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A-Xing Zhu

Dick J Brus

Murray Lark

Jing Liu

The Newsletter of the Pedometrics Commission of the IUSS

Alex reported that over 3000 people attended the 31th Brazilian Congress of Soil Science (actually more than 3500, see the exciting news about this at the IUSS website). Recently (Aug. 18 and 19), I attended the annual conference hosted by the soil remote sensing and soil geography specialty groups of the Chinese Society of Soil Sciences. The number of attendants at this conference reached its record high, from the usual 100 some to over 250. It seems to me that soil science is experiencing a growth which is great and exciting. The reasons for this exciting growth are many but the introduction of digital technology into soil science, I thought to myself, has brought new life into soil science, thus this got to be one of the most important, if not the most important, reason. In this regard, we, pedometricians, should give ourselves a pat on the back, saying "Hmmm, here is good one after all these efforts!" Pedometron No. 33, August

Great but the question "how do we, the Pedometrics Commission, can benefit from and continue to foster this growth?" I would like to take this opportunity to share some thoughts. Certainly, this is to start the discussion. In a Chinese saying, this is "抛砖引玉", "Sending the bricks to lure the jades). In other words, using my primitive ideas (bricks) to get your precious advice and polished ideas (the jades). I am writing this for two purposes. The first is to offer an introduction to the new comers about the Pedometrics Commission and to ask our existing pedometricians to help to distribute our welcome to anyone who is interested in pedometrics, and tell them how vibrant this group is and to capture the growth and to foster the growth.

Here is the introduction. Pedometrics, as an organization, is a commission of Division One (Soil in Space and Time) in the International Union of Soil Sciences (IUSS). As a field, based on the definition in Wikipedia Pedometrics is the application of mathematical and statistical methods for the study of the distribution and genesis of soils. It might need to be revised to include modern spatial information gathering and processing techniques (such as global positioning systems, remote sensing, and geographic information systems, now collectively referred to as Geographic Information Science or GIScience) because this (GIScience) becomes increasingly important in pedometrics.

The Commission is one of the most vibrant, if not the most vibrant group in IUSS. In addition to itself, it also houses three working groups directed at emerging areas of pedometrics: Digital Soil Mapping Working Group, Proximal Soil Sensing Working Group, and Soil Monitoring Working Group. The Commission hosts three well attended global scale conferences:

Pedometrics Conference: held by the commission itself every two years, focusing on all aspects of pedometrics;

Global workshop on digital soil mapping: held by the Digital Soil Mapping Working Group every two years (offset the Pedometrics conference by a year), focusing on the techniques and issues for digital soil mapping;

Global Workshop on Proximal Soil Sensing: held by

Issue 33, August 2013

the Proximal Soil Sensing Working Group, focusing on proximal soil sensing.

The Commission grants two awards: the Richard Webster Medal, in honor of Dr. Richard Webster, the founder of pedometrics. The Medal is to honor outstanding researchers in pedometrics. The Award is given every four years (at the World Congress of Soil Sciences). Details of this award can be found at the IUSS website. The other award is the best paper award which is to honor the best paper in pedometrics every year. The award is given at the Pedometrics Conference.

The Commission also offers three communication platforms: the website (www.pedometrics.org), this newsletter, and email list (the pedometrics google group). The website contains a rich set of materials

From the Chair ranging from job ads to archived articles. The newsletters is a place for people to share stories, reports, research thoughts and comments on issues. In this issue we have three of these for you to enjoy.

How to be part of it? Easy, just sign up at the pedometrics googlegroup and come to the conferences, participate in elections and in the award activities, and even help to organize the conferences. For those of you who cannot access googlegroup from where you are, send us (Dick Brus dick.brus@wur.nl and A-Xing Zhu azhu@wisc.edu) an email. We will sign you up.

So, come and be part of this exciting group! Best wishes, A-Xing Zhu

Pedometron No. 33, August 2013 2

Pedometrics goes to the Tropics

By Leigh Winowiecki International Center for Tropical Agriculture (CIAT)

Pedometrics Conference 2013

As you are all aware, the Pedometrics conference 2013 will be co-hosted by the International Center for Tropical Agriculture (CIAT) and the World Agroforestry Centre (ICRAF) in Nairobi, Kenya (https://sites.google.com/a/cgxchange.org/pedometrics 2013/home). This conference will include a two-day pre-conference workshop on methods for digital soil mapping, a tour of the ICRAF soil and plant spectroscopy laboratory, three full days of conference presentations, a poster session, keynote presentations by the East African Soil Science Society and lead scientists in soil mapping, and a field trip to one of the most spectacular rangeland areas in north-central Kenya. Both CIAT and ICRAF have been working in Kenya for over 25 years and this conference will bring together international scientists and local Kenyan University students to discuss key advances in pedometrics. The conference organizers are happy to announce that five student scholarships were awarded to African students and a reduced conference fee is available for students.

The Soils of Kenya

Kenya is an incredibly diverse country, both ecologically and culturally, with an area of approximately 582,600 km 2 and a population of about 30 million (from 2000 census data). Just under 70% of its population live in rural areas.

Continental maps produced by ISRIC and JRC (Figure . 1) and country-level maps produced by the Kenya Soil Survey (Figure . 2) indicate that most of the WRB Reference Groups exist in Kenya. This diversity of soils is due to the geographically and climatically diverse regions of Kenya which include the humid regions (> 1000 MAP), sub-humid regions (< 1000 MAP) and semi-arid regions (450-900 MAP), as well as arid to very arid regions (150-500 MAP), combined with a high geologic diversity. The most impressive ecological features of Kenya include the Great Rift Valley, which extends 6,000 km from northern Syria to Mozambique, and Africa's second highest peak, Mt. Kenya (5,199 m). Escarpments to the east and west border Kenya's rift valley, and the floor contains volcanoes, some still active, as well as a series of sodic lakes that are important breeding grounds for great white pelicans and feeding areas for lesser flamingos. The soils of the Rift Valley have volcanic parent material, and most are classified as Andosols, with high P-sorption, high aluminum saturation and also high fluoride content.

The Lake Victoria basin, which covers an area of 1988). While soils of the Kano plains within the basin are generally Luvisols, Vertisols, Planosols, Cambisols, and Solonetz (FAO- UNESCO, 1988). The Lake Victoria basin has been plagued by high soil erosion rates, increasing land degradation, which are contributing to both decreased agricultural productivity in the region and diminishing water quality of Lake Victoria (World Agroforestry Centre, 2006). When viewed from the air the gilgais can appear in characteristic patterns, as in Figure 2, with each roughly circular and separated from its neighbors by similar distances. One can imagine that their distributions represent distortions of hexagonal close packing, and that prompts one to ask whether there is some regularity in the repetition, and if there is then what its characteristic wavelength might be. But what of the soil itself? The soil in the gilgais is typically wetter than that on the intervening plain. It can differ in other respects, too; in clay content, depth to carbonate or gypsum, and salinity, for example. It occurred to me that these properties might also repeat in a periodic way. So I pursued the idea by sampling I recorded the height of the soil surface above a local datum and the morphology, pH and electrical conductivity of the soil down to 1 m on cores at frequent intervals. All of the properties showed strong spatial dependence at that scale. More interesting, however, was that the correlograms of several fluctuated in an apparently periodic manner and that their power spectra, obtained as transforms of the correlograms, had peaks at a frequency corresponding approximately to the distances between the centers of neighboring gilgais, about 34 m -see Webster (1977). It seemed that there was indeed a degree of regularity in the spatial pattern. Margaret Oliver and I (Webster and Oliver, 2007) had already come to that view after modelling the variogram; after all, for a second-order stationary process the variogram and the power spectrum contain the same information.

Pedometrics goes to the Tropic

Electrical conductivity on the Bland Plain

Here are the results of analysis of the electrical conductivity (EC) measured in the 30~40-cm layer of the soil at 4-m intervals and converted to their common logarithms. The general equation for the transformation of the experimental correlogram 𝜌 (𝑘) to its power spectrum is:

𝑔 𝑓 = 1 + 2 𝜌 𝑘 𝜔 𝑘 cos (2𝜋𝑓𝑘) 𝐾 𝑘=1
Spatial analysis of gilgai patterns sufficient data. An alternative was to process the images optically, a technique then being explored by Preston and Davis (1976) for analyzing photomicrographs of sedimentary rocks.

Optical Fourier transformation

The principle of the method is based on the interference of light waves refracted through a lens and projected on to a screen, which is the physical analogue of the Fourier transformation.

Let us suppose that a gilgai pattern has a characteristic wavelength, which seems reasonable given the photographs reproduced in Figure 2. Let us denote this wavelength as ω. When transformed through the optical system a ring of light should appear in the plane of the Fourier transform at a distance d from the center given by

𝑑 = 𝜔 λ𝑓 2𝜋 , ( 2
)
where λ is the wavelength of light and f is the focal length of the lens.

I made transparencies of several aerial photographs, including ones of the scenes in Figures 2. I put them on a light bench, shone a coherent monochromatic beam of light from a laser through them and converted the images to their Fourier transforms in this way. The results were in all instances the same. The transforms were dominated by bright centers away from which the light gradually diminished towards the peripheries; there were no evident subsidiary peaks to indicate periodicity. They were disappointing.

Modern digitizing equipment, hugely increased computing power and new mathematical developments such as wavelet analysis enable us to revisit the question. Alice Milne and Murray Lark and for frequency 𝑓 in the range 0 to 1/2 cycle and lags from 1 to a maximum of K. The quantity 𝜔 𝑘 is a weight that depends on the limit K and on the shape of the window within which the transform is computed. For present purposes I have set K = 60 and used the Parzen window to minimize the leakage. In addition to my measuring the height of the land, I recorded at each sampling point the nature of the land surface as 'plain', 'depression', i.e. gilgai, or 'puff'. [START_REF] Lark | Spatial analysis of categorical soil variables with the wavelet transform[END_REF] analyzed and modelled these records, and he obtained periodic empirical variograms of both plain and depressions with wavelengths of 8 to 9 sampling intervals (32~36 m). The patterns are two-dimensional, of course, and the results of the one-dimensional analysis led to the question: is the two-dimensional arrangement of the gilgais regular?

Measuring properties of the soil, such as its electrical conductivity, at enough sites for a two-dimensional spectral analysis was prohibitively expensive. An alternative was to analyze the photographic images.

At the time, the 1970s, however, the available digitizing equipment proved too unreliable to furnish ,

I took advantage of the opportunity. We described in detail our search for an answer in the Australian Journal of Soil Research (Milne et al., 2010), and here I mention the highlights.

Digital analysis of aerial photographs

The photographs showing gilgai patterns that we analyzed were taken of the Bland Plain by the New South Wales Department of Lands and Surveys in the late 1960s. One is of Caragabal station, which I sampled originally, the other is several km to the west at Back Creek. We digitized electronically a rectangular patch corresponding to about 25 ha on the ground of each photograph at a resolution of 157 pixels per cm (≈ 1.3 m diameter of a pixel) and recorded the grey level in the range 0 to 256. The results are as shown in Figure 2.

Correlograms and spectral analysis

We first computed variograms and correlograms from the pixel data for individual rows and columns in the images by the method of moments. Figure 5 shows examples, one from Caragabal and one from Back Creek. Notice how both fluctuate in an apparently periodic way. Their spectra, Figure 6, contain strong peaks corresponding to the lengths of the periods in the correlograms.

uncorrelated information in the images, just as the optical transforms did. Both, however, have subsidiary rings surrounding the bases of the peaks at distances from 0.025 to 0.04 cycles per pixel. These frequencies correspond to wavelengths on the ground We also computed the two-dimensional variograms on grids. Again, the autocorrelations are obtained as the complements of the semivariances.

Spatial analysis of gilgai patterns

Figures 7 and8 show the two-dimensional correlograms. Notice how both correlograms decay from their central peaks but with pronounced waves on them.

From these correlograms we computed the twodimensional spectra. The spectra appear in Figures 9 and 10. Both have peaks at their origins arising from of 52 to 32 m. So, we found periodicity in the twodimensional spectra confirming our visual impression from the photographs with wavelengths that accord.

Wavelet analysis

My colleagues and I took the analysis a stage further with wavelets. As you may know, spectral analysis loses all information on the positions of the features in images. The correlogram and spectrum average the statistics over the whole field of data. Wavelet analysis, in contrast, retains local information by decomposing data into separate components of both frequency (called 'scale' in the jargon) and location as locally compact wavelets move over the data. In this way it can reveal where characteristics of an image or series of data change. For this reason it requires no assumption of stationarity.

There is a great deal to wavelet analysis, just as there is to geostatistics, and we cannot describe here all aspects of wavelets. We can at best refer you to some of our papers [START_REF] Lark | Analysis and elucidation of soil variation using wavelets[END_REF], 2001[START_REF] Lark | Analysing soil variation in two dimensions with the discrete wavelet transform[END_REF]Milne et al., 2010; and the excellent book by Percival and Walden, 2000).

Basically, we have a set of wavelet functions

𝛹 λ,𝑥 = 1 λ 𝛹 𝑢-𝑥 𝜆 , λ>0 (3)
in which 𝛹(𝑥)is a 'mother wavelet' centerd at position x, λ is a scale parameter that controls the width over which the wavelet takes non-zero values, and u represents a displacement from x. By convolving the wavelet with the data we obtain wavelet coefficients:

𝑊(λ, 𝑥) = 𝑧(𝑢) 1 λ 𝛹 𝑢-𝑥 λ d𝑢 ∞ -∞ . ( 4
)
Varying x moves the wavelet over the space to provide coefficients at each position. By changing λ we change the scale at which we view the variation.

The smaller is the λ the finer is the scale at which we describe the variation about x. Increasing λ dilates the wavelet and coarsens the scale.

In the discrete wavelet transform λ is incremented in a series of powers of 2, thus: λ = 2 j ; j = 1, 2, … to some maximum usually set by the extent of the data. The result of the convolution at each value of j produces a smooth representation and a detailed component of the data, and by changing j we obtain a multi-resolution analysis.

Figures 11 and show our results for Caragabal and Back Creek respectively.

Wavelets also have variances attached to them, and for present purposes we have calculated them by the maximal overlap discrete wavelet transform (MODWT) of Percival and Guttorp (1994). They are given by

, ( 5
)
where n j is the number of data involved in the computation, and is the MODWT coefficient at position x for the j th scale.

We calculated the variances in three directions, namely along the rows, down the columns and across the diagonals, and in Figure 13 we plot them against scale. The graphs are similar for the two images. We note first that the maximum variances along the rows and down the columns are similar and larger than those on the diagonals. These show that the variation is isotropic. More importantly, perhaps, both have pronounced peaks at the 16~32 pixel scale, and these accord with the visibly largest variation in the detail components at that scale in Figures 11 and13. Further, this scale of maximal variation corresponds with the wavelengths estimated in the correlation analysis and peaks in the spectra. So, we can conclude that the gilgai patterns that appear in plan to be periodic and isotropic are indeed regular and isotropic with wavelengths that we can estimate with confidence. The paper by Jenny et al. (1968) was the first chapter in this book, available at http://tinyurl.com/begpa2h (Jenny's appendix paper on "The image of soil in landscape art, Old and New" in the same book is better-known than this paper). In the study, Jenny collected 97 soil samples across a moisture transect in the Sierra Nevada, California where the variation in the factors of soil formation were to some degree controlled. The mean annual precipitation (MAP) is between 80 and 2000 mm, and mean annual temperature (MAT) between 10 and 16℃. The flora was restricted to pine tree and grass. The aspect is always southeast, with slopes varying from 0 to 30%. The parent materials are acidic and basic igneous rocks. Jenny used this data to quantitatively fit "an integrated clorpt model" where all factors were simultaneously modelled in the form of a multivariate linear regression:

Spatial analysis of gilgai patterns

s = a + k 1 MAP + k 2 MAT + k 3 Parent Material + k 4 slope + k 5 Flora + k 6 Latitude
In addition, Jenny also realised there would be correlation among the independent factors: "When the independent variables X 1 , X 2 , X 3 , … are highly self-correlated (collinear) the slope coefficients b become unstable, even meaningless as to sign.

Regressing for example N against Precipitation (P) (in.) and Temperature (T) (℉) gives 𝑁 = 0.350 + 0.0012𝑃-0.0055𝑇

with R 2 = 0.324. Introducing Leaching value (Li, in.), which is highly collinear with P, results in 𝑁 = 0.375 + 0.0037𝑃 -0.0062𝑇-0.0029𝐿𝑖

The slope coefficient of P has tripled and that of Li is negative, which is absurd from the viewpoint of soil leaching. R 2 remains essentially unchanged as 0.327. 1901 andHotelling (1933). However it was not until the 1960s with the availability of computers that the analysis became practical. Earlier papers on soil can be found on mostly on factor analysis (rotated principal components or principal factors that are not necessarily orthogonal). The thrust for factor analysis was largely from the social sciences (Psychometrics) rather than the physical ones. Rayner's 1966 paper was on principal coordinate analysis which involves finding the principal components corresponding to similarity matrices. This analysis was invented by John Gower at Rothamsted principally to help James Rayner with the soil similarity problem, however as it turns out it is virtually the same as multidimensional scaling which was invented in the 1950's by the psychologists. The first use of multivariate statistical methods for soil (data) that we know of is [START_REF] Cox | Use of a discriminant function for differentiating soils with different azotobacter populations[END_REF]

Pygmy Forest to Random Forests

Research in digital soil mapping now has moved from carefully controlled environmental factors to "realworld" soil data, either collected from stratified random sampling or using legacy soil data. Models of the Jenny et al. ( 1968) type are still being developed (Gray et al., 2012), while others prefer to use datamining techniques. Data-mining models are usually treated as a black-box as they are complex and cannot be easily or explicitly written out. However, the results can be expressed as significant predictors or variables of importance and usually interpreted as 'knowledge discovery' from databases which are then sometimes justified a posteriori by principles of soil genesis. As opposed to a process-based model, where the process needs to be specified first, the data-mining approach is said to "learn" the process through the data. As an example, the Random Forests technique has been used a lot in digital soil mapping as it is freely available and it has been claimed that "Random forests does not overfit. You can run as many trees as you want" (From the Random Forests Manual by Breiman and Cutler). In addition, the author also made claims that it is: "The most accurate current prediction", "a complex predictor can yield a wealth of 'interpretable' scientific information about the prediction mechanism and the data."

An example of the use of Random Forests is given in Figure 2, which shows the prediction for some surface soil carbon data in the Hunter Valley, NSW, Australia, where the fit is excellent on the training data (using 100 trees), R 2 = 0.94. The variable of importance indicated that in addition to indices calculated from Landsat bands, terrain attributes of MrVBF (Multiresolution Valley Bottom Flatness) and TWI (topographic wetness index) play important roles. The map confirmed this and it is in accordance with our pedological knowledge, where carbon concentration is expected to be larger in areas with higher moisture and areas of deposition (knowledge discovery).

Jenny, PCA and Random Forests

But wait a minute, Figure 3, shows the fit on an internal validation (out of bag estimates) and an independent validation data, where there is no fit at all. The soil carbon data has very little correlation with any of the terrain attributes and is very weakly correlated with some Landsat imagery. It is obvious that Random Forests can easily overfit the data.

Overfitting implies the model describes the noise in the data (perfect fit on the training data), while has poor predictive capability in the validation data. (The data and R code are available to download from here, and you can experiment yourself with the notion that RF can fit anything). It is quite interesting that scientists take the statement "Random Forests does not overfit" as the truth, and repeatedly quote this in many papers without any question.

A recent news article mentioned the latest breakthrough in technology: "With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally getting smart." Perhaps we should tell you that we need to explore Deep Learning tools for pedometrics. But we think now we need to remind ourselves that explicit linear models should be at least considered as a starting point for exploratory data analysis before trying the fancy tools. There is no magic algorithm that can fit everythingyet not overfit.

Figure 2. The prediction of soil carbon content in the Hunter Valley using random forests, its predicted map, and variable of importance (for prediction). Plotting R 2 and RPD, we can see the exact relationship. For a normally distributed variable, and large sample size, RPD = (1-R 2 ) -0.5 .

Another great myth in pedometrics 1 is the use of RPD as a measure of the goodness of fit. RPD, Ratio of Performance to Deviation, is the ratio of the standard error in prediction to the standard deviation of the samples, which is frequently used in (soil) NIR literature, and now in digital soil mapping for assessing the usefulness or goodness-of-fit of calibration models. It attempts to scale the error in prediction with the standard deviation of the property.

If the error in estimation is large compared with the standard deviation, then the model is not performing well.

RPD was initially used by [START_REF] Williams | Variables affecting near-infrared reflectance spectroscopic analysis[END_REF] for assessing the goodness of fit for NIR calibration (in agricultural and food products). Batten (1998) wrote "Williams (pers. comm.) suggested that RPD values greater than 3 are useful for screening, values greater than 5 can be used for quality control, and values greater than 8 for any application."

Then a paper by [START_REF] Chang | Near-infrared reflectance spectroscopy -Principal components regression analyses of soil properties[END_REF] used RPD for assessing the ability of NIR spectra to predict soil properties. In that paper the authors made 3 arbitrary categories: Category A: RDP > 2.0, Category B: RDP 1.4-2.0 and Category C: RDP< 1.4. This somehow was interpreted by other authors as the 'standard' classification and referred excellent models when RPD >2; fair models when 1.4 < RPD < 2; and nonreliable models when RPD <1.4. Since then some other authors also have slightly modified this to make a new criterion for general quality of soil prediction.

There is no statistical or utilitarian basis as to how these thresholds were determined. And people have tend to use these RPD classes to designate their models as 'excellent' (RPD > 2 becomes the golden standard) with no further questions asked. Veronique Bellon Maurel et al. ( 2010) questioned the use of RPD and pointed that the normalization in RPD only works assuming normally distributed values. For different soil properties, due to the difference in their statistical distributions, the std. deviation will not have the same interpretation in terms of the range of values. It does not correctly represent the spread of the population. They recommended the use of RPIQ (ratio of performance to interquartile range/ IQ = Q75 -Q25).

It is a better index than RPD, based on quartiles, which better represents the spread of the population.

Looking again to the definition of RPD, we can see 1 One other myth is that random forests never overfit.

   n i i i f y n 1 2 ) ( 1     n i i y y n 1 2 ) ( 1 1    n i i i f y 1 2 ) (    n i i y y 1 2 ) (
So we might as well say that if R 2 >0.75 (equal to RPD >2) the model fits (predicts) quite well and if R 2 < 0.5 (equal to RPD < 1.4) it doesn't fit so well, rather than using the RPD classification.

Pedometricians, please 1) do not quote both RPD and R 2 , they are the same measure,

2) do not use the classification of RPD to justify that your models are excellent or poor, it is no different than using R 2 and there is no basis for this classification. It is all relative!

3) The important measure is how uncertain is the prediction, or what is the prediction interval. This is rarely calculated.

Workshop1 (Aug. 26):

Title: Basic Geostatistics with R Instructor: Gerard Heuvelink (ISRIC World Soil Information)

Brief Description:

This workshop reviews basic geostatistical methods and shows how these methods are implemented in the R language for statistical computing. It requires no prior knowledge about geostatistics but assumes that participants are familiar with basic statistical concepts such as probability distribution, mean and variance, correlation and linear regression.

The main topics addressed are: relationship between spatial variation and semivariogram, semivariogram estimation from point observations, ordinary kriging and regression kriging. The first part of the workshop is a lecture that explains the theory and illustrates it with real-world examples. The second part is a computer practical in which participants execute all steps of a basic geostatistical analysis in R using a soil pollution dataset from the Netherlands.

Contents:

• Lecture: basic statistics and geostatistics, including variogram estimation and ordinary and regression kriging • Computer practical: introduction to R, basic statistics and geostatistics with R Workshop2 (Aug. 27):

Title: Spatial prediction of soil variables using 3D regression-kriging -GSIF and plotKML packages for R

Instructor: Tom Hengl (ISRIC World Soil Information)

Brief Description:

This workshop continues with more advanced geostatistical methods that can be implemented in the R environment for statistical and geographical computing, namely 3D regression-kriging (soil properties) and multinomial logistic regression (soil classes). The focus of this workshop is on using R packages for daily work, i.e. operational soil mapping. The lecturer will use two packages for R (GSIF and plotKML for Google Earth) that have been developed for the purpose of automating soil mapping and that have been used for 3D mapping of key soil properties in Africa at 1 km resolution. The first part of the workshop is a lecture that explains the design and main functionality of the GSIF package. In the second part participants will use GSIF and plotKML for 3D regression-kriging of soil properties in the Ebergötzen area, Germany. This workshop will also include a demonstration of how the soil grids at 1 km were derived for the African continent using the WorldGrids repository of covariates and the AfSP database that contains over 15,000 African legacy soil profiles.

Contents:

• Demo: spatial prediction of soil properties and classes for Africa

Contents:

• DSM Using SoLIM: Framework

The Working Group on Soil Monitoring

In March 2014 an international workshop entitled 'Soil Change Matters' will be organized by the IUSS WGs on Soil Monitoring and Global Soil Change, Soil Science Australia and the Victorian Department of Primary Industries (VDPI). The meeting will be hosted by VDPI in Bendigo, Australia. It will explore the extent to which soil change can be quantified and explained through monitoring and modelling. The meeting will bring together policy makers and scientists to discuss policy needs, the limitations of our current understanding and the implications for future research.

Further details on all of these forthcoming events can be obtained from the WG secretary.

Contacts

Chairman: Dominique Arrouays, Dominique.Arrouays@orleans.inra.fr Secretary: Ben Marchant, benmarch@nerc.ac.uk This new subdivision will be organizing sessions for the 2014 EGU convention in Vienna (27th April-2nd

European Geosciences Union, Soil System Sciences Division

May 2014). While the programme has yet to be approved and finalized, the following proposed sessions might be of particular interest to pedometricians.

• Communication of uncertainty about information in earth sciences During a field campaign collecting validation data, pedometrician Ganlin runs into a farmer. "You are a clever and educated guy", says the farmer, "so you should be able to solve my problem". The farmer explains that he has four sons who are quite jealous about each other and who constantly quarrel about the management of their father's land (see shape below). He decides to divide the land give each of his sons an equal share, but this must be done in a way that not only the area of each of the four resulting parcels is the same, but also their geometry. How to do this?

(from Gerard Heuvelink)

Problem 2 (difficult)

One of the problems with legacy soil profile data is that their geographic coordinates may not always have been recorded properly. Recently, legacy data officer Johan came across a very odd recording. The metadata stated: "Go to the drinking well and walk from there to the old elm tree, measure the distance, take a right turn (90 degrees), walk the same distance and mark the point that you get to. Go back to the drinking well, walk to the pine tree, measure the distance, take a left turn (90 degrees), walk the same distance and mark this point too. The profile is located exactly half way the straight line connecting the two marked points." One day Johan happens to be in the neighbourhood and decides to trace the position of the soil profile. Arriving at the spot, he finds that the elm tree and pine tree are still there, but that the drinking well did not survive the passage of time: it had completely disappeared. Johan is still wondering about and looking for the location of the profile, can you help him?

(from Gerard Heuvelink)

Problem 3

Alf and Bert the soil surveyors are about to go on a trip to map a remote corner of Ruritania. Alf does all the planning, but the weekend before they are due to travel he wins a big mathematical bet off Flossie the barmaid and wakes up on Monday morning with a severe hangover. Bert has to pack the Landrover, using Alf's notes.

All is well until he comes to the equipment list which you can see below.

Bert collects 2 augers, 3 spades etc. but realizes that all is not as it seems when he gets to 7 plane tables. Alf only ever takes one plane table when soil surveying, which he likes to have in case the GPS should break down or he requires shelter from particularly heavy rain. Furthermore, why would Alf want 13 GPS? The institute doesn't have that many and, anyway, regulations only allow one GPS to be taken on any one expedition. The numbers cannot mean what they seem to mean, and what does "FTA-Code for Ruritania survey" mean? It's not the project number, it's not a set of co-ordinates, it's not even the budget in Ruritanian Reuros (too small a number for turns list over and reads:

Can you work out how many of each item on the list Alf intended to pack?

(from Murray Lark)

Problem 4

1) If circle is clever, whatever cross is (only one answer)

2) If circle and cross are stupid and choose the location randomly (give a probability)

3) If circle is stupid and choose the location randomly and cross is clever (give a probability)

The first winner will receive a unique tee-shirt with the drawing.

(from Dominique Arrouays and Anne Richer de Forges)

Solutions for Pedometron 32 Problem 1 (easy)

Answer: There is as much red wine in the white barrel as there is white wine in the red barrel. This is simply because at the start there was as much red there was white wine, and so it just has to be that all the volume taken up by the red wine in the white barrel must be equal to the volume taken up by the white wine in the red barrel, because no got lost. We can also show it mathematically: The amount of litres of red wine in the white barrel is 1 - Answer: Philippe can be certain that Marc will have to pay the bill by playing it cleverly. This problem was in fact quite difficult. It is a variant of the Chinese Nim game, see https://en.wikipedia.org/wiki/Nim for an explanation of the game and a winning strategy. The winning strategy is best explained by writing the number of beer glasses of each row in binary format: 001 010 011 100 101 110 Adding the number of ones in each column gives 3 -3 -3. What Philippe should do is remove that many beers such that the sum for each column becomes even. He can do this by drinking five beers from the bottom row, which gives: 001 010 011 100 101 001 This gives a sequence of sums 2 -2 -4, which are all even. Whatever is Marc's next move, each time Philippe must make sure that after his move the sum of the number of ones in each of the columns of the binary representation is again even. This is always possible. With this strategy, Philippe is guaranteed to win. Try it out yourself with your friends or colleagues and impress them! More details and explanation of the Sprague-Grundy theorem (which states that "every impartial game under the normal play convention is equivalent to a nimber"!) at https://en.wikipedia.org/wiki/Nim.

(by Gerard Heuvelink) Problem 3: Answer: The number of presents received on day n is the nth triangular number Tn. A triangular number is the number of points in one of the series of triangular arrays created by adding, at the nth step, a row of n the base of the previous triangle in the series, so that the first few terms of the series are:

I always find it easier to think of the nth triangular number as the number of elements on above the diagonal of an n×n matrix, i.e. + 𝑛 = 𝑛(𝑛 + 1)/2. Easy, and who needs R?

Before thinking about the second question, note that if we double the nth triangular number T n we get n(n+1) which is called the nth pronic number. Now, since the two integers provided by Flossie are odd, and not equal to each other, we can write the larger and the smaller as 2i+1 and 2j+1 respectively where i and j are integers and i>j. The number of pints that Guinevere orders is therefore: (2i+1) 2 -(2j+1) 2 = {4i 2 +4i +1} -{4j 2 +4j +1} =4{i(i+1)-j(j+1)}. Now, note from above, that the two terms in the braces are the ith and jth pronic numbers, and so the number of pints is =4{2T i -2T j } =8{T i -T j }

Since the triangular numbers are obviously all integer, it follows that, what ever odd numbers Flossie provides, the number of pints is exactly divisible by eight, so Alf is certain to win the bet.

Figure 1 .

 1 Figure 1. Major Soil Types of Africa

  Dominating Kenya's landscape are the dryland ecosystems, which are the arid and semi-arid lands of north-central and eastern Kenya. These areas are inhabited by nomadic pastoralists. The dominant soil types include: Regosols, Plansosols, Lixisols, Solonchaks, Calcisols, Arensosols, among other soil types that are characterized by high sand content. Recent studies have indicated that these soils have low carbon content, high erosion prevalence and are particularly vulnerable to over grazing and compaction (Vå gen et al., 2012). Current efforts to curb this degradation with community pastoralists groups have made strides toward rehabilitation and improved productivity. The field trip on the Pedometrics Conference will highlight the soils of this region as well as the conservation efforts (https://sites.google.com/a/cgxchange.org/pedometrics 2013/home/field-trip.) For a set of recently developed maps of soil condition and land degradation for sub-Saharan Africa you can visit the website of the World Agroforestry Centre's GeoScience Lab (http://gsl.worldagroforestry.org/).

Figure 2 .

 2 Figure 2. Generalized Soil Map of Kenya (from Kenya Soil Survey) References: (Turn to Page 15)

Figure 1 .

 1 Figure 1. Ground photograph of gilgai on the Bland Plain That idea came to Russell and Moore (1972) more than 40 years ago. Russell and Moore measured the height of the land above a local datum at frequent intervals along transects and estimated the average wavelength and amplitude of the micro-relief by Fourier analysis of their data.

Figure 2 .

 2 Figure 2. Parts of two digitized aerial photographs of the Bland Plain; at Caragabal (left) and at Back Creek (right).

  Figure. 3 displays the variogram and its corresponding correlogram computed by the method of moments; they show distinct waves. A nested model including a periodic component with a wavelength of 34.7 m fits the experimental semivariogram well.

Figure. 3

 3 Figure. 3 shows the experimental variogram, with a nested model containing a periodic component fitted to it, and the correlogram. The power spectrum derived from the latter appears in Figure 4. Notice the peak at a frequency of ≈ 0.12, which is equivalent to wavelength of 8.4 sampling intervals or 34 m.

Figure 3 .

 3 Figure 3. Empirical variogram of log 10 electrical conductivity at Caragabal with four components of the fitted model shown separately. Note in particular the periodic component.

Figure 4 .

 4 Figure 4. Correlogram of log 10 electrical conductivity at Caragabal (left) and its corresponding spectrum computed with the Parzen lag window and bandwidth 60.

Figure 5 .

 5 Figure 5. Correlograms of transects across the digitized images of (a) Caragabal and (b) BackCreek.

Figure 6 .

 6 Figure 6. Power spectra computed from the correlograms in Figure 5: (a) Caragabal and (b) Back Creek..

Figure 7 .

 7 Figure 7. Two-dimensional correlogram of the Caragabal image.

Figure 8 .

 8 Figure 8. Two-dimensional correlogram of the Back Creek image.

Figure 9 .

 9 Figure 9. Two-dimensional spectrum of the Caragabal image.

Figure 10 .

 10 Figure 10. Two-dimensional spectrum of the Back Creek image.

  Multi-resolution analysis of the Caragabal image; (a) smooth representations, and (b) detail components. Milne, A.E., Webster, R. and Lark, R.M. 2010. Spectral and wavelet analysis of gilgai patterns from air photography. Australian Journal of Soil Research, 48, 309~325. Percival, D.B. and Guttorp, P. 1994. Long-term memory processes, the Allan variance and wavelets. In: Wavelets in Geophysics (editors E. Foufoula-Georgiou and P. Kumar), pp. 325~344. Academic Press, New York. Percival, D.B. and Walden, A.T. 2000. Wavelet Methods for Time Series Analysis. Cambridge University Press, Cambridge, UK. Preston, F.W. and Davis, J.C. 1976. Sedimentary porous materials as a realization of a stochastic process. In: Random Processes in Geology (editor D.F. Merriam), pp. 63{86. Springer-Verlag, New York. Russell, J.S. and Moore A.W. 1972. Some parameters of gilgai microrelief. Soil Science, 114, 82~87. Webster, R. 1977. Spectral analysis of gilgai soil. Australian Journal of Soil Research, 15, 191~204. Webster, R. and Oliver, M.A. 2007. Geostatistics for Environmental Scientists, 2nd edition. John Wiley & Sons, Chichester. Multi-resolution analysis of the Back Creek image; (a) smooth representations, and (b) detail components.

Figure 13 .

 13 Figure 13. Two-dimensional wavelet variances for Caragabal and for Back Creek. The points are plotted at the lower bounds of the scale range on the abscissa, and the lines that join them are for visual clarity only.

Figure 1 .

 1 Figure 1. Illustration of converting original variables (aci, C) to first and second principle components (from Jenny et al., 1968).

Figure 3 .

 3 Figure 3. The out of bag fit vs. observed carbon content fitted using random forests (up) and the fit for an independent validation dataset (down).

  -Land Inference Model, is a new technology for digital soil mapping (DSM). It makes use of the state-ofthe-art techniques in geographic information processing techniques and artificial intelligence techniques for predictively mapping under fuzzy logic. The focus of this workshop is the operation of SoLIM Solutions 2013 for DSM. SoLIM Solutions 2013 encompasses most of the recent developments in DSM techniques under the SoLIM framework. The workshop will also provide a brief introduction of the SoLIM framework and an operational perspective of the new system in deploying the SoLIM technology, the CyberSoLIM, an effort for bridging the digital divide in DSM. The workshop will be given by A-Xing Zhu and assisted by three other key members of the SoLIM group. The workshop will provide attendees with the full version of SoLIM Solutions 2013.

  At its Annual General Meeting at the 2013 European Geosciences Union (EGU) meeting in Vienna the Soil System Sciences Division of EGU voted to establish a new subdivision called Soils: Informatics and Statistics.

  second question let 𝑅(𝑘) be the amount of red wine in the red barrel after 𝑘 mixings. We then have that: 𝑅 𝑘 + Since 𝑅(0) = 20we can calculate how large k should be until 𝑅(𝑘) = 0.51 • 20 = 10.2 or smaller. This happens at 𝑘 = 40. Philippe has a lot of work! (by Gerard Heuvelink) Problem 2 (medium)

  I found this question (without the answer, I should point out) in a maths examination paper set in Oxford in 1854 and printed in facsimile in Mathematics in Victorian Britain by Flood, Rice and Wilson (OUP, 2011).

Jenny, PCA and Random Forests

  By Budiman Minasny & Alex. McBratney University of SydneyThe handicap of self-correlation can be overcome by computing "principal components" (Some of the content of this paper is later used in the last chapter of Jenny's 1980 The Soil Resource Book, pp. 361-363).Gary in his talk indicated that this was the first paper that used PCA in soil studies. Intrigued by his comment, we tried to find out whether there are predecessors. Jim Wallis one of the co-authors of Jenny's paper (who then worked at I.B.M. Watson Research Center, Yorktown Heights, N.Y.) wrote to Gary (email message from Jim Wallis to Gary Sposito, May 15, 2011):

	A sidelight on how I arrived at the methodology
	follows. There was a Professor Meredith in the
	Psychology Department at the time, he taught a
	graduate course in Factor Analysis which I
	unofficially audited, and it seemed to me that if one
	used principal-component regression to determine the
	number of factors at work in soil formation
	(eigenvalues >1) and rotated the matrix into the
	variable space by Varimax that you would have a
	quantitative measure of Jenny's CLORPT equation. I
	wrote a 120-variable computer program to do just
	that. Jenny was not on campus that year, but he came
	back in the spring of 1966, got excited by its
	possibilities for pedology, although I had little to do
	with the writing of our joint paper, beyond a few
	conversations and notes that did not get preserved. He
	demanded that I give a seminar to the Soils
	Department on the subject, so I did."
	Jim Wallis introduced PCA to hydrology in a 1965
	paper 1 (Wallis 1965), and also wrote a FORTRAN
	program called WALLY1.

"It is not possible for me to say definitely that my work/paper was the first use of principal-components regression in soil science, but the probability that it was is extremely high. What is certain is that very few people at the time seemed able to understand the methodology or provide references to similar work. It seemed that it would help me with my dissertation on accelerated soil erosion, and I used it in my dissertation -it was highly controversial at the time. Jim Wallis is a well-known hydrologist who wrote the first paper on fractal in hydrology with Benoit Mandelbrot (1968), and he was the president of the Hydrology section at the AGU. ______________________ 1 The hydologists always seem to be a couple of years ahead of the soil scientists

The earliest references to techniques in Principal Component Analysis (PCA) were Karl Pearson in

2013 Pre-conference Workshops The

  Working Group on Soil Monitoring was formally established during the last World Congress of Soil Science in 2010. The aims of the group are to encourage inter and intra disciplinary collaborations into the design, implementation and interpretation of soil monitoring networks. An article describing the key issues to be addressed by the group has been published in Pedosphere[START_REF] Arrouays | Generic Issues on Broad-Scale Soil Monitoring Schemes: A Review[END_REF].

	The first activity arranged by the group was a special
	session at the 2011 Pedometrics meeting in Trest,
	Czech Republic. The session concentrated upon
	mathematical and statistical issues of soil monitoring
	and consisted of seven research talks and a keynote
	reviewing the outstanding research challenges. The
	research talks illustrated how diverse threats to soil
	quality such as compaction and contamination could
	be monitored.
	A symposium addressing more general issues in soil
	monitoring was arranged at the Eurosoils 2012
	meeting in Bari. This tacked fundamental problems
	such as the design of soil monitoring networks and the
	requirements and challenges of monitoring key soil
	parameters such as soil carbon and bulk density.
	Contributions from this symposium and the
	accompanying poster session will be included in a
	special issue of the European Journal of Soil Science
	which is currently in preparation.
	Further sessions have been arranged for the 2013
	IUSS Division 1 Congress in Ulm Germany and the
	2014 World Congress in Jeju, South Korea. The Ulm
	symposium is concerned with the interdisciplinary
	challenges of soil monitoring whereas the Jeju
	meeting will explore how soil monitoring can benefit
	mankind and the environment.

• DSM Using SoLIM: Operation • DSM Using SoLIM: New Advancesthe CyberSoLIM system Pedometrics

Flyers The first pedometric marriage, fusing European and Australian soil in the greatest soil profile in the world!!! Problem 1 (not difficult)

  Sessions for the 2014 congress will be announced later in 2013. For more information about these, and about the new subdivision, visit http://gsoil.wordpress.com/ or contact the Subdivision Chair mlark(at)bgs.ac.uk

• Sampling in space and time

• Modelling and visualization: new informatics tools for soil science

• Digital soil sensing, assessment and mapping: novel approaches for spatial prediction of key soil properties and for spatial assessment of soil functions

• Soil system modelling: strategies and software

• Complexity and nonlinearity in soils

• Scaling Connectivity

• Dynamic Soil Landscape Modelling

• Soil mapping perspectives: soil spatial information for land management and decision making

• Carbon sequestration in agricultural soils: the need for a landscape scale approach • Soil Apps.

The Third Global Workshop on Proximal Soil Sensing 26-29 May, 2013, Potsdam, Germany.

The workshop was organised to bring together global community devoted to advancements in technologies related to measurements by sensors placed in proximity to the soil being tested. As with the previous workshops, it was held under auspices of international union of soil science (IUSS) working group on proximal soil sensing (WG-PSS). Locally, it was organised by Leibniz-Institute of Agricultural Engineering (ATB), Leibniz-Institute of Vegetable and Ornamental Crops (IGZ Großbeeren), and the University of Potsdam (Potsdam, Germany). Over 80 researchers from various disciplines and 23 countries were present. The workshop included two full days of presentations, a field demonstration event and a number of networking and discussion sessions.

Summary

We've come a long way from Jenny's pedological study in the Pygmy Forest to using Random Forests for making soil predictions. Technology has advanced, powerful computers that can handle complex algorithms and there is now widespread availability of high-resolution covariates. We still stick to the same principle that while we need to make use of all of the new technologies, common sense and parsimony must prevail over fancy tools.

Jenny, PCA and Random Forests

No. J451 of the Iowa Experimental Station, pp. 323-332. Gray, J., Bishop, T., Smith, P., Robinson, N., Brough, D., 2012

Postscript by Alex.

The availability of principal components and more general multivariate methods for looking at soil took off fairly quickly after the sixties. When I did my first serious pedometrics work, which was in the long hot summer of 1976, with Dick Webster at Yarnton, software for doing PCA, discriminant analysis, principal coordinates etc. was readily available in programs such as Genstat, BMDP, SPSS and SAS. They were the powerful forerunners of S and then R.

In my alma mater at Aberdeen another mentor the soil physical chemist Michael Court very much favoured principal factor analysis over principal components analysis. Largely with Dick Webster's help I learned the mechanics of the multivariate methodsand they continue to serve well. They should be in any pedometrician's toolbox.

access those articles through the links below the titles.) The working group has been actively involved in the organising of workshops and sessions at different conferences:

• IUSS WG-PSS session at 19th WCSS, Brisbane 1-6 August 2010 We have also produced some publications:

• Book from papers of the 1st global workshop on high resolution soil sensing and mapping, Sydney 2008

Activities of the Proximal Soil Sensing Working Group (WG-PSS) We have a website with general information on the WG and on PSS as well as our meetings: www.proximalsoilsensing.org

Flyers


Marinus Te Pas

Bénédicte Lebret

Marie Damon

Bo Thomsen

Mariusz Pierzchala

Agnieszka Korwin-Kossakowska

Kui Li

Lars Kristensen

Jette Feveile

Birthe Pedersen

Predicting meat quality with biomarkers

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


Odile Cabaret

Olivier Puel

Françoise Botterel

Michel Pean

Stéphane Bretagne

Marcel Delaforge
email: marcel.delaforge@cea.fr

Contribution of uniformly 13 C-enriched sterigmatocystin to the study of its pulmonary metabolism †

Mycotoxins are secondary metabolites of filamentous fungi which can cause a wide range of systemic effects. Human health effects of inhaled mycotoxins remain poorly documented, despite the large amounts present, associated with airborne particles. Among these mycotoxins, sterigmatocystin is one of the most prevalent. Because its chemical structure is close to that of the aflatoxins, we studied its metabolism and its cellular consequences when in contact with the airway epithelium, using the mass spectral signature from the 10% [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C uniformly enriched sterigmatocystin. The metabolism was studied in vitro, using recombinant cytochrome P450s enzymes, and in porcine tracheal epithelial cell (PTEC) primary cultures at an air-liquid interface. The metabolites were analyzed by high-performance liquid chromatography coupled with tandem mass spectrometry detection. Expressed enzymes and PTECs were exposed to uniformly [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] Cenriched sterigmatocystin to confirm the relationship between sterigmatocystin and its metabolites because this isotopic cluster shape is conserved for all metabolites and their product ions. Incubation of sterigmatocystin with recombinant cytochrome P450 1A1 led to the formation of three metabolites identified as monohydroxysterigmatocystin, dihydroxysterigmatocystin and one glutathione adduct, the latter after the formation of a transient intermediate. In the PTEC cultures, sterigmatocystin metabolism resulted in a glucuro-conjugate. Two other products were detected, a sulfo-conjugate and a glucuro-conjugate of hydroxysterigmatocystin upon cytochrome P450 1A1 induction. This is the first study to report sterigmatocystin metabolism in airway epithelium, and it suggests that, contrary to the aflatoxins, sterigmatocystin is mainly detoxified into its conjugates and is unable to produce significant amounts of reactive metabolites in respiratory cells, at least in pigs. Copyright © 2011 John Wiley & Sons, Ltd.

Mycotoxins are secondary metabolites of filamentous fungi which can cause a wide range of acute and chronic systemic effects. As xenobiotics, these compounds can be metabolized by some of the host enzymes. These enzymes, primarily hepatic cytochromes P450 (CYP), ensure the oxidation of many hydrophobic exogenous compounds, increasing their polarity and thus assuring their biliary or urinary elimination. This transformation can involve a reduction of the parental compound toxic effects (reduction of ergotism by oxidation of the ergot alkaloids, e.g. [START_REF] Peyronneau | High affinity of ergopeptides for cytochromes P450 3A. Importance of their peptide moiety for P450 recognition and hydroxylation of bromocriptine[END_REF] ) or, in other cases, can involve a toxic activation, as in the case of aflatoxins. [START_REF] Isin | Complex reactions catalyzed by cytochrome P450 enzymes[END_REF] These modified compounds are then conjugated to polar compounds in conjugation reactions. Glucuronic acid moieties are transferred to substrate molecules that contain oxygen, nitrogen, sulfur or carboxyl groups. Sulfonic acid moieties are transferred from a donor molecule to an acceptor alcohol or amine and the conjugation of reduced glutathione to electrophilic centers, on a wide variety of substrates.

Most publications on mycotoxins deal with hazards due to toxic effects after their gastrointestinal uptake. [3] There are fewer data available concerning toxicity and metabolism after inhalation, despite the detection of mycotoxins in bioaerosols and dust at workplaces and in homes [START_REF] Bloom | Molds and mycotoxins in dust from water-damaged homes in New Orleans after hurricane Katrina[END_REF][START_REF] Bunger | Cytotoxicity of occupationally and environmentally relevant mycotoxins[END_REF] and their association with inhaled spores. [START_REF] Khoufache | Verruculogen associated with Aspergillus fumigatus hyphae and conidia modifies the electrophysiological properties of human nasal epithelial cells[END_REF][START_REF] Sorenson | Trichothecene mycotoxins in aerosolized conidia of Stachybotrys atra[END_REF] Among mycotoxins, sterigmatocystin (STG) is one of the most prevalent in the environment. STG has been detected in carpet dust from damp dwellings [START_REF] Engelhart | Occurrence of toxigenic Aspergillus versicolor isolates and sterigmatocystin in carpet dust from damp indoor environments[END_REF] and in dwellings after water flooding. [START_REF] Tuomi | Mycotoxins in crude building materials from water-damaged buildings[END_REF][START_REF] Nielsen | Production of mycotoxins on artificially and naturally infested building materials[END_REF] Aspergillus versicolor is the most efficient producer of STG, which is a biochemical precursor in the biosynthesis of aflatoxin. [START_REF] Yabe | Enzyme reactions and genes in aflatoxin biosynthesis[END_REF] After ingestion, STG is carcinogenic in animal models [START_REF] Adamson | Induction of hepatocellular carcinoma in nonhuman primates by chemical carcinogens[END_REF][START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF][START_REF] Mabuchi | Sequential hepatic changes during sterigmatocystin-induced carcinogenesis in the rat[END_REF] and it is classified as possibly carcinogenic in humans. [15] STG is activated by CYP to a liver carcinogen. [START_REF] Mcconnell | DNA adducts of aflatoxins, sterigmatocystin and other mycotoxins[END_REF] To date, STG metabolites have never been studied in airway epithelium.

The aim of this study was to elucidate the metabolism of STG after contact with airway epithelium and to assess any possible cellular consequences. For this purpose, we used uniformly [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C-enriched STG to confirm the metabolic relationship to STG. Preliminary studies consisted of identifying STG metabolites in recombinant CYP enzymes, and thus the metabolism upon exposure of porcine tracheal epithelial cell (PTEC) primary cultures to STG.

EXPERIMENTAL Chemicals

Ham's F-12 nutrient medium, Dulbecco's modified Eagle's medium, penicillin/streptomycin, amphotericin B, fetal calf serum, 0.05% trypsin-EDTA (1X) and phosphate-buffered saline were purchased from Invitrogen Life-Technologies (Cergy-Pontoise, France). Gentamicin, collagen IV, commercial [START_REF] Adamson | Induction of hepatocellular carcinoma in nonhuman primates by chemical carcinogens[END_REF] C STG (molecular weight (MW) = 324.3) with purity of 98%, acetonitrile, reduced nicotinamide adenine dinucleotide phosphate (NAPDH), glutathione (GSH), β-naphthoflavone, and ammonium acetate were obtained from Sigma (Saint Quentin Fallavier, France). Ultroser G was purchased from BioSepra SA (Cergy-Saint-Christophe, France); dimethyl sulfoxide (DMSO) from Prolabo (VWR International, Fontenay sous Bois, France).

Production of uniformly 13 C-enriched STG

Production of uniformly [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C-enriched STG was performed using autoclaved 10%- [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C wheat grains (Triticum aestivum cv. Ardente) as the exclusive nutrient source for the fungal culture, as previously described. [START_REF] Bravin | Production and use of mycotoxins uniformly enriched with stable isotopes for their dosage in biological samples[END_REF] The grains were moistened to a water activity value of 0.98 before sterilization. Around 30 g of this plant material was inoculated with a suspension of 2.10 5 Emericella nidulans conidia. STG was extracted from 14-day-old cultures with chloroform. The raw extract was fractioned by automated flash chromatography (Teledyne Isco, Lincoln, NE, USA). The fractionation was performed on a normal-phase RediSep 12 g column (Teledyne Isco) at a flow rate of 25 mL/min with linear gradient. The solvent system was 10% acetic acid in toluene as solvent A and 10% acetic acid in ethyl acetate as solvent B. Initially solvent B was present at 0% and increased to 45.5% within 10 min. The fraction volume was set to 5 mL. The presence of STG in fractions was checked by high-performance liquid chromatography (HPLC) with diode-array detection (Kontron Instruments, Neufahru, Germany) with a 250 × 4.6 mm Modul-cart Strategy 5 µm C18-2 column (Interchim, Montluçon, France). Gradient chromatography (run of 46 min) was performed with 0.2% acetic acid in water (eluent A)/acetonitrile (eluent B) as the mobile phase at a flow rate of 1.5 mL/min. The compounds were eluted by starting from 31% solvent B for 30 minfollowedbyalineargradientupto90%within5 min.After an isocratic elution for 5 min, the gradient was reduced to its initial value within 5 min, and remained at this value for the final 5 min. The 13 C STG was purified from positive fractions by semiquantitative HPLC. The HPLC unit was fitted with a 250 × 7.8 mm Modul-cart Strategy 5 µm C18-2 column (Interchim) and a diode-array detector. The gradient program was the same as above, except that the flow rate was increased to 4.5 mL/min. After production of uniformly [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C-enriched STG the presence of an isotopic cluster close to that expected from theoretical evaluation [START_REF] Bravin | Production and use of mycotoxins uniformly enriched with stable isotopes for their dosage in biological samples[END_REF] was checked by mass spectrometry (MS).

Metabolite studies by human cytochrome P450 enzymes

Bactosome ® -expressed human cytochromes P450 (CYP) were obtained from Cypex (Tebu-bio, Le Perray en Yvelines, France). We tested the main human CYP differing by their active site and thus by their substrate specificity. [START_REF] Guengerich | Human Cytochrome P450 Enzymes[END_REF] Based on the structural homology of STG with aflatoxins, we considered the CYP isoforms 1A, 2A and 3A, reported to metabolize aflatoxin B 1 . [START_REF] Crespi | Human cytochrome P450IIA3: cDNA sequence, role of the enzyme in the metabolic activation of promutagens, comparison to nitrosamine activation by human cytochrome P450IIE1[END_REF][START_REF] He | Efficient activation of aflatoxin B1 by cytochrome P450 2A13, an enzyme predominantly expressed in human respiratory tract[END_REF][START_REF] Kelly | Aflatoxin B1 activation in human lung[END_REF][START_REF] Van Vleet | Metabolism of aflatoxin B1 by normal human bronchial epithelial cells[END_REF] The metabolism of both commercial [START_REF] Adamson | Induction of hepatocellular carcinoma in nonhuman primates by chemical carcinogens[END_REF] C STG and [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C STG was studied as previously described. [START_REF] Cabaret | Metabolic detoxication pathways for sterigmatocystin in primary tracheal epithelial cells[END_REF] STG metabolism in porcine tracheal epithelial cells (PTECs) Preparation of porcine primary cultures was performed with trachea obtained from the Centre de Recherche Chirurgicale of the Henri-Mondor Hospital (Créteil, France) as previously described. [START_REF] Khoufache | Primary in vitro culture of porcine tracheal epithelial cells in an air-liquid interface as a model to study airway epithelium and Aspergillus fumigatus interactions[END_REF] After proliferation during the first week, the PTECs reached the differentiated state during a second week of culture with the presence of an apical side at an air interface and a basal side at a liquid contact. PTECs were exposed when cell differentiation was stable (after 14 days of culture) for 48 h to 1 mL of 1 μM U10% [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C STG through the basal side of the culture. After this exposure the medium was collected for extraction. To examine the effect of amounts of CYP1A1, the cells were pre-treated for 24 h with 25 μM βnaphthoflavone, a well-known CYP1A inducer, which led to an increase of CYP1A expression, or with 0.2% DMSO as control. The basal medium was then removed and the STG incubated for an additional 48 h. Controls were obtained after 48 h incubation with [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C STG in coated wells without cells and with 0.2% DMSO incubation in cell culture medium. Before extraction, the proteins were precipitated by addition of 500 μL cold methanol. After 30 min at -20°C, the mixture was centrifuged at 10 000 rpm for 5 min. Then 3 mL of water were added to the supernatant and the resulting solution was purified with a SepPack C18 cartridge (Waters Corp., Milford, MA, USA). The samples were eluted with 1 mL acetonitrile. This extract was reduced to dryness by speedvac concentration (Savant SC-200, Savant Instruments, Farmingdale, NY, USA) and then dissolved in the HPLC solvent (50% 10 mM ammonium acetate in water, 50% acetonitrile).

HPLC/MS/MS analysis

HPLC/MS/MS analyses were performed on an Esquire ion trap mass spectrometer (Bruker Daltonics GmbH, Bremen, Germany) with an electrospray ionization source operating in alternative positive/negative ionization mode, coupled with a 150 × 2.1 mm C18 Kromasil column (Interchim). The flow rate was 0.2 mL/min of a 25 min linear gradient ranging from 10 to 80% B (90 % acetonitrile in water) into A (90% 10 mM ammonium acetate in water, 10% acetonitrile). Electrospray ionization was performed at 4.5 kV and the capillary temperature was set to 310°C using a nitrogen gas flow rate of 40 mL/min and an auxiliary gas flow of 10 mL/min. Collision-induced dissociation of the selected precursor ion was carried out in the presence of helium with a collision Metabolism of U 13 C-sterigmatocystin in vitro and ex vivo wileyonlinelibrary.com/journal/rcm voltage of 0.70 V using a trap isolation width of 3 m/z units in order to isolate the whole isotopic cluster. Optimizations of MS and MS n signals were carried out using infusion of commercial STG. Injections were performed in 50:50 acetonitrile/water containing 10 mM ammonium acetate. Data acquisition and reprocessing were performed using Bruker Daltonics Data Analysis 3.2 software.

The SMARTCyp 1.5.3 program [25] was used to predict CYP metabolism. Mass Frontier 7.0 Spectral Interpretation Software (Thermo Fisher Scientific, San Jose, CA, USA) was used to simulate MS fragmentation in positive ion mode.

RESULTS AND DISCUSSION

Uniform [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C enrichment in STG Before its use in metabolic studies, we checked that [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] Cenriched STG presented an isotopic cluster characteristic of 10% [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C enrichment. The MS spectrum in positive ion mode contained the [M + H] + ion at m/z 325 and also M + 1 (m/z 326), M + 2 (m/z 327), M + 3 (m/z 328), M + 4 (m/z 329) and M + 5 (m/z 330) ions (Fig. 1). These peaks are in a 0.50:1.00:0.93:0.57:0.23:0.02 ratio relative to the most intense ion at m/z 326, and the isotopic ratios are close to those expected from theoretical evaluation for 10 % 13 C enrichment. [START_REF] Bravin | Production and use of mycotoxins uniformly enriched with stable isotopes for their dosage in biological samples[END_REF] The accuracy on the obtained isotope pattern determined for 15 different patterns was inferior to 20% for each peak.

Metabolite studies by human P450 enzymes

In order to avoid the use of human microsomes which contain around 20 different CYP isoforms, we used recombinant human CYP which are obtained from insect cells transfected with only a specific CYP cDNA. A 30 min incubation of either commercial [START_REF] Adamson | Induction of hepatocellular carcinoma in nonhuman primates by chemical carcinogens[END_REF] C or 10%- [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C STG with 0.2 μM recombinant human CYP1A1 enzyme in the presence of NADPH led to the significant formation of two metabolites M1 and M2. These metabolites, having retention time (t R ) = 19.5 min (M1) and 15.5 min (M2), were more polar than STG (t R = 24.8 min). They had a molecular weight difference of one (+16 Da) (MW = 340) or two oxygen atoms (+32 Da) (MW = 356), respectively, relative to STG (Table 1). The metabolites formed using 10%- [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C STG exhibited isotopic clusters similar to that of STG. Their [M + 1] + and [M + 2] + ions were more intense than [M] + . Collision-induced fragmentation of protonated M1 led to product ions at m/z 313, 299, 285, and 271 in positive ion mode (Fig. 2). Incubations in the presence of GSH led to the formation of a third metabolite (M3) having t R = 11 min and an ion at m/z 644 in negative ion mode, which corresponds to an increase of 321 Da relative to the molecular weight of STG (Table 1). Incubations of STG with other human CYP isoforms led to the formation of M1 and M3 for CYP1A2, M3 for CYP3A4, and no metabolism for CYP2A6 and CYP2A13. All the metabolites exhibited an isotopic cluster profile in their mass spectra (Fig. 2), which confirmed their connection to STG. The MS 2 product ions also conserved an isotopic cluster (Fig. 2).

Based on its mass, its polarity and the monooxygenase activity of CYP, M1 was assigned as monohydroxysterigmatocystin (Table 1). CYP metabolism prediction using the SMARTCyp program indicates that two sites (C3 and C12) in STG bear a labile proton for CYP hydroxylation. The fragmentation pathway of protonated M1 in positive ion mode is in accordance with hydroxylation on C12 (Fig. 2). Interestingly, it had been demonstrated that CYP1A2 oxidizes aflatoxin B 1 in the same position, leading to aflatoxin M 1 . [START_REF] Guengerich | Activation and detoxication of aflatoxin B1[END_REF] M3 was assigned as a GSH adduct of an oxidized metabolite (Table 1). This oxidation may occur in the 1-2position since the STG-glutathione adduct formed on STG 1,2-oxide has been observed. [START_REF] Raney | Oxidation of aflatoxins and sterigmatocystin by human liver microsomes: significance of aflatoxin Q1 as a detoxication product of aflatoxin B1[END_REF] Other possible structures include oxidation of the phenol ring to a reactive quinone which can then form a glutathione adduct. [START_REF] Krol | Oxidation of 4-alkylphenols and catechols by tyrosinase: ortho-substituents alter the mechanism of quinoid formation[END_REF] The detection Figure 1. Representative HPLC/MS chromatograms and isotopic cluster of uniformly [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C-enriched sterigmatocystin after 10% [START_REF] Fujii | Tumor induction by a single subcutaneous injection of sterigmatocystin in newborn mice[END_REF] C enrichment (A) and natural [START_REF] Adamson | Induction of hepatocellular carcinoma in nonhuman primates by chemical carcinogens[END_REF] C sterigmatocystin with its structure (B). of a glutathione adduct after in vitro metabolism through CYP1A and CYP3A4 activities strongly suggests the formation of a transient reactive epoxide of STG, as has been described using either human hepatic microsomes or the Ames test. [START_REF] Raney | Oxidation of aflatoxins and sterigmatocystin by human liver microsomes: significance of aflatoxin Q1 as a detoxication product of aflatoxin B1[END_REF][START_REF] Shimada | Activation of chemically diverse procarcinogens by human cytochrome P-450 1B1[END_REF] Such metabolic pathways have been described for aflatoxin B 1 hepatotoxicity. [START_REF] Wang | DNA damage by mycotoxins[END_REF] These mutagenic properties could be related to the pulmonary carcinomas described in mice after oral administration of STG. [15,[START_REF] Zwicker | Long-term administration of sterigmatocystin and Penicillium viridicatum to mice[END_REF] M2 was assigned as dihydroxysterigmatocystin (Table 1). M2 may be formed by hydrolysis of M3 or by M1 hydroxylation. CYP metabolism prediction using the SMARTCyp program indicates that C3 is the most probable site for CYP hydroxylation in monohydroxysterigmatocystin (M1). Thus, M2 could also be a catechol or a para-diphenol on the phenyl ring, and it may be metabolized to a quinone.

STG metabolism in PTECs

After 48 h incubation with PTECs, only one STG metabolite, M4, was formed and this was identified as a glucuro conjugate. M4 was more polar than STG (at t R = 11.5 min) and had an isotopic cluster shifted to M + 176 relative to STG (MW = 500) (Fig. 3, Table 1). Fragmentation of deprotonated M4 in negative ion mode led to the formation of the MS cluster of deprotonated STG. The glucuronide formation was confirmed after hydrolysis β-glucuronidase (data not shown). Only glucuronide conjugation with the free hydroxyl function on C8 is compatible with the mass of M4. Glucuronidation has been involved in metabolism of STG, and a STG glucuronide detected in the urine of rat or monkey. [START_REF] Olson | Immunochemical Studies of Urinary Metabolites of Sterigmatocystin in Rats[END_REF][START_REF] Thiel | Urinary excretion of the mycotoxin, sterigmatocystin by vervet monkeys[END_REF] Glucuronidation has been identified as an important pathway in the respiratory detoxification of environmental carcinogens such as polycyclic aromatic hydrocarbons. [START_REF] Dellinger | Importance of UDP-glucuronosyltransferase 1A10 (UGT1A10) in the detoxification of polycyclic aromatic hydrocarbons: decreased glucuronidative activity of the UGT1A10139Lys isoform[END_REF] After 24 h exposure of PTECs to 25 μM β-naphthoflavone, 48 h STG incubation led to the formation of two new polar metabolites, M5 and M6, detected in negative ion mode. These metabolites had t R values of 12.5 and 5.5 min and an isotopic cluster shifted to M + 96 (MW = 420) and M + 192 (MW = 516), respectively, relative to STG (Fig. 3). M5 was identified as a sulfoconjugate (M + 16 + 80) and M6 as a glucuro-conjugate (M + 16 + 176) of monohydroxysterigmatocystin (Table 1). The product ions of deprotonated M5 and M6 show an M + 16 isotopic cluster, which could correspond to a monohydroxysterigmatocystin product (M1 or hydroxylation on another location). Therefore, CYP1A1 metabolism may result in M1 formation, with rapid transformation into M5 and M6. Evidence for the epoxide formation and the subsequent formation of dihydroxysterigmatocystin (M2) or the glutathione conjugate (M3) was not found in porcine primary cells.

CONCLUSIONS

Our study shows that STG is metabolized in porcine epithelial airway cells through phase 1 and 2 metabolism (Fig. 4, Table 1). This is the first study to report metabolite formation and detoxification pathways in the pulmonary stage using primary cells at an air-liquid interface. This metabolism seems close to that of aflatoxin B 1 with the formation of oxidized metabolites, a glutathione adduct and glucuronides. However, further studies are necessary to precisely locate the sites of oxidation. STG is mainly detoxified through glucuronidation (glucuronidation of STG and monohydroxysterigmatocystin). We also suggest that the metabolism of STG in the porcine airway tract in vitro is unable to produce significant amounts of reactive epoxide metabolites.

Finally, the uniformly enriched molecules with stables isotopes are very powerful tools for metabolism studies. Since the isotopic cluster signature is preserved for all the enriched metabolites, mass spectrometric analysis is easier than the classical approach.

Figure 2 .

 2 Figure 2. Representative HPLC/MS, MS 2 and MS 3 chromatograms of the ion at m/z 341 in positive ionization mode (M + 16, metabolite M1); with natural 12 C sterigmatocystin on the left and with 13 C-enriched sterigmatocystin on the right (A). Proposed fragmentation pathways of protonated-M1 in positive ion mode (B).

Figure 3 .

 3 Figure 3. Representative HPLC/MS/MS chromatograms showing formation of sterigmatocystin metabolite in PTEC exposed to 1 μM sterimatocystin through the basal side of cultures (A): m/z 499 in negative ionization mode (M4) and MS 2 spectrum of M4 metabolite. Proposed fragmentation pathways of deprotonated M4 in negative ion mode (B).

Figure 4 .

 4 Figure 4. Putative pulmonary sterigmatocystin metabolism. Pathway A (top) indicates formation of a sterigmatocystin-glucuro conjugate (metabolite M4) in 'normal' airway epithelial cell cultures. Pathway B indicates formation of a sulfo conjugate and a glucuro conjugate of monohydroxysterigmatocystin (metabolites M5 and M6, respectively) in 'CYP1A1-induced' airway epithelium cultures. CYP1A1 amounts were increased by 24 h exposure to 25 μM β-naphthoflavone. For high levels of CYP1A1 and in the presence of glutathione-S-transferases and glutathione, transient epoxide formation led to the glutathione conjugate or to dihydrodiol (pathway C, bottom).

Table 1 .

 1 Molecular weights (MW) for the compounds described in this study

	MW	Detection
	(Da)	(m/z)

-) O. Cabaret et al. wileyonlinelibrary.com/journal/rcm Copyright © 2011 John Wiley & Sons, Ltd. Rapid Commun. Mass Spectrom. 2011, 25, 2704-2710

Acknowledgements

We thank Sandrine Blondel and Anaïs Roussel for technical assistance. These studies were supported in part by AFSSET (Grant Numbers EST-2007-63 and EST-2008-27). We thank Andrew Lilley for English correction.