bibliography.bib

@article{theobald_using_2007,
	title = {Using {GIS} to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications},
	volume = {40},
	issn = {0364-152X, 1432-1009},
	url = {http://link.springer.com/article/10.1007/s00267-005-0199-x},
	doi = {10.1007/s00267-005-0199-x},
	abstract = {Sampling of a population is frequently required to understand trends and patterns in natural resource management because financial and time constraints preclude a complete census. A rigorous probability-based survey design specifies where to sample so that inferences from the sample apply to the entire population. Probability survey designs should be used in natural resource and environmental management situations because they provide the mathematical foundation for statistical inference. Development of long-term monitoring designs demand survey designs that achieve statistical rigor and are efficient but remain flexible to inevitable logistical or practical constraints during field data collection. Here we describe an approach to probability-based survey design, called the Reversed Randomized Quadrant-Recursive Raster, based on the concept of spatially balanced sampling and implemented in a geographic information system. This provides environmental managers a practical tool to generate flexible and efficient survey designs for natural resource applications. Factors commonly used to modify sampling intensity, such as categories, gradients, or accessibility, can be readily incorporated into the spatially balanced sample design.},
	pages = {134--146},
	number = {1},
	journaltitle = {Environmental Management},
	shortjournal = {Environmental Management},
	author = {Theobald, David M. and Stevens, Don L. and White, Denis and Urquhart, N. Scott and Olsen, Anthony R. and Norman, John B.},
	urldate = {2016-09-21},
	date = {2007-05-22},
	langid = {english},
	file = {Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/7Z8HG4UB/s00267-005-0199-x.html:text/html}
}

@article{cushman_parsimony_2008,
	title = {Parsimony in landscape metrics: Strength, universality, and consistency},
	volume = {8},
	issn = {1470-160X},
	url = {http://www.sciencedirect.com/science/article/pii/S1470160X07001306},
	doi = {10.1016/j.ecolind.2007.12.002},
	shorttitle = {Parsimony in landscape metrics},
	abstract = {Ecologists can be overwhelmed by the number of metrics available to quantify landscape structure. Clarification of interrelationships and redundancy is needed to guide metric selection and interpretation for the purpose of landscape monitoring. In this study we identified independent components of class- and landscape-level structure in multiple landscapes in each of three large and geographically disjunct study areas. We used {FRAGSTATS} and principal components analysis ({PCA}) to identify independent components of landscape structure, and cluster analysis to group the components. We then calculated the universality, strength, and consistency of the identified landscape structure components. At the class-level we identified 24 independent configuration components. Seven of these components were nearly universal and consistent in interpreted meaning. At the landscape-level there were 17 independent structure components. Eight of these components were universal and consistent. These results indicate that there are consistent combinations of metrics that universally describe the major attributes of landscape structure at the class- and landscape-levels.},
	pages = {691--703},
	number = {5},
	journaltitle = {Ecological Indicators},
	shortjournal = {Ecological Indicators},
	author = {Cushman, Samuel A. and {McGarigal}, Kevin and Neel, Maile C.},
	urldate = {2016-09-21},
	date = {2008-09},
	keywords = {Ecological indicators, {FRAGSTATS}, landscape metrics, Landscape monitoring, Landscape pattern, Landscape structure},
	file = {ScienceDirect Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/SUN8MRHH/S1470160X07001306.html:text/html}
}

@article{mcgarigal_spatial_1995,
	title = {Spatial pattern analysis program for quantifying landscape structure},
	url = {http://www.umass.edu/landeco/pubs/mcgarigal.marks.1995.pdf},
	journaltitle = {Gen. Tech. Rep. {PNW}-{GTR}-351. {US} Department of Agriculture, Forest Service, Pacific Northwest Research Station},
	author = {{McGarigal}, Kevin and Marks, Barbara J.},
	urldate = {2016-09-21},
	date = {1995},
	file = {[PDF] umass.edu:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/J6P5NW45/McGarigal and Marks - 1995 - Spatial pattern analysis program for quantifying l.pdf:application/pdf}
}

@article{langford_map_2006,
	title = {Map Misclassification Can Cause Large Errors in Landscape Pattern Indices: Examples from Habitat Fragmentation},
	volume = {9},
	issn = {1432-9840, 1435-0629},
	url = {http://link.springer.com/article/10.1007/s10021-005-0119-1},
	doi = {10.1007/s10021-005-0119-1},
	shorttitle = {Map Misclassification Can Cause Large Errors in Landscape Pattern Indices},
	abstract = {Although habitat fragmentation is one of the greatest threats to biodiversity worldwide, virtually no attention has been paid to the quantification of error in fragmentation statistics. Landscape pattern indices ({LPIs}), such as mean patch size and number of patches, are routinely used to quantify fragmentation and are often calculated using remote-sensing imagery that has been classified into different land-cover classes. No classified map is ever completely correct, so we asked if different maps with similar misclassification rates could result in widely different errors in pattern indices. We simulated landscapes with varying proportions of habitat and clumpiness (autocorrelation) and then simulated classification errors on the same maps. We simulated higher misclassification at patch edges (as is often observed), and then used a smoothing algorithm routinely used on images to correct salt-and-pepper classification error. We determined how well classification errors (and smoothing) corresponded to errors seen in four pattern indices. Maps with low misclassification rates often yielded errors in {LPIs} of much larger magnitude and substantial variability. Although smoothing usually improved classification error, it sometimes increased {LPI} error and reversed the direction of error in {LPIs} introduced by misclassification. Our results show that classification error is not always a good predictor of errors in {LPIs}, and some types of image postprocessing (for example, smoothing) might result in the underestimation of habitat fragmentation. Furthermore, our results suggest that there is potential for large errors in nearly every landscape pattern analysis ever published, because virtually none quantify the errors in {LPIs} themselves.},
	pages = {474--488},
	number = {3},
	journaltitle = {Ecosystems},
	shortjournal = {Ecosystems},
	author = {Langford, William T. and Gergel, Sarah E. and Dietterich, Thomas G. and Cohen, Warren},
	urldate = {2016-09-21},
	date = {2006-04-17},
	langid = {english},
	file = {Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/ZR8MRVF4/s10021-005-0119-1.html:text/html}
}

@article{sanchez_meador_new_2011,
	title = {A New Method for Delineating Tree Patches and Assessing Spatial Reference Conditions of Ponderosa Pine Forests in Northern Arizona},
	volume = {19},
	issn = {1526-100X},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1526-100X.2010.00652.x/abstract},
	doi = {10.1111/j.1526-100X.2010.00652.x},
	abstract = {Multiscale spatial patterns of forest reference conditions offer insight on how historical and environmental processes have influenced forest stand dynamics. Yet, spatial information is often either unavailable or partial, because many studies provide nonspatial reference condition information, whereas others only report the local (i.e. observed at plot extent) spatial arrangement of trees. However, knowledge of multiscaled spatial patterns, including stand-, among-patch-, and within-patch-level patterns, is needed to ensure that forest management strategies suit a wide range of objectives in ecosystem restoration. In this study, we propose a new framework for delineating tree patches based on common stand attributes. We found that patch reference conditions for presettlement ponderosa pine in northern Arizona ranged from 10 to 27 patches/ha, accounted for 62–75\% of the total historic stand basal area (m2/ha) and varied in size from 0.01 to 0.15 ha. Lastly, discussion was made on the importance of using a patch-delineation framework, such as the one proposed in this study, as a basis for quantifying stand- and patch-level patterns of reconstructed presettlement and current forest conditions and how this information should guide spatially explicit restoration management (e.g. thinning prescriptions).},
	pages = {490--499},
	number = {4},
	journaltitle = {Restoration Ecology},
	author = {Sánchez Meador, Andrew J. and Parysow, Pablo F. and Moore, Margaret M.},
	urldate = {2016-09-21},
	date = {2011-07-01},
	langid = {english},
	keywords = {Arizona, G. A. Pearson, Pinus ponderosa, presettlement, spatial reference conditions, tree patches, T. S. Woolsey, Jr., Woolsey plots},
	file = {Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/HIUTZI38/abstract.html:text/html}
}

@article{saura_sensitivity_2001,
	title = {Sensitivity of landscape pattern metrics to map spatial extent},
	volume = {67},
	pages = {1027--1036},
	number = {9},
	journaltitle = {Photogrammetric engineering and remote sensing},
	author = {Saura, Santiago and Martinez-Millan, Javier},
	date = {2001},
	file = {[PDF] upm.es:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/WWJKRZC8/Saura and Martinez-Millan - 2001 - Sensitivity of landscape pattern metrics to map sp.pdf:application/pdf}
}

@misc{williamson_biophysical_2012,
	title = {Biophysical monitoring for the desired conditions of the Four Forest Restoration Initiative},
	author = {Williamson, M and Huffman, D and Smith, E and Waltz, A and {McKinnon}, T},
	date = {2012}
}

@article{wu_effects_????,
	title = {Effects of changing scale on landscape pattern analysis: scaling relations},
	volume = {19},
	issn = {0921-2973, 1572-9761},
	url = {http://link.springer.com/article/10.1023/B:LAND.0000021711.40074.ae},
	doi = {10.1023/B:LAND.0000021711.40074.ae},
	shorttitle = {Effects of changing scale on landscape pattern analysis},
	abstract = {Landscape pattern is spatially correlated and scale-dependent. Thus, understanding landscape structure and functioning requires multiscale information, and scaling functions are the most precise and concise way of quantifying multiscale characteristics explicitly. The major objective of this study was to explore if there are any scaling relations for landscape pattern when it is measured over a range of scales (grain size and extent). The results showed that the responses of landscape metrics to changing scale fell into two categories when computed at the class level (i.e., for individual land cover types): simple scaling functions and unpredictable behavior. Similarly, three categories were found at the landscape level, with the third being staircase pattern, in a previous study when all land cover types were combined together. In general, scaling relations were more variable at the class level than at the landscape level, and more consistent and predictable with changing grain size than with changing extent at both levels. Considering that the landscapes under study were quite diverse in terms of both composition and configuration, these results seem robust. This study highlights the need for multiscale analysis in order to adequately characterize and monitor landscape heterogeneity, and provides insights into the scaling of landscape patterns.},
	pages = {125--138},
	number = {2},
	journaltitle = {Landscape Ecology},
	shortjournal = {Landscape Ecology},
	author = {Wu, Jianguo},
	urldate = {2016-09-21},
	langid = {english},
	file = {Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/RTVDB74A/BLAND.0000021711.40074.html:text/html}
}

@article{wickham_sensitivity_1995,
	title = {Sensitivity of landscape metrics to pixel size},
	volume = {16},
	issn = {0143-1161},
	url = {http://dx.doi.org/10.1080/01431169508954647},
	doi = {10.1080/01431169508954647},
	abstract = {Analysis of diversity and evenness metrics using land cover data are becoming formalized in landscape ecology. Diversity and evenness metrics are dependent on the pixel size (scale) over which the data are collected. Aerial photography was interpreted for land cover and converted into four raster data sets with 4, 12, 28, and 80m pixel sizes, representing pixel sizes up to that available on Landsat-{MSS}. Analysis of covariance was used to determine the effect of changing pixel size on landscape metrics. The results indicate that landscape metrics should not be dramatically affected by the change in pixel size up to 80m, provided that identical land cover classifications could be generated by sensors with different spatial resolving powers (e.g. Landsat-{TM} and {MSS}).},
	pages = {3585--3594},
	number = {18},
	journaltitle = {International Journal of Remote Sensing},
	author = {Wickham, J. D. and Riitters, K. H.},
	urldate = {2016-09-21},
	date = {1995-12-01},
	file = {Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/SMA9HQWP/01431169508954647.html:text/html}
}

@article{canny_computational_1986,
	title = {A Computational Approach to Edge Detection},
	volume = {{PAMI}-8},
	issn = {0162-8828},
	doi = {10.1109/TPAMI.1986.4767851},
	abstract = {This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.},
	pages = {679--698},
	number = {6},
	journaltitle = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
	author = {Canny, J.},
	date = {1986-11},
	keywords = {Detectors, Edge detection, Feature extraction, Gaussian approximation, Image edge detection, image processing, Machine vision, multiscale image analysis, Performance analysis, Shape measurement, Signal synthesis, Signal to noise ratio, Uncertainty},
	file = {IEEE Xplore Abstract Record:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/JMEXEKIZ/4767851.html:text/html}
}

@article{haralick_textural_1973,
	title = {Textural Features for Image Classification},
	volume = {{SMC}-3},
	issn = {0018-9472},
	doi = {10.1109/TSMC.1973.4309314},
	abstract = {Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite ({ERTS}) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.},
	pages = {610--621},
	number = {6},
	journaltitle = {{IEEE} Transactions on Systems, Man, and Cybernetics},
	author = {Haralick, R. M. and Shanmugam, K. and Dinstein, I.},
	date = {1973-11},
	keywords = {Application software, Crops, Earth, Humans, Image classification, Image resolution, Piecewise linear techniques, Satellites, Spatial resolution, Testing},
	file = {IEEE Xplore Abstract Record:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/RU4FX2V5/4309314.html:text/html}
}

@article{conners_segmentation_1984,
	title = {Segmentation of a high-resolution urban scene using texture operators},
	volume = {25},
	issn = {0734-189X},
	url = {http://www.sciencedirect.com/science/article/pii/0734189X8490197X},
	doi = {10.1016/0734-189X(84)90197-X},
	abstract = {A study aimed at segmenting a high-resolution black and white image of Sunnyvale, California, is described. In this study regions were classified as belonging to any one of nine classes: residential, commercial/industrial, mobile home, water, dry land, runway/taxiway, aircraft parking, multilane highway, and vehicle parking. The classes were selected so that they directly relate to the Defense Mapping Agency's Mapping, Charting and Geodesy tangible features. To attack the problem a statistical segmentation procedure was devised. The primitive operators used to drive the segmentation are texture measures derived from cooccurrence matrices. The segmentation procedure considers three kinds of regions at each level of the segmentation: uniform, boundary, and unspecified. At every level the procedure differentiates uniform regions from boundary and unspecified regions. In the assigns a class label to the uniform regions. The boundary and unspecified regions are split to form higher level regions. The methodologies involved are mathematically developed as a series of hypothesis tests. While only a one-level segmentation was performed studies are described which show the capabilities of each of these hypothesis tests. In particular an 83\% correct classification was obtained in testing the labeling procedure. These studies indicate that the proposed procedure should be useful for land use classifications as well as other problems.},
	pages = {273--310},
	number = {3},
	journaltitle = {Computer Vision, Graphics, and Image Processing},
	shortjournal = {Computer Vision, Graphics, and Image Processing},
	author = {Conners, Richard W. and Trivedi, Mohan M. and Harlow, Charles A.},
	urldate = {2016-10-25},
	date = {1984-03-01},
	file = {ScienceDirect Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/9VEKT57E/0734189X8490197X.html:text/html}
}

@article{anselin_local_1995,
	title = {Local Indicators of Spatial Association—{LISA}},
	volume = {27},
	issn = {1538-4632},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.1995.tb00338.x/abstract},
	doi = {10.1111/j.1538-4632.1995.tb00338.x},
	abstract = {The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems ({GIS}) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association ({LISA}) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The {LISA} statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a {LISA} statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.},
	pages = {93--115},
	number = {2},
	journaltitle = {Geographical Analysis},
	author = {Anselin, Luc},
	urldate = {2016-10-25},
	date = {1995-04-01},
	langid = {english},
	file = {Full Text PDF:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/S5FKGBHK/Anselin - 1995 - Local Indicators of Spatial Association—LISA.pdf:application/pdf;Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/UBXM3X9Q/abstract.html:text/html}
}

@article{farr_shuttle_2007,
	title = {The Shuttle Radar Topography Mission},
	volume = {45},
	issn = {1944-9208},
	url = {http://onlinelibrary.wiley.com/doi/10.1029/2005RG000183/abstract},
	doi = {10.1029/2005RG000183},
	abstract = {The Shuttle Radar Topography Mission produced the most complete, highest-resolution digital elevation model of the Earth. The project was a joint endeavor of {NASA}, the National Geospatial-Intelligence Agency, and the German and Italian Space Agencies and flew in February 2000. It used dual radar antennas to acquire interferometric radar data, processed to digital topographic data at 1 arc sec resolution. Details of the development, flight operations, data processing, and products are provided for users of this revolutionary data set.},
	pages = {RG2004},
	number = {2},
	journaltitle = {Reviews of Geophysics},
	shortjournal = {Rev. Geophys.},
	author = {Farr, Tom G. and Rosen, Paul A. and Caro, Edward and Crippen, Robert and Duren, Riley and Hensley, Scott and Kobrick, Michael and Paller, Mimi and Rodriguez, Ernesto and Roth, Ladislav and Seal, David and Shaffer, Scott and Shimada, Joanne and Umland, Jeffrey and Werner, Marian and Oskin, Michael and Burbank, Douglas and Alsdorf, Douglas},
	urldate = {2016-10-25},
	date = {2007-06-01},
	langid = {english},
	keywords = {1824 Hydrology: Geomorphology: general, 6924 Radio Science: Interferometry, 9805 General or Miscellaneous: Instruments useful in three or more fields, interferometry, radar, topography},
	file = {Full Text PDF:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/9WHJ5XRA/Farr et al. - 2007 - The Shuttle Radar Topography Mission.pdf:application/pdf;Snapshot:/Users/lukezachmann/Library/Application Support/Zotero/Profiles/0eszmh88.default/zotero/storage/2GSDDT4N/abstract.html:text/html}
}

@article{breiman_random_2001,
	title = {Random Forests},
	volume = {45},
	issn = {08856125},
	url = {http://link.springer.com/10.1023/A:1010933404324},
	doi = {10.1023/A:1010933404324},
	pages = {5--32},
	number = {1},
	journaltitle = {Machine Learning},
	author = {Breiman, Leo},
	urldate = {2016-10-25},
	date = {2001}
}

@article{de_martino_combining_2008,
	title = {Combining multivariate voxel selection and support vector machines for mapping and classification of {fMRI} spatial patterns},
	volume = {43},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2008.06.037},
	abstract = {In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. Here we employ a strategy for classifying functional imaging data based on a multivariate feature selection algorithm, Recursive Feature Elimination ({RFE}) that uses the training algorithm (support vector machine) recursively to eliminate irrelevant voxels and estimate informative spatial patterns. Generalization performances on test data increases while features/voxels are pruned based on their discrimination ability. In this article we evaluate {RFE} in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures. Using simulated {fMRI} data, we show that the recursive approach is suitable for mapping discriminative patterns and that the combination of an initial univariate activation-based (F-test) reduction of voxels and multivariate recursive feature elimination produces the best results, especially when differences between conditions have a low contrast-to-noise ratio. Furthermore, we apply our method to high resolution (2 x 2 x 2 mm(3)) data from an auditory {fMRI} experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temporal gyrus in encoding the information of sound categories. In line with the simulation results, our method outperforms univariate statistical analysis and statistical learning without feature selection.},
	pages = {44--58},
	number = {1},
	journaltitle = {{NeuroImage}},
	shortjournal = {Neuroimage},
	author = {De Martino, Federico and Valente, Giancarlo and Staeren, Noël and Ashburner, John and Goebel, Rainer and Formisano, Elia},
	date = {2008-10-15},
	pmid = {18672070},
	keywords = {algorithms, Artificial Intelligence, Brain, Brain Mapping, Evoked Potentials, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, multivariate analysis, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}
}