Quantitative Analysis of and Discussion on Social Security Price of Farmland in Land Requisition Price

Yapeng Zhou1 Lin Liu [START_REF] Liegan | Predicament and Way Out of Land-lost Farmers in Guangxi Province[END_REF][START_REF] Liu Qiaoqin | Discussion on Rational Compensation Standard of Requisition of Suburb Land in China[END_REF] Yang Yang [START_REF] Dai Shuangxin | Constructing Two-way Land Requisition Compensation System: Requisition and Purchase[END_REF] Ying Chen [START_REF] Dai Shuangxin | Constructing Two-way Land Requisition Compensation System: Requisition and Purchase[END_REF] Hao Xu [START_REF] Dai Shuangxin | Constructing Two-way Land Requisition Compensation System: Requisition and Purchase[END_REF] zhouyp@hebau.edu.cn,zwt1964b@sina.com Abstract: This paper points out that the irrational calculation of the social security price of farmland leads to the relatively low price of the land requisition price. This paper begins with the analysis of the social security function of farmland to farmers and puts forward the idea that the social security value of farmland should include the following four aspects, namely, the value of the endowment insurance, the medical insurance, the job security and the inheritance of property. Based on the principle that the urban residents and the rural residents are equal and taking the pricing level of the social security system of residents into account, this paper quantifies the value of the four aspects of the social security value of farmland, adds up the four aspects, analyzes them and gets the social security price of farmland. Keywords: Farmland; Social Security; Quantitative Analysis With the unceasing development of the market economy in China, radical changes have occurred to the employment system of enterprises and the traditional compensation and placement system cannot function well. Therefore, how to calculate the land requisition price of farmland in the new era rationally has become a major problem that needs resolving urgently. The social security price of farmland is an important part of its price structure, but the research into its calculation method is still limited to the theoretical research. Therefore, the research into the rational calculation method of social security price of farmland will provide a basis for building a sound land requisition pricing system of farmland [START_REF] Dai Shuangxin | Constructing Two-way Land Requisition Compensation System: Requisition and Purchase[END_REF][START_REF] Liegan | Predicament and Way Out of Land-lost Farmers in Guangxi Province[END_REF] .

Definition of Social Security Price of Farmland

The social security system is the social service and measures provided by a state or a community so as to improve and enhance the material standards as well as the living standards of its residents, and it is also a sign of social civilization and social progress.

On March 22, 2005, the No. 233 file, Technical Guide to Setting Comprehensive Land Price in Requisition Area (Exposure Draft) issued by the General Office of Ministry of Land and Resources defined the social security price of farmland as "the due compensation accepted by farmers of whom land is requisitioned and does not get the basic living allowances, to receive education and reemployment training" [START_REF] Liegan | Predicament and Way Out of Land-lost Farmers in Guangxi Province[END_REF][START_REF] Liu Qiaoqin | Discussion on Rational Compensation Standard of Requisition of Suburb Land in China[END_REF] . Therefore, the social security price of farmland is monetized functions of the four aspects, namely, the endowment insurance, medical insurance, job security and inheritance of property for farmers.

The Price Structure of Social Security Price of Farmland

The Function of Endowment Insurance

Land provides not only a means of production for farmers in the contemporary age, but also a basis for their children to live by cultivating the land and support their elders.

The Function of Medical Insurance

Family has become the basic unit for farmers to get medical insurance. For most rural families, land is the most basic and most important means of production and land revenue is the most important or even the sole source of family security. Therefore, land is of important medical security function for farmers.

The Function of Job Security

Land is the important place for farmers to work on and live on and also basis for their subsistence. By means of working on land, farmers can obtain the basic security for their food and closing. They all amply show that land is of important job security function or basic subsistence guarantee function for farmers.

The Function of Property Inheritance

In accordance with relevant laws, land is of property inheritance function for farmers, it not only guarantees the basic subsistence of farmers, but also provides their offspring with the basic survival conditions [START_REF] Liu Qiaoqin | Discussion on Rational Compensation Standard of Requisition of Suburb Land in China[END_REF][START_REF] Lifang | Constructing Compensation and Placement Mechanism with Social Security Function for Land Requisition[END_REF] .

The Calculation of Social Security Price of Farmland

The new edition of The Law of Land Administration adopts the "method of multiplicity of production value" to calculate the placement and compensation fee (i.e. the social security price). This kind of calculation is very subjective and lacks a reasonable basis, and thus the result fails to reveal the social security value of land.

Therefore, this paper will calculate the endowment insurance price of land, medical insurance price of land, job security price of land and property inheritance price of land respectively, then add them up, modify them reasonably and finally obtain the social security price of land [START_REF] Lifang | Constructing Compensation and Placement Mechanism with Social Security Function for Land Requisition[END_REF] .

At present,more and more farmers will leave their land, and thus the social security value of land will gradually be replaced by the social security system of the state, and farmers will enjoy the same social security as that of the urban residents. Therefore, when the social security price of land is calculated, the choice of the parameters as well as the value can consult the social security standards of the urban residents, i.e. reach uniformity between urban residents and rural residents.

Calculation of Endowment Insurance Price

The endowment insurance price of the requisitioned land should be calculated based on the amount of money paid for the individual endowment insurance. By reference to the endowment insurance system of urban residents and taking the individual endowment insurance, scale of premium rate of Taipingshengshi Changshou endowment insurance A (see Table 1) of China Pacific Insurance Co., Ltd.

as an example, the per capita single premium of endowment insurance can be calculated based on the following equation.

Ye=(Yma×Bm+Ywa×Bw)×Mi/Mo

In this equation, Ye-the per capita single premium of endowment insurance a-average age Yma-base number of the single premium of endowment insurance of male citizens at the age of a Ywa-base number of the single premium of endowment insurance of female citizens at the age of a Bm-the proportion of male population to the total population Bw-the proportion of female population to the total population Mi-basic allowances of farmers (insurance benefit standard per month) Mo-base number of the premium per month

Calculation of Job Security Price

Land is the place of employment for most farmers and is of job security function for them. Therefore, the loss of land for farmers is equal to the loss of jobs for urban residents, so the job security price of land is the price of unemployment insurance.

The unemployment insurance is a government action and now is not taken over by

Calculation of Property Inheritance Price

The Q-land area a farmer owns per capita At present, in some areas where fine agriculture or characteristic agriculture is developed, the income of farmers from farming is far more than that of the ordinary land cultivation. However, fine agriculture and characteristic agriculture are the key supported industries of the local government, the production and management land of which will not be requisitioned. Therefore, this kind of sampling point should be eliminated and not be calculated as an exception.

In the process of farmland requisition, the insurance function of farmland to farmers is often ignored or in the actual operation, the calculation method of retirement pension of insurance companies is adopted, which result in some problems such as the extremely low compensation price. In the process of calculating endowment insurance price, medical insurance price, job security price and property inheritance price, some factors such as the inflation of prices and inflation are ignored.

With the development of economy, the inflation rate and inflation of prices might change dramatically and modification should be made according to the practical situation when the social security price is calculated. Whether the structure of the social security function of farmland is complete is determined by whether the social security system of China is complete. With the perfection of social security system of China and the disappearance of limit between urban areas and rural areas, the structure of social security price of farmland will change accordingly.

  Wenting Zhao 1* Na Hao 1 1College of Natural Resources & Environment Sciences, Agricultural University of Hebei, Baoding 071001, China 2School of Land Science and Technology, China University of Geosciences, Beijing 100083, China 3Shijiazhuang Engineering Technology School, Shijiazhuang 050061, China;

  insurance companies by means of charging a single premium. When the unemployment insurance is calculated, the years before the retirement of farmers should be worked out based on the average age of farmers and during these years, farmers should be treated as unemployed, and thus the government should pay them one-off unemployment compensation. As the number of years to receive unemployment compensation is determined by the number of years residents paying unemployment insurance before unemployment, the maximum number of years is only 24 months. If the resident fails to find a job in 24 months, he should receive the minimum living allowances instead of unemployment compensation. As farmers have never bought any unemployment insurance, the unemployment compensation of 2 years at most can be ignored and they can receive the minimum living allowance of 2 years. By reference to the minimum living allowances from the time of unemployment to the retirement, the equation is as follows: Yuc=M×(dm×Bm+dw×Bw-a) In this equation, Yuc-per capita job security price M-minimum living allowance per capita a-average age of citizens dm-age of retirement of male citizens Bm-the proportion of male population to the total population dw-age of retirement of female citizens Bw-the proportion of female population to the total population In this equation, the value of M, the minimum living allowance per capita, is the sum of annual basic living expenses per capita, annual basic medical care expenditure per capita (referring to the treatment of common diseases, not including treatment for major diseases) and annual educational expense per capita under the current education level (including compulsory education and job training).

  Law of the People's Republic of China on Land Contract in Rural Areas currently in effect has given farmers the long-term right of use of the collective land and their right of use of land is the survival guarantee that farmers leave with their offspring. The property inheritance value of the original farmland can be calculated by calculating the education expenses that ensure the offspring of farmers receive the same education as that of the local urban residents and enable them to have the same career competence as the offspring of the urban residents. The education expenses refer to the expenses that should be used to pay for the job training which enables the offspring of farmers to receive the same education as that of the urban residents other than the nine-year compulsory education. The equation is as follows:

Table 1

 1 Scale of Premium Rate of Taipingshengshi Changshou Endowment Insurance A of China Pacific Insurance Co., Ltd.(Male citizens get insurance benefit when they reach 60 years old, while female citizens reach 55 and both get 100 yuan each month) unit:yuan

	Purchasing	Single	Single	Purchasing	Single	Single
	Insurance at	Premium for	Premium for	Insurance at	Premium for	Premium for
	the Age of	Males	Females	the Age of	Males	Females
	0	3642	5278	30	7956	11283
	1	3719	5412	31	8170	11573
	2	3816	5550	32	8390	11871
	3	3916	5692	33	8616	12177
	…	…	…	…	…	…
	27	7347	10454	57	16173
	28	7545	10723	58	16575
	29	7747	11000	59	16981
	3.2 Calculation of Medical Insurance Price
	The medical insurance price of the requisitioned land should be calculated based
	on the amount of money paid for the individual medical insurance. Therefore, by
	reference to the individual medical insurance, scale of premium rate of

Table 2

 2 Scale of Premium Rate of Taipingshengshi Changjian Medical Insurance A of China Pacific Insurance Co., Ltd.(insured till 70 years old)unit:yuan

	Purchasing	Single	Single	Purchasing	Single	Single
	Insurance at	Premium for	Premium for	Insurance at	Premium for	Premium for
	the Age of	Males	Females	the Age of	Males	Females
	16	865	743	39	1329	1128
	17	884	759	40	1347	1140
	18	903	775	41	1364	1151
	…	…	…	…	…	…
	35	1250	1071	58	1335	1008
	36	1270	1086	59	1295	971
	37	1290	1101	60	1246	928
	38	1310	1115

The paper is supported by the Hebei Provincial Department of education funding for a project, (project number is

2009451)


Koldo Morante

Augusto Bellomio

David Gil-Cartón

Lorena Redondo-Morata

Jesús Sot

Simon Scheuring

Mikel Valle

Juan Manuel González-Mañas

Kouhei Tsumoto
email: tsumoto@bioeng.t.u-tokyo.ac.jp

Jose M M Caaveiro

Identification of a membrane-bound prepore species clarifies the lytic mechanism of actinoporins

Keywords: pore forming protein, cytolysin, lipid-protein interaction, protein structure, oligomerization, atomic force microscopy, lipid vesicle

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Pore-forming toxins (PFT) are proteins designed for defense and attack purposes found throughout the eukaryote and prokaryote kingdoms [START_REF] Anderluh | Disparate proteins use similar architectures to damage membranes[END_REF][START_REF] Dal Peraro | Pore-forming toxins: ancient, but never really out of fashion[END_REF]. These proteins function by opening pores across the cell membranes, triggering processes conducive to cell death [START_REF] Tilley | The mechanism of pore formation by bacterial toxins[END_REF]. PFT are commonly classified into αand β-types according to the secondary structure of the transmembrane portion of the pore [START_REF] De | Crystal structure of the Vibrio cholerae cytolysin heptamer reveals common features among disparate pore-forming toxins[END_REF][START_REF] Gilbert | Membrane pore formation at protein-lipid interfaces[END_REF][START_REF] Mueller | The structure of a cytolytic alpha-helical toxin pore reveals its assembly mechanism[END_REF]. The classical route of pore-formation begins with the interaction of the water-soluble monomer with the outer leaflet of the cell membrane, followed by the in-plane oligomerization of toxin subunits and conformational changes to assemble the lytic transmembrane pore [START_REF] Rojko | How lipid membranes affect pore forming toxin activity[END_REF]. Among the intermediate species that populate this pathway, prepore particles bound to lipid bilayers have been described in the class of β-PFT [START_REF] Fang | The heptameric prepore of a Staphylococcal alpha-hemolysin mutant in lipid bilayers imaged by atomic force microscopy[END_REF][START_REF] Hotze | Monomer-monomer interactions drive the prepore to pore conversion of a beta-barrel-forming cholesteroldependent cytolysin[END_REF][START_REF] Leung | Stepwise visualization of membrane pore formation by suilysin, a bacterial cholesterol-dependent cytolysin[END_REF][START_REF] Sonnen | Incomplete pneumolysin oligomers form membrane pores[END_REF][START_REF] Gilbert | Protein-lipid interactions and non-lamellar lipidic structures in membrane pore formation and membrane fusion[END_REF]. In the class of α-PFT, prepore structures of Cytolysin A from Escherichia coli have been proposed [START_REF] Mueller | The structure of a cytolytic alpha-helical toxin pore reveals its assembly mechanism[END_REF][START_REF] Eifler | Cytotoxin ClyA from Escherichia coli assembles to a 13-meric pore independent of its redox-state[END_REF][START_REF] Fahie | A Non-classical assembly pathway of Escherichia coli pore-forming toxin cytolysin A[END_REF], although direct visualization still remains elusive. For actinoporins, a class of α-PFTs secreted by sea anemones, it is unclear whether a stable prepore is assembled before formation of the lytic transmembrane pore. Structurally, actinoporins are composed of a rigid β-sandwich core (mediating binding to the membrane) and two flanking αhelices, as shown for the actinoporin FraC (20 kDa, 179 residues). The N-terminal region of FraC is made of an amphipathic α-helix and neighboring residues that insert collectively in the bilayer and, together with structural lipids from the membrane, line the lytic pore [START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF]. This remarkable metamorphosis is fully reversible under certain environmental conditions [START_REF] Tanaka | Bidirectional Transformation of a Metamorphic Protein between the Water-Soluble and Transmembrane Native States[END_REF].

A variety of membrane-bound species have been proposed in the mechanism of actinoporins [START_REF] Subburaj | Toxicity of an alpha-poreforming toxin depends on the assembly mechanism on the target membrane as revealed by single molecule imaging[END_REF][START_REF] Wacklin | Neutron reflection study of the interaction of the eukaryotic pore-forming actinoporin equinatoxin II with lipid membranes reveals intermediate states in pore formation[END_REF][START_REF] Antonini | Functional characterization of sticholysin I and W111C mutant reveals the sequence of the actinoporin's pore assembly[END_REF].

However, the nature of some of the intermediate species and the order at which they appear during pore-formation remains unclear. Some authors have proposed that the protein subunits should first assemble into an oligomeric prepore, followed by the concerted insertion of the N-terminal region in the lipid bilayer that gives rise to the functional pore (20). Other authors, on the contrary, have suggested that the α-helix inserts deeply in the membrane prior to the oligomerization step and, therefore, this model does not contemplate the appearance of stable prepores (Figure 1) [START_REF] Rojko | Membrane damage by an alpha-helical pore-forming protein, equinatoxin II, proceeds through a succession of ordered steps[END_REF].

Here, we have visualized a non-lytic oligomer of Liposome preparation and leakage assays. LUVs of 100 nm were formed by extrusion as described previously [START_REF] Morante | A pore-forming toxin requires a specific residue for its activity in membranes with particular physicochemical properties[END_REF]. The lipid concentration was determined according to Bartlett (25). The images were CTF-corrected by flipping phases after estimation of CTF parameters in EMAN [START_REF] Ludtke | EMAN: Semiautomated software for high-resolution single-particle reconstructions[END_REF]. The 2D images were classified by maximum-likelihood and hierarchical clustering procedures within the XMIPP software package [START_REF] Scheres | Image processing for electron microscopy single-particle analysis using XMIPP[END_REF]. The starting 3D model was generated using reference-free alignment, classification, and common-lines procedures implemented in EMAN.

MATERIALS AND METHODS

Materials

This was followed by iterative refinement using a projection matching scheme in SPIDER package [START_REF] Baxter | SPIRE: The SPIDER Reconstruction Engine[END_REF].

The rigid-body fitting was performed by maximization of the sum of map values at atom positions and by improvement in the coefficient of correlation between simulated maps from the atomic structures and the cryo-EM density map in Chimera [START_REF] Pettersen | UCSF chimera -A visualization system for exploratory research and analysis[END_REF]. The correlation between the atomic structure of FraC and cryo-EM map suggested a resolution of ~30 Å.

Preparation of giant unilamellar vesicles (GUVs)

for confocal fluorescence microscopy. GUVs made of DOPC/DPPC (20:80) were prepared by electroswelling on a pair of platinum wires by a method first developed by Angelova and Dimitrov [START_REF] Angelova | Liposome electroformation[END_REF], modified as described previously [START_REF] Montes | Electroformation of giant unilamellar vesicles from native membranes and organic lipid mixtures for the study of lipid domains under physiological ionic-strength conditions[END_REF]. A temperature-controlled chamber was used following previous methodology [START_REF] Montes | Electroformation of giant unilamellar vesicles from native membranes and organic lipid mixtures for the study of lipid domains under physiological ionic-strength conditions[END_REF]. Briefly, a mixture of 0.2 µg/µl lipid and 0.2 % DiIC18 was spread on the chamber and dried under vacuum. The sample was then covered with 10 mM HEPES, 200 mM NaCl, pH 7.5 at 61 ºC. This temperature was selected to prevent lipid demixing. To form the vesicles, current was applied in three steps under AC field conditions and a sinusoidal wave function:

(i) 500 Hz, 0.22 V (35 V/m) for 6 minutes, (ii) 500

Hz, 1.9 V (313 V/m) for 20 minutes, (iii) 500 Hz, Small oscillation free (Afree) and set point (Aset) amplitudes of about 1 nm and 0.9 nm, respectively, were employed to achieve minimum tip-sample interaction. Typically, pixel sampling ranges from 100 × 100 pixels and 200 × 200 pixels and frame rate between 500 and 800 ms per frame.

AFM data analysis. AFM data was analyzed in

ImageJ and with self-written image analysis scripts (movie acquisition piezo drift correction) in ImageJ [START_REF] Husain | Software for drift compensation, particle tracking and particle analysis of high-speed atomic force microscopy image series[END_REF]. To obtain the high resolution images shown in [START_REF] Edman | A protein sequenator[END_REF]. Electron density gradient analysis and protease digestion assays suggest a close association of the N-terminal region with the membranes in a position approximately parallel to the plane of the membrane as was described before for other actinoporins [START_REF] Gutiérrez-Aguirre | Membrane insertion of the N-terminal alpha-helix of equinatoxin II, a sea anemone cytolytic toxin[END_REF][START_REF] Alegre-Cebollada | Sea anemone actinoporins: The transition from a folded soluble state to a functionally active membrane-bound oligomeric pore[END_REF]. Evidence that this oligomer precedes pore formation is inferred from a previous study carried out with the actinoporin equinatoxin II. It was shown that the addition of phospholipase C to vesicles of PC decorated with toxin promoted vesicle lysis by the in situ generation of lipid domains [START_REF] Barlic | Lipid phase coexistence favors membrane insertion of equinatoxin-II, a poreforming toxin from Actinia equina[END_REF]. Our results suggest a model where the N-terminal α-helices penetrate the bilayer in a concerted manner (Figure 8), an alternative mechanism to that in which helix penetration occurs before protein oligomerization [START_REF] Rojko | Membrane damage by an alpha-helical pore-forming protein, equinatoxin II, proceeds through a succession of ordered steps[END_REF].

RESULTS

Interaction of

  non-lytic oligomeric intermediate known as prepore plays an essential role in the mechanism of insertion of the class of β-PFT. However, in the class of α-PFT like the actinoporins produced by sea anemones, evidence of membrane-bound prepores is still lacking. We have employed single-particle cryo-electron microscopy (cryo-EM) and atomic force microscopy (AFM) to identify, for the first time, a prepore species of the actinoporin fragaceatoxin C (FraC) bound to lipid vesicles. The size of the prepore coincides that of the functional pore, except for the transmembrane region, which is absent in the prepore. Biochemical assays indicated that, in the prepore species, the N-terminus is not inserted in the bilayer but exposed to the aqueous solution. Our study reveals the structure of the prepore complex in actinoporins, and highlights the role of structural intermediates for the formation of cytolytic pores by an α-PFT.

FraC bound to large

  unilamellar vesicles (LUVs) by using cryo-EM, and to supported planar bilayers by AFM. The overall dimensions of the cryo-EM model and the high-resolution AFM images indicates that the prepore is made of eight protein subunits, a result consistent with the oligomerization number of the active pore. Biochemical assays indicate that, in the prepore species, the first few residues of the N-terminal region are not embedded in the lipid phase, but exposed to the aqueous environment. Our results reinforce the idea that protein oligomerization occurs prior to the complete insertion of the Nterminal region into the membrane, thus clarifying a critical aspect of the lytic mechanism of actinoporins.

.

  Sphingomyelin (SM) from porcine brain and chicken egg, 1,2-dilauroyl-sn-glycero-3phosphocholine (DLPC), 1,2-dipalmi-toyl-snglycero-3-phosphocholine (DPPC), and 1,2dioleoyl-sn-glycero-3-phosphocholine (DOPC) were from Avanti Polar Lipids (AL, USA). 8-Aminonaphthalene-1,3,6-trisulfonic acid (ANTS), p-xylene-bis-pyridinium bromide, 1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate (DiIC18), and Alexa Fluor 633 succinimidyl ester were from Thermo Fisher Scientific (MA, USA). Proteinase K (PK) was purchased from Sigma Aldrich (St. Louis, MO, USA). Protein expression and purification. Expression and purification of FraC was carried out as previously described (22). Briefly, FraC expression was induced in E. coli BL21 (DE3) cells and then purified to homogeneity by ion-exchange and sizeexclusion chromatography. Oxidation of the double-cysteine mutein was carried out as described by Hong et al. (23). Protein labeling. To visualize FraC by fluorescence microscopy, the protein was labeled with the amine-reactive fluorescent dye Alexa Fluor 633 succinimidyl ester. The succinimidyl ester moiety of the reagent reacts with non-protonated aliphatic primary amine groups of the protein. The protein-dye conjugate was prepared following the instructions supplied by the manufacturer. Briefly, 740 µl of FraC at 180 µM in 90 mM bicarbonate buffer (pH 8.3) were mixed with 45 µl of the fluorescent dye previously dissolved in DMSO. The mixture was incubated for 1 hour at room temperature with constant stirring. To stop the reaction and remove weakly bound probes from the unstable conjugates 80 µl of freshly prepared 1.5 M hydroxylamine was added and incubated for 1 hour at room temperature. Unreacted labeling reagent was separated from the conjugate by elution of the reaction mix through a Sephadex G-15 (GE Healthcare) packed column. The protein-conjugate was tested for activity using surface pressure measurements and hemolysis assays, showing a similar behavior to that of the unlabeled protein (data not shown), ruling out detrimental effects by the dye.

  For the leakage assays, four different populations of LUVs made of DLPC, DPPC, DOPC, and SM/DOPC (1:1) were prepared as described above in a buffer containing 10 mM HEPES (pH 7.5), 50 mM NaCl, 25 mM ANTS (the fluorescent probe), and 90 mM p-xylene-bis-pyridinium (the quencher), followed by washing the liposomes with isosmotic buffer 10 mM HEPES, 200 mM NaCl, pH 7.5 in a PD-10 column (GE Healthcare). Leakage of encapsulated solutes was assayed as described by Ellens et al. (26). Briefly, LUVs were incubated with FraC at room temperature for 30 minutes to ensure, as much as possible, completion of vesicle lysis at each protein concentration employed. Upon solute release to the external medium, the dilution of quencher and fluorophore results in an increase of the emission of fluorescence of ANTS. The fluorescence was measured in a PHERAstar Plus microplate reader (BMG LABTECH, Ortenberg, Germany) with excitation/emission wavelengths of 350/520 nm. Complete release of the ANTS was achieved by solubilization of the liposomes with Triton X-100 (0.1% w/v). The percentage of leakage was calculated as: % leakage = (Ff -F0 / F100 -F0) × 100 where Ff is the fluorescence measured after addition of the toxin, F0 the initial fluorescence of the liposome suspension and F100 the fluorescence after addition of detergent. Surface pressure measurements. Surface pressure measurements on lipid monolayers made of pure DLPC, DPPC, or DOPC were carried out using a Micro-Trough-S instrument (Kibron, Finland) at room temperature with constant stirring. In these experiments, the lipid was spread over the air-water interface to the desired initial surface pressure. The protein (1 µM) was injected into the aqueous subphase and the increase of surface pressure recorded. The maximum surface pressure (πmax) was determined with the following equation: πmax = π0 + (Δπ•"x / b + x) where π0 is the initial surface pressure, Δπ is the change in surface pressure, x is time, and b is the time necessary to reach Δπ/2 (27). The critical pressure (πc) corresponded to the initial surface pressure of the lipid monolayer at which the protein no longer penetrates the surface, calculated by least squares fitting as the intercept when Δπ= 0. Cryo-EM of FraC Inserted in Model Membranes. For cryo-EM imaging, LUVs composed of DOPC were incubated with FraC (5 µM) at a protein/lipid ratio of 1:160 for 30 minutes. Holey-carbon grids were prepared following standard procedures and observed in a JEM-2200FS/CR transmission electron microscope (JEOL Europe, Croissy-sur-Seine, France) operated at 200 kV at liquid nitrogen temperature. A set of 1,562 individual pore particles were manually selected and recorded on CCD camera under low-dose conditions at 60,000 × magnification resulting in a final pixel size of 1.72 Å. An in-column omega energy filter was used to improve the signal to noise ratio of the images.

5. 3 V

 3 (870 V/m) for 90 minutes. After vesicle formation the chamber was left to settle at room temperature. Inverted confocal fluorescence microscopy. For the visualization of GUVs and labeled protein the chamber for GUV formation was placed on a D-Ellipse C1 inverted confocal fluorescence microscope (Nikon, Melville, NY, USA) and the samples visualized at room temperature. The excitation wavelengths used were 561 nm (for DiIC18) and 633 nm (for Alexa Fluor 633). The fluorescence signal was collected into two different channels with band pass filters of 593/40 nm and 650 nm long pass. The objective used was a 60 × oil immersion with a NA of 1.45. Image treatment was performed with the EZ-C1 3.20 FreeViewer software. Preparation of GUVs for AFM. GUVs made of SM/DOPC (1:1) were prepared by the electroswelling technique (34). A volume of 30 µl of 1 mg/mL lipids dissolved in chloroform: methanol (3:1) were deposited in two glass plates coated with indium tin oxide (70-100 Ω resistivity, Sigma-Aldrich) and placed in the desiccator at least 120 minutes for complete solvent evaporation. A Ushape rubber piece of ~1 mm thickness was sandwiched between the two indium tin oxide side slides. Then the formed chamber was filled with ca. 400 µl of 200 mM sucrose and exposed to 1.2 V AC current (12 Hz sinusoidal for 2h, 5 Hz squared for 10 minutes). The resulting suspension was collected in a vial and used within several days. Supported lipid bilayer preparation for AFM. A total of 1 µL of a suspension of GUVs was deposited onto freshly cleaved 1 mm 2 mica pretreated with 1 µL of 10 mM Tris-HCl, 150 mM KCl, pH 7.4 (imaging buffer) and incubated for 15 minutes at room temperature. The resulting supported lipid bilayers were carefully rinsed with imaging buffer before image collection and always kept under aqueous environment. During imaging, FraC toxin was injected into the fluid cell to give a final concentration of ~10 µM. AFM imaging. AFM was performed at room temperature on a high-speed AFM 1.0 instrument (RIBM, Japan) equipped with short high-speed AFM cantilevers (~8 µm, NanoWorld, Switzerland) with nominal resonance frequency of ~1.2 MHz and ~0.7 MHz in air and liquid, respectively, and a nominal spring constant of ~0.15 Nm -1 . Image acquisition was operated using optimized feedback by a dynamic PID controller.

Figure 5 and

 5 Figure 5 and Figure 6, five consecutive frames were time-averaged. All further analysis, i.e. histogram distributions were analyzed in Igor and Origin. Protease susceptibility assay. Proteinase K (PK) (50 µM) was incubated with FraC (50 µM) for 24 hours at room temperature in 50 mM Tris, 200 mM NaCl, 5 mM CaCl2 at pH 7.4. In the assays with lipids, FraC was incubated with the appropriate LUVs (7.5 mM) made of either DOPC or

  FraC with PC membranes -Because actinoporins are specifically activated by membranes containing the lipid SM, the use of lipid compositions in which SM is absent but where the toxin maintain a strong interaction with the vesicles may reveal structural intermediates not detectable by other means. We first evaluated the interaction of FraC with vesicles made of various types of the lipid phosphatidylcholine (PC, a lipid displaying the same phophocholine headgroup moiety as that of SM) to determine the optimum PC species yielding the highest possible association between protein and liposomes. The three PC lipid species examined were DLPC, DPPC, and DOPC each differing in the length and degree of saturation of their acyl chains. To evaluate the degree of interaction of FraC with these lipids we measured the magnitude of the insertion of the protein in a monolayer of lipid molecules at the water-lipid interface (Figure2A). We determined the surface pressure at which the protein will no longer penetrate, known as critical pressure (πc)[START_REF] Caaveiro | Differential interaction of equinatoxin II with model membranes in response to lipid composition[END_REF]. The lipid composition at which πc was highest corresponded to that of monolayers composed of DOPC (πc = 36.1 ± 1.6 mN/m) followed by that of monolayers made of DLPC (πc = 31.1 ± 1.3 mN/m).The insertion of FraC in DPPC monolayers was meager (23.6 ± 1.0 mN/m). These data suggest that the toxin associates more readily with lipids in the liquid-expanded phase such as DOPC and DLPC than those in the liquid-condensed phase (DPPC)[START_REF] Calvez | Parameters modulating the maximum insertion pressure of proteins and peptides in lipid monolayers[END_REF][START_REF] Kakiuchi | Phase-transition and ion permeability of phosphatidylcholine monolayers at the polarized oil-water interface[END_REF][START_REF] Pedrera | Sticholysin I-membrane interaction: An interplay between the presence of sphingomyelin and membrane fluidity[END_REF]. However, the protein does not generate pores in LUVs when SM was absent regardless of the lipid phase (Figure2B). A close association between actinoporins and lipid monolayers thus does not guarantee effective formation of pores in a lipid bilayer system as there are other physicochemical properties involved in pore formation, such as lipid phase coexistence and the presence of SM[START_REF] Barlic | Lipid phase coexistence favors membrane insertion of equinatoxin-II, a poreforming toxin from Actinia equina[END_REF].Additional evidence describing the lipid preference of FraC was gathered by visualizing the binding of the fluorescently-labeled toxin to GUVs composed of DOPC/DPPC (20:80). In these experiments, the fluorescent dye conjugated to the toxin co-localized with the DOPC domains (dark areas in Figure2C) indicating that the toxin preferentially binds to the fluid phase domains over the gel domains, a result consistent with the observations made with monolayers for FraC and leakage assays for sticholysin II[START_REF] Palacios-Ortega | Regulation of Sticholysin II-Induced Pore Formation by Lipid Bilayer Composition, Phase State, and Interfacial Properties[END_REF]. Based on these results, membranes composed of DOPC were selected for structural studies analyzing the conformation of FraC bound to membranes in a non-lytic environment.Cryo-EM -To visualize the structure of membranebound FraC, vitrified samples of toxin-treated DOPC liposomes were imaged by cryo-EM. The observed ring-shaped particles covering the lipid vesicles were attributed to protein oligomers (Figure 3A). A total of 1,562 top-and side-view images were selected to build a three-dimensional model of the protein oligomer. The model was built by common-lines procedures, followed by iterative refinement using projection matching of the classaveraged images and the density map projections (Figure 3B). A second classification method based on maximum-likelihood (42) and hierarchical clustering approaches (43) (Figure 3C) rendered images similar to those used to generate the final density map.The reconstructed image consisted of a doughnutshaped ring with an external and internal diameter of ~11 and ~ 5 nm, respectively (Figure3D and 3E).These dimensions are very similar to those of the crystallized pore in the active state[START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF]. However, unlike the cryo-EM model of the pore bound to SM/DOPC (1:1) liposomes (20), the oligomer bound to DOPC vesicles does not span the lipid membrane (see below). This architecture is consistent with a non-lytic oligomeric species resembling a prepore. A rigid-body fitting of an octameric model of FraC based on the atomic structure of the transmembrane pore of FraC achieved a high cross-correlation coefficient (cc = 0.82, Figure3D, E)[START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF]. The oligomeric model fits well within the perimeter of the cryo-EM map, except for the N-terminal region, which lies outside the electron density map suggesting that it is either resting on the surface of the membrane or inserted in the hydrophobic core of the membrane[START_REF] Rojko | Membrane damage by an alpha-helical pore-forming protein, equinatoxin II, proceeds through a succession of ordered steps[END_REF][START_REF] Hong | Two-step membrane binding by Equinatoxin II, a pore-forming toxin from the sea anemone, involves an exposed aromatic cluster and a flexible helix[END_REF][START_REF] Gutiérrez-Aguirre | Membrane insertion of the N-terminal alpha-helix of equinatoxin II, a sea anemone cytolytic toxin[END_REF]. A nonamer of FraC mimicking the structure of a crystallized oligomer of FraC in the presence of detergents (20,45) was also fitted in the cryo-EM maps, yielding a cross-correlation coefficient only slightly worse (cc = 0.81) than that of the octamer. The fitted nonamer displayed a few clashes between protomers, in contrast to an oligomer made of ten units in which the numerous collisions between protein chains made the decamer prepore unfit for this electron density. From these data we cannot rule out the existence of a minor population of nonameric prepore species preceding a hypothetical nonameric pore, as discussed previously[START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF]. A comparative analysis of the electron density distribution along the central section of the oligomer of FraC in DOPC membranes and in SM/DOPC (1:1) clearly shows that the differences between pores and prepores occur in the critical transmembrane region. To perform this comparison we employed the previously reported cryo-EM map of the active pore (20). For the analysis, 3D volumes focused on the pore regions (shown within yellow rectangles in Figures 4A and 4B) were projected into 2D images, and the gray values of the images (resulting from the accumulation of 3D density values) were plotted in 1D profiles (Figures 4C and 4D). The structure of FraC in DOPC membranes (Figure 4A) reveals a peak of higher density values in a central lobe at the membrane level below the vestibule of the oligomer (Figure 4C), whereas in SM/DOPC (1:1) membranes (Figure 4B), the same region is characterized by lower density values (Figure 4D). These features are consistent with the absence (in DOPC membranes) or the presence (in SM/DOPC (1:1) membranes) of a transmembrane pore. In the former, the high-density central region likely represents the accumulation of membrane lipids and N-terminal α-helices detached from the β-core of the toxin. In contrast, in membranes containing SM the central region of the oligomer displays lower electron density because the αhelices span the membrane from top to bottom, and consequently the lipids are cleared off producing an aqueous pore. AFM -In the presence of supported lipid bilayers composed of the equimolar mixture SM/DOPC (1:1), WT FraC assembles in a dense array of closely packed oligomers as determined by AFM (Figure5). These oligomers, presumably corresponding to pore particles, cover the SM-rich domains in an arrangement previously observed in FraC and other actinoporins[START_REF] Mechaly | Pores of the toxin FraC assemble into 2D hexagonal clusters in both crystal structures and model membranes[END_REF][START_REF] Mancheño | A complementary microscopy analysis of Sticholysin II crystals on lipid films: Atomic force and transmission electron characterizations[END_REF] or the SMspecific PFT lysenin[START_REF] Yilmaz | Real-time visualization of assembling of a sphingomyelin-specific toxin on planar lipid membranes[END_REF]. The cross-section profile of the oligomeric complexes reveals an average diameter of 7.5 ± 0.6 nm, a value in good agreement with the mean diameter (average of outer and inner diameter) of the pore determined by X-ray crystallography (~ 8 nm). Eight protein chains are observed in three well-resolved pore particles encountered (see for example Figure5C). Because prepores of FraC were not resolved in DOPC, probably caused by high-diffusivity preventing AFM contouring, a construct of FraC bearing a double cysteine mutation (V8C/K69C, termed 8-69 OX ) was instead examined on supported membranes made of SM/DOPC (1:1).Under oxidizing conditions, the N-terminal segment of this mutein is covalently attached to the protein core by means of a disulfide bond, preventing the protein from generating a transmembrane pore, and thus inactivating the toxin[START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF][START_REF] Hong | Two-step membrane binding by Equinatoxin II, a pore-forming toxin from the sea anemone, involves an exposed aromatic cluster and a flexible helix[END_REF]. As with WT FraC, the construct 8-69 OX also gave rise to a dense array of pore-like particles (Figure6), indicating that the protein readily oligomerizes in the presence of membranes even if the N-terminal region remains attached to the protein. The average diameter of these particles (6.2 ± 0.7 nm) is somehow smaller than that of WT protein, reflecting the influence of the N-terminal region attached to the β-core region. Because of the constrains imposed by the disulfide bond, the conformation of the N-terminal region in 8-69 OX is likely to differ from that of WT FraC bound to liposomes made of DOPC (Figure3D, E). To further investigate this question we employed biochemical assays (see below).Protease susceptibility of the membrane-boundtoxin -It was shown that FraC bound to LUVs exhibits different susceptibility to proteinase K (PK) depending on the lipid composition of the membrane[START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF]. The incubation of FraC with PK generated a product of smaller size when the toxin was bound to DOPC vesicles as compared to those generated in the presence of SM/DOPC (1:1) vesicles[START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF], although the basis of this difference was not explained. In view of the new prepore oligomeric species described herein, we hypothesized that the N-terminus of this prepore is located in a solvent-exposed environment accessible to PK, whereas in the pore the Nterminus is deeply inserted in the membrane and thus inaccessible to the protease. To verify this hypothesis and determine the extent of the digestion, we incubated samples of FraC with PK followed by their separation by SDS-PAGE and Nterminal sequencing. The incubation of PK with FraC in the presence of DOPC vesicles yields a fragment of smaller molecular weight than that of the untreated protein (shown in the 20 kDa region). In contrast, in the presence of SM/DOPC (1:1) vesicles, the bands of treated and untreated toxin display the same molecular mass (Figure 7A). The mutein 8-69 OX bound to membranes was also employed, since its N-terminus remains exposed to the solvent constrained by the disulfide bond. As expected, 8-69 OX was also susceptible the proteolytic activity of PK in DOPC and SM/DOPC (1:1) vesicles. To determine the cleavage point the proteins were sequenced from their N-terminus. The sequencing data revealed that, in the presence of vesicles of DOPC, FraC WT and 8-69 OX were cleaved at the Nterminus by PK, rendering products in which the first four and first eleven residues, respectively, were missing (Figure 7B, C). FraC bound to SM/DOPC (1:1) was not digested by PK as expected from the position of the band in the SDS-PAGE gel, whereas 8-69 OX was cleaved at the same position seen in vesicles of DOPC. These results demonstrate that the N-terminus of FraC in DOPC vesicles (prepore configuration) is accessible to PK, i.e. this region is not embedded in the lipid bilayer. DISCUSSION Structural intermediates that populate the pathway leading to the formation of a functional transmembrane pore in PFT are key species that can help to elucidate the details of pore formation. Membrane-bound oligomeric structures poised for membrane disruption are commonly referred to as prepores and have been visualized in lipid bilayers only for β-PFT. In contrast, the existence of prepores in α-PFT is controversial. An example is the family of actinoporins, where a strong debate is held about the existence or not of these non-lytic oligomers (20,21,48). Until now, the evidence supporting a prepore in actinoporins was based on the crystal structure of a non-lytic nonameric ensemble solved for FraC (20). Herein, we have described a low-resolution membrane-bound oligomer consistent with the ability of FraC to assemble as a prepore on biological membranes. We employed a protein concentration above physiological levels to ensure a large and homogeneous population of pre-pore species bound to the liposomes, thus facilitating their visualization by cryo-EM and AFM. The prepore structure could explain the readiness of actinoporins to induce lysis in liposomes made of PC upon generation of lipid domains in situ (40).The size and stoichiometry of the prepore in the cryo-EM (Figures3, 4) and AFM (Figure6) images were in the range of those of the crystallized pore species[START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF]. The cryo-EM reconstruction map is not consistent with a transmembrane pore, an argument strengthened by the comparison side-byside with cryo-EM data of pores of FraC embedded in vesicles of SM/DOPC (1:1) (20).

FIGURESFigure 1 .

 1 ABBREVIATIONS PFT, pore-forming toxins; FraC, fragaceatoxin C; SM, sphingomyelin; DLPC, 1,2-dilauroyl-sn-glycero-3-phosphocholine; DPPC, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine; DOPC, 1,2-dioleoyl-sn-glycero-3-phosphocholine; DiIC18, 1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate; LUVs, large unilamellar vesicles; GUVs, giant unilamellar vesicles; AFM, atomic force microscopy; cryo-EM, single-particle cryo-electron microscopy; PK, Proteinase K; CTF, contrast transfer function.

Figure 2 .

 2 Figure 2. Interaction of FraC with model membranes. (a) Change in surface pressure of lipid monolayers composed of DOPC (red), DLPC (green), or DPPC (blue) after treatment with FraC (1 µM). The parameter πc corresponds to the value of π0 where the regression line intersects the abscissa. The inset shows a representative example of the kinetic profile of insertion of FraC in DOPC monolayers (π0 = 20 mN/m, gray trace). The experimental data was fitted to a hyperbola (red line) from which the value of Δπ was determined. (b) Lytic activity of FraC in LUVs made of DOPC (red) or DPPC (blue). The data obtained with SM/DOPC (1:1) represents a positive control (black). The LUVs made of DLPC are permeable to encapsulated dyes in the absence of protein (spontaneous leakage) and thus the data obtained with them was not considered. For the experiments in panels (a) and (b) the mean and standard deviation of three independent measurements was plotted. (c) Binding of FraC to GUVs made of DOPC/DPPC (20:80) supplemented with 0.2% DiIC18. This probe partitions in the ordered phase regions (yellow domains) (53,54). Protein (red) was added to a final concentration of 1.3 µM. Lipid and protein were visualized with a 593/40 nm band pass filter (yellow, left panel), or with a 650 nm long pass filter (center panel), respectively. Merged images are shown on the right panel. The white arrows point at liquid disordered regions (dark domains) where FraC is preferentially located. The scale bar represents 5 µm.

Figure 3 .

 3 Figure 3. Structure of the oligomeric prepore of FraC bound to DOPC vesicles. (a) Representative image of FraC in DOPC vesicles obtained by cryo-EM. Top-and side-views of the protein oligomers were selected (red squares) for subsequent classification analysis. The scale bar corresponds to 100 nm. (b) Density map projections (top row) and 2D class-averaged particles (bottom row) employed to build a 3-dimensional model of the protein oligomer (see below). (c) Set of particles obtained by maximum-likelihood (ML2D, top row) and hierachical clustering (CL2D, bottom row) procedures. (d) Top-and (e) side-views of the 3-dimensional model of the prepore of FraC bound to vesicles of DOPC. The atomic model of FraC was built as an octamer using the coordinates of the protomer of FraC prior to pore formation (entry code 4TSL).

Figure 4 .

 4 Figure 4. Electron density of FraC bound to vesicles. Side view (Z-projection) of oligomers of FraC bound to vesicles of (a) DOPC, or (b) SM/DOPC (1:1). The yellow square indicates the region where the 1-dimensional profile of the Z-projection (shown in c, d) was calculated. The intensity of the electron density is expressed in gray values. Panels (b) and (d) correspond to the analysis carried out with published data (20), although we note that the analysis presented here has not been shown elsewhere.

Figure 5 .

 5 Figure 5. Visualization of pores of WT FraC with AFM. (a) Two-dimensional packing of ring-shaped oligomers of WT FraC on supported lipid bilayers composed of the lipid mixture SM/DOPC (1:1). (b) Diameter distribution analysis (peak-to-peak distances of the protein protrusion in the height profile). The average diameter of the particles was 75 ± 6 Å (mean ± SD from the Gaussian distributions). Inset: detail of the particles inside the white dashed rectangle in panel (a). (c) Magnification (13 nm frame size) of a single FraC oligomer in panel (a) (white dashed square). (d) Cross-section profile (left to right) of FraC oligomers shown in panel (a) (white dashed line). The molecules are packed with a center-to-center distance of ~112 Å.

Figure 6 .

 6 Figure 6. Visualization of prepores of 8-69 OX FraC with AFM. (a) Two-dimensional packing of ring-shaped oligomers of 8-69 OX FraC on supported lipid bilayers composed of the lipid mixture SM/DOPC (1:1). (b) Diameter distribution analysis (peak-to-peak distances of the protein protrusion in the height profile). The average diameter was 62 7 Å (mean SD from the Gaussian distributions). Inset: detail of the particles inside the white dashed rectangle in panel (a). The slightly smaller diameter compared to the WT suggests a tighter association of the subunits in the 8-69 OX FraC mutant. (c) Magnification (12 nm size frame) of a single prepore of FraC 8-69 OX (white square in panel a). (d) Cross-section profile (left to right) of prepore particles of FraC 8-69 OX (white line in panel a). The molecules packed with a center-to-center distance of ~108 Å.

Figure 7 .

 7 Figure 7. Protection of FraC from PK in the presence of liposomes. (a) SDS-PAGE of the products obtained after the incubation of WT and 8-69 OX FraC with DOPC vesicles in the absence and in the presence of PK (lanes 1-4) or with SM/DOPC (1:1) (lanes 5-8). (b) N-terminal sequence of FraC after digestion with PK. The circled number before the sequence corresponds to the lane of the same number in the SDS-PAGE. Residues highlighted in red were digested by PK. The first 16 residues of the recombinant WT protein expressed in E. coli are ADVAGAVIDGAGLGFD (55). (c) The location of the residues digested by PK are depicted in the three dimensional structure of the monomer of FraC (PDB code 3VWI).

Figure 8 .

 8 Figure 8. Model for pore formation by FraC. A toxin monomer binds the membrane. The membrane promotesprotein-protein interactions between monomers to produce a dimer[START_REF] Tanaka | Structural basis for self-assembly of a cytolytic pore lined by protein and lipid[END_REF] leading to prepore upon successive addition of monomer and/or dimers to the growing oligomer. The N-terminal α-helices in the prepore embedded on the surface on the membrane with the N-terminus exposed to the aqueous solution. The conversion to the transmembrane pore would be achieved by the concerted penetration and elongation of the helices across the lipid bilayer. The structures of the monomer, dimer, and pore were retrieved from the PDB with entry codes of 3VWI, 4TSL, and 4TSY, respectively. The structure of the prepore at high resolutions has not been determined experimentally. In this figure the structure of the prepore is drawn to illustrate the model consistent with the experimental data reported in our study.

ACKNOWLEDGEMENTS

this region) in the hydrophobic core of biological membranes would be strongly disfavored (50)(51)(52).

In conclusion, our study clarifies the structure of a key intermediate, known as prepore, in the route of pore formation by actinoporins belonging to the group of α-PFTs. The characterization of the prepore in actinoporins highlights similarities with the mechanism for pore formation of the group of β-PFT, despite these two groups having quite distinct architectures at the transmembrane region.

AB is a staff scientist from the CONICET (Argentina) and received a visiting scientist fellowship from the Basque Government while conducting this work. We thank Dr. S. Kudo for expert advice. This work was supported by a Grant-in-Aid for Scientific Research A (25249115 to KT) and a Grant-in-Aid for Scientific Research C (15K06962 to JMMC). KM was a recipient of a fellowship from the Spanish Ministerio de Ciencia e Innovación during the beginning of this study. Work in the Scheuring-Lab was supported by a European Research Council Grant (#310080, MEM-STRUCT-AFM). This work was also supported by grant BFU2015-66326-P from the Spanish Ministry of Economy and Competitiveness to MV. AB, DGC, LRM, and JS performed experiments. All authors designed experiments and analyzed the data.

KM and JMMC wrote the paper with input from all other authors. All authors reviewed the results and approved the manuscript.

FOOTNOTE

The present address of AB is Instituto Superior de Investigaciones Biológicas (INSIBIO, CONICET-UNT) e Instituto de Química Biológica "Dr. Bernabé Bloj", Facultad de Bioquímica, Química y Farmacia, Universidad Nacional de Tucumán, Chacabuco 461, San Miguel de Tucumán. Argentina.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest with the contents of this article.

AUTHOR CONTRIBUTIONS

KM, JMGM, and JMMC conceived the study. KM, JMGM, KT and JMMC coordinated the study. MV and DGC analyzed and performed cryo-EM studies. SS and LRM analyzed and performed AFM studies. KM,


Yuechan Shi

Guijun Yang

Haikuan Feng

Renli Wang

Remote Sensing of Forest LAI from Multitemporal Optical Satellite Images over Mountain Area

Keywords: remote sensing, landsat TM, forest, LAI, seasonal variability

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Leaf area index (LAI) is an important ecological parameter used for characterizing the interface between a vegetation canopy and the atmosphere. And it is also crucial for quantifying the energy and mass exchange of terrestrial ecosystems [START_REF] Sellers | A simple biosphere model (SiB) for use within general circulation models[END_REF][START_REF] Running | Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation[END_REF][START_REF] Bonan | Importance of leaf area index and forest type when estimating photosynthesis in boreal forests[END_REF]Turner et al., 1999). Chen and Black (1992) defined LAI was a half of the total leaf area per unit ground surface area.

All the methods of obtaining LAI values can be classified into two categories: direct or indirect way [START_REF] Hongbo | Estimating Method of Forest Leaf Areas Index on Different Space Scales[END_REF][START_REF] Jiahua | Vegetation and Ecological Remote Sensing[END_REF]. The traditional direct way by destructive sampling and litter collection are time-and strain-consuming. Both the optical instruments and remote-sensing method are classified as the indirect way. The optical instruments (e.g. the LAI-2000, TRAC, and hemispherical photography) were used to measure LAI through the gap fraction method [START_REF] Jonckheere | Review of methods for in situ leaf area index determination Part I[END_REF]. But, this method is limited due to the extent of observation only representing small-scale area. On the contrary, the remote sensing method could acquire the images at a moment covering large area.

Most of previous researches on estimating LAI from optical satellite data are based on two approaches: empirical models and physical models [START_REF] Weiss | Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data[END_REF][START_REF] Liang | Estimation and validation of land surface broadband albedos and leaf area index from EO-1 ALI data[END_REF]Xing, 2010;Yang, 2010). The empirical model involved the relationship between LAI and vegetation indices, while physical model uses the inversion of canopy reflectance models. Due to the quantitative methods usually faced the problem of ill-posed inversion because too many parameters should be providing before inverting the model. Therefore, many scientists preferred to use the empirical model. The empirical model method, as a simple and practical way, is widely used [START_REF] Jing | Compact Airborne Spectrographic Imager (CASI) used for mapping biophysical parameters of boreal forests[END_REF]Turner, 1999;[START_REF] Zhi-Meng | Retrieving Leaf Area Indices for Coniferous Forest in Xingguo County, Jiangxi Province, in Use of Landsat ETM + Images[END_REF][START_REF] Stenberg | Boreal forest leaf areas index from optical satellite images: model simulations and empirical analyses using data from central Finland[END_REF].

In this study, forest LAI was measured using Hemispherical Photography (Cambridge, UK) at experiment station and multitemporal Landsat5 TM images are acquired simultaneously. All image data were geometrically and radiometrically corrected with the DEM data. Three typical vegetation indices (NDVI, EVI and TGDVI) were calculated and selected through the correlative analysis method. This model was applied in mapping multitemporal forest LAI.

Study Areas

In-situ LAI measurements

The study area (Figure 1) is located in the northwest mountain areas of Beijing, China (116°0'38"E-116°8'43"E,40°0'48"N-40°4'59"N) with an elevation of 150-1265 m. With the semi-humid continental monsoon climate in warm temperate zone, the woodland area is 823.04 hectares and the rate of forest coverage reached 96.2%. Broadleaf and broadleaf-coniferous mixed forests, shrub and meadow extensively distribute in the study area in relatively homogenous spatial patterns.

Five ground-based forest LAI measurements were conducted using Hemispherical Photography over study areas from April to October, 2011.The histogram of LAI seasonal variability was given in Figure 2. Each plot was designed 30 m×30m, which is the same size with the pixel of Landsat5 TM data. The geographic coordinates of the centre place of plots were measured with GPS (Trimble Geo XT). The forest type was also recorded. Hemispherical photography is a technique for studying plant canopies via photographs acquired through a hemispherical (fisheye) lens with 180 º field of view from beneath the canopy (oriented towards zenith) or placed above the canopy looking downward. The DEM data covering the study area with a resolution of 30 m were received from the NASA Land Processes Distributed Active Archive Center (LP DAAC), which were produced from optical stereo data of the Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER). ASTER GDEM is in geotiff format with geographic lat/long coordinates (WGS84 datum). According to the zenith angle, azimuth angle and imaging time, the DEM data was used to calculate the slope, aspect, skyview and shadow factors which are the parameters of terrain radiometric correction.

3

Analyze and Result

Inversion LAI model

Three vegetation indices (NDVI, EVI and TGDVI) (Table 2) were employed for correlative analysis to LAI [START_REF] Chong | The Applicability of Different Methods for Retrieving Leaf Area Index of Forests in Red Soil Hilly Regions[END_REF][START_REF] Gao-Long | Forest canopy leaf area index in Maoershan Mountain: Ground measurement and remote sensing[END_REF]Robert, 2003). Normalized Difference Vegetation Index (NDVI).Compared to NDVI, Enhanced Vegetation Index (EVI) adds blue band, which used to further correction for aerosol residue and the impact of soil background on vegetation index (Li, 2010). According to the vegetation and soil spectral characteristics, Tang et al (2003) advanced Three Gradient Difference Vegetation Index (TGDVI) and indicated this index has advantages in a high saturation point and eliminating background effects.

In our study, vegetation indices, which extracted from matched spectral reflectance image using the GPS points, are used for corrective analysis in two cases: individual observed period and the whole observations. The accuracy in Table 3 indicated that the vegetation indices were little sensitive to LAI especially in August and September. Nerveless, when all the LAI observations are used to established model, the significance was increasing largely and they reached significant level (Table 4). The LAI inversion model based on NDVI showed the most performance (Fig. 3), the equation as follows. LAI = 0.042e 4.6 * NDVI (R 2 =0.487) (1) In Figure 4, the retrieved results were compared with the available LAI measurements with R 2 of 0.44 and RMSE of 0.38. The significance of retrieved LAI models improved considerably using all the LAI, which can be explained by: (1) the error caused by the signal LAI value makes stronger influence on the trend of NDVI with LAI changes in individual observed period than that in all observations. This situation can be detected in September in Figure 5 that the sample located in small NDVI value but large LAI value. (2) In contrast with individual observed period, the total dataset located in 2D space of NDVI-LAI uniformly, showing the trend of NDVI with LAI changes clearly. For example, 80% of LAI values ranged from 1.3m 2 m -2 to1.8m 2 m -2 while NDVI from 0.7 to 0.78 in June. 68% of LAI values distributed from 0.7m 2 m -2 to1.1m 2 m -2 while NDVI from 0.64 and 0.7 in October. A similar situation also existed in other months. But, the multitemporal samples extend the dynamic range obviously which LAI values change from 0.12m 2 m -2 to 3.01m 2 m -2 and NDVI from 0.46 to 0.92. Further, showing in Figure 5, the April values are relatively in low value areas of 2D space of NDVI-LAI while the June, August and September values are relatively in high value areas, resulting in the trend of NDVI with LAI changes.

Inversion of forest LAI and analysis of seasonal variability

The retrieved forest LAI (Figure 6) show the seasonal variability from April to October. The estimated LAI value changes from 0-3.1m 2 m -2 and the April and October values(less than 1.5m 2 m -2 ) are lower than those in June, August and September (0.5m 2 m -2 to 3.1m 2 m -2 ). Both the dynamic range and seasonal variation of the retrieval LAI are consistent with the situation of ground-based LAI measurements (Figure 2). Those proved that LAI model based on multitemporal LAI values can be used for estimating individual seasonal map.

To further confirm applicability of this model, 180 samples are extracted from each estimated LAI map and used to explore the seasonal variability of estimated LAI values (Figure .7). We can see that LAI increased from April to August and decreased from August to October. The values reached the peak in August which is similar to the trend of ground-based LAI observations in Figure 2.

From figure 6, the most spatial distribution variation areas of forest LAI mainly located in Mentougou, Fangshan, Miyun, Huairou, Yanqing and Changping. In April, the value is obviously lower in Mentougou and Fangshan (less than 0.5m 2 m -2 ) than other regions (less than 1.5m 2 m -2 ). Through investigation, the difference is caused by the differences in vegetation type: Mentougou and Fangshan are mainly covered by bush while Huairou dominated covered by shelterbelts. With the growth of the vegetation, this difference becomes smaller especially in August. In this part, the sensitivity of the models between vegetation indices and LAI were tested for the broadleaf forest, coniferous and mixed forests, respectively.Whatever the vegetation types is, the LAI value increased with NDVI and the values exceed in certain rang in LAI-NDVI 2D space (NDVI: 0.6-0.9; LAI: 0.5 m 2 m -2 -2.5 m 2 m -2 ) in Figure 8.We found that LAI model based on NDVI in exponential form is more sensitive than other models: broadleaf forest R 2 =0.601, coniferous R 2 =0.432 and mixed stands R 2 =0.667(Table 5). Compared with aforementioned results which didn't take into account the effect of vegetation type, the significance of the relationship increased slightly both in broadleaf and mixed stands and decreased a little in conifer stand. However, the accuracy of all models has reached a significant level.

Conclusions

The purpose of this paper is to retrieve forest LAI from multitemporal optical satellite images over mountain areas in Beijing. In-situ Forest LAI were measured using Hemispherical Photography. Three typical vegetation indices were employed, and the best index was selected through the correlative analysis method. The selected best LAI model is used to estimate forest LAI in this study. In the preprocessing of images, both terrain and atmospheric correction are conducted using DEM data. The major conclusions were drawn from this study as follows: the significance of the relationship between NDVI and LAI using all observations is better than others (R² =0.487). The retrieved LAI maps presented seasonal variability, forest LAI increased from April to August and decreased from August to October, which was similar to the trend of observed LAI. These situations proved that LAI model based on multitemporal LAI measurement could more suitable be used in the inversion of individual seasonal LAI. The sensitivity of the models between vegetation indices and LAI were tested for the broadleaf forest, coniferous and mixed forests, respectively. The results show that the accuracy was improved both in broadleaf and mixed forests if we take care of different vegetation types, except for conifer stands with little accuracy decreasing. However, the accuracy of all models has reached a significant level.

Fig. 1 . 2 .

 12 Fig.1. The location of tudy areas and in-situ Fig.2. The histogram of observed Measurements LAI with the seasonal variability

Fig. 3 . 4 . 5 .

 345 Fig. 3. Exponential model Fig.4. Estimated and Fig.5. The scatter of multitemporal Between NDVI and LAI observed LAI LAI and NDVI

Fig. 6 .Fig. 7 . 9 .

 679 Fig.6. Multitemporal Maps of retrieved forest LAI (A) Forest LAI map in April;(B) Forest LAI map in June;(C) Forest LAI map in August;(D) Forest LAI map in September;(E) Forest LAI map in October

  This work was supported in part by the State Key Basic Research and Develop ment Program (2011CB311806), the National Natural Science Foundation of Chi na (41071228, 41271345), the Beijing Municipal Natural Science Foundation (41 02021), the Beijing Municipal Talents Training Funded Project (2012D00202000 0007),the Special Funds for Technology innovation capacity building sponsored b y the Beijing Academy of Agriculture and Forestry Sciences (KJCX201104012), a nd the State Key Laboratory of Remote Sensing Science sponsored by the Institut e of Remote Sensing Applications of Chinese Academy of Sciences, through its o pen funds(OFSLRSS201109).

Table 3 .

 3 The accuracy of the relationship between LAI and vegetation index in different observed time

	Observed	NDVI			TGDVI		EVI
	Time	Lin.	Exp.	Log.	Lin.	Exp.	Log.	Lin.	Exp.	Log.
	20110414	0.062	0.047	0.049	0.195	0.05	0.166	0.272 *	0.133	0.223
	20110610	0.123	0.154 *	0.128	0.143	0.143	0.140	0.141	0.154 *	0.149 *
	20110811	0.001	0.002	0.001	0.001	0.002	0.001	0.003	0.002	0.002
	20110908	0.038	0.031	0.030	0.002	0.001	0.001	0.002	0.001	0.002
	20111024	0.001	0.003	0.001	0.022	0.021	0.031	0.049	0.043	0.037

Note: * a significant level in 0.05. Lin., Exp. and Log. indicate Liner, Exponent and Logarithm model.

Table 4 .

 4 The accuracy of the relationship between all LAI values and vegetation index

	Ve getation	Liner(R 2 )	Exponent(R 2 )	Logarithm(R 2 )
	Index
	NDVI	0.456**	0.487**	0.454**
	TGDVI	0.164**	0.102**	0.177**
	EVI	0.175**	0.130 **	0.176**

Note: ** a significant level in 0.001

Table 2 .

 2 Three selected vegetation indices

	Vegetation Index	VI formulate
	NDVI
	( Normalized Difference
	Vegetation Index)
	EVI
	(Enhanced Vegetation Index)
	TGDVI
	( Three Gradient Difference
	Vegetation Index )

Table 5 .

 5 The accuracy of the relationship between LAI and vegetation index in different vegetation types (R 2 )

	Vegetat-	Mixed forest		Broadleaf forest		Conifer forest
	ion index	Lin.	Exp.	Log.	Lin.	Exp.	Log.	Lin.	Exp.	Log.
	NDVI	0.578 **	0.667 **	0.585 **	0.579 **	0.601 **	0.566 **	0.402 **	0.432 **	0.418 **
	TGDVI	0.143 *	0.074 *	0.136 *	0.250 **	0.110 **	0.261 **	0.103 *	0.112 *	0.111 *
	EVI	0.122 *	0.076 *	0.106 *	0.300 **	0.193 **	0.303 **	0.128 *	0.141 *	0.145

*

Note: * a significant level in 0.05; * * a significant level in 0.001.

Acknowledgment


Liping Yang

Honghai Guo
email: honghaig@163.com

Xinhua Li

Shubo Wan

A Discussion on Spatial Distribution Differentiation Law of Peanut Quality in China Based on GIS Technology

Keywords: Peanut quality, Protein content, GIS technology, Spatial distribution

Abstract:In order to realize spatial differentiation law of peanut quality in China by quantitative analysis method based on GIS technology, a spatial analysis model for peanut quality was established based on 9 ecological factors influencing peanut quality (light, temperature, water and soil, etc). The results showed that it can be achieved in the analysis of peanut quality differentiation law in space with GIS technology. Taking peanut protein content as an example, the spatial distribution of peanut protein content gradually increases from north to south, high-content region distributes mainly in south of middle-lower Yangtze region; The results have instructive significance for production and breeding department to improve peanut protein quality, establish peanut special production base, optimize regional production layout, and provide a reference for quality rating and planting of other crops.

Introduction

Peanuts are major oil and food crops in the world, also major oil and cash crops in China. Since the 1990s, China's peanut production has ranked first around the world, accounting for about 1/3 of global production. Peanuts are widely cultivated all over China, except for Qinghai and Ningxia [START_REF] Yang | Present Status and Outlook of Peanut Production and Trade in China[END_REF][START_REF] Shubo | Peanut Quality Science[END_REF] .

The differences in peanut quality spatial distribution are formed under some ecological conditions, resulted from the combined effects of temperature, light, water, soil and other ecological factors which vary among regions, so geographical differences in peanut quality exist. Scholars at home and abroad mostly concern about species, farming systems and fertilizers related to peanut quality, however, there are contradictions such as regional imbalances, general purpose and special varieties, production and processing, so it is necessary to clarify the spatial distribution of peanut quality. As a result, Arcgis software is used to explore the spatial distribution of peanut quality based on ecological and geographical principles. In this paper, Peanut protein content, an important index of peanut quality, was used. A spatial analysis model for protein content is established based on 9 ecological factors influencing protein content, and spatial distribution of peanut protein content in China is analyzed by GIS technology. The results have instructive significance for production and breeding department to improve peanut protein quality, establish peanut special production base and optimize regional production layout.

2

Spatial analysis model for peanut protein content

Index system of spatial analysis model for peanut protein content

Academic research suggests that temperature is the dominant factor influencing protein content whose accumulation is significantly positively correlated with temperature; as temperature rises, protein content increases [START_REF] Zhen | The Effect of Climatic Factors on Protein and Oil Content of Peanut Seeds[END_REF] . During seedling stage, mild and moderate drought stresses can increase seed protein content, but severe drought conditions have little impact [START_REF] Yan | Effects of Drought during Seedling Stage on Physiological Traits, Yield and Quality of Different Peanut Cultivars[END_REF] ; during pegging stage, drought has a larger impact on peanut protein: drought increases fat content but decreases protein content [START_REF] Yao | Study on peanut early variety critical moisture in different growth stages[END_REF][START_REF] Yao | Study on appropriate water of Summer Peanut[END_REF] ; pod maturity, which determines peanut quality largely, is affected by accumulated temperature above 15℃ [START_REF] Shi | Preliminary study on meteorological condition of peanut quality[END_REF] ; shading processing can reduce peanut crude protein content [START_REF] Lin | Nutritive Quality and Morphological Development under Partial Shade of Some Forage Species with Agroforestry Potential[END_REF] ; under similar climatic factors, soil type, texture and fertility levels become important factors determining peanut quality [START_REF] Zhao | Study on the low between peanut and soil texture[END_REF][START_REF] Hu | Studies of the Effect Factors on Peanut QualitiesⅡ. Cultural Factors[END_REF][START_REF] Zhang | The Impact of Different Soils and Fertilizers on Peanut Quality Traits[END_REF] . The spatial analysis model for peanut protein content is established based on 9 ecological factors influencing protein content, in which the meteorological factor only covers the whole growth period.

Table 1. Index system of peanut protein content analysis model from the Institute of Soil Science, Chinese Academy of Sciences [START_REF] Shi | Cross-Reference System for Translating Between Genetic Soil Classification of China and Soil Taxonomy[END_REF][START_REF] Shi | Soil Database of 1:1,000,000 Digital Soil Survey and Reference System of the Chinese Genetic Soil Classification System[END_REF] . Use extraction command in Arcgis9.2 software to extract the value of the sampling points.

Analysis model

In spss13.0 software, Regression was used to establish the linear analytical model of peanut protein content, in which regression uses the enter method to include all factors into the equation. According to goodness-of-fit test of the model (Table 2) and ANOVA (Table 3), R=0.408, R 2 =0.166, sig=0.046, the model passes the significance

Spatial analysis for peanut protein content

Determination of analysis unit

The analysis unit is required to indicate the spatial difference of peanut protein content. Each unit shall have its unique characteristics, including both variability and comparability. In the principle of quality consistency, this paper uses 2KM×2KM raster as the unit, and to enhance accuracy, all data were masked by 1:1000000 countrywide farmland vector layer, so only the farmland part was analyzed.

Factor processing

Spatial distribution analysis for peanut quality

Analyze the spatial distribution of peanut protein content for countrywide farmland with the classification function of Arcgis and the result shows a significant spatial distribution trend of peanut protein content: the minimum value which appears in Jilin Province is 13.3%; the maximum value which appears in Taiwan Province is 32.9%; the average value is 26.9%. As shown in Fig. 2, the countrywide peanut protein content increases from south to north, with an average value of 28.3%, higher value mainly in south of middle-lower Yangtze region, and slightly lower value in Inner Mongolia, Ningxia, Shaanxi, Gansu and Xinjiang, which is consistent with the research conclusion drawn by Xiao Changzhen etc. that peanut protein content is inversely related to the latitude [START_REF] Xiao | Analysis on Main Biochemical Quality of Peanut in China[END_REF] .

Discussion

A spatial analysis model for peanut quality based on 9 ecological factors influencing The method of quantitatively analyzing spatial distribution of peanut quality with GIS technology discussed in this paper is scientific and feasible, but the accuracy of this model and the result needs to be further improved.

3 : 4 :

 34 Sunshine hours(the whole growth period of peanut) Sunshine hours accumulated during the whole growth period of peanut X Diurnal variation of temperature(the whole growth period of peanut) An average daily diurnal variation of temperature during the whole growth period of peanut Soil factors X 5 : Soil organic matter The data of sampling points of 2008 X 6 : Soil pH X 7 :Soil available phosphorus X 8 :Soil available potassium X 9 :Soil clay content National 2KM×2KM grid data 2.2 Data source of index system 140 sampling points (sampling points are as shown in Fig. 1) were set in major peanut production areas of China, the pods and corresponding soil samples of dominant peanut cultivars were collected, and then pod protein content, soil organic matter, available phosphorus, available potassium, PH, and other indexes were determined in accordance with national standards. The soil clay content is 2KM×2KM raster data

Fig. 1 .

 1 Fig. 1. Sampling points distribution map of 2008

  protein content was established to analyze the spatial distribution of peanut quality in China (farmland), which breaks the old way of analyzing the relationship between peanut quality and environment on field scale and qualitatively describing spatial distribution of peanut quality.

Fig. 2

 2 Fig. 2 Spatial distribution map of peanut protein content in China (cultivated land)

Table 2 .

 2 Summary table of peanut protein content analysis model

	Model	R	R Square		Adjusted R Square	Std. Error of the Estimate
	1	0.408	0.166		0.084	2.311
		Table3. ANOVA table of peanut protein content analysis model
	Model		Sum of Squares	df	Mean Square	F	Sig.
		Regression	97.051	9	10.783	2.02	0.046
	1	Residual	485.862	91	5.339
		Total	582.912	100
	Linear regression model of peanut protein content is as shown below:
	Y=32.432+0.0001X 1 +0.00014X 2 -0.00104X 3 -0.30594X 4 -0.04503X 5 -0.33025X 6 -0.027
	15X 7 -0.00351X 8 +0.01884X 9			(1)

Peanut Quality Spatial Distribution Analysis (based on protein content).Upon the

  Soil indexes are 2KM×2KM raster data from Institute of Soil Science of Chinese Academy of Sciences.Raster Calculator command of Spatial Analyst of Arcgis9.2, apply the model to carry out raster calculation, gain the value of each unit, which is protein content data for 2KM×2KM raster of countrywide farmland, thus the spatial distribution map for countrywide farmland is gained.

	Meteorological factor processing. The meteorological indexes are processed based
	on 30-year log data of peanut growth period during 1978-2007 (from 756 stations in
	China, provided by China Meteorological Data Sharing Service System). The
	2KM×2KM raster data are acquired through Kriging interpolation calculation of the
	756 stations' meteorological indexes under the Geostatistical Analyst module of
	Arcgis9.2.
	Meteorological and soil factors are both masked by 1:1000000 countrywide
	farmland vector layer to obtain the farmland raster data.

Soil factor processing.


Weidong Zhuang

Chun Wang

Xi Wang

Research and Implementation of Measurement Data Wavelet De-noising and 3D Visualization of Farmland

Keywords: Precision agriculture, farmland, Wavelet de-noising, 3D visualization

The precision agriculture decision-making needs of geographic information visualization analysis for farmland. In order to get 3D visualization of farmland, the Matlab software and wavelet transform were used for 3Dterrain survey data processing. Haar, DB6, Sym8, coif3 wavelet were used on the 3 layer decomposition of elevation data de-noising test, DB6 wavelet was used on the two to seven layer decomposition de-noising. The measurement plane scatter plots, contour map, 3D surface chart of the farmland was drew. The research shows that using a db6 wavelet five level decomposition denoising for elevation data measured by DGPS continuously was suitable for the 3D-terrain farmland surveying and mapping in which farmland have the more moderate wavy. This may well reflect the farmland terrain features.

Introduction

The goal of precision agriculture (PA) is the management of crop and soil variability to increase profitability and reduce environmental impact. The PA decision-making needs of geographic information visualization analysis for farmland. The measurement of elevation data for sloping farmland's 3D topography uses real-time differential GPS system in order to reduce the cost of measuring equipment and make it convenient and easy. The positioned GPS observation is a set of sequence related to time and the discretization of time functions. GPS measurement error contains the ionosphere delay, troposphere delay, multi-path effect, receiver software, hardware and so on. The difference technique can eliminate most of the system error except for random measurement error that belongs to the high-frequency measurement noise. If the high frequency measurement noise was removed, it can reduce the disturbance of positioning precision, which is caused by measurement noise, as a result of improving the accuracy and reliability of the GPS.

The real-time differential GPS system was used to gather 3D topography information for farmland in the field of No. 4-3-1-4, Sinograin northern agriculture development Co, LTD. Nenjiang County, Heilongjiang Province, China on April 27th, 2011. The farmland is the typical sloping field and its square is 61.4hm 2 . The measuring speed is between 20 to 30 kilometers per hour. It is equipped with AgGPS332 receiver that can receive the signal of Satellite Based Augmentation Systems (SBAS). The GPS position data was recorded by computer once per second, 1302 points were collected. The thesis researches the measurement data processing and visualization of farmland's 3D topography.

Measurement data wavelet de-noising

Wavelet is the wave of small area, limited length and zero mean value. Wavelet transformation is the space frequency of localization analysis. It takes signal to Multiscale Analysis through the idea of dilation and translation in order to achieve the applied time in both high and low frequency and get the analysis in both time and frequency domain. It also can be focused to the signal in any detail, thus extracting information effectively. It is called "Math microscope". At present the analysis of wavelet is widely used in Mathematics study, signal analysis, image processing, classification and recognition, synthetic music and language, medical imaging and diagnosis and so on.

Principle of wavelet noise elimination

It usually contains non-use signal gathering Data signals from the computer, which is called noise signal. The signal noise elimination prevents non-use part of signal Elimination and reserves useful part of signal enhancement. A d signal model that contains noise shows in this form:

s(k)=f(k)+σ• e(k) k=0,1,…,n-1 (1)
In the formula, f(k) is useful signal, e(k)is noise, s(k) is signal including noise. In fact, f(k) is usually low frequency or some stable signal, e(k) is usually high frequency. So there are three procedures to deal with noise elimination for a d signal.

(1): It resolve Original signal into wavelet so that the noise usually belongs to high frequency coefficient. (2): It takes quantization process for high frequency coefficient of Wavelet decomposition in some form, such as threshold. (3): It takes reconstruction of a d wavelet for the bottom of low frequency coefficient and high frequency coefficients of every layer of Wavelet decomposition in order to eliminate noise.

Choice of wavelet basis function

The process of reducing the noise of signal is a operation that the signal is decomposed and restructured. We should search for a set of functional form that stands for the features of the signal. Wavelet function is an infinite number as well as wavelet basis function. That is because different wavelet basis function has different characteristics of the time frequency. And different wavelet basis function may come out different results when using the same method to analysis problem. We choose the proper wavelet basis according to the characteristics and application of wavelet function in practice.

Choosing wavelet basis we should consider its Mathematics characteristics including support, symmetry, orthogonality, regularity, vanishing moments and wavelet itself. There are Common wavelet basis in the Matlab toolkit including haar,dbN,symN,CoifN.

The premises of GPS data processing of changing wavelet are discrete values observed quantity. So wavelet basis function has the characteristics of Discrete wavelet transformation and orthogonality, symmetry. In order to position GPS data processing fast and precisely we could choose wavelet basis function, such as Haar、 dbN、symN、CoifN.

Wavelet De-noising

In order to study the effect of GPS data noise elimination of changing wavelet, wavelet de-noising is analyzed.

Using elevation data to the variable z1 with assignment, then Haar, DB6, Sym8, coif3 wavelet were used on the three layer decomposition. In addition, choosing threshold model of sure and scale noise to eliminate noise. The original elevation curve data and wavelet de-noising curve of elevation show in Fig. 1.

Fig. 1. Wavelet de-noising curve of elevation

It can be seen from the Fig. 1 that the wavelet including haar、db6、sym8、coif3 has an effect on noise elimination and filter out outliers. Wavelet Db6 and sym8 for noise elimination is more smooth, so is the effect.

In order to study the GPS elevation curve data de-noising effect of the layering wavelet, using elevation data to the variable z1 with assignment, then the wavelet db6 is used from the two layers to seven layers for de-noising. The original elevation data and the wavelet db6 de-noising curve of elevation show in Fig. 2.

Fig. 2. The db6 wavelet de-noising using multilayer level for elevation

It can be seen from the Fig. 2 that the more layers and more smooth after wavelet de-noising. However, with the increasing of layers, the more detail data lost. For example, the seven layers wavelet de-noising is too much smooth. The five layers wavelet de-noising reserves the trend of changing data, and the curve is relatively smooth.

Because slop farmland generally have more moderate undulating and field 3D topographic survey is continuous sampling, the five layers db6 wavelet de-noising can be used for farmland elevation data.

Drawing farmland's 3D topography

The latitude and longitude coordinates of the GPS positioning acquisition of data points in the program using the UTM projection, the transformation to the plane rectangular coordinates.

In this study, using Matlab as data processing and drawing tools. First, projection coordinates and elevation values of the collected data points were assigned to three variables to x, y, and z. In the operation, export Access data to Excel, then to Matlab. In another way, assign the variable x、y、z with Matlab, then copy and paste the corresponding data series to the variable value table of Matlab. Compared with the former, the latter is that a large amount of column values fast assign the variable value of Matlab.

Plane scatter plots

In order to show each sampling point location, need to draw a plane scatter plot of the sampling points. Draw blue scatter diagram, Matlab command sentences are as follows.

plot(x,y,'*b'); xlabel('x coordinate'); ylabel('y coordinate');

Plane scatter diagram that is made up of 1302 data points shows in Fig. 3.

Fig.3. The sampling points scatter diagram

Data interpolation

Sampling points is an irregular distribution practically in farmland. In order to generate isoline and isosurface with Matlab, we should conduct data interpolation and gridding. Matlab command sentences of data interpolation are as follows.

X=linspace(min(x),max(x),100); Y=linspace(min(y),max(y),100);

Linspace is one of the instructions that are used to produce Line vector between two points. Take 100 points from minimum to maximum in the experiments. Matlab command sentences of data gridding are as follows.

[X,Y]=meshgrid(X,Y); Z=griddata(x,y,z,X,Y,'linear');

The effect of meshigrid(X,Y)is that conducting a matrix for line vector X and column vector Y, generating a cartesian product space and drawing the frame through two points on the axis.

We use designated algorithm. There are four kinds. (1) 'linear': Base on the linear

(2)'cubic': triangle-based cubic interpolation;

(3)'nearest': the nearest interpolation;

(4)'v4':Lattice point spline function interpolation algorithm. Through gathering 3D topography information for farmland, the curve that is made up of 'v4' interpolation is more smooth. But the interpolation of the four corners will be larger deviation, the curve that is made up of 'linear' interpolation is more ideal.

Drawing contour map

Drawing contour map is an important branch of visualization technology, we can achieve contour map fast with Matlab. The contour map shows in Fig. 4. surf(X,Y,Z); surf(X,Y,Z5);

Three-dimensional topographic surface map and three-dimensional topographic surface map of elevation de-noising show in Fig. 6 and Fig. 7. It can be seen from the Fig. 6 and Fig. 7 that three-dimensional topographic surface map of elevation de-noising is better than before. This method was suitable for the sloping farmland, which has the more moderate wavy. The wavelet db6 is used on the fifth layer for de-noising by farmland's 3D topography, and then draw 3-D surface chart. It has a good effect on drawing Geomorphology.

Conclusions

It takes the measures of wavelet transformation to process the data of farmland's 3D topography with Matlab. Haar, DB6, Sym8, coif3 wavelet were used on the 3 layer decomposition of elevation data de-noising test, DB6 wavelet was used on the 2 ~ 7 layer decomposition de-noising. The research shows that the wavelet db6 was used on the 5 layer decomposition for de-noising, which could achieve elevation data of farmland.

The precision agriculture decision-making is widely needed in geographic information visualization analysis for farmland. Matlab has the function of Data calculation and drawing. By the technology, plane scatter plots, contour map, 3-D surface map of the farmland are drawn.

The research shows that using the wavelet db6 5 level decomposition de-noising for elevation data measured by DGPS continuously was suitable for the 3D-terrain farmland surveying and mapping in which farmland have the more moderate wavy. This may well reflect the farmland 3D topography features.

Fig. 4 .

 4 Fig.4. The contour map of elevation DB6 wavelet was used on the fifth layer decomposition de-noising. Then make an interpolation. The contour map of elevation denoised shows in Fig.5. Matlab command sentences are as follows.contour(X,Y,Z); colorbar; z5=wden(z,'heursure','s','one',5,'db6');

Fig. 5 .

 5 Fig.5. The contour map of elevation denoisingIt can be seen from the Fig.4and Fig.5that the contour map of elevation data denoising is better than before.

  surface chart can be fast achieved with Matlab. Matlab command sentences of the3-D surface chart, which is made up of interpolation, are as follows.

Fig. 6 .Fig. 7 .

 67 Fig.6. Three-dimensional topographic surface map

Acknowledgments

We wish to thank twelve five years science and technology plan for rural area project No.2011BAD20B03, No.2011BAD20B06 and No.2012BAD04B01, China, for funding this research. We also wish to thank the anonymous reviewers for their useful suggestions that greatly improved this paper.