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Biolog, DGGE, microbial community structure, PLFA, tea garden soil

Biolog, DGGE, microbial community structure, PLFA, tea garden soil
Biolog, DGGE, microbial community structure, PLFA, tea garden soil

Pedosphere18(5):653–663,2008

ISSN1002-0160/CN32-1315/P

c 2008Soil Science Society of China

Published by Elsevier Limited and Science Press

Soil Microbial Community Structure in Diverse Land Use Systems:A Comparative Study Using Biolog,

DGGE,and PLF A Analyses?1

XUE Dong1,2,YAO Huai-Ying1,?2,GE De-Yong1and HUANG Chang-Yong1

1Key Laboratory of Polluted Environment Remediation and Ecological Health,Ministry of Education,College of Envi-ronmental and Resource Sciences,Zhejiang University,Huajiachi Campus,Hangzhou310029(China).E-mail:xue-dong78@https://www.wendangku.net/doc/4014145722.html,

2Department of Environmental and Chemical Engineering,Luoyang Institute of Science and Technology,Luoyang471023 (China)

(Received November20,2007;revised June14,2008)

ABSTRACT

Biolog,16S rRNA gene denaturing gradient gel electrophoresis(DGGE),and phospholipid fatty acid(PLFA)analyses were used to assess soil microbial community characteristics in a chronosequence of tea garden systems(8-,50-,and90-year-old tea gardens),an adjacent wasteland,and a90-year-old forest.Biolog analysis showed that the average well color development(AWCD)of all carbon sources and the functional diversity based on the Shannon index decreased(P<0.05) in the following order:wasteland>forest>tea garden.For the DGGE analysis,the genetic diversity based on the Shannon index was signi?cantly lower in the tea garden soils than in the wasteland.However,compared to the90-year-old forest,the tea garden soils showed signi?cantly higher genetic diversity.PLFA analysis showed that the ratio of Gram positive bacteria to Gram negative bacteria was signi?cantly higher in the tea garden soils than in the wasteland,and the highest value was found in the90-year-old forest.Both the fungal PLFA and the ratio of fungi to bacteria were signi?cantly higher in the three tea garden soils than in the wasteland and forest,indicating that fungal PLFA was signi?cantly a?ected by land-use change.Based on cluster analysis of the soil microbial community structure,all three analytical methods showed that land-use change had a greater e?ect on soil microbial community structure than tea garden age.

Key Words:Biolog,DGGE,microbial community structure,PLFA,tea garden soil

Citation:Xue,D.,Yao,H.Y.,Ge,D.Y.and Huang,C.Y.2008.Soil microbial community structure in diverse land use systems:A comparative study using Biolog,DGGE,and PLFA analyses.Pedosphere.18(5):653–663.

INTRODUCTION

Soil microorganisms play a crucial role in the cycling of almost all the major plant nutrients and the energy?ow of either natural or anthropogenically altered soils(Smith and Paul,1990).Soil microbial community is an important measure of sustainable land use and is sensitive to changes in the soil chemical properties(Wolters and J¨o ergensen,1991;Wardle,1992;Bauhus and Khanna,1994;Beck et al.,1995).

Tea(Camellia sinensis)is an important economic crop and is planted widely on acidic red soils in the tropical and subtropical zones of China.Tea gardens are usually grown as a monoculture and receive considerable amounts of nitrogen fertilization,root exudates,and leaf litter.Several studies have shown that the soil physico-chemical properties undergo a marked change with the development of tea garden ecosystems(Liao,1998;Shi et al.,1999;Yu et al.,2004).Soil pH decreases gradually, whilst aluminium,?uorin,and hydroxybenzene accumulate in tea garden soil.The soil is also relatively de?cient in microelements and in basic cations,such as calcium and magnesium.Little information is available regarding the microbial community characteristics in tea garden soil ecosystems.

?1Project supported by the National Natural Science Foundation of China(Nos.30671207and40371063).

?2Corresponding author.E-mail:huaiyingyao@https://www.wendangku.net/doc/4014145722.html,.

654 D.XUE et al.

Methods,such as Biolog,16S rRNA gene denaturing gradient gel electrophoresis(DGGE),and phos-pholipids fatty acid(PLFA)pro?ling,have been developed only recently for studying rapidly complex microbial communities.Biolog analysis is based on the premise that microorganisms vary in the pattern and the rate at which they utilize carbon sources.Therefore,carbon(C)utilization patterns can be used as a measure of microbial community structure and functional potential.DGGE analysis,targeting rRNA sequences by polymerase chain reaction(PCR)ampli?cation coupled with rDNA-fragment anal-yses by DGGE(Krave et al.,2002;R?nn et al.,2002),is based on length or sequence polymorphisms to produce a visual?ngerprint of the microbial community.PLFA analysis is used as an indicator of the microbial community structure,since certain groups of microorganisms have di?erent‘signature’fatty acids(Tunlid and White,1992).Therefore,changes in the PLFA pro?le represent changes in the total soil microbial community.All three methods yield a?ngerprint-type data set that may be used for further qualitative or quantitative analysis.Since each of these approaches o?ers a focus on speci?c aspects of the soil microbiological characteristics,they represent an independent analysis of the di?erences or changes in the soil microbial community structures and functions.

Tea gardens of di?erent ages represent practical soil systems and can be used to evaluate temporal changes in soil microbial community characteristics.Inclusion of an adjacent forest and wasteland will facilitate assessment of the ecological sustainability of the tea garden ecosystems and the relative importance of tea garden management versus land-use change in soil microbial community structure. Our objectives were therefore to compare the results obtained using the three methods and to evaluate the changes in the soil microbial community structure in response to tea garden age and land-use change.

MATERIALS AND METHODS

Site description

Soil samples were collected from the Meijiawu tea area(30?11 N,120?05 E),one of the original regions of Longjing Tea production,located in the West Lake district of Hangzhou,Zhejiang Province in southeast China.The area is characterized by a subtropical wet monsoon climate with a mean annual temperature of16?C and a mean annual rainfall of about1500mm.To assess the e?ect of tea garden age on the soil microbial community structure,three tea gardens were selected as study sites.The tea gardens were established on wasteland in1914,1954,and1996and were90,50,and8years old, respectively,when soil samples were taken.Each tea garden was made up of several plots separated by a foot path.All tea garden soils received annual applications of nitrogen(N)averaging approximately 450kg N ha?1year?1,usually in split applications(i.e.,two or three times per year).Although early records for the sites were not available,it is likely that the oldest tea garden received lower amounts of N per year during the?rst half of the20th century.The tea gardens were also fertilized with phosphorus and potassium,and treated with pesticides when required.

Vicinal wasteland and forest were also chosen as study sites to evaluate the e?ect of land-use change on the soil microbial community structure.The wasteland in this red soil area was covered with sparse grasses.The90-year-old mixed-conifer forest was established on wasteland in1914.The forest was essentially unmanaged,i.e.,no fertilization,pesticide,irrigation,or harvest.All soils investigated were classi?ed as red soil by the Chinese Classi?cation System(Ultisols in US soil taxonomy)and were derived from the same parent material,namely,quartzose sandstone interbedded with shale.

Sample collection and preparation

Soils were collected from three sampling plots chosen randomly within the8-,50-,and90-year-old tea gardens,wasteland,and the90-year-old forest in September2004.Twenty cores(5cm diameter×20cm length)were taken from each sampling plot and mixed.

The15bulked samples were transported on ice to the laboratory where they were sieved through a2-mm mesh to remove plant debris and soil fauna.Biolog assays were performed immediately on

LAND USE EFFECT ON MICROBIAL COMMUNITY STRUCTURE655 fresh soil,and a portion of soil was immediately frozen for subsequent DGGE and PLFA analyses.The remaining soil samples were stored at4?C for analyzing the chemical properties(Table I).

TABLE I

Physico-chemical properties of the tested soils collected from wasteland(soil1),8-year-old tea garden(soil2),50-year-old tea garden(soil3),90-year-old tea garden(soil4),and90-year-old forest(soil5)

Soil sample pH Organic C Total N C/N Available P NH+

4-N NO?

3

-N

g kg?1mg kg?1

Soil1 5.167.40.858.7 1.7 5.88 6.6

Soil2 4.2213.9 1.3510.317.88.0246.6

Soil3 4.0122.2 2.0510.819.57.0556.1

Soil4 3.7126.3 2.2911.554.5 4.4140.3

Soil5 3.9427.5 1.7515.723.99.2013.5

LSD0.050.02 1.30.090.60.10.10.1 Soil chemical property and community substrate utilization analyses

Soil pH was measured by a combination glass electrode(soil:water=1:2.5).Total nitrogen was determined by Kjeldahl digestion(Keeney and Nelson,1982)and quanti?ed using a continuous?ow analyzer(Skalar,The Netherlands),and total organic C was determined by dichromate oxidation(Nelson and Sommers,1982).Available phosphorus analysis was done by the method of Olsen and Sommers (1982).Inorganic N(NH+4-N and NO?3-N)was extracted with2mol L?1KCl by shaking for1h at200 r min?1and?ltered through a0.45-μm polysulfone membrane.The KCl-extracted N was determined colorimetrically in a continuous?ow analyzer(Skalar,The Netherlands).

Biolog EcoPlates(Biolog Inc.,Hayward,CA,USA)were used to study the substrate utilization pattern of soil microbial communities as described by Girvan et al.(2003).Brie?y,10g of fresh soil was added to100mL of distilled water in a250-mL?ask and shaken at200r min?1for10min to achieve a10?1dilution.Ten-fold serial dilutions were prepared and the10?3dilution was used to inoculate the Biolog EcoPlates.The plates were incubated at25?C for7days and color development was read as absorbance daily with an automated plate reader(VMAX,Molecular Devices,Crawley,UK)at a wavelength of590nm,and the data were collected using Microlog4.01software(Biolog Inc.).

DNA extraction,PCR,and DGGE

DNA was extracted by bead beating based on the method of Zhou et al.(1996)with slight modi?-cations.The crude DNA was suspended in100μL of TE(10mmol L?1tris-HCl;1mmol L?1EDTA; pH8.0)and puri?ed using Sephadex G200,as described previously by Cahyani et al.(2003),based on the method of Jackson et al.(1997).To assess the DNA yield and quality(average molecular size),the DNA was run on8g L?1agarose gels with a molecular size marker as the reference.The DNA purity was assessed using ampli?cation by PCR as the criterion.

The universal bacterial primers,PRBA338f and PRUN518r,located at the V3region of the16S rRNA genes of bacterioplankton(?vreas et al.,1997),were used to amplify the variable V3region of 16S rDNA.A GC-rich clamp attached to the forward primer prevented the complete melting of the PCR products during subsequent separation in DGGE.PCR mixtures were prepared with1μL puri?ed DNA template(10ng),5μL10×PCR bu?er,2.25mmol L?1MgCl2,0.8mmol L?1deoxyribonucleotide triphosphate(dNTP),0.5μmol L?1of each primer,2.5U Taq DNA polymerase,and sterile?ltered milli-Q water to a?nal volume of50μL.The PCR cycles included a4min initial denaturation at94?C,followed by30cycles of denaturation at94?C for1min,annealing at55?C for30s,extension at 72?C for1min,and a7-min?nal extension step at72?C.Finally,the PCR samples were held at4?C until removal from the thermal cycler.

DGGE was performed using the Dcode TM Universal Mutation Detection System(Bio-Rad Labo-

656 D.XUE et al. ratories,Hercules,CA,USA).PCR products were loaded onto a100g kg?1polyacrylamide gel with a35%–60%denaturing gradient,where100%denaturant contains7.0mol L?1urea and400g kg?1 deionized formamide.The electrophoresis was performed at70V for16h at60?C in1×TAE bu?er (40mmol L?1tris,pH7.4;20mmol L?1sodium acetate;1mmol L?1EDTA).After electrophoresis, the gel was silver-stained(Bassam et al.,1991)and visualized with a Bio-Rad Gel Doc documentation system.

Molecular analysis of soil PLFAs

Lipid extraction and PLFA analyses were performed using the modi?ed Bligh and Dyer-method (Bligh and Dyer,1959;Frosteg?ard et al.,1993b).Brie?y,2.0g of freeze-dried soil was extracted with a chloroform-methanol-citrate bu?er mixture(1:2:0.8),and the phospholipids were separated from other lipids on a silicic acid column.The phospholipids were subjected to a mild alkaline methanolysis and the resulting fatty acid methyl esters were prepared according to the MIDI protocol and analyzed using the MIDI Sherlock R Microbial Identi?cation System(MIDI,Newark,DE).Fatty acids nomenclature follows that of Tunlid and White(1992).Individual fatty acids were designated according to convention by the total number of carbon atoms:number of double bonds,followed by the position of the double bond from the methyl-end of the molecule.The pre?xes i and a indicate iso-and anteiso-branching, respectively,and cy indicates cyclopropane fatty acid.Me refers to the position of methyl group from the carboxyl-end of the chain.

The ratios of Gram positive to Gram negative bacteria were calculated by taking the sum of the predominant Gram positive bacterial PLFAs16:010Me,17:010Me,18:010Me,i15:0,a15:0,i16:0,i17:0, and a17:0divided by the sum of the predominant Gram negative bacterial PLFAs16:1w5c,16:1w7t, 16:w9c,cy17:0,18:1w5c,18:1w7c,and cy19:0(Wilkinson,1988).The sum of the PLFAs considered to be predominant of bacterial origin(i15:0,a15:0,15:0,i16:0,16:1w7c,16:1w5c,i17:0,a17:0,cy17:0,17:0, 18:1w7c,cy19:0)were chosen to represent the bacterial biomass(Frosteg?ard et al.,1993a).The fatty acid18:2w6,9was used to represent the fungal biomass(Frosteg?ard and B?a?ath,1996;Olsson,1999). The fatty acid18:010Me was used as an indicator of actinomycetes(Zogg et al.,1997).

Statistical analysis

All values reported are the arithmetic means of the three determinations expressed on an oven-dried soil basis(105?C).Means,least signi?cant di?erences(LSD)of5%level were calculated by a one-way ANOVA.

The diversity of microbial community was assessed by the Shannon index(H )(Shannon and Weaver, 1949),calculated for each soil sample using the following formula:

H =?

s

i=1

(p i ln p i)

where p i is the number of individuals of species i,and s is the number of species found in the community pro?le.For Biolog data,p i is the proportional color development of the i th well relative to the total color development of all wells.For DGGE data,p i is the ratio of the speci?c band intensity to the total intensity of all bands in a lane sample.In the case of PLFA data,p i is the concentration of i th individual fatty acid relative to the concentration of all fatty acids.

The average well color development(AWCD)value of Biolog data was calculated for each sample at each time point by dividing the sum of the optical density data by31(number of substrates),as described by Garland(1996a).

The Quantity One R software(Bio-Rad)used for DGGE gel image acquisition was also employed for analysis.Following the removal of background intensity and setting of noise levels,the software automatically obtains a density pro?le through lanes and the percentage similarity among lanes after

LAND USE EFFECT ON MICROBIAL COMMUNITY STRUCTURE657

each band has been identi?ed.

Cluster analysis of the Biolog,DGGE,and PLFA pro?les of the microbial community was performed using hierarchical clustering according to the Ward-method with the software package SPSS10.0for Windows.For Biolog data,the absorbance values at equivalent AWCD from di?erent times of incubation were compared and were also transformed by dividing by the AWCD to avoid bias between samples with di?erent inoculum density(Garland,1997).For DGGE data,the band intensity was normalized by dividing the intensity of the individual bands by the mean band intensity comprising a particular community pro?le.The PLFA data were expressed as mole percentage of individual fatty acids. RESULTS

Biolog analysis

The AWCD of all C sources,as a measure of the total microbial activity,generally followed the same‘s’pattern with incubation time(Fig.1).However,the AWCD of all C sources and the functional diversity based on the Shannon index decreased(P<0.05)in the following order:wasteland>forest >tea garden.The AWCD and Shannon index also decreased with increase in the tea garden age(Table II).

Fig.1The average well color development(AWCD)of the Biolog EcoPlates at590nm for the tested soils collected from wasteland(soil1),8-year-old tea garden(soil2),50-year-old tea garden(soil3),90-year-old tea garden(soil4),and 90-year-old forest(soil5).

TABLE II

Shannon index calculated from the Biolog,denaturing gradient gel electrophoresis(DGGE),and phospholipids fatty acid (PLFA)of soil microbial community in the tested soils collected from wasteland(soil1),8-year-old tea garden(soil2), 50-year-old tea garden(soil3),90-year-old tea garden(soil4),and90-year-old forest(soil5)

Soil sample Shannon index from Biolog Shannon index from DGGE Shannon index from PLFA Soil1 3.17 3.60 2.94

Soil2 2.73 3.17 2.88

Soil3 2.61 3.28 2.80

Soil4 2.29 3.16 2.81

Soil5 2.86 2.76 2.93

LSD0.050.110.060.14 Cluster analysis according to31carbon sources of Biolog EcoPlates revealed that land-use change could considerably a?ect the microbial community pro?les of potential carbon utilization(Fig.2).The three tea garden soils(soils2,3,and4)presented microbial pro?les strictly linked together at a low Eu-clidean distance of2and they linked together with the90-year-old forest(soil5)at Euclidean distance of3.However,the wasteland was distinctly di?erent,having microbial pro?les at a considerably higher

658 D.XUE et al.

Fig.2Cluster analysis of community level physiological pro?les of the tested soils collected from wasteland(soil1), 8-year-old tea garden(soil2),50-year-old tea garden(soil3),90-year-old tea garden(soil4),and90-year-old forest(soil 5).Scale indicates Euclidean distance.

Euclidean distance.

DGGE analysis of16S rRNA gene fragments

There were signi?cant di?erences in the Shannon index obtained from the DGGE analysis of16S rRNA gene fragments in response to land use and tea garden age(Table II).The Shannon index was signi?cantly lower in the tea garden soils than that in the wasteland.However,compared to the90-year-old forest,the tea garden soils showed signi?cantly higher community diversity.In the tea garden soil ecosystems,there was an increasing trend in the Shannon index from the8-year-old tea garden to the50-year-old tea garden,and a decreasing trend from the50-year-old tea garden to the90-year-old tea garden.

Dice coe?cient from DGGE pro?les was used to represent the percentages of similarity among lanes (Table III).The Dice coe?cient was the highest among the three tea garden soils and the least between the wasteland and the forest.Moreover,the tea garden soils had greater similarity with the forest soils than with the wasteland.

TABLE III

Dice coe?cient from denaturing gradient gel electrophoresis pro?les of the tested soils collected from wasteland(soil1), 8-year-old tea garden(soil2),50-year-old tea garden(soil3),90-year-old tea garden(soil4),and90-year-old forest(soil 5)

Soil sample Soil1Soil2Soil3Soil4Soil5 Soil110046354827 Soil246100586149 Soil335581006053 Soil448616010054 Soil527495354100

Cluster analysis showed that the three tea garden soils had the closest association with a low Eu-clidean distance of2and were related to the90-year-old forest at Euclidean distance of10,but the wasteland was distinctly di?erent from the other systems(Fig.3).The result clearly demonstrated that DGGE pro?les revealed marked di?erences in the response of soil microbial communities under di?erent land use systems.

Phospholipid fatty acid analysis

Based on the Shannon diversity index calculated from the PLFA data set,there was no signi?cant change in population diversity owing to land use and tea garden age(Table II).The PLFA pro?les of the whole microbial community showed that the tested soils contained a variety of PLFAs composed

LAND USE EFFECT ON MICROBIAL COMMUNITY STRUCTURE659

Fig.3Cluster analysis of the denaturing gradient gel electrophoresis pro?les of16S rDNA ampli?ed from the tested soils:wasteland(soil1),8-year-old tea garden(soil2),50-year-old tea garden(soil3),90-year-old tea garden(soil4),and 90-year-old forest(soil5).Scale indicates Euclidean distance.

of saturated,unsaturated,methyl-branched,and cyclopropane fatty acids(Fig.4).Some PLFAs varied signi?cantly in their relative abundance in response to land use and tea garden age.The conversion from the wasteland to the tea garden soils resulted in disappearance of some fatty acids(i16:1G,18:1w5c) and synthesis of new fatty acids(i16:1H,17:0,19:010Me,20:1w9c).Some of the fatty acids(19:0 10Me,i19:0,10:02OH)were unique to the tea garden soils.Gram positive bacteria,Gram negative bacteria,bacteria,and actinomycete were signi?cantly less in the tea garden soils than those in the wasteland(Table IV).The ratio of Gram positive bacteria to Gram negative bacteria was signi?cantly higher in the tea garden soils than that in the wasteland,and the highest value was found in the90-year-old forest.In addition,the ratio showed a decreasing trend with the tea garden age.Fungal PLFAs were

Fig.4Mole percentage of phospholipid fatty acids(PLFAs)(n=3)in the tested soils collected from wasteland(soil1), 8-year-old tea garden(soil2),50-year-old tea garden(soil3),90-year-old tea garden(soil4),and90-year-old forest(soil 5).

660 D.XUE et al.

TABLE IV

Abundance of a variety of phospholipid fatty acids(PLFAs)in the tested soils collected from wasteland(soil1),8-year-old tea garden(soil2),50-year-old tea garden(soil3),90-year-old tea garden(soil4),and90-year-old forest(soil5) Microorganism PLFA LSD0.05

Soil1Soil2Soil3Soil4Soil5

Gram positive bacteria34.528.226.627.138.7 1.80 Gram negative bacteria10.3 6.47.07.2 6.50.90 Bacteria31.126.625.726.733.0 1.60 Fungi 1.52 2.65 2.31 2.40 1.680.27 Actinomycete 4.12 2.72 2.47 2.67 2.940.38 Gram positive bacteria/Gram negative bacteria 3.36 4.38 3.80 3.79 5.930.40 Fungi/bacteria0.050.100.090.090.050.18

signi?cantly a?ected by land use change.Both the fungal PLFA and the ratio of fungi to bacteria were signi?cantly higher in the three tea garden soils than those in the wasteland and the forest.

Similar to cluster analyses based on Biolog and DGGE data(Fig.5),cluster analysis of the PLFA data signi?cantly discriminated the microbial communities from the three land use types.The three tea garden soils with di?erent ages strictly linked together at a low Euclidean distance,while similarities with other systems were found at considerably higher distances.

Fig.5Cluster analysis of the phospholipids fatty acid pro?les of the tested soils collected from wasteland(soil1),8-year-old tea garden(soil2),50-year-old tea garden(soil3),90-year-old tea garden(soil4),and90-year-old forest(soil5). Scale indicates Euclidean distance.

DISCUSSION

Biolog,DGGE,and PLFA were used to assess the microbial community characteristics in soil samples obtained from tea gardens di?ering in age,and from a neighbouring forest and wasteland.However, the results obtained using the respective methods were not in perfect agreement.This,however,may not be surprising since each method analyzes a di?erent aspect of the soil microbial community and is associated with its own advantages and disadvantages in determining microbial diversity and community structure.

Biolog analysis assesses the functional diversity of the soil microbial community.Although it is relatively easy to use,is reproducible,and can produce a large amount of data re?ecting the metabolic characteristics of the communities(Zak et al.,1994),it su?ers from several disadvantages.Only some metabolically active and culturable bacteria can be detected,whereas soil fungi and slow-growing bac-teria can not be assessed(Garland and Mills,1991;Yao et al.,2000).The technique is sensitive to inoculum density(Garland,1996a)and can not re?ect the potential metabolic diversity in situ(Gar-land and Mills,1991).Additionally,the carbon sources and pH of the medium in the Biolog plates may not be representative of those present in the soil(Yao et al.,2000).Nevertheless,the Biolog method

LAND USE EFFECT ON MICROBIAL COMMUNITY STRUCTURE661 is useful in studying the functional diversity of a microbial community and is a valuable tool especially when used in conjunction with other methods.

DGGE analysis re?ects the genetic diversity of a microbial community and has the advantages of being reliable,reproducible,rapid,and allows screening of multiple samples(Muyzer,1999;Nakatsu et al.,2000).There are,however,some disadvantages associated with DGGE analysis,including e?ect of variable DNA extraction e?ciency on DGGE pro?les(Theron and Cloete,2000),similarities in the mobility characteristics of the polyacrylamide gel of DNA fragments with di?erent sequences(Gelsomino et al.,1999;Maarit-Niemi et al.,2001),and dependence of DGGE pro?les on soil type being tested and choice of primers(Nakatsu et al.,2000).In the study,the PRBA338f and PRUN518r primers chosen were universal bacterial primers.However,it is well known that fungi are predominant in tea garden and forest soils.Therefore,it is possible that our results based on16S rDNA-DGGE only provided a measure of the bacterial community structure.Nevertheless,the dominant fungi may not be detected.

PLFA analysis reveals the structural characteristics of a living microbial community at the time of sampling and is suitable for detecting rapid changes in microbial communities.However,temperature and nutrition can in?uence the cellular fatty acid composition of microbes,and plant organisms may confound the PLFA pro?les(Graham et al.,1995).Although some fatty acids can be used as signatures for certain broad groups of organisms(Zelles,1997),individual fatty acids can not be used to represent speci?c species since the same fatty acids can occur in more than one species(Bossio et al.,1998).

In the present study,the Shannon diversity index calculated based on Biolog and DGGE data showed signi?cant di?erences in microbial community response to land-use change and tea garden age.However, no signi?cant di?erence was detected in the Shannon diversity index from the PLFA data.This may be because even though the community structure was di?erent,its diversity was not,or it could represent some problems using fatty acid pro?les to measure the diversity(Bossio et al.,1998).

Chemical analysis of the tested soils(Table I)indicated that tea garden and forest soil samples had a lower pH,but higher organic C content when compared to soil samples obtained from the wasteland. The low pH and high organic matter can favor fungal growth(Gray and Williams1971).Therefore, the tea garden and the forest will be expected to have a similar ratio of fungi to bacteria.However,our results based on the PLFA pro?les of the soil microbial communities showed that the ratio of fungi to bacteria was signi?cantly higher in the three tea garden soils than those in the wasteland and the forest soils,and no di?erence was found between the wasteland and the forest.An explanation of this result could be that land management a?ected the soil microbial communities in the tea gardens,since PLFA pro?les were demonstrated previously to be highly sensitive to management e?ects(Zelles et al.,1992; Bossio et al.,1998;Ponder and Tadros,2002).

The8-year-old tea garden soil was reclaimed from wasteland in a short time.Therefore,the soil microbial community of the8-year-old tea garden should be closer to the wasteland than the50-or 90-year-old tea garden.However,cluster analysis based on all the three methods showed that the three tea gardens with di?erent ages had more similarity in microbial community characteristics than the wasteland and forest.The result suggested that a change in the microbial biomass was not always accompanied by a change in the microbial community diversity and activity,and land-use change had more severe e?ects on the soil microbial community structure than tea garden age.Several factors may be involved.On one hand,land-use conversion involving considerable disruption of the soil,such as cultivation,compaction,and fertilizer application,may have a?ected the soil microbial community. On the other hand,several studies have shown that plant species have a major selective in?uence on microbial communities in their rhizospheres(Garland,1996b;Smalla et al.,2001;Yao et al.,2001;Berg et al.,2002).The three land use types di?er in plant species composition,which is therefore likely to exert strong selective pressures on the soil microbial community.Finally,the chemical properties of the soils showed that only the available N and soil C/N were distinctly di?erent in response to land-use conversion(Table I).This result may suggest that soil N fertilizer application a?ects the soil microbial community.

662 D.XUE et al.

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制作依据与应用说明 一、本样式根据《中华人民共和国民事诉讼法》第七十二条的规定制定,供各级人民法院在执行案件中,委托有关专业单位进行鉴定时使用。 二、委托书应写明、写全案号、案由。 三、委托书后应附相关法律文书。 四、委托鉴定的具体事项必须写清楚。 五、人民法院对专业性问题认为需要鉴定的,应当交法定鉴定部门鉴定;没有法定鉴定部门的,由人民法院指定的鉴定部门鉴定。 六、鉴定部门和鉴定人应当提出书面鉴定结论,在鉴定书上签名或盖章。鉴定人鉴定的,应当由鉴定人所在单位加盖印章,证明鉴定人身份。

73.价格评估委托书样式 ××××人民法院 价格评估委托书 (××××)××执×字第××号 ××××(受委托单位名称): 我院执行的……(写明当事人的姓名或名称和案由)一案,需对附件清单所列财产进行价格评估。依照《最高人民法院关于人民法院执行工作中若干问题的规定(试行)》第47条和《最高人民法院关于人民法院民事执行中拍卖、变卖财产的规定》第四条的规定,请你单位对附件清单所列财产进行价格评估,并将书面评估报告一式×份及时报送我院。 附:委托评估财产清单 ××××年××月××日 (院印) 本院地址:邮编: 联系人:联系电话:

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东方矿产资源评估系统 北京中东方矿业开发科技有限公司 2009.4

内容 一、公司简介 二、系统介绍 三、创新之处 四、功能特点 五、售后服务

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二、系统介绍 ?采用现代数据库技术、GIS技术、三维地质构模技术、地质统计学方法和科学计算可视化技术; ?为地测采等专业人员提供一套针对固体矿产资源的地质勘探、矿山地测、矿山三维资源评价等的先进实用的地质勘探数据管理、图件绘制与资源储量估算的三维交互可视化工具; ?集数据管理、矿体圈定、三维建模、储量估算、经济评估、自动成图、自由报表一体化的计算机管理,为建立数字矿山奠定基础; ?被收入由国土资源部储量司组织编著,于2000年4月由地质出版社发行的《矿产资源储量计算方法汇编》;?2004年4月通过由国土资源部储量司组织的专家评审,并以“国土资储函(2004)20号”发文至相关单位及部门。 ?应用涉及国内外多种矿床类型的数十家用户单位,能满足不同客户的各种需求,应用效果显著;

符合国情 ?我国矿山以中小、多金属矿床为主,矿体形态较为复杂,要求对矿体描述更为细腻,需要采用多品位指标圈矿,用国外软件实施难度大,需要结合我国矿山的设计情况开发适用、有效的软件系统;?完全从现场的实际应用出发,解决了地质、测量、采矿过程中手工难以解决的问题,便于工程师进行辅助设计、协同办公,提高了工作效率。 ?提供多种储量计算方法和灵活、方便、简单的报表设计; ?图形、报表符合提交地质报告要求;

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4. 安玛西亚电泳仪等。 三、实验试剂 1. 试剂:请使用高质量产品,推荐日本东洋坊TOYOBO公司的相关产品 DNA提取试剂盒; EcoRI酶,Msel酶,T4连接酶试剂盒; Taq 酶,dNTP, PCR reactio n buffer; 琼脂糖电泳试剂:琼脂糖,无毒GeneFinder核酸染料替代传统EB染料;超纯水(18.2M ? ? cm 2. 其他实验需要物品 微量移液枪(一套及相应尺寸Tip头,PCR管,冰浴等。 四、实验流程 1、总DNA提取 使用DNA提取试剂盒提取植物基因组DNA,通过紫外分光光度计检测或用标准品跑胶检测。一般来说,100ng的基因组DNA作为反应模板是足够的。 2、EcoR1酶消化(20ul体系/样品 EcoR1 1ul

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2. 同时,在如图所示的路径中找到“”文件; 3.开始安装:运行 ⑴Begin→Next→输入序列号: →Next→Next ⑵到这步 选择“Custom Setup”项 ⑶到这步 选择4个安装项(如图所示):(先全不选,在进行勾选) 选择安装;“BusinessObjects”、“Designer”、 “Document Agent”、在Data Access中选择 “Oracle”(共4项) ⑷在弹出的文件夹中找到“Business objects” 快捷方式至对方桌面; ⑸关闭多余文件夹; 4.将本机bo_rep文件夹复制到对方D盘根目 录下; 5.双击“Business objects(快捷方式)”

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操作流程说明文档

操作流程说明文档内部编号:(YUUT-TBBY-MMUT-URRUY-UOOY-DBUYI-0128)

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学生网上选课操作步骤及说明【模板】

学生网上选课操作步骤及说明 1.操作步骤: 1.1 在任意一台可访问校园网的计算机上打开IE 浏览器,在地址栏输入:******/ 登录教务处网站,然后点击“学生综合系统”。 1.2 输入自己的用户名(即学号)及密码,确认进入。 1.3 选课分为预选、正选和补、退选三个阶段 预选:学生先从选课手册上查出本人可选的课程号、课序号,然后再进行选课。 注意,在选课手册中,每一个课程号代表一门课程,每一个课序号代表一个教学班,同一课程号的不同课序号代表同一门课程有多个教学班,在选课时,一门课程只能选一个课序号。 正选:所有参加预选的学生都必须参加正选。对于选课人数小于课容量的课程,系统将自动置为选中,而对那些选课人数大于课容量的课程必须进行抽

签。当学生所选课程因名额(即课容量)限制抽签时未能选中,则可查询选课手册,改选该课程的另一课序号(必须为所在院系可选的课序号)。 补、退选:正选结束后即进入补、退选阶段,凡未选中课程或对已选课程不满意者,可在此阶段补选或退选。 1.4 预选 依次点击导航条中的【选课管理】→【选课方案】→【方案课程】按钮,进入“方案课程”选课页面,在“计划学年学期”下拉列表框中选择相应学年学期,根据所显示的课程名和课序号,对照所发选课手册中所列课程,选择本专业教学计划规定的相应课程,然后单击【确定】即可。(也可直接在“课程号”和“课序号”中,输入课程号和课序号过滤出欲选择的课程) 1.5 正选 学生依次点击【选课管理】→【已选课程】按钮,若页面“选课结果列表”中所看到的选课状态为“选中”,此时表明该门课程已经选定,无需抽签;若页面“选课结果列表”中所看到的选课状态为“拟选”,此时表明该门课程需抽签。

百威软件操作流程说明教学内容

百威软件操作流程说明 【STEP 1】进入后台管理系统 双击进入系统,此时系统提示输入管理员密码,实验室POS机无需密码,直接点击确定进入后台管理系统 【STEP 1】系统主要功能

从系统主界面上可以看出,POS收银后台系统可以实现多种功能。这里主要介绍以下几种。 [商品分类] 商品分类是用来设置本公司所有商品的类别,分类必须明确方便易记。 操作方法:进入此界面后如果要新建大类,就点击新的大类按钮,如果需新建子类,则首先点到需某大类,然后在该大类下面新建子类。需修改类别名称,首先点击该类别然后按修改类别,如需删除某类,首先点击到该类,然后按删除类别即可,删除类别时要注意,如果该类中已有子类或者有商品,则该类不能删除。 以上操作全部作完后,如果需要保存,则按确定按钮,如果不需要保存,则按取消。 [类别代码] 类别代码是用来为每个类别指定一个唯一的代码,代码可以是英文字母,也可是数字,一般不要用中文。

操作方法是:首先点击到该类,然后按设置按钮或双击该类,系统会弹出输入框,可在此框中输入类别代码,输入完后按确定即可。此处需注意的是类别代码不能重复,如果该类中已有商品,类别代码不可更改。 如果打印商品类别列表,可按打印按钮即可进行打印。 [商品档案] 商品档案是用来设置本公司所有的商品信息,包括商品的供应商,条码,价格以及包装规格等。操作方法如下: 进入信息档案 商品档案,出现如下界面。

商品档案可进行新建、删除、打印、复制、导出、表格设置 新建:如果需增加商品档案,可按新建按钮,新建商品档案时首先要选择要新建商品所属的类别,然后再点击新建,系统会弹出新建商品的输入界面。 (基本信息) ◇自编码/条码:表示商品的条形码或者是商品的店内码(要用条形码)◇商品名称:表示商品的名称或者是叫法 ◇单位:表示商品的计量单位,在这里也是商品的最小单位 ◇进价:表示商品的参考进货价格 ◇零售价:表示商品的零售价格 ◇供应商:表示该商品的供货商 ◇所属类别:表示该商品属于哪个类别,以后还可更改 ◇库存上限:表示商品的库存最高限度,如果超过此限度,系统将会在库存积压报表中体现出来 ◇库存下限:表示商品的库存最低限度,如果低于此限度,系统将会在库存不足报表中体现出来 ◇期初库存:表示在开业前该商品的实际库存 ◇此商品只按零售价销售:表示该商品在前台销售时只允许按零售价销

平台操作使用手册

平台操作使用手册 一、增加用户 如果本级管理员想增加下级用户,先要增加“工程组织机构”,才能增加用户。 1)选中左侧树形菜单的“工程组织机构”,再单击右上角的“增加”,在弹出的“新建机构”对话框中填写要增加用户所属机构的相关信息。 2)选中左侧树形菜单中的“用户管理”,单击右上角的“增加”,在机构选项的菜单栏中选择刚才增加的“机构”,填写用户的其他信息既可以增加用户。 二、高级研修班申报 1)选择左侧“高级研修班初审/申报”,再单击右侧的“新建申报”即可以填写申报的相关信息。

三、开班通知和方案申报 1)选择“开班通知和方案上报”,在右侧的栏目中选择需要上报通知的高研班,点击“上报”,在弹出的对话框中即可选择通知上传。 2)开班通知和方案请至少提前一个月上传到平台,以便审批。如有其他要求请跟专技司联系。 四)结业上报 1)选择左侧“结业上报”,在右侧中选择需要上报的高研班,点击“上报”,即可上报总结报告、经费结算表和考核学员名单。总结报告、经费结算表和考核学员名单可以分别上传。

2)上报学员名单时首先请点击“下载导入模板”,将模板下载到电脑中,然后填入学员名单、课时等等,里面栏目不要做任何修改。名单填好后再点击“导入学员”,在弹出的菜单中点击“上传”,选择刚才的模板,然后点击“提交”即可将学员导入到系统中,此时学员名单并未提交到人社部专技司,只有本地管理员可以看到。此时本地管理员还可以修改、添加、删除学员信息。 3)确定学员名单完整后,就可以再次点击最下方的“提交”按钮,此时名单即提交到人社部专技司,平台管理员就可以生成证书编号。一旦点击此“提交”按钮,学员信息任何人(包括平台管理员)都无法更改,也不能对学员进行任何添加或者删除。慎重建议各管理员在提交人员名单确定好,如提前提交出错后果自负!

操作手册产品使用说明

JBKL型燃烧器PVC全自动 操作手册 大庆国科盛鑫节能环保设备制造有限公司 前言 我国是全世界自然资源浪费最严重的国家之一,在59个接受调查的国家中排名第56位。另据统计,中国的能源使用效率仅为美国的26.9%,日本的11.5%。为此,近年来我国推行了多项节能减排政策措施。目前,为了实现“十一五”规划中确定的单位GDP能耗降低20%的目标、主要污染物排放总量减少10%的约束性指标,国务院发布了继续加强节能工作的决定,节能减排工作迫在眉睫。 在举国重视节能减排工作的大形势下,我公司自主创新,目前已经自主研发9项国家专利技术,全部是节能减排燃油燃气燃烧器技术。我公司发展势头强劲,不断创新探索,为全国节能减排事业做出自己应有的责任。 我公司研发节能减排燃烧器过程中发现,目前小型取暖锅炉普遍使用的国内外燃烧器采用的程序控制工艺是:锅炉出口水温度到达给定值上限后,电磁阀关闭,炉灭火。锅炉出口水温度降到下限时锅炉重新启动,送风机进行3-5分钟炉膛扫线,这时大量冷风进入炉膛里,把炉膛温度大幅降下来,扫完炉膛后,重新喷燃气点火升温,这样消耗燃气量增加。因此我公司燃烧器程序控制是采用自动调节阀来控制燃气喷大小量,锅炉出口水

温平稳,安全运行,提高节能减排数据。 JBKL型燃气燃烧器的设计说明 JBKL型燃烧器主要是针对目前燃气燃烧器喷咀存在的问题而设计的。存在的问题是: 1、目前国内外使用的燃气喷咀是直线喷燃气方式,国际上燃烧器技术较发达的意大利、法国、德国等国家的相关技术也是直线喷燃气方式。燃气是靠自身压力通过燃气喷咀直线喷入炉膛里的,燃气压力而产生的冲力使燃气与空气在推进的一段距离内不容易混合好,因此燃气在逐步扩散中与空气边混合边燃烧,这样炉膛内的火型长,高温度热量停留在炉膛内的受热时间短,使排烟温度升高,导致热效率降低。当加负荷增加燃气压力时冲力增大,烟气在炉膛内的流速加快,排烟温度迅速升高,热效率更低。 2、目前油田加热炉、炼油厂加热炉使用的配风器都是配直流风方式,直流风和燃气混合时出现各走各的现象,完全燃烧所需要的时间长,需要大量的配风才能满足燃烧,在运行时高温度烟气向前的推动力很大,当加负荷加大配风量时,推动力更大,这是加热炉热效率低的重要因素。 针对这样的问题,我们紧紧抓住安全运行、稳定燃烧、快速完全燃烧、配备最佳空气、控制最佳烟气流速和提高炉热效率的关键因素,对锅炉燃烧器相关的结构和部位进行研究和开发,并采取了以下几点措施: 1、燃气压力设计在燃气喷枪管内,运行时燃气冲力产生真空度,利用这个动力把空气吸进来,燃气和空气提前有效地混合,缩短了燃烧的过程和时间,喷出的混合气体立即迅速燃烧,高温度的能量停留在炉膛内的时间长,排烟温度低,提高热效率。

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