MOSFLM program has been developed to simplify the processing of macromolecular diffraction data. The interface, iMOSFLM, allows data processing via a series of clearly defined tasks and provides visual feedback on the progress of each stage.A new graphical user interface to the  iMOSFLM is a graphical user interface to the diffraction data-integration program MOSFLM. It is designed to simplify data processing by dividing the process into a series of steps, which are normally carried out sequentially. Each step has its own display pane, allowing control over parameters that influence that step and providing graphical feedback to the user. Suitable values for integration parameters are set automatically, but additional menus provide a detailed level of control for experienced users. The image display and the interfaces to the different tasks  are described. The most important parameters for each step and the best way of assessing success or failure are discussed.                The overall structure of the GUI and a more detailed description of the individual tasks and their graphical output are given below.The new interface was designed to provide an intuitive route to data processing, so that inexperienced users are guided in a logical fashion through the stages of data processing and alerted to potential problems. At the same time it was felt important to provide the full functionality of the 2.iMOSFLM acts as a ‘front end’ to the MOSFLM program itself. iMOSFLM generates the MOSFLM commands for particular tasks, based on user input, and then passes these commands to MOSFLM, which carries out all the computation. The results of these tasks are then passed back to iMOSFLM for display, either on completion of the task  or while the task is still in progress (e.g. cell refinement and integration). iMOSFLM and MOSFLM are run as separate processes, with the MOSFLM process being started by iMOSFLM. This means that iMOSFLM always retains all the parameters relevant to a particular task, so that if MOSFLM encounters an error that causes it to fail, iMOSFLM can restart MOSFLM and the user can attempt to recover from the failure.            2.1.iMOSFLM task has its own pane where relevant parameters can be set and the results are displayed. The available tasks  are listed on the vertical icon bar on the left-hand side of the GUI  once any necessary preceding actions have been carried out. For example, indexing can only be selected once images have been added to the session using the Add Images button in the Images pane. This displays a file browser and selecting any image file will add all images that have the same filename template. Images with different filename templates can also be added but will be assigned to different sectors. The start and end oscillation angles of each image are listed and once an image has been processed the missetting angles  are added. The cell, space group, mosaicity and mosaic block size are also listed in the Images pane (and are user-editable).Each UI Fig. 1 and can 2.2.e.g. the backstop shadow), spot search region and resolution limits, while other icons control zooming and panning, a selection tool, manual addition of spots, the definition of masked regions, the definition of the centre of a circle and the erasure of either masks or manually added spots. Additional icons control image zoom (about the current centre), image size and image contrast. Any image can be selected for display from a drop-down list. A useful feature of the zoom window is that the zoomed region is always preserved when changing from one image to another. The View option in the menu bar allows the size of the image display to be changed, while the Tools option allows a reflection with particular indices to be located. The selection tool displays the resolution at any point in the image (when the Ctrl key is depressed) and when over a predicted reflection also displays the Miller indices. It can be used to make ‘drag-and-drop’ adjustments to the spot-search areas, resolution limits, direct-beam position and masked areas.As soon as one or more images have been added to the session, the first image will be displayed in the Image Display window Fig. 2. Buttons2.3.via the menu bar. The Session drop-down menu allows the user to start a new session, save the current session or load a previously saved session. A new session is started each time iMOSFLM is run or when selected by the drop-down menu. Saving a session stores all the information about the current state of the interface, including the images that have been read, the current values of all refineable parameters and processing options and the graphical information for all steps carried out during the session. The View menu allows access to the various settings dialogues that allow experimental and detector parameters to be defined (under Experiment Settings) and a large number of parameters and options influencing the processing stages to be set (under Processing Options). Some of these will be described in later sections.The Session and View menus are always accessible 3.iMOSFLM issues commands to find spots on two images: the first image in the series and the image that is as close as possible to a 90° rotation away from the first. Found spots are displayed both in the Image Display window and in a representation of the images in the Indexing pane  and therefore the assignment of symmetry (above triclinic) is an assumption that needs to be tested after images have been integrated.On selecting the Indexing task, ne Fig. 3. Spots aIf the appropriate option is set in the Processing Options dialogue, the indexing and mosaicity-estimation processes will be carried out automatically after spot finding without the requirement to click the Index button.3.1.e.g. to 2.0σ), increasing the minimum number of pixels to 20–30, reducing the r.m.s. spot variation to 1.0 and setting the minimum spot separation to values around 1.5 mm .Parameters that control spot finding are listed in the Spot finding tab of the Processing Options dialogue. The search area is set by default to be between circles of radii corresponding to 5 and 95% of the radius of the inscribed circle centred on the direct-beam position, but this can be adjusted graphically with the spot-finding button on the Image display or by setting the values explicitly. The default threshold value for a pixel to be considered part of a spot is set to 5.0σ above the background (where σ is determined by counting statistics) and a variety of rejection criteria are applied to distinguish true Bragg spots from noise features in the image. These include a minimum number of pixels, minimum and maximum sizes (mm), a minimum r.m.s. variation of pixel values within the spot, a maximum anisotropy in spot dimensions, a minimum spot separation and a maximum peak separation within spots. The default values have been optimized for images collected on synchrotron beamlines, where the spots tend to be smaller than for images collected on a laboratory source. For the latter, if the spots are large and diffuse then better results can be obtained by decreasing the threshold . These are normally below the threshold for use in auto-indexing and can be ignored, but reducing the size of the local background region (e.g. to 20 × 20 pixels) will often eliminate these spurious spots. Even when the local background method is being used, an initial radial stripe background is determined to set parameters associated with the background determination, so it is important that this stripe (which can be displayed on the image with the spot-finding search area button) does not lie over a large shadow on the detector.Two algorithms are available for determining the local X-ray background. The simpler one assumes that the background is circularly symmetric about the direct-beam position and determines the background in a radial stripe 50 pixels wide. The orientation of this stripe is at 90° to the direction of the rotation axis in order to avoid any shadow arising from a solid backstop support , but this can be changed by ‘drag and drop’ in the Image display or e.g. owing to zingers) and the minimum spot size is also decreased. If there are only a few true Bragg spots present, the inclusion of only a small number of noise spots can lead to failure of the indexing. Finally, the intensity threshold for spots to be included in the auto-indexing is automatically set to 5, 10 or 20 depending on the strength of the diffraction (on the last image processed), which also decreases the chance of indexing failure. All of these options can be overridden in the Processing Options dialogue.The inclusion of spots arising from crystalline ice can easily result in the failure of auto-indexing. To avoid this problem, spots within narrow resolution shells centred on the principal reflections of hexagonal crystalline ice are automatically excluded. In addition, if the diffraction is very weak the resolution limit for spot finding is automatically reduced to 4.5 Å in order to avoid including any noise features that might occur at higher resolution  and then to run POINTLESS . Note that unless the mosaicity has been estimated (see §3.4.3.4.1.e.g. not within the backstop shadow). The beam position can be adjusted manually (using the selection tool to drag and drop) and in favourable cases  it can easily be positioned close enough to the correct position to allow indexing. If ice rings are present a tool is available in the Image display to determine the centre of the ring (and hence the direct-beam position) by circle-fitting a small number of points on a ring . The accuracy required for the beam coordinates depends on the cell dimensions, with larger unit cells requiring more accurate coordinates. To avoid mis-indexing, the coordinates need to be known to an accuracy corresponding to less than one half of the spot separation for the longest cell axis. For cell axes longer than about 250 Å it can be very difficult to detect from the results of the indexing if the pattern is mis-indexed by one index along the long axis. However, integration of even a small rotation range and subsequent symmetry detection with POINTLESS will immediately detect this error, as even Friedel pairs will give poor agreement. As an alternative to manually defining the direct-beam position a two-dimensional grid search can be carried out where, by default, the beam coordinates will be varied by ±1.0 mm in 0.5 mm steps  and the indexing carried out for each new position. The positional residual and consistency between the refined beam coordinates can be used to select the most probable correct beam position. This procedure works best if at least two images (preferably widely separated in rotation angle) are used.The most common cause of indexing failure is having incorrect values for the direct-beam coordinates (these are read from the image-file header). The direct-beam position is indicated in the Image display (as a green cross) and so it is immediately obvious if this is seriously in error  image can result in successful indexing, while inclusion of both images fails. In the case of multiple lattices, it may only be necessary to increase the intensity threshold for spots to be included in the indexing, with values of up to 100 sometimes being necessary. The same approach works well for crystals with very high mosaicity (no distinct lunes visible). In marginal cases, usually those with very weak diffraction resulting in very few spots above the threshold, varying the threshold slightly from the default value can make the difference between failure and success. The use of more than two images can also be helpful in difficult cases, ideally with the additional images well separated in rotation angle Φ from those already being used. All of these modifications to the default behaviour are readily achieved Another possible cause of indexing failure, especially if the diffraction is strong and there is no evidence of other problems, is that the direction of spindle rotation is opposite to that conventionally used. This is the case on a number of synchrotron beamlines. In this case, indexing should be successful when only a single image is used, but if the next image in the series is inspected the prediction will not match the observed spots. This can be dealt with checking a box in the Detectors tab of the Experiment Settings dialogue and repeating the spot search for all images used in indexing to ensure that the correct Φ values are assigned.3.5.iMOSFLM always uses two images by default.Caution is required if only a single image is used for indexing since for low-symmetry space groups it is possible to find an indexing solution that will correctly predict one image but will not correctly predict images that differ significantly in Φ. This is why When inspecting the predicted pattern, it is also important to check if pseudo-centring or lattice repeats have resulted in a particular class of reflections being systematically weak. If only strong reflections are used in the indexing, the resulting unit cell will be too small to predict the weak reflections and the indexing should be repeated with a reduced intensity threshold.3.6.POINTLESS . If the unit cell is large, the total intensity will drop for large values of the mosaic spread as some reflections become flagged as overlapped and are not integrated . The mosaicity is refined during cell refinement and data integration, so the accuracy of this initial estimate is not critical.An initial estimate of the crystal mosaicity is obtained  this has the effect of changing the apparent crystal mosaicity as a function of resolution  using a total Φ rotation that is significantly less than that strictly required for that Laue group, especially if the crystal is oriented so that none of the principal axes are aligned with the rotation axis. For example, in an orthorhombic space group a 60° rotation in two 30° segments is generally sufficient for 95% overall completeness . The option is therefore provided to collect the data in up to three distinct segments with a total rotation of between 30 and 90°. Various statistics on completeness and multiplicity as a function of resolution and total rotation angle are presented in graphical form. An interactive graphical representation of the suggested rotation segment(s) is displayed in the lower left area of the pane  will calculate the maximum oscillation angle per image that will avoid spatial overlaps, based on the current values for the resolution, minimum spot separation and crystal mosaicity, as a function of Φ for the suggested rotation range. Alternatively, the percentage of overlapped reflections can be calculated for different values of the oscillation angle. In both cases, the results are displayed graphically as histograms in the lower right area of the pane.5.et al., 1979et al., 1979Y direction of the detector), both for each image and separately for each cycle of refinement. Indicators of a successful refinement are a decrease in the r.m.s.d. values, YSCALE values close to unity for all images  and consistent detector distances for all images.The cell parameters determined from the auto-indexing step are based on spot positions and are limited in accuracy as they are highly correlated with the crystal-to-detector distance, which is not refined by default as, except for very high-resolution data, this distance is not well determined. The Cell Refinement task Fig. 7 allows tIn situations where the data are too weak or the resolution is too low for successful post-refinement, the cell parameters obtained from the auto-indexing should be used, and in these cases including three or four images (widely separated in Φ) may improve the accuracy of the cell parameters.6.POINTLESS  and preliminary scaling  until processing is complete. The option is available in the Processing Options dialogue to write a separate MTZ file for every block of images. These files can be used to run either POINTLESS or SCALA before the integration has finished and there is an option to run POINTLESS automatically after integration of each block of images.Data integration proceeds in blocks of images, with typically 5–10 images in a block. The detector parameters are refined for each image and the pixel values for the measurement boxes for all predicted spots are written to a scratch file. The standard profiles are formed using all the images in the block and each image is then integrated in turn. Normally, the results of integration are written to a single MTZ file that cannot be used , Φ(y), Φ(z)] are refined independently for every image . It is not unusual to see changes of a few tenths of a degree in these angles for a complete data set. This can either reflect genuine small changes in orientation or can be a consequence of the rotation axis not being exactly orthogonal to the X-ray beam (which is an implicit assumption), which results in apparent changes in missetting angles that repeat with a periodicity of 360°. Providing that the change in orientation between successive images is less than approximately one-tenth of the crystal mosaicity, these changes in orientation will have no effect on data quality. The cell parameters are normally fixed during integration because for technical reasons there are not sufficient data available to define the values of all of the cell parameters accurately. The crystal mosaicity is refined during integration, as the mosaicity can be anisotropic and can increase as a result of radiation damage. The mosaicity refinement can be unstable, refining towards a value close to zero if there are errors in either the cell dimensions or the crystal orientation. For this reason, during the first cycle of cell refinement ] is plotted separately for all reflections and for reflections in the highest resolution bin as a function of image number for both fully and partially recorded reflections  profile can be viewed by clicking on the relevant profile. Parameters controlling the profile averaging can be altered 6.6.I/σ(I) values in different resolution ranges are plotted as a histogram for each image, with separate plots for fully recorded and partially recorded reflections. This plot can provide an estimate of the resolution limit of the data as the resolution at which the mean I/σ(I) drops to below 2.0, but the final resolution limit will depend on the multiplicity of the data and the presence of radiation damage, so this is only an approximate indicator.The mean 6.7.POINTLESS  to optimize the data integration.Once a series of images has been integrated, the QuickSymm button in the tool bar launches a run of the program 6.8.POINTLESS to determine the correct Laue group and then run SCALA .            iMOSFLM is written is object-oriented Tcl/Tk and makes use of a number of extensions to the core language. Several of the widgets used are built on code provided by the Iwidgets package. The image display and the customized buttons make use of parts of the tkImg extension (for displaying the diffraction image rendered in JPEG format and the buttons either as PNG or GIFs). The results from processing and the current state of the GUI are stored in an internal tree structure using the TreeCtrl package. All the XML produced by MOSFLM and received through the socket by iMOSFLM is parsed using the tDom package.            iMOSFLM can be run using any version of Tcl/Tk from version 8.4, although some minor versions have particular bugs that militate against their use. For example, Tk version 8.4.13 has an error in the image-display routines that make it unusably slow. Tcl/Tk 8.5 can be used, but the extensions discussed above are not included in most distributions and thus would need to be installed separately.            9.iMOSFLM interface provides an intuitive easy-to-use approach to processing diffraction data. The software is undergoing active development to improve overall performance, to allow processing of images containing multiple lattices, to provide improvements to the strategy calculations where data are collected from multiple crystals and to add new task panes to allow more control over running POINTLESS and SCALA and displaying the results from these programs. The ability to launch multiple parallel background jobs to process entire data sets very rapidly and display the results graphically is also being investigated. The software is distributed with the CCP4 package (Winn et al., 2011http://www.mrc-lmb.cam.ac.uk/harry both as source code and as precompiled executables for Windows, Mac OSX and Linux platforms.The


This paper presents the application of a frequency-domain reflectometry (FDR) sensor designed for soil salinity assessment of sandy mineral soils in a wide range of soil moisture and bulk electrical conductivity, through the determination of soil complex dielectric permittivity spectra in the frequency range 10–500 MHz. The real part of dielectric permittivity was assessed from the 380–440 MHz, while the bulk electrical conductivity was calculated from the 165–325 MHz range. The FDR technique allows determination of bulk electrical conductivity from the imaginary part of the complex dielectric permittivity, without disregarding the dielectric losses. The soil salinity status was determined using the salinity index, defined as a partial derivative of the soil bulk electrical conductivity with respect to the real part of the soil complex dielectric permittivity. The salinity index method enables determining the soil water electrical conductivity value. For the five sandy mineral soils that have been tested, the relationship between bulk electrical conductivity and the real part of dielectric permittivity is essentially linear. As a result, the salinity index method applied for FDR measurements may be adapted to field use after examination of loam and clayey soils. The concept of the soil salinity index sX, describing soil salinity status, was introduced by Malicki and Walczak [bC with respect to the soil bulk or apparent dielectric permittivity bε , which helped to prepare appropriate soil samples with variable water content ranging from approximately 10% to near saturation. The soil material of various moisture levels was packed into the containers in small portions and pressed with a 0.2 kg rubber hammer to achieve homogenous density distribution in the soil sample [zThe tested material included five mineral soils that were air dried and put through a 2 mm sieve. The basic physical characteristics of these materials are given in 3 volume of air-dry soil to achieve soil samples of a desired water content, from 10% to near saturation.The values of saturation water content by mass for the selected soils were determined using capillary rise. Three samples of each soil of a known dry weight placed in measurement containers with holes at the bottom, were gathered in separate plastic bowls with porous bottom sides. The containers with soil, covered to minimize excessive evaporation, were immersed for 72 hours in distilled water allowing capillary rise to achieve saturation. Then they were weighed to determine the mass of water saturating each soil sample. Finally, after simple calculations it was possible to determine the mass of water for adding to the 120 cmsC enabled changing the real and imaginary parts of dielectric permittivity of the tested material to achieve the assumed range of variability of the soil electrical conductivity of 100–400 mS m−1, and of soil water content—from air dryness to near saturation. The samples of chosen soils fully filled 120 cm3 containers (plastic cylinder 4.6 cm in diameter and 7 cm in height) equipped with a sealed cover, and wetted by distilled water and the KCl solutions.On the basis of generally available conversion tables and pilot measurements of soil electrical conductivity performed by a TDR meter, three KCl solutions and distilled water were prepared for wetting soil samples . ApplicaThe material samples in containers were mixed with distilled water and KCl solutions, covered with a sealing cap, weighed and conditioned at 40 °C for 72 hours to ensure uniform water content in the sample volume. Then, after leaving the soil containers for several hours at room temperature, the filled containers were again weighed to make weight corrections caused by possible water evaporation loss. Next, the containers were opened to perform FDR measurements of the complex dielectric permittivity of the material. Each FDR measurement was made three times by inserting the probe rods in various locations of the material in the container. All measurements were made in a laboratory with a controlled temperature 21 ± 1°C.2.1.i.e., 3 cm against 10 cm. The probe with such short rods applied with a TDR meter would require even shorter rising time of the pulse, which is possible in extremely sophisticated and expensive TDR devices. With a probe of this type, the measured quantities pertained to a greater volume of the sample under test than in the case of an open-ended coax probe [S11 of the signal reflected from the probe inserted into the sample. This reflection coefficient is defined as:pZ is the impedance of the probe and Zc = 50 Ω. The impedance of the probe depends on the complex dielectric permittivity of the material in which the sensor was inserted. Therefore, the measured reflection coefficient is also a function of the dielectric permittivity of the sample. It is possible to obtain the inverse relation: ε* = f(S11), which allows determining the complex dielectric permittivity from the measured reflection coefficient for each applied frequency. For calibration purposes, measurements of short circuit, open (air) and acetone were used, which reduced the measurement errors [The applied FDR sensor, measurement details and calibration techniques were described earlier by Skierucha and Wilczek . The proax probe . The soit errors .3.3.1.ε*. They are related to resonances in the experimental set-up, due mostly to the length of the coax cable connecting the sensor with the VNA; the details and discussion are presented in [The FDR measurement technique allowed determining the real and imaginary parts of the dielectric permittivity directly and independently. ented in . This waThe real part of the dielectric permittivity is strongly related to the soil water content, namely the square root of the real part of the dielectric permittivity (the refractive index) depends linearly  on the v3.2.ε* is related to bulk electrical conductivity of the sample bCThe imaginary part of the complex dielectric permittivity may be used to infer the bulk electrical conductivity of the sample. According to f:One can multiply both sides of the above equation by the frequency, so that the whole relation is a linear function of dε is not frequency dependent in the analyzed frequency range up to 500 MHz, one can find the values of bC by fitting a straight line into a plot of the above function, as shown in R2 and standard error of regression σ, defined as the square root of the sum of the squared residuals divided by the number of degrees of freedom, were presented on the graph. To calculate bulk electrical conductivity with the artifacts removed, frequencies from 165 to 180 and from 245 to 325 MHz were selected. In this frequency range, which is narrow enough and distant from relaxation frequencies of various polarization mechanisms which may occur in the tested soils, the assumption that the dielectric loss dε does not depend on frequency is reasonable and gives good fits, as presented by the example in Assuming that bC of all tested soil samples.This procedure was applied to determine the bulk electrical conductivity 3.3.bC of each sample versus the real part of dielectric permittivity ε′ = Re(ε*) was plotted for all moistening solutions and a straight line was fitted through each set of data points. The results are presented on the left panel of R2 and standard error of regression σ, defined as in the previous section, were presented on the graphs. For all points, error bars are present. The vertical error bars represent the standard error of determination of bC. Taking into account these errors and high values of R2 (as presented on the graph), it transpires that for all tested samples the relations between bC and ε′ are essentially linear. Thus, the salinity index defined as:b vs. ε′C graph and, for linear b vs. ε′C dependence, is independent of the water content. The difference between bC actually denoted bulk (or apparent) electric conductivity determined from attenuation of the TDR pulse with the assumption that the dielectric loss dε = 0.In order to test the concept of salinity index introduced by Malicki and Walczak  with ressC for the examined soils are presented on the right panel of sX was determined from the slope of the regression equations of bC against ε′, which were shown on the left panel of sX depends linearly on the electrical conductivity of the moistening solution sC. When the salinity index sX and the slope l of the s vs. CsX relation are known, one can calculate the conductivity of soil water from the following formula:The relations between the salinity index and moistening solution conductivity SIX ≠ 0, due to some residue conductivity of ions dissolved from dry soil. As was shown in [rC may be found by extrapolating the s vs. CsX relation to the horizontal axis. Then:One may notice that for the samples wetted with distilled water the salinity index is equal to some initial value shown in , this rewC is a sum of the residue electrical conductivity and the conductivity of the moistening solution:Therefore, one may expect that the electrical conductivity of soil water This assumption will be tested in a subsequent part of this paper.3.4.b vs. ε′C is linear, one need not take a series of measurements of samples of different water contents of the soil under question to determine the value of the salinity index—the measurement of complex dielectric permittivity of a single sample will suffice. It transpires that the lines from the left panel of bε′,C) data points, for a given soil and all applied solutions, cross at certain limiting values of the real part of the dielectric permittivity and bulk electrical conductivity. These limiting values are denoted Iε and IC, respectively. Once these values are known for a given soil, it is possible to calculate the salinity index by determining the value of the partial derivative from an appropriate difference quotient:ε′ and bC measured from a single sample of previously unknown salinity and water content. Therefore, combining To calculate the salinity index using the method presented above, it is necessary to take a series of measurements of the same soil moistened with the same solution to various water contents. Obviously, this procedure has little practical use, since a measure of soil salinity applicable for field conditions should provide an accurate estimate based on a single measurement of a single soil sample. However, similarly to the method shown in , if the Iε and IC for a given soil. The values, obtained in this experiment for the five tested soils, depend on the soil properties. It was found that Iε depends on the specific surface s of the soil under question, with regression equation given by I =ε 0.02s + 3.44 with R2 = 0.99, where s is given in units from IC is affected by the clay content c. The appropriate regression equation is IC = 0.65c + 3.23, with R2 = 0.89 and clay content given in units from Iε and IC are included only to present the variability of these parameters with the soil texture and are not used in further calculations. The accuracy of the values of Iε and IC is especially important for soils which are either very dry, or have low bulk electrical conductivity. This is because from the form of the last term of bC and ε′ close to IC and Iε, respectively, the error of the final value is the most affected by the errors of all these four quantities.To apply the formula above in the field, it is necessary to know the values of wC from the salinity index SX in the field conditions from l needs to be known beforehand too. As was the case with Iε and IC, the parameter l also depends on the soil properties. For the five tested soils, the best fit was obtained for a straight line of equation l = −0.00008c + 0.009, where c is the clay content, as for IC. However, R2 equals only 0.56. It is obvious that in order to minimize the error of wC determined in field conditions, additional soils of different properties  should be tested in order to achieve better regression equations.To calculate the electrical conductivity of soil water 3.5.wC for all tested soils are shown. The “linear model” phrase at the top of the figure means that the values of wC were calculated with the assumption that the relation b vs. ε′C is linear as in the left panel of wC  calculated from SX obtained from the regression equations from the left panel of l values determined from the slopes of the regression lines from the right panel of ε′) by the same solution, the value of wC is from definition independent of the water content . The errors of wC, represented by the dashed lines, depend on the standard error of determination of SX as a slope of the fitted straight line and on the standard error of determination of slope l of the S vs. CSX relation.On the left panel of wC calculated from l were taken as in the previous linear model. The errors of wC, represented by the error bars, depend on the standard errors of all quantities used in l. As can be seen, these errors are much greater than in the previous case, where SX from regression were used. Because of the form of wC for the two samples with the least water content for each soil and moistening solutions; therefore, these values have not been presented in The data points shown on the left panel of wC obtained from the linear field model are roughly independent of the water content and agree with the values calculated from SX obtained from regression. A notable exception to this is soil no. 589 moistened with solution no. 4 (the most concentrated KCl solution), as seen on the left panel of wC calculated from SX obtained from regression are still encompassed within the error bars of the field model values.The values of 3.6.b vs. ε′C relation for all measurement series. However, some possible quadratic effects may be lost. In order to assess the possible impact of quadratic terms on the wC values, a quadratic model of the salinity index has been examined.On the left panel of b vs. ε′C is not linear, it is still possible to use the salinity index defined as in bε′, C) data points for all measurement series (not presented on figures), the salinity index calculated as a derivative takes the form:l of the S vs. CSX relations, for each value of ε′. Fortunately, it transpired that the l vs. ε′ is linear (R2 = 0.9988 for the worst fit for soil no. 568), which enables calculating wC for arbitrary values of ε′. The values of electrical conductivity of soil water obtained with the use of the quadratic model of the salinity index are presented on the right panel of wC values calculated from SX obtained from regression from the linear model are also presented on the plots. One may notice that the values of wC either remain constant with ε′  or decrease with the increase in ε′. Also, for most measurement series, the values of wC obtained by all three approaches are comparable.Even when the dependence wC calculated from the quadratic model, which depend mostly on the errors of the salinity index calculated from wC, determined from the salinity index obtained from: (i) regression from the linear model, (ii) calculated from wC obtained for loess for all four applied moistening solutions, for samples with similar values of the real part of dielectric permittivity. For other tested soils, these errors behave similarly. This suggests that the inclusion of the quadratic term in the b vs. ε′C relation does not improve the wC estimation.The errors of wC than the quadratic one; however, not for all cases, as can be seen in Generally, only the linear field model may exhibit greater errors for 3.7.SC from rC (taken as conductivity of soil water for samples wetted with distilled water) from the conductivity of soil water wC:To further test the linear salinity index model, introduced by Malicki and Walczak in , one maySC assumed from the molarity of the solutions (b vs. ε′C relation (linear model), are presented in R2 and the standard errors σ presented in the figure describe how well the assumed values of SC explain the calculated quantities. As can be seen from the values, the agreement is very good. The worst case is soil no. 568, which exhibits both high errors in the calculated SC, as shown by the error bars, and the worst fit for the assumed values. The best case is soil no. 605, which has 95% sand fraction. This occurrence, along with the errors of wC presented in The results can be then compared with olutions . The val4.The present study deals with the measurement of soil salinity using the FDR technique. It enables directly obtaining real and imaginary parts of complex dielectric permittivity. The obtained The salinity index method, introduced in  for soilThe linear salinity model by definition gives electrical conductivity of soil pore water independent of the real part of dielectric permittivity and thus of water content. This ignores the possible ion adsorption and desorption processes occurring on the surfaces of the soil solid particles. Even though the chosen soils were sandy, they differed in specific surface area values (as shown in


Based on extensive site investigations and modeling of present hydrological conditions, the effects of different processes on future site hydrology are quantified. As expected, shoreline displacement has a strong effect on local hydrology  in areas that change from sea to land. The comparison between present and future land areas emphasizes the importance of climate variables relative to other factors for main hydrological features such as water balances.This paper presents an analysis of present and future hydrological conditions at the Forsmark site in Sweden, which has been proposed as the site for a geological repository for spent nuclear fuel. Forsmark is a coastal site that changes in response to shoreline displacement. In the considered time frame (until year 10 000 The online version of this article (doi:10.1007/s13280-013-0394-6) contains supplementary material, which is available to authorized users. Analyses of hydrological processes such as groundwater and surface water flow and water exchanges between atmosphere, regolith, and vegetation are often fundamental components in environmental assessments and geoscientific research studies. Applications may be focused on the water itself , or consider cases where water primarily is of interest as a carrier of chemical substances  or as an environment where biogeochemical processes take place. In applications like these, hydrological models are often developed and used to predict or otherwise describe the future. The time periods considered in most of these studies of future hydrology are relatively short, on the order of a few decades up to, say, one hundred years. However, in recent years there has been an increasing interest in issues involving much longer time scales, such as long-term climate change and risk assessments of long-lived contaminants. When considering these longer time perspectives, also changes in the studied systems as such must be taken into account; for example, Rowland et al.  emphasizThis paper presents modeling of present and future hydrological conditions at the Forsmark site in Sweden. In 2009, Forsmark was selected by the Swedish Nuclear Fuel and Waste Management Company (SKB) as the intended site for the planned geological repository for spent fuel from the Swedish nuclear power plants, and an application for constructing and operating the repository was submitted to the authorities in 2011 , i.e., the mean difference between the daily values in the measured and modeled time series (could be positive or negative) for a particular measurement station, and the mean absolute error, MAE, which is the mean of the absolute differences  between the time series. The calibration targets for the groundwater level in the regolith were to obtain average ME and MAE of less than 0.20 and 0.40 m, respectively, for the groundwater monitoring wells. For cumulative surface water discharges, the calibration target was to be below a maximum difference of 15 % between measured and modeled water volumes at the end of the considered time period. Finally, another important criterion was to keep the hydraulic variables within physically realistic ranges, not diverging from site observations.Hydrological models describing future conditions at the site were developed to produce parameter values  for the calculations of radionuclide transport and associated radiation doses to be carried out within the biosphere modeling in the safety assessment. Other purposes of the numerical modeling were to investigate possible future surface water and groundwater conditions, and to what extent lakes, wetlands, and other objects of interest might differ from those observed at the site today. The modeling of future site hydrology used the description of the present state as a basic input. The other main input was the assessment and quantitative modeling of the various processes driving the succession of the regolith and hydrological objects at the site.ad and 10 000 ad were produced; the modeled shore levels are −15.0 m at 5000 ad and −31.4 m at 10 000 ad (expressed in the Swedish RHB70 elevation system). Figure ad . The hydrological modeling considered fixed geometrical and geological conditions at these times, i.e., ‘snap shots’ in the dynamic development of the site. This means that surface hydrology was not modeled continuously in the changing landscape, in contrast to some of the dynamic processes determining, for example, the geometrical and geological conditions . In order to keep simulation times relatively short, the horizontal grid resolution was changed from 40 m to 80 m. As a reference, and to enable comparisons with the previous site descriptive modeling and an evaluation of the effects of the coarser resolution, the present situation was modeled also with the enlarged hydrological model.The MIKE SHE model area used in the safety assessment modeling of the future Forsmark is also shown in Fig. Different combinations of shoreline locations (corresponding to different times and proportion of land and sea), regolith descriptions (present and future conditions), and climate conditions were considered, in order to evaluate potential effects of different changes of the system. Three different climate states were simulated: (i) the present temperate climate, (ii) a future warm and wet temperate climate, and (iii) a future periglacial climate with permafrost. Input data to the description of the present climate were taken from the site investigation, whereas climate modeling presented by Kjellström et al.  providedElectronic Supplementary Material), where also some example results are given.Results from the MIKE SHE modeling in terms of water fluxes from bedrock to regolith and between different parts of the surface system were required as input data to the radionuclide transport modeling. The radionuclide transport and resulting radiation doses were to be calculated using a compartment model that uses a simplified representation of the landscape objects, typically consisting of a lake surrounded by a mire, where modeling indicated that discharge of potentially radionuclide-bearing groundwater could occur Lindborg . These oDescriptive modeling of present site hydrology entailed extensive comparisons between numerical modeling results and measured data, using a relatively detailed model. This modeling also served as a reference in the safety assessment modeling otherwise focused on future site conditions where a larger model of lower spatial resolution was employed. In the following, some results of the model calibration and the comparison between model results for present conditions are presented; the site description and safety assessment models are referred to as SDM and SA models, respectively.Electronic Supplementary Material).The calculated accumulated discharges for the four surface water discharge stations Fig.  show difThe mean MAE values for all the groundwater monitoring wells in regolith that were included in the calibration process were 0.31 m for the SA model and 0.26 m for the SDM model, whereas the mean ME values were 0.00 m and −0.04 m for the SA and SDM models, respectively. The differences between measured and observed groundwater head elevations in the bedrock were larger in both models. The mean MAE in bedrock was 0.70 m and the ME −0.69 m in the SA model, and the corresponding values were 0.68 m (MAE) and −0.66 m (ME) in the SDM model. A hydraulic pumping test performed at the site during the summer 2006 . The model results for present conditions indicate that 80 % of the model area has groundwater depths within 1.5 m of the ground surface (cf. 20 % cumulative frequency), as compared to 1 m for the same fraction in the field observations. This difference can be explained by the fact that the model area covers a larger range in ground surface elevation than the area represented by the groundwater monitoring well locations. This means that the model area includes a larger fraction of higher-elevation areas, where the depth to the groundwater table is larger.The groundwater level observations within the catchment show that approximately 80 % of the observations (daily average groundwater depth) is within 1 m of the ground surface and thus have a strong correlation to surface topography . This is in good agreement with the long-term annual water balance estimated using local site data and regional meteorological observations, where the runoff is 160 mm and the evapotranspiration is 400 mm for a precipitation slightly lower than that characterizing the modeled reference year (cf. above).The water balance of the present Forsmark area has been calculated for the part of the model area constituting land today. Approximately 30 % of the precipitation leaves the model volume as runoff and the evapotranspiration constitutes approximately 70 % of the precipitated water. The water balance calculation for a selected reference year with a precipitation (P) of 583 mm and a potential evapotranspiration (PET) of 420 mm resulted in a runoff (R) of 175 mm and an evapotranspiration of 405 mm ; the simulation cases with their P, T, and PET input data, and the results in terms of ET and R for each case are listed in Table S2 . The simulations of future hydrology were carried out with and without taking landscape succession into account, which means that the regolith was either taken from the model for present conditions  or from the modeled future regolith distributions , and indicate that the variations in the overall water balance are small as long as the climate is the same. The calculated distribution of the precipitation that falls during the year was approximately 30 % runoff and 70 % evapotranspiration for the normal temperate climate, more or less independently of the shoreline position and the regolith model considered. Differences related to the regolith model used were observed in the results, but these were not large enough to have significant effects on the overall water balances; for example, the variations in runoff among the cases studied were within ca. 10 %.The results in terms of water balances extracted for different times (shoreline positions), areas (present and future land and specific catchments), and climate conditions are also presented in Table S2 . This case also included higher temperatures and much higher potential evapotranspiration, which led to comparatively small changes in the relative proportions of evapotranspiration (75 % of the precipitation) and runoff (25 %). However, there was a large absolute increase in the yearly runoff, 395 mm compared to 196 mm for the 10 000 Some significant changes in the overall water balance appeared in the model results for a cold periglacial climate, where the climate conditions included a yearly precipitation of c. 400 mm. When applying a periglacial climate and permafrost conditions in the model [see Bosson et al.  for detaad simulation with normal temperate climate, the model area is divided into subareas indicating where groundwater recharge and discharge may take place. The different areas are identified using the calculated head difference between the two regolith layers in the model , such that discharge areas are identified as areas with an upward hydraulic gradient and recharge areas are those with a downward gradient. The model results indicate that new lakes and streams, formed due to landscape succession, are discharge areas and the relatively higher-altitude areas are recharge areas, i.e., the same pattern as observed for present conditions at the site. The distribution of recharge and discharge areas changes somewhat with shoreline displacement, such that some areas with larger vertical gradients form in the new land areas  it is seen that the groundwater depths are in general larger in the models describing future site conditions. As the shoreline becomes more distant the groundwater levels drop. The cumulative frequencies of groundwater depth show that the groundwater table is deeper in the future models, but still 80 % of the model area has groundwater depth within 2 m of the ground surface at both 5000 ad and 10 000 ad.The groundwater table is correlated to the topography also in the future models. However, comparing the depth to the groundwater table for present and future conditions in the area constituting land today  representing different distances from the present coastline, different stages in the on-going succession of the objects can be observed and modeled. Historical records of the main processes driving site development, such as shoreline displacement, can be collected and used to construct descriptions of possible futures. The confidence in models describing the future relies to a large extent on the ability of the models to describe the present state and reproduce measured data and other observations from the site. Extensive model calibration and other comparisons between measurements and model results were performed as a part of the modeling presented herein, and the resulting numerical models showed good agreement with field data for realistic values of model parameters.Shoreline displacement is a major process in the development of the (presently) coastal Forsmark site. It determines the overall distribution of land and sea, and when a certain area emerges from the sea and hence when lakes and streams are formed. The change from sea to land also has a significant influence on the groundwater flow pattern. The results presented in this paper emphasize the importance of the climate relative to other factors for general hydrological features such as water balances. Clearly, to further investigate the sensitivity of site hydrology to meteorological variables is an important topic for additional studies. Other processes affecting the hydrology, primarily processes related to lake succession and terrestrialization, are obviously of large significance when performing more detailed studies, especially of the future development of individual hydrological objects.Changes in the climate may have more profound effects on flow systems than just to change magnitudes of flow components. In particular, for a cold climate Bosson et al.  showed tElectronic Supplementary Material) represent the most detailed model scale considered in the present work. However, these results are extracted from larger-scale, distributed hydrological models, in this case MIKE SHE models covering many potential biosphere objects. An obvious next step from the present use of an ‘average object’ is to perform flow calculations for the specific biosphere objects included in the radionuclide transport model, and then use the results in an object-specific parameterization in the transport model. The hydrological models are also used to support the subdivision of biosphere objects into compartments; this idealization of the system should be revisited when developing new models. Furthermore, the possibilities of providing fully dynamic flow modeling results, i.e., hydrological fluxes that change during the succession from lake to mire and further, should be investigated.The calculations of hydrological fluxes for biosphere objects Below is the link to the electronic supplementary material.


Contact maps have been extensively used as a simplified representation of protein structures. They capture most important features of a protein's fold, being preferred by a number of researchers for the description and study of protein structures. Inspired by the model's simplicity many groups have dedicated a considerable amount of effort towards contact prediction as a proxy for protein structure prediction. However a contact map's biological interest is subject to the availability of reliable methods for the 3-dimensional reconstruction of the structure.Cβ atoms constitute the most accurate representation of the 3D structure. The reconstruction process does not provide a single solution to the problem but rather an ensemble of conformations that are within 2Å RMSD of the crystal structure and with lower values for the pairwise average ensemble RMSD. Interestingly it is still possible to recover a structure with partial contact information, although wrong contacts can lead to dramatic loss in reconstruction fidelity.We use an implementation of the well-known distance geometry protocol to build realistic protein 3-dimensional models from contact maps, performing an extensive exploration of many of the parameters involved in the reconstruction process. We try to address the questions: a) to what accuracy does a contact map represent its corresponding 3D structure, b) what is the best contact map representation with regard to reconstructability and c) what is the effect of partial or inaccurate contact information on the 3D structure recovery. Our results suggest that contact maps derived from the application of a distance cutoff of 9 to 11Å around the Thus contact maps represent a valid approximation to the structures with an accuracy comparable to that of experimental methods. The optimal contact definitions constitute key guidelines for methods based on contact maps such as structure prediction through contacts and structural alignments based on maximum contact map overlap. For over 30 years ,2 contacAlthough they constitute a simple 2-dimensional representation of the molecule, contact maps still capture all important features of a protein fold. As such they are an invaluable tool for the analysis of biological macromolecules. They provide a computationally tractable representation of an otherwise complex problem, with the important advantage of being structural descriptors independent of the coordinate frame. Thus providing a sort of internal coordinates description, rotationally and translationally independent. However the simplified representation loses on accuracy as compared to the original 3-dimensional model. Multiple applications can be found in the literature that make use of the concept. Contact maps have been used for development of structural alignment algorithms , for autSeveral methods have been proposed in the past for the reconstruction of contact maps. Most of them develop around the common mathematical theory of distance geometry first applied to chemistry by Blumenthal . The theCα traces.However the problem of reconstructability of protein contact maps has not been fully addressed in the literature. A few studies -26 have Our aim here is twofold. We would like to find what is the reconstruction accuracy for an average protein so that the limits of the utility of contact maps in protein structure prediction can be precisely assessed. As a second aim we are looking for optimal criteria in the definition of a contact map decomposition model: atoms selected as interaction centres and distance cut-off. By decomposing a representative set of PDB protein structures into residue interaction graphs and then reconstructing them based purely on the contact information we should be able to assess the accuracy and loss of information in the decomposition process by comparing to the original native structure . Firstly we decomposed the native proteins into contact maps with different contact type definitions and for several distance cut-offs. Then we used our reconstruction software to recreate the 3D structures based solely in the information supplied by the contact maps.Cα atoms over all residues, independent of whether the reconstructions were based on Cα contact maps or not. This seems to be a well-established way of measuring the similarity between two structures especially when they are closely related and should facilitate the comparison to other published work. Another well-established method for structure comparison, GDT )Cα, Cβ and Cα + Cβ cases  experiment [Melo et al.  studyingperiment .Cβ as the interaction centre of choice. It seems natural that Cβ is better in order to derive empirical potentials as it spans both the backbone and the side-chain. But also it is a superior point of choice for embedding a 3D structure from interatomic distance restraints. The interaction centre is able to capture geometrical information for the backbone positioning as well as for the orientation of the side-chain leading to a more precise 3D description.Our study, purely based on the 3D geometrical information content of the contacts, confirms the preference for Cα and Cβ contacts leads still to better reconstruction performance, indicating that there is some more backbone information not contained in the Cβ restraints. This suggests an approach in the homology modelling of proteins based on distance restraints (see [Cβ.Also of interest is the fact that the combination of both nts see -35): usi: usiCα aβ group is significantly better than that of the other three. Interestingly the median values are not very far away for the 4 classes but the variances are hugely different especially for the all-beta case. Contrary to this result, in a similar study Saitoh et al. [β class. Vassura et al. [a class, which we also observe here.We then address the question of whether the reconstruction process is dependant of the type of protein. In order to do so we separate our 60 proteins into the four SCOP classes to which they belong to, each of the classes containing 15 structures. Figure h et al.  stated ta et al.  did findThe reconstruction process inherently leads to a non-unique solution fully matching the contact map. We studied the variance of the ensemble of reconstructed structures. The average spread of the pairwise RMSD among the ensemble structures is in most cases below 2Å. In Table As seen in Figure Cα atoms. In contrast here we are constructing full atom protein chains with realistic bonds and angles. This leads to higher RMSD values as more geometrical constraints need to be fulfilled.For completeness of this work we compare our results to those of two previously published reconstruction methods ,25. In FAs a final part of the study we then address the question of reconstruction of contact maps in the more realistic scenario of incomplete or noisy maps, which is likely to be the case when the input is a predicted set of contacts. To do this instead of using real predictions, for instance from homology or machine learning methods, we simulate incomplete and noisy contact maps to thoroughly explore the effect of noise in the process of reconstruction.Figure Cβ and different cut-offs. The loss of accuracy with lower percentage sampled subsets seems to decrease with higher cut-offs. Thus for the same percentage deletion one can recreate the original structure better with contact maps of higher cut-offs, i.e. the redundancy is higher. The second test that we perform intends to asses the robustness of the 3D recovery process with respect to the presence of noise, the case of a more realistic prediction with false positives. Figure Cβ definition behaves better at all levels of noise.Interestingly enough there seems to be a non-linear relationship in the information redundancy with respect to cut-off. Figure An existing application  is reporThe tests performed here are based on randomly generated inaccurate contact maps which in principle differ significantly from ab-initio predictions. However from our results here we could conclude that with adequately precise ab-initio contact predictions one could produce reasonable models. In fact we applied successfully some of these ideas in the CASP8 community-wide experiment for structure prediction . In thatIn this work we have studied the viability of computing 3D protein models from contact maps. We assessed the performance of a reconstruction procedure based on the well known distance geometry protocol used extensively in NMR protein structure determination.We perform a comprehensive evaluation covering a representative set of the PDB spanning the 4 SCOP classes. We then explore several possible contact map definitions and evaluate the accuracy of the reconstructions based on RMSD to the available native structure.Cβ atoms are a better description of the 3-dimensional model than those based on Cα, confirming other studies that used one-body and two-body empirical contact-based potentials for fold recognition to find this optimum. Reconstruction accuracy can be further improved by using the two contact definitions together Cα + Cβ.We found that contacts based on the With regards to contact cut-offs we found that the optimal lies in the region from 9 to 11Å. We do not observe, contrary to previous studies  that theβ SCOP class yields very good accuracies across all its members as compare to the other classes, leading to the conclusion that some topologies are more amenable to be described in terms of single atom distance restraints.Interestingly the accuracy of the reconstruction seems to be different for different classes of proteins. Particularly the all-These results are particularly valuable for the contact prediction community. As contact prediction ultimately aims at obtaining 3-dimensional models of protein structures the usage of our optimal contact definition findings should contribute to better accuracies of the predictions. At the same time the results can be useful in the structural alignment of proteins through contact map overlap [Further our 3D recovery procedure seems to perform also very well even if only a partial subset of the contacts is available. With as little as 40% of the contacts reasonably good models can be produced. On the contrary the method is very sensible to the presence of non-real contacts. The introduction of restraints at random points in the chain is simply fatal for the recovery of the original structure. This indicates that contact predictions should focus on accuracy rather than coverage.http://dasher.wustl.edu/tinker. In particular the distgeom [This study is based on the TINKER molecular dynamics package , availabdistgeom  program An interface to the TINKER package was developed (Java) providing a single command line executable as a one stop solution for contact map reconstruction, taking contact maps as input and outputting PDB files. The software is multiplatform  and only requires a working copy of the TINKER package locally installed.http://www.molgen.mpg.de/~lappe/reconstruct.We have made our program freely available under the terms of the GPL v.2 at 2 for the force constant). The restraints were then fed into distgeom to generate a total of 30 models per structure using simulated annealing for refinement. The extensive study performed required a substantial amount of computation as we had 60 proteins, 3 contact-type definitions and 19 cutoff bins from 6 to 15 with 0.5 step. This gave a total of 3420 contact maps, for each of them we computed 30 structures in order to have a statistically meaningful sampling of the reconstruction space, resulting in a total of 102,600 models. The computations were carried out in a distributed fashion on a Linux cluster with over 100 CPUs.We generated distance restraints from the contact maps in the form of lower and upper bounds restraints for pairs of atoms (with standard value of 100.0 kcal/ÅThe conformations found through the distance geometry protocol can not distinguish between the 2 enantiomers of the molecule, as chirality information is simply not present in the contact map. We overcome this problem by comparing to the native molecule through RMSD. The RMSD values for the conformation ensemble are found to be distributed bimodally, by simply choosing the lowest third of models as ranked by RMSD we are sure not to be falling into the wrong enantiomer.Cα and Cβ. Two atoms were considered to constitute a contact when their euclidean distances where below the given cut-off. In the Cα model the backbone Cα atom for each residue is chosen, whilst for the Cβ model the Cβ atom of the side chain of each residue is taken, except for Glycine where we use the Cα atom.We used two definitions of contact maps in this study: Cα atoms or Cβ atoms for each of the cases above. As upper bound of the restraint we used directly the distance cut-off, while for the lower bound value we used distance statistics derived from the PDB database. We proceeded by plotting the distance distribution for all Cα or Cβ atoms and then choosing as our lower cutoff the value of the 90th percentile of the distribution.For the reconstruction procedure we then need the contacts to be translated into distance restraints. Restraints were generated only for pairs of atoms corresponding to the contacts: The distance geometry procedure in TINKER is an implementation of the established distance geometry algorithms used for NMR protein structure determination, see . CrippenIn the selection of the data set we aimed at covering a diverse set of structures to ensure generality of the results obtained. We used a non-redundant PDB dataset of 60 proteins selected from SCOP release 1.73 . Only moJD performed the bulk of the analysis, developed the software and drafted the manuscript. RS performed the analysis related to error tolerance of reconstruction. HS developed the software and participated in the design of the study. IF selected the protein subset and contributed drafting the manuscript. ML initiated the study and participated in its design. All authors read and approved the final manuscript.


We have used computer simulation to study the relationship between the muscle fiber diameter and parameters: peak-to-peak amplitude and duration of the negative peak of the muscle fiber action potential. We found that the negative peak duration is useful in the determination of fiber diameter via the diameter dependence of conduction velocity. We have shown a direct link between the underlying physiology and the measurements characterizing single fiber potential. Using data from simulations, a graphical tool and an analytical method to estimate the muscle fiber diameter from the recorded action potential has been developed. The ability to quantify the fiber diameter can add significantly to the single fiber electromyography examination. It may help study of muscle fiber diameter variability and thus compliment the muscle biopsy studies. Single fiber EMG (SFEMG) is a powerful technique to study the pathophysiology of the motor unit. Two types of measurements are made using this technique: fiber density (FD) and jitter. The FD is useful to study the grouping of muscle fibers of a motor unit within its territory. In neuropathy, FD increases due to reinnervation. The “jitter” measurements are used to assess the efficacy of the neuromuscular transmission .In myopathy, the FD may be increased slightly due to fiber splitting, regeneration of muscle fibers, etc. The jitter is usually normal. In this manner, SFEMG is not particularly useful in the diagnosis of myopathy. It is often used to rule out other diseases such as a neuropathy or a neuromuscular junction disease . A primaThe abnormalities of muscle fiber diameter are observed indirectly on electrodiagnostic studies. On routine needle EMG examination, increased variability gives motor unit potentials with polyphasic waveforms. Atrophy can give low amplitude  potentials , 8, 9.The variability of muscle fiber diameter may be studied indirectly by investigating the muscle fiber conduction velocity. The muscle fibers are stimulated directly using an intramuscular needle, and their action potentials are recorded at a few millimeter distance. The latency of the potential is used to compute the velocity .V(t) is computed as [i(t) is the transmembrane current and φ is a weight function. The transmembrane current is proportional to the square of the fiber diameter [In principle, the shape of the single muscle fiber action potential also contains information about the muscle fiber diameter. In the so-called line source mode, the extracellular muscle fiber action potential puted as :1\documediameter . Thus, ldiameter . Higher The weight function is the potential recorded by the electrode as a unit current source propagates from the endplate to the tendon. The waveform of this function is thus dependent on the propagation velocity of muscle fiber, and hence the fiber diameter. The change in weight function waveform with radial distance and with fiber diameter has not been investigated systematically.In this study, we have used computer simulation to study the relationship between the muscle fiber diameter and parameters of potential: peak-to-peak amplitude and duration of the negative peak of SFP. These relationships are used to develop a graphical and an analytical tool to estimate the muscle fiber diameter from the recorded action potential. Although our goal is similar, our method of analysis is quite different from that used by Rodriguez et al. , 7. TheyThe line source model described by Nandedkar and Stålberg  was usedV recorded at the electrode at time t, coming from a single fiber, is given by a convolution of a weight function φ(t) and current i(t) given by  and radial distance (50–500 μm). The range of fiber diameters corresponds to the data from muscle biopsy 5–90 μm a. Hence tFor the simulation we have used our own software which is based on the formulation of the simulation method presented by Nandedkar and Stålberg . In thistz.The amplitude was measured from maximum positive to maximum negative peaks Fig. . The zertz, we obtain parametric curve with coordinates:d and r on a for a fixed tz. Similarly by fixing a, we may obtain parametric dependence of r and d on tz:d, r, a, and tz as follows: the negative peak duration was approximated by the following two bi-quadratic polynomials:The change in the amplitude and duration with radial distance and muscle fiber diameter was investigated. In the experimental recordings, the peak duration and amplitude are measured while the muscle fiber diameter and radial distance are the “unknown” variables. Hence we may write:In our approach, the atic Eq.  with \doing Eqs.  and 8)  \documenWe have found experimentally that the form of dependence given by Eqs.  and 8)  8) is beIn order to calculate the diameter, mentclass2pt{minimThe results of simulation studies are shown in Figs. The amplitude and negative peak duration dependencies are shown on a single graph Fig. . The solFor example, if a potential with amplitude of 0.7 mV and a negative peak duration of 0.6 ms is recorded, we could determine the distance and diameter from the intersection of the curve for constant amplitude = 0.7 mV (solid line) and constant duration = 0.6 ms (dashed line). The point of intersection gives the fiber diameter (roughly 65 μm) and the radial distance (150 μm).While in principle the graphical tool, provided that the curves for constant amplitude or negative peak duration are dense enough, may be used to determine fiber diameter and distance from needle we have approximated the data with analytical formulae which makes them more suitable for use. The graph, however, may be used to verify that the curves are smooth, nearly linear and that the two variables The graph may be readily used to compare the diameters for two or more SFPs. If two SFPs have the same amplitude, so on the graph we are moving along one of the constant amplitude curves, then the one that has shorter negative peak duration is due to larger fiber. This is because the negative peak duration depends on conduction velocity which in turn is proportional to fiber diameter. Hence the larger the fiber, the shorter the negative peak duration.In order to explain the dependence of fiber diameter on amplitude for a constant negative peak duration one has to note that the amplitude depends primarily on two factors—the distance from the electrode and fiber diameter see Fig. b. For a In order to calculate the values of the coefficients formulae  and the mentclass2pt{minimfrom Eq.  is \docu for Eq.  it is \dUsing this method to approximate the data it is found that the root mean square error of the determination of diameter is 2 μm and for radial distance it is 6 μm. The maximum error for  by Eqs.  and 8)  \documenFor example, using these equations for a simulated SFP with fiber diameter In order to estimate the effect of measurement errors of From the simulation studies it has been found that the negative peak duration of SFP is a quantity which is useful in the determination of fiber properties and in particular of fiber diameter. Rodriguez and co-workers , 7 have otential  and sourWe have derived a set of analytical formulae using which it is possible to determine fiber diameter (and fiber to electrode distance) from the measurement of the SFPs amplitude and negative peak duration. The analysis of the sensitivity of the determination of fiber diameter to the errors in the measurement of amplitude and negative peak duration shows that a sampling rate of 25 kHz is required so that the error in the determined fiber diameter is less than The ability to quantify the fiber diameter can add significantly to the SFEMG examination. It may help one to study muscle fiber diameter variability in many different muscles and thus compliment the muscle biopsy studies.


In one model, each scene point is treated independently in the reconstruction; in the other, the pertinent variable is the spatial relationship between pairs of points. Participants viewed a simple environment from one location, were transported  to another part of the scene and were asked to navigate back. Error distributions varied substantially with changes in scene layout; we compared these directly with the likelihood maps to quantify the success of the models. We also measured error distributions when participants manipulated the location of a landmark to match the preceding interval, providing a direct test of the landmark-location stage of the navigation models. Models such as this, which start with scenes and end with a probabilistic prediction of behaviour, are likely to be increasingly useful for understanding 3D vision.It is often assumed that humans generate a 3D reconstruction of the environment, either in egocentric or world-based coordinates, but the steps involved are unknown. Here, we propose two reconstruction-based models, evaluated using data from two tasks in immersive virtual reality. We model the observer’s prediction of landmark location based on standard photogrammetric methods and then combine location predictions to compute  Many studies on 3D representation assume that the parietal cortex generates representations of the scene in an egocentric frame, the hippocampus does so in a world-centred frame, and coordinate transformations account for the passage of information from one frame to another . We compare this to the equivalent distribution that is inferred as part of the modelling of the first experiment. Section Section Participants viewed a simple scene in immersive virtual reality and were then teleported to a different location in the scene from where they had to return to the original location. The paradigm is similar to a previous experiment by Waller et al. . Waller Five participants took part in the experiment, all with normal or corrected-to-normal visual acuity. Participants viewed the virtual scene using an NVIS SX111 head-mounted display with horizontal field of view of 102nnerster . The sizThe layouts of the poles we used are illustrated in Fig. A trial would start when the participant was within a 20 cm mentclass2pt{minimThe main results of Experiment 1 are shown at the end of the paper in Figs. In order to gather data that could be plotted in the clear way shown here, i.e. with many trials repeated using exactly the same goal point, we adapted the protocol slightly. Instead of defining the goal point based on the participant’s location in the viewing zone of interval one when they pressed the button, we inserted an “interval 1a” during which the participant saw a static, stereo image of the scene from a fixed viewpoint, and this defined the goal point to which they should try to return in the “homing” interval.The real data . In this formulation, for a given pole configuration, the red-to-green vector will then be identical for any position of the viewing point along a line from the green pole, since this line defines the orientation of the coordinate frame. Figure We can see that with the “basic” 3D model, defined above, the condition on which its predictions looked least convincing was Fig. Find a description of landmarks from the goal point in egocentric coordinates: Transform these means into relative-location means by taking pairwise differences , i.e. adds, e.g.: Transform the associated covariance for each mean, remembering that the uncertainty Stack the three 2D Gaussians to give a single 6D shape-based description of the view of the landmarks from this goal location.Once the description has been found for the goal point, the Bhattacharyya difference between this and the descriptions calculated for other points on the 2D ground plane can be found exactly as for the “basic” model.The algorithm for creating a shape-based description of this type isThe shape-based model predictions for the navigation data are shown in Fig. In some ways, the “relative” or “shape-based” model described here is a minor extra step added to the basic model and we are deliberately treating it as such in this paper. This means that the uncertainty that arises, for example, in estimating the location of a pole in the basic model will propagate through and affect the predictions of the shape-based model. That is what makes this a type of reconstruction-based model. However, in another sense, because it is starting to use relative rather than absolute position information, this model is a step down a quite different road, ultimately leading to the abandonment of any type of reconstruction. For example, one could use the relative image locations of pairs of poles as input features to the model and consider independent noise on each of these input measurements. That would be an entirely different, view-based approach, as raised in the Introduction and discussed in a previous paper  and two of the poles remained in the same place while the third pole was displaced. It was always the same pole that was displaced throughout a whole run although the displacement varied from trial to trial. Participants were told in advance which pole would be displaced. The participant’s task was to move the shifted pole back to the location it had occupied during the first interval. They did this using a hand-held pointing device with which they could “push” or “drag” the pole in two dimensions while pressing a button on the device. Participants indicated that they were satisfied that the location of the pole matched that in the first interval by pressing a different button on the device, advancing them to the next trial. The moving pole always remained vertical, so participants could only manipulate its Examples of the data gathered from Experiment 2 are shown in Fig. In general, this pattern of position uncertainty fits the predictions of the reconstruction model described in Sect. The free parameters, In optimizing the data likelihood with respect to these parameters, we found slightly higher likelihoods for the observed data when With the viewing-strip width fixed, Figure }λ Sect. , are givmentclass2pt{minimIt is not inevitable that the agreement between the two approaches should be so close (i.e. with and without incorporating parameters derived from the data from Experiment 2). For example, if the covariance matrices for the pole positions are rotated by We have discussed the visual comparison of the two models provided in Fig. Figure entclass1pt{minimamentclasspt{minimaboth reconstruction-based models provide an explanation for a good deal of the variation observed in the human navigation error dataset, neither model is able to outperform the other consistently, and overall they have a tendency to complement one another.What is clear from Fig. We have demonstrated the extent to which a reconstruction algorithm can account for participants’ performance in a simple navigation task. Any algorithm that is to predict human behaviour successfully in this case must vary its output according to changes in the visual scene and make explicit the way that noise at various stages in the reconstruction process will affect the predicted spatial distribution of errors in the task. We are not aware of algorithms that fulfil these criteria other than those based on the principles of photogrammetry, as we have used here.post-hoc rather than describing a reconstruction process and the noise associated with its different stages. For example, Foley (Many papers have assumed that the brain generates a 3D reconstruction of the scene without providing a model of the process underlying its construction in the way that we have done here Luneburg . While oe, Foley  presentse, Foley . A more The distortions of space that these models describe Luneburg  do not pNavigation often involves proprioception and vestibular cues in addition to vision (Foo et al. Instead, our model relies on matching of representations generated from visual data. In the end, a full description of human navigation will have to account for multiple sources of sensory information and show how these are integrated. This process will almost certainly incorporate a mechanism for weighting different cues according to their reliability (Landy et al. As we raised in the Introduction, reconstruction models are not the only approach to explaining 3D representation and performance in our scene-matching task. In a subsequent paper, we will compare directly the performance of a reconstruction algorithm with that of a quite different, view-based approach.


These methods were tested on data acquired using a novel purpose-built phantom for deformable registration and clinical CT/CBCT data of prostate and lung cancer patients. The Dice similarity coefficient (DSC) between manually drawn contours and the contours generated by a derived deformation field of the structures in question was compared to the result obtained with rigid registration (RR). For the phantom, the piecewise methods were slightly superior, the featureletNC for the intramodality and the featureletMI for the intermodality registrations. For the prostate cases in less than 50% of the images studied the DSC was improved over RR. Deformable registration methods improved the outcome over a rigid registration for lung cases and in the phantom study, but not in a significant way for the prostate study. A significantly superior deformation method could not be identified.We present an evaluation of various non-rigid registration algorithms for the purpose of compensating interfractional motion of the target volume and organs at risk areas when acquiring CBCT image data prior to irradiation. Three different deformable registration (DR) methods were used: the Demons algorithm implemented in the  The development and clinical introduction of radiation delivery units with integrated imaging option has stimulated research for the management and compensation of inter- and intrafractional patient movements, which is the primary goal of image guided adaptive radiotherapy (IGART) In general, a DR algorithm consists of (i) a rigid registration step, where translations and rotations are carried out for a gross alignment of the volume image data and if necessary also scaling is done and (ii) an algorithm to improve the match of the volume data content by defining a vector field that compensates for non-rigid motion of tissue iPlan Software . The validation took place using datasets of an especially designed deformable phantom. Intramodality and intermodality examples were studied as well as patients datasets from prostate and lung cancer consisting of planning CT and CBCT datasets acquired during the treatment course.In this paper, we present a competitive validation of various non-rigid registration algorithms using a novel phantom setup and clinical data. In detail, a groupwise rigid registration algorithm 22.1An implementation of the featurelet-based deformable registration method suggested by Söhn et al. In the first step of the algorithm temporal subvolumes are created in both images. These are the featurelets (or megavoxels) of size A in the moving image – that is, the image that undergoes the spatial transform – and a search-region of size B defined on the reference image. In Fig. The featurelets of the moving image are then rigidly registered to its corresponding search-region on the reference image using a translation transform, a regular steepest gradient descent optimizer and either a Normalized Correlation Metric (NC) or a Mutual Information Metric (MI) to obtain the final displacement vectors of the megavoxels. Fig. Once the displacement vectors . This method will be referred to as iPlan throughout this document. The deformable registration was done with the default parameters of the system and for the whole body.The Demons algorithm for deformable image registration was first presented by Thirion 2.33 with three opaque and three transparent walls, one of them being the removable top (A purpose-built pelvis-shaped deformable phantom was designed. It consisted of a cubic plastic box of 40 × 40 × 29 cmable top . For sim2 FOV and 4 mm slice spacing). For every acquisition the volume inside the balloons and the position of the structures was varied to obtain internal deformation. Then three CBCT images  where also obtained changing the relative position of the structures inside the phantom .Three CT images  of the phantom where acquired with a multislice CT scanner  treatment planning system and rigidly registered. The bladder balloon, the prostate-shaped polystyrene item and the rectum-like bag where delineated manually on the six datasets. CT1 was defined as the planning CT and the other five used as the consecutive deformed datasets. The structures delineated on CT 1 where deformed using three different sets of the deformation fields obtained by the three algorithms per image set.The six image datasets where imported in the a) one of the internal deformations of the phantom can be found; here, CT1 and CT5 are overlaid and the displacement of the prostate-like structure can be clearly seen. Also, the pseudo pelvic and rectal structures are apparently coincident. Fig. b) and Fig. c) correspond to the overlap after performing deformable registration, for iPlan method and featureletMI respectively. It can be observed that the featureletMI method is deforming the central prostate-shaped structure but is not bending the box structure as much as the iPlan method.In Fig. 2.4iPlan Image Fusion application. Prostate, rectum and bladder were delineated on the panning CT and on all CBCTs of all data set by one radiation oncologist on the same treatment planning system. All structures defined on the CBCTs were mapped to the planning CT. The contours drawn on the planning CT were deformed with respect to the CBCT images; the deformation fields were obtained by a DR between the reference CT and the correspondent CBCTs.The images of nine patients treated for prostate cancer were arbitrarily selected for this study. The dataset consisted of one planning CT acquired with a multislice CT scanner  using 120 kV, 200 mAs and 4 mm slice spacing. Furthermore, 7 weekly CBCTs  were taken for all cases, resulting in 63 CBCT scans. Acquisition parameters were chosen according to recommended prostate protocol without bow-tie filter and an axial field of view (FOV) of 42 cm and 12 cm scan length. The reconstructed volume was converted to 4 mm slice thickness. Every CBCT was rigidly registered to the planning CT using the 2.5Ten arbitrarily selected patients undergoing stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC) or lung metastasis were selected for this study. Image acquisition was done with patients positioned in the BodyFIX system  for imaging and treatment. CT and CBCT imaging was performed under free-breathing conditions.iPlan, which was used for contouring.Treatment planning CT images were again acquired with a Siemens Somatom Volume Zoom CT scanner  and with intravenous contrast . CBCT images  were obtained without contrast before each treatment fraction on the linear accelerator. The CBCT acquisition protocol  was optimized for thorax imaging with a field of view of 42 cm. The duration of CBCT acquisition was approximately 2 minutes, whereas CT imaging was performed in 15 seconds. The reconstructed volume from CBCT was converted to 4 mm slices and transferred to the treatment planning system For each patient, the treatment planning CT and one randomly selected CBCT set (out of three available) were chosen for analysis. The gross target volume (GTV) was delineated in both the CT and the CBCT images of all the ten cases. The DR methods were applied to the GTV contour of the CT to establish a correspondence to the CBCT.2.6Vd is the deformed volume and Vm is the reference volume. In our case Vm corresponds to the volume obtained from the contour manually drawn on the CBCTs and Vd corresponds to the volumes obtained by deforming the contours from the CT.For analysing all the contours obtained by the different deformation parameters of the CT delineations on the three different dataset groups, the Dice similarity coefficient (DSC), which is sometimes also named volume overlap index (VOI), was used. This index is defined as:Hausdorff distanceAs a second tool for performance assessment of the registration methods the iPlan (RR).For all test conditions the starting point for making the comparisons is the rigid registration performed in The deformable phantom was used to analyse the performance of the algorithms in two different deformation scenarios; mainly, we were looking to achieve a better contouring precision than in clinical images due to the high contrast of the phantom images in both CT and CBCT acquisitions. For the intramodality registration analysis of the deformable phantom, CT1 was considered to be the reference (or fixed) image and CT2 and CT3 the moving images. After performing the rigid and deformable registrations between these images the DSC and the Hausdorff-distance were quantified. Statististical significance was determined using a Wilcoxon signed rank test.Both validations are critical for the quantification of contour propagation, which is an essential tool for the assessment of the total dose delivered.33.1MI method was not changing the average DSC value for this case, keeping it under 35. The featureletNC and the iPlan method were increasing the coefficient to 52 and 54, respectively. For the rectum the reference DSC is 100 because the structure is not moving at all and the rigid registration is performed in such a way that the coincidence of the bone in both images is fully achieved. For the three methods the value was slightly decreasing down to 96 for the iPlan method. For the prostate the initial or reference average DSC was 39.4, and had an considerable increase to 78.6, 57.8 and 74.5 for the featureletNC, featureletMI and iPlan methods respectively.The results of the average DSC for the phantom studies can be found in Figure MI was improving the DSC, showing a considerable improvement for the bladder, going from a 51.8 to 60.8.For the intermodality cases, CT1 was considered to be the reference or fixed image and CT4, CT5 and CT6 the moving images that correspond to the three CBCTs acquired in the treatment room. For all the structures only the featureletMI method as well as the iPlan method were not changing the average Hausdorff-distance value in this case. The featureletNC was decreasing the distance to 15.74 pixels. For the rectum the reference Hausdorff-distance was 0 pixels since the structure is not moving at all and the rigid registration is performed in such a way that the coincidence of the bone of both images is fully achieved. For the three methods the value was increasing up to 2.67 pixels for the iPlan method. For the prostate the initial Hausdorff-distance was 16.8, and had a considerable decrease to 10.37, 14.49 and 12.77 pixels for the featureletNC, featureletMI and iPlan methods respectively.The results of the average Hausdorff-distance for the phantom studies can be found in iPlan method was the one modifying the volume of the prostate to the largest extent which resulted on average change of the volume of 35%. The featureletNC method was changing it by 10% and the featureletMI method by less than 5%. The displacement of the center of mass of the prostate volume for the CT-CT images was 47.7 ± 14.7 mm on average. After featureletNC deformable registration it was 17.3 ± 12.4 mm, after featureletMI it was 37.1 ± 22.4 mm and after iPlan it was 20.6 ± 3.6 mm. For the CT-CBCT cases the original displacement on average was 26 ± 12.9 mm, and 23.1 ± 10, 24.2 ± 10.7 and 29.8 ± 10.2 mm after featureletNC, featureletMI and iPlan registration respectively.The prostate type polystyrene object did not vary its shape or size so also a study of the volume in voxels and the position of the center of mass was done. The 3.2NC method was deforming the structures in a way that no improvement over the initial RR could be found. (a) The featureletMI and iPlan methods are not significantly different from the result of RR for the rectum. (b) For the prostate on average all of the deformable registration methods show a deterioration of registration results compared to RR. (c) For the bladder, the featureletMI method is not significantly different compared to the RR but the iPlan method is significantly improving the Hausdorff distance.The featureletNC method was on average significantly worse than the original DSC value after doing RR and the other methods were not changing the result considerably – only an increase of outliers was achieved after performing the deformable registration of iPlan. For the prostate all the contours generated by the three deformations gave a worse DSC value than the RR  of the same importance. In most advanced radiotherapy approaches tolerance doses to OAR drive the computerized optimization approaches in treatment planning. The current knowledge on tolerance doses for organs at risk is based on static images, volumes defined at the time of treatment planning from these static images, dose volume relations extracted from that information, which is in the final stage related to observed toxicity. This methodological approach is the basis for data in the recently published QUANTEC report The number of recently published papers on DIR and dose accumulation is considerable However, one potential application still requiring more detailed examination on the usefulness of DIR is lung motion; while tracking approaches using local rigid registration do exist Beside phantom based validation of deformable registration other approaches e.g. landmarks in multiple datasets like 4D-CT for lung and liver annotated by a physician are used iPlan method had a very similar performance, although NC was slightly better. On the other hand the results in For our phantom study, we divided the evaluation into two main groups, the intra-modality and the inter-modality. Three volumes were analysed in both groups, the bladder, rectum and prostate. For the first phantom group see also  using imMI algorithm, and the 6, 5 and 17 points increase for the iPlan method in our study.It is a well known fact that for prostate cases the image contrast is very poor in either CT and CBCT modalities, efforts for auto-segmenting structures in this treatment area have been done and studied As mentioned before for the clinical lung case with all the deformable registration methods a significant improvement was achieved on the DSC see .This sug5iPlan -adaptive software. Despite the fact that very good results for deformable registration on phantoms and clinical data have been reported iPlan method performed best.In this work we presented an analysis of 3 deformable registration algorithms In general, featurelet algorithms that are based on piecewise registration methods were found to be comparable to the Demons algorithm implemented in the