; the addition of a quadratic term for age did not improve the ROC curve. Using the cutpoint which produces a sensitivity of 80%, models A and B increase the positive predictive value to 5860%, compared with 42% for a model which includes only age. Of the three models which include age plus a single ovarian age indicator, at the cutpoint corresponding to a sensitivity of 80%, the positive predictive value is highest (55%) for the model with ovarian volume.
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Discussion |
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The strengths of the study are several. First, all participants were of demonstrated fecundability and 71% had at least one live birth, avoiding the potential biases of assisted reproduction samples. Second, all ovarian age indicators were assessed without knowledge of maternal characteristics, eliminating the possibility of bias. Third, reliability was excellent: the procedure for counting antral follicles was repeatable; the intra- and inter-assay CV for the hormone assays were high. Fourth, ovaries were imaged on day 57 rather than, as in previous studies, during the early follicular phase, maximizing the ability to detect developing follicles. (Earlier, follicles are often too small to see; later, the emerging dominant follicle may obscure smaller follicles.)
Three aspects of the study design are of possible concern. First, the sample over-represents women with spontaneous abortions. We do not believe this feature limits generalizability since spontaneous abortion is unrelated to these ovarian age indicators (Kline et al., 2004) and all results persisted when analyses were adjusted for the index pregnancy. Moreover, as noted above, nearly all women had at least one previous live birth. Second, because all women had conceived recently (0.32.5 years prior to the study sonogram, mean 0.9 years), at older ages (the late 30s and 40s) our sample may over-represent women of high fecundability. This over-representation may alter the patterns of change observed at older ages. Third, we measured hormones and counted follicles at different times during the follicular phase, an approach which may limit the clinical utility of regression equations which draw on measures from two different days.
In our sample, as expected, ln(1+antral follicle count) showed the strongest association with chronological age (r=0.52). Age was modestly associated with ln(FSH) (r=0.35), ln(1+follicle maximum diameter) (r=0.26) and ln(ovarian volume) (r=0.26). Age was statistically significantly associated with ln(E2) (r=0.18) and ln(inhibin B) (r=0.16), although both correlations were small. The association of age with follicle surface area was entirely explained by follicle count.
For comparison, we consider the two largest of the aforementioned studies. Scheffer et al. (2003), in the Netherlands, recruited 162 regularly menstruating volunteers, 2546 years; they obtained ovarian age measures on day 14 of the cycle. Erdem et al. (2002)
, in Turkey, drew on a sample of 108 menstruating women, 3550 years, with minor gynaecological problems; all measures were made on day 3 of the cycle. The two studies differ from ours in several respects. First, both obtained follicular and ovarian measures on a single day during the early follicular phase. The range of follicle sizes in both studies was small to moderate (210 mm in the Netherlands, <10 mm in Turkey), whereas we counted all follicles (of which 96% were 210 mm). The difference in follicle size is likely a function of the timing of the measures. Second, in both studies, follicular and ovarian measures were taken during the scan, leaving open the potential for bias related to awareness of the woman's age. Data from the Netherlands (Scheffer et al., 2002
), however, show both high inter-observer agreement for counts obtained during a scan and high intra-observer agreement between real-time counts and subsequent counts from stored scans. Third, both studies analysed ovarian age indicators without logarithmic transformation.
Average follicle count (our computations from published data) is 3.3 per woman in the Turkish sample, about nine in the Netherlands sample and 18.7 in our sample. The differences most likely reflect the higher proportions of older women and the earlier time-period of scanning in the two previous studies, when a smaller proportion of developing follicles are large enough to detect by sonography.
Observations in the Netherlands sample are roughly compatible with ourschronological age is more strongly associated with follicle count than with FSH, inhibin B, E2 or with measures of the ovary or follicles (Scheffer et al., 2003). As in our data, chronological age is modestly associated with ovarian volume and FSH, but the correlation of age with E2 is stronger. The higher proportion of women aged >40 years in the Netherlands sample probably accounts for the somewhat higher correlations of chronological age with follicle count and E2.
In the Turkish sample (Erdem et al., 2002), chronological age is more strongly associated with FSH than with follicle count, ovarian volume or E2. Moreover, the association with E2 is inverse, rather than positive as in our study and the Netherlands study. This pattern of results probably reflects the age distribution of the sample. The inverse correlation of age with E2 was confined to women aged 4550 years, suggesting that many, though menstruating, were close to their final menstrual periods.
There is little doubt that antral follicle count declines monotonically with age, but various models describe the shape. In our sample, the association of chronological age with ln(1+antral follicle count) is well fitted by either of two modelsa simple exponential model (R2=0.30) or a model which includes both linear and quadratic terms (R2=0.32). While the quadratic age term is marginally statistically significant, it accounts for only 1.6% of the variance in count, leaving uncertain which model most fairly represents the data. The latter equation suggests that follicle count declines more steeply after about age 27 years than before. One intuitively appealing feature of the equation is the suggestion of a natural upper limit to antral follicle count. Our models contrast with those fitted by Scheffer and colleagues, who also fitted two modelsa biphasic model in which ln(count) decreases more steeply beginning at age 38 years than before (Scheffer et al., 1999) and a linear model relating age to count until age 46 years (Broekmans et al., 2004
). (It is beyond the scope of this paper to combine the evidence from these studies with autopsy and hysterectomy data to model the relation of age to antral follicle count.)
Age explained only a small proportion of the variance in ovarian volume and FSH in our sample. Nevertheless, our data are consistent with previous observations. Data from a large series of women enrolled in a cancer screening project show that average ovarian volume declines between ages 25 and 91 years (Pavlik et al., 2000). Like previous studies (Reyes et al., 1977
; Metcalf and Livesey, 1985
; Lee et al., 1988
; Lenton et al., 1988
; Cramer et al., 1994
; Broekmans et al., 1998
), our data indicate that FSH levels increase beginning around age 40 years.
With respect to inhibin B, our data indicate that the association with age is not monotonic; rather, inhibin B levels are constant or increasing until about age 40 years and then decrease. These results are compatible with two studies which show that inhibin B levels are lower in older regularly cycling women than in younger women (Klein et al., 1996b; Welt et al., 1999
); at later ages (i.e. 3952 years in one cross-sectional study), levels appear to decline linearly with age (Danforth et al., 1998
).
In our sample, adjusting for age, ln(inhibin B) is modestly correlated with ln(1+follicle count) (r=0.24) and slightly more strongly correlated with ln(1+follicle surface area) (r=0.34), results consistent with those from the Netherlands sample (Scheffer et al., 2003). Taken together, the two studies suggest that inhibin B measured on day 14 is not a good indicator of the number of developing follicles. Since inhibin B is produced by the developing cohort of antral follicles (Groome et al., 1996
; Burger, 2000
), measurements later in the follicular phase may more accurately reflect the size and quality of the developing cohort.
Our data show only a weak positive relation between age and E2. This result joins an already inconsistent body of evidence, among menstruating women, showing a positive association (Welt et al., 1999; Scheffer et al., 2003
), no association (Klein et al., 1996b
) or an inverse association (Erdem et al., 2002
). This inconsistency is probably explained by methodological disparities.
Clinical application
From among the easily obtained indicatorsage, ovarian volume and levels of FSH, inhibin B and E2we identified the combination that best predicts ln(1+antral follicle count). Other informative hormones might also be easy to obtain and useful. For example, two recent studiesone in presumably fecund women (de Vet et al., 2002) and the other in infertile women (van Rooij et al., 2002
)suggest that anti-Müllerian hormone levels may be a useful predictor of antral follicle count.
Of the five indicators examined, only E2 was not significantly related to ln(1+follicle count). The regression model which included age, ovarian volume, FSH and inhibin B explained 49.6% of the variance in count. Age and ovarian volume were the strongest predictors, followed by FSH. The association between ovarian volume and count is not surprising, since the ovary enlarges to accommodate the developing follicles. Inhibin B, though significantly associated with count, was responsible for only a small proportion of its variance. Removing this term from the equation decreased the proportion of variance explained only trivially, to 48.3%.
To predict which women have 10 antral follicles, models that include ovarian age indicators as well as chronological age improve markedly on the model that includes chronological age alone. Two modelsA (chronological age, ovarian volume, FSH and inhibin B) and B (chronological age, ovarian volume and FSH)are virtually indistinguishable in their ability to discriminate between women with low and high count. For the clinician, these models have the disadvantage that they require measures of both ovarian volume and hormones. Moreover, our regression equation derives from ovarian and hormone data which were collected on two different days of the cycle. Model D, which includes only chronological age and hormone levels, performs nearly as well as models A and B.
All models performed less well in younger (<35 years) women than in older (35 years) women, as expected given the prevalence (13.9%) of low antral follicle count among younger women. For a presumably fecund young woman who wants to defer childbearing, our models do not improve upon knowledge of her age alone for predicting whether or not she will encounter problems when she later tries to conceive. For an older woman who wants to know how long she can postpone childbearing or who is trying to conceive and wants to know whether to expect difficulties, our models improve upon prediction based on chronological age alone. In our sample, in which 52% of older women had low count, the best model has a positive predictive value of 79%, a marked improvement over the positive predictive value (60%) of the model based only on chronological age. Thus, our models help to identify women who might benefit from expedited evaluation (for example, actually counting follicles).
Three caveats are in order. First, our predictive regression equations, which derive from a single sample, need replication to determine validity. Second, strictly speaking our equations apply to women of demonstrated (or presumed) fecundability; it remains to test whether or not they are useful to women seeking treatment for infertility. Third, an underlying assumption for this work is that antral follicle count predicts fecundability. While it is generally thought that low count is associated with low fecundability, data are limited to women seeking care for infertility and their response to assisted reproduction treatment. These data do not readily translate into predictions about conception for women of known or presumed fecundability, nor are they generalizable. It remains to test this assumption in samples not selected for difficulty conceiving.
Finally, one aspect of the analytical approach merits elaboration. Although we defined low count as 10 antral follicles, the choice was not guided by empirical evidence; the biologically relevant cutpoint may be lower. In our sample, antral follicle count of 10 corresponds to the median at age 39 yearswhen the risk of infecundity is
20% (Menken et al., 1986
) and the proportion of women taking >1 year to conceive may be on the order of 50% (Schwartz and Mayaux, 1982
). If our equations are valid, they will improve the prediction of time to conception in older women beyond that provided by chronological age alone.
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Acknowledgements |
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Submitted on February 14, 2005; resubmitted on March 22, 2005; accepted on April 1, 2005.
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