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Crop Science 42:1737-1744 (2002)
© 2002 Crop Science Society of America

PLANT GENETIC RESOURCES

RAPD Marker Diversity among Cultivated and Wild Soybean Accessions from Four Chinese Provinces

Zenglu Lia and Randall L. Nelson*,b

a Dep. of Crop Sciences, 1101 W. Peabody Dr., Univ. of Illinois, Urbana, IL 61801
b USDA-ARS, Soybean/Maize Germplasm, Pathology, and Genetics Research Unit, Dep. of Crop Sciences, 1101 W. Peabody Dr., Univ. of Illinois, Urbana, IL 61801, USA

* Corresponding author (rlnelson{at}uiuc.edu)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Wild soybean (Glycine soja Siebold & Zucc.) is the ancestor of cultivated soybean [Glycine max (L.) Merr.] and is widely distributed in China, Japan, Korea, Taiwan, and eastern Russia. In North America, modern soybean cultivars are derived from a very limited germplasm base. Use of more soybean introductions and G. soja lines in breeding programs to expand the genetic base of and incorporate specific traits into commercial soybean cultivars could be beneficial. This study was conducted to evaluate the genetic variation between and within annual Glycine species and to determine geographical patterns of variation within and between the annual Glycine species. Forty G. max and 40 G. soja accessions from four Chinese provinces, Heilongjiang, Shandong, Jiangsu, and Shanxi, were surveyed with random amplified polymorphic DNA (RAPDs). The results indicated that the genetic distance (GD) within the G. soja group was larger than that within the G. max group, but smaller than that between the G. max and G. soja groups. Twenty-three more polymorphic RAPD fragments were detected within the G. soja group than within the G. max group. Nine fragments were present only in G. soja lines. On the basis of AMOVA analysis, 18% of the total variation could be accounted for by species, 15% by populations within species, and 67% by individuals within populations. Cluster and principal component analyses completely separated the G. max and G. soja groups. The groups formed by cluster analyses generally reflected the geographical regions of origin. Glycine max and G. soja lines for the same province were not more genetically related than were accessions of different species from different provinces, but the GDs between the G. soja lines from Heilongjiang and the G. max accessions from all other provinces were less than for the G. soja accessions from any other provinces. The results of this study could be used for exploiting the genetic diversity in the two species in breeding programs and in sampling and managing germplasm collections.

Abbreviations: AFLP, amplified fragment length polymorphism • AMOVA, analysis of molecular variance • GD, genetic distance • HHH, Huang Huai Hai region in China (east central) • MG, maturity group • NE, northeast region in China • PCR, polymerase chain reaction • RAPD, random amplified polymorphic DNA • SMC, simple matching coefficient • UPGMA, unweighted pair group method using arithmetic average.


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
WILD SOYBEAN is the ancestor of cultivated soybean on the basis of cytogenetics, seed proteins, morphological data, and mitochondrial DNA analysis (Hymowitz and Bernard, 1991; Palmer et al., 1996) and is widely distributed in China, Japan, Korea, Taiwan, and eastern Russian (Hymowitz and Singh, 1987). Soybean and wild soybean belong to the subgenus Soja. Both species contain 2n = 40 chromosomes and can produce vigorous, fertile F1 hybrids (Hymowitz and Singh, 1987). The basic distinction between wild and cultivated soybean is that G. soja is a twining vine with small, hard, black seeds and G. max is a bushy and rather coarse annual plant with larger seeds (Palmer et al., 1996).

Several thousand soybean accessions from the USDA Soybean Germplasm Collection (Urbana, IL) have been evaluated for agronomic and seed composition traits as well as disease resistance (Nelson et al., 1987; Nelson et al., 1988; Juvik et al., 1989; Bernard et al., 1998). Although a large number of G. max lines are available to soybean breeding programs, discovering and transferring novel, favorable genes from G. soja into G. max could be a successful approach for enhancing the genetic variability and improving cultivated soybean (Carpenter and Fehr, 1986). Identifying useful diversity is a challenge. Molecular characterization of soybean germplasm has been performed with isoenzymes, proteins, and DNA markers. Estimates of genetic relationships on the basis of the enzymes and molecular markers have been shown to be consistent with expectations based on origin and pedigree information (Griffin and Palmer, 1995; Maughan et al., 1995, 1996; Doldi et al., 1997; Thompson et al., 1998).

Keim et al. (1989) surveyed 58 G. max and G. soja accessions with 17 restriction fragment length polymorphism (RFLP) markers and found that the molecular diversity was the least among cultivated soybeans and greatest between species. Using simple sequence repeat (SSR) and amplified sequence length polymorphism (ASLP) markers, Maughan et al. (1995) reported that five microsatellite markers detected a total of 79 alleles in a sample of 94 accessions of wild and cultivated soybean. Allelic diversity for the SSR loci was greater in wild soybean than in cultivated soybean. Overall, 43 more SSR alleles were detected in wild than in cultivated soybean. Maughan et al. (1996) evaluated 23 accessions of wild and cultivated soybean using amplified fragment length polymorphism (AFLP). Among the 759 AFLP fragments scored, 17% were polymorphic in G. max and 31% were polymorphic in G. soja. Their results also indicated that AFLP phenotypic variation was greater in wild soybean than in cultivated soybean. Griffin and Palmer (1995) screened more than 1200 G. max and G. soja plant introductions with eight enzymes. The numbers of alleles per locus and average gene diversity were greater in the G. soja samples than in the G. max samples.

Approximately 23 000 G. max accessions and 6100 G. soja accessions have been collected in China. With the large number of accessions available, identifying geographical patterns of diversity among G. max and G. soja accessions would be useful to sample genetic diversity efficiently. Thompson and Nelson (1998) identified a core set of RAPD primers with high polymorphism information content scores that would be useful in surveying a broad spectrum of soybean accessions for genetic diversity. The objectives of this research were to: (i) measure genetic variation between and within annual Glycine species and (ii) to determine geographical patterns of variation within and between the annual Glycine species.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 REFERENCES
 
Ten cultivated soybean and 10 wild soybean accessions (Table 1) from each of four provinces in China were selected from the USDA Soybean Germplasm Collection. Heilongjiang (44–53° N, 121–135° E) province is located in northeast China, whereas Shanxi (35–40° N, 110–114° E), Shandong (35–38° N, 115–123° E), and Jiangsu (31–35° N, 116–122° E) are located in the Huang Huai Hai (HHH) region of east-central China. On the basis of climatic conditions and cropping systems, these two regions are considered to represent diverse ecological systems for soybean production. The three provinces in the HHH region are geographically closer to each other than to Heilongjiang province. All accessions selected were considered as landraces or primitive varieties except two improved breeding lines from Heilongjiang province. Because there were a limited number of wild soybean accessions available from Shandong, Jiangsu, and Shanxi provinces in the USDA Collection, wild soybean accessions from neighboring provinces were selected to complement the accessions from each of the above three provinces. One line from Hebei and three lines from Henan were selected to represent Shandong, one line from Zhejiang was added to the accessions from Jiangsu, and two lines from Shaanxi were used for Shanxi province. The G. max accessions selected from the HHH region are in the U.S. maturity groups (MG) I through V and the G. soja accessions selected from this region are in MG II through VII. Both G. max and G. soja accessions selected from Heilongjiang province are in MG 000 through II (Table 1).


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  		Table 1. Plant introduction (PI) number, origin, and maturity group for selected cultivated and wild soybean accessions characterized with RAPD markers.

 
Genomic DNA was isolated from fresh unifoliolate leaf tissue of 10 greenhouse-grown plants for each accession. The CTAB (cetyltrimethylammonium bromide) method (Keim et al., 1988) with minor modification was used for all accessions. Total genomic DNA of each line was standardized to a uniform concentration (10 ng µL-1) for the polymerase chain reactions (PCR) with a spectrometer.

Thirty-five decanucleotides selected for high diversity scores with diverse soybean lines (Thompson and Nelson, 1998) along with two additional primers, OPK-14 and OPS-09 (Operon Technologies, Alameda, CA) were used. The PCR procedure reported by Williams et al. (1990) and modified by Kresovich et al. (1994) was followed with a Perkin-Elmer GeneAmp PCR System 9600 or 9700 (Perkin-Elmer Corporation, Norwalk, CT); however, 50 ng of template DNA instead of 25 ng was used in each PCR reaction. Amplified products were electrophoresed on 1% (w/v) agarose gels in 1x TBE buffer at 96 V for approximately 3 h. The gels were stained with ethidium bromide, viewed under ultraviolet light, and photographed. Fragment size was estimated by means of a 100-base pair DNA ladder (GibcoBRL, Life Technologies, Carlsbad, CA).

Amplified DNA fragments were scored as either present (1) or absent (0). Simple matching coefficients (SMC), Sij = (a + d)/(a + b + c + d), were used to calculate the similarity coefficients between each pair of genotypes, where a = number of fragments in common between lines; d = number of fragments absent in both lines, and b and c = number of fragments not in common between two lines (Sokal and Michener, 1958). All scoreable polymorphic and monomorphic fragments for each genotype were included for the computation of similarity coefficients.

A SAS macro (Mumm and Dudley, 1995) was used to compute the similarity matrix on the basis of the SMC. Euclidean distances (GD), Dij = (1 - Sij)1/2, were calculated on the basis of the similarity coefficients. The average GDs and standard deviations between and within two species were obtained by the MEANS procedure (SAS Institute, 1989a). The entries were clustered on the basis of two hierarchical cluster analysis methods: unweighted pair group method using arithmetic average (UPGMA) (SAS Institute, 1989a) and Ward's minimum variance method (Ward, 1963). The TREE procedure was used to generate a dendrogram for both the UPGMA and Ward's procedures. Specific clusters were defined by means of a constant measure of dissimilarity within each procedure that was consistent with a biological interpretation of the data. The nonhierarchical cluster analysis, VARCLUS procedure (SAS Institute, 1989b), was also used with the original data as input to calculate the covariance matrix. The clusters generated with each procedure were assigned numbers independently. The variation among regions and between species was also determined by principal component analysis (SAS Institute, 1989b).

An analysis of molecular variance (AMOVA) (Schneider et al., 1997) was used to partition the genetic variation between species, among regional populations (species–province group) within species, and among individuals within regional populations. Population pairwise genetic distance, Fst, was calculated for the two species and tested for significance using the AMOVA program (Excoffier et al., 1992, Schneider et al., 1997). Fst expresses the proportion of variation explained by each population and is the average interpopulation distance between any two populations. The squared Euclidean distance was used as input in this procedure.


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
REFERENCES
 
To compare the polymorphic ratio across data sets, both polymorphic and monomorphic fragments were included in the analyses. From the 37 selected RAPD primers, 269 fragments were scored and 172 were polymorphic (64%). Among the 172 polymorphic fragments, 14 fragments were polymorphic within the G. soja group and monomorphic within the G. max group and two fragments were polymorphic within the G. max group and monomorphic within the G. soja group. Nine fragments (OPH-12620, OPK-14750, OPK-14450, OPK-14300, OPO-14610, OPO-19450, OPR-07600, OPS-091080, and OPS-09890) were only present in the G. soja lines. The frequencies of these fragments ranged from 0.05 to 0.62. DNA from 80 additional G. max accessions (landraces from four other Chinese provinces in the HHH region, Japan, and S. Korea) was amplified with the primers that produced the unique fragments in the G. soja lines. None of the G. soja specific fragments was found in any G. max line. It is possible that these unique G. soja fragments, especially those with high frequencies, indicate genetic losses that occurred during the domestication of soybean. Four fragments (OPK-031100, OPK-031400, OPO-01650, and OPR-10550) were only present in the G. max lines with frequencies of 0.10 to 0.20. Because of the low frequencies of the unique fragments within the G. max accessions, these differences could be the results of mutation after the species were separated or they could be due to sampling errors in accession selection.

The mean frequency for the polymorphic RAPD fragments among all of the accessions was 0.56. The frequencies of RAPD fragments present in the G. max and G. soja groups were quite evenly distributed in the accessions in this research, but with more RAPD fragments with intermediate frequencies within the G. soja group than within the G. max group. These results are different from those in a set of tomato (Lycopersicon esculentum Mill.) accessions assayed with RAPDs by Villand et al. (1998) who indicated that most RAPD fragments had very low or very high frequencies, and these frequencies were exhibited consistently across subpopulations. Because fewer than 5% of the fragments were unique to each species, the distinct separation of the species was primarily dependent on the differences in the frequencies of specific fragments. Analysis without the data from the fragments unique to each species did not substantially change the results.

GDs between individuals ranged from 0.12 between two G. soja lines from Heilongjiang province to 0.57 between G. max and G. soja lines from Heilongjiang and Shanxi provinces (Table 2) . The mean GD among all accessions was 0.46. The average GD within the G. max group (0.40) was less than the average distance within the G. soja group (0.46), but both intraspecific distances were less than the average GD between the G. max and G. soja groups (0.49) (Table 2). Both the minimum and maximum genetic distances within a species occurred between G. soja accessions (Table 2). The two most highly related G. soja lines (GD = 0.12) were PI 464866A and PI 458535 collected from the same county of Heilongjiang province (Bernard et al., 1989) and could have evolved from a common source. The most distantly related pair of G. soja accessions (GD = 0.57), PI 468398C and PI 522183A, originally came from Shanxi and Heilongjiang provinces, respectively, which are separated by a large geographical distance. The two most highly related G. max lines (GD = 0.19) were PI 567738A and PI 567775A from neighboring counties of Jiangsu province (Anonymous, 1980). Like the most diverse pair of G. soja lines, the most diverse pair of G. max lines (GD = 0.49), PI 291312 and PI 567443, came from Shanxi and Heilongjiang province, respectively, but the difference was less than that of the most diverse G. soja lines. The minimum distance between a G. max and a G. soja line (GD = 0.39) was more than twice the minimum GD within a species, but the maximum GD between a G. max and a G. soja line (GD = 0.57) was nearly identical to the most diverse pair of G. soja lines. This demonstrates the large GD that exists between the two species, and also the great genetic variation within G. soja. Our observations of greater variation within G. soja group agreed with the results of genetic diversity in soybean from previous reports (Kiang and Gorman, 1983; Keim et al., 1989; Griffin and Palmer, 1995; Maughan et al., 1995, 1996).


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  		Table 2. Genetic distances within and between Glycine species calculated from 269 RAPD fragments and the t test results comparing the mean genetic distances.

 
Several cluster procedures have been used in molecular marker data analysis (Lubbers et al., 1991; Grabau et al., 1992; Kresovich et al, 1994; Griffin and Palmer, 1995; Gizlice et al., 1996; Thompson et al., 1998). UPGMA, Ward's, VARCLUS, and principal component analysis are widely used procedures. In this study, analyses tended to identify similar numbers of groups but the actual cluster members were different for each procedure (Table 3) . The UPGMA procedure defined 10 groups with four outliers, all G. soja accessions, at the 0.9 level dissimilarity level. Twelve groups with no outliers were defined by Ward's method at 0.024 variation level. By VARCLUS procedure, 14 groups accounted for 73% of the total variation. All of the procedures completely separated the G. max and the G. soja accessions at the selected dissimilarity level (Table 3). Previous research on genetic diversity between G. max and G. soja has also shown a clear separation between the species using isozymes (Griffin and Palmer, 1995), AFLPs (Maughan et al., 1996), and SSRs (Maughan et al., 1995). Keim et al. (1989) using RFLPs reported that the average GD between the G. max and G. soja accessions was similar to the average GD within G. max lines. This may have been caused by the small numbers of RFLP markers (17 markers) used. This research and previously reported results with other markers consistently indicate that the G. soja and G. max are different gene pools. UPGMA clustered eight G. max accessions from Heilongjiang in one cluster, and all other G. max accessions in a second cluster. Both Ward's and UPGMA identified the Heilongjiang cluster. Ward's split the remaining G. max lines into two clusters, whereas VARCLUS formed three clusters. The consistency of all procedures to identify the Heilongjiang cluster indicates a strong genetic relationship among those lines. Although the actual collection date of each of the Heilongjiang accessions is unknown, they were imported into the USA from 1926 to 1982. Two members of this cluster are improved breeding lines including one in MG 000. This demonstrates the power of DNA markers to identify genetic groups that would not be obvious from other data. The remaining G. max accessions clustered differently in each procedure. VARCLUS produced the most G. soja clusters (10) and UPGMA the fewest (8). Only three G. soja clusters were consistently identified by all procedures (Clusters 7, 12, and 13). As with the G. max clusters, looking for agreement among the multiple procedures may provide a more complete classification scheme.


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  		Table 3. Group assignments for Glycine max and Glycine soja accessions from three clustering procedures and assigned clusters based on results form all procedures.

 
Using all three analyses, we established 17 consensus clusters (Table 3). The 40 G. max accessions were divided into five clusters. Each cluster was dominated by accessions from a single province or region. Cluster 1 consisted of eight accessions from Shanxi province and Cluster 9 included eight accessions from Heilongjiang province. In Cluster 2, three of the five accessions originated from Jiangsu. Seven of the eight accessions included in Cluster 11 originated from Jiangsu and eight of the eleven accessions in Cluster 5 were from Shandong. The 40 G. soja accessions also clustered according to origin but were divided into 12 clusters and 1 outlier indicating a more diverse group of accessions than the G. max lines (Table 3). In two clusters (8 and 17), UPGMA defined four accessions as outliers, but considering all analyses only one accession from Jiangsu was classified as an outlier (Table 3). There were three clusters (4, 13, and 14) with only accessions from Heilongjiang, and three other single province clusters (6, 16, and 10) with accessions from only Shandong, Jiangsu or Shanxi provinces, respectively. There were two clusters (3 and 15) with accessions from both Shandong and Shanxi. These provinces do not share a common border but are connected by the Yellow River that can be a conduit for seeds. The one accession included from Zhejiang clustered with accessions from Jiangsu in Cluster 12. Zhejiang and Jiangsu share a common border. The three accessions from Henan clustered together with one accession from the neighboring province of Shandong (Cluster 7). Two clusters contained accessions that do not share similar geographical regions. Cluster 17 is a two-member cluster with one accession each from Shanxi and Jiangsu provinces, and Cluster 8 has five accessions from four noncontiguous provinces.

On the basis of the results of the principal component analysis (Fig. 1) , the G. max and G. soja groups were distinctly separated but the G. max lines were closely clustered, whereas the G. soja lines were widely scattered. The G. max accessions from Heilongjiang province were closer to the G. soja lines from Heilongjiang than any of the other accessions of different species from the same province. The first principal component accounted for 35% of the variation, and the second principal component explained 8% of the variation. These results were consistent with that from the cluster analyses but highlight the differences in genetic diversity within species.



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  		Fig. 1. Scatterplot of G. max and G. soja accessions from four Chinese provinces determined on the basis of principal component analysis of the 269 RAPD fragments. Observations are coded as follows: H = Glycine max and L = Glycine soja from Heilongjiang; S = G. max and D = G. soja from Shandong; J = G. max and U = G. soja from Jiangsu; X = G. max and I = G. soja from Shanxi; N = G. soja from Henan; B = G. soja from Hebei; Z = G. soja from Zhejiang; A = G. soja from Shaanxi.

 
Cluster and principal component analyses separated accessions on the basis of species and origin. The AMOVA program was used to estimate and partition total molecular marker variances between species, among provinces, and within provinces as well as to test the significance of partitioned variance components using a permutation procedure (Excoffier et al., 1992; Schneider et al., 1997). Variation between species accounted for 18% of the total variation, among provinces within species 15% of the total variation, and within species–province populations 67% of the total variation (Table 4) . The variation for all three levels was significant (P = 0.05). Although there are large morphological differences between G. max and G. soja species, the variation accounted for by species with molecular marker data was much smaller than that within populations but larger than that explained by populations within species. These results were consistent with the report from Maughan et al. (1995). After partitioning the diversity within and between groups in a set of G. max and G. soja lines on the basis of 79 SSR alleles detected, Maughan et al. (1995) indicated that approximately 7 to 19%, depending on the locus, of the variation observed was accounted for by between-group differentiation.


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  		Table 4. Analysis of molecular variance design and results for Glycine max and Glycine soja accessions characterized with RAPD markers.

 
To visualize the relationships among regional populations, an interpopulation distance matrix, generated from the AMOVA program (Table 5) was submitted for cluster analysis by the UPGMA procedure and the dendrogram was plotted (Fig. 2) . The G. max accessions from the three HHH provinces formed a cluster, and the G. soja accessions from the same provinces formed a separate cluster. This clearly shows that the accessions from Heilongjiang were distinct from the other provinces.


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  		Table 5. Population distance pairwise comparisons for Glycine max and Glycine soja accessions from four Chinese provinces calculated from the analysis of molecular variance.

 


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  		Fig. 2. Dendrogram generated from the UPGMA procedure using population distances generated from analysis of RAPD fragments depicting the relationships among populations of annual Glycine species from four provinces in China. HL = Heilongjiang, SD = Shandong, JS = Jiangsu, SX = Shanxi, suffix M = Glycine max; S = Glycine soja.

 
Because of the lack of availability of G. soja lines in the USDA Soybean Germplasm Collection (Urbana, IL), some G. soja accessions used in this study were sublined from the same original seed lot obtained from China. These accessions had the same PI number with different letter suffixes and had been previously separated on the basis of some obvious morphological differences. It is not surprising that among the nine sets of sublines included, seven clustered together. There are two sets of sublines that consisted of three accessions each and each set formed a distinct cluster (Clusters 5 and 9) as did one set of two sublines (Cluster 15). There were two sets of sublines that were split into different clusters. In one set, one of the sublines (JSS-8) did not consistently cluster with any other accession and was classified as an outlier. It also had a higher than average GD (0.48) with all other G. soja accessions. This GD was nearly equal to the average distance between G. max and G. soja accessions. In the other case (SDS-4 and SDS-5), the GD between the two sublines was 0.37, which is much less than the average distance among G. soja accessions but was much greater than the minimum distance found between two G. soja accessions from Heilongjiang (GD = 0.12). When germplasm accessions are maintained as mixed samples it is always possible that undetected seed contamination can occur. These highly different sublines could be the result of that type of sample contamination or it demonstrates the highly variable nature of some G. soja populations.

World wide, the number of G. soja accessions held in germplasm collections is small compared with the number of G. max accessions. In this research, much greater genetic diversity was found within moderately sized geographical regions (provinces) for G. soja accessions than for G. max accessions. Data from G. soja sublines derived from a seed lot presumably collected from a single population demonstrated that considerable variation could also be found among G. soja accessions originating from a very small area. These results indicate that more intense sampling of G. soja, both within populations and in total number of samples collected per region, is likely justified if germplasm collections are to sample the genetic diversity adequately that exists within the species.

Limited genetic diversity could be a major factor that restricts genetic gains in soybean breeding programs. Currently, G. soja lines have been used only for the improvement of some specific traits in soybean (Ertl and Fehr, 1985; LeRoy et al., 1991). Because of high levels of variability and specific useful traits, G. soja lines are potential sources of valuable genetic variation. Crosses between G. max and G. soja lines could create populations with the greatest variability. Past attempts to utilize G. soja lines for cultivar improvement have not been successful (Carpenter and Fehr, 1986) because of the difficulty in overcoming the many negative contributions of the G. soja parents such as vining, shattering, and low yield. Molecular markers are proving to be useful in tagging genes conferring both quantitative and qualitative traits. The use of marker techniques and the eventual identification of specific loci important for yield or disease resistance will greatly increase the likelihood that useful variation will be found in G. soja and transferred to improved cultivars. The results from this research provide breeders with both general and specific information to help identify useful genetic diversity from both G. max and G. soja accessions. Unique fragments in G. soja accessions could imply the presence of loci that are not present in G. max. The much greater genetic diversity within G. soja than within G. max demonstrates the potential that exists within this species to improve the cultivated soybean despite the lack of past success.


   ACKNOWLEDGMENTS
 
Funding for this research was provided in part by the United Soybean Board and by the Illinois Soybean Program Operating Board.


   NOTES
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ABSTRACT
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 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
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Mention of a trademark, proprietary product, or vendor does not constitute a guarantee or warranty of the product by the USDA or the University of Illinois and does not imply its approval to the exclusion of other products or vendors that may also be suitable.

Received for publication September 20, 2000.


   REFERENCES
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