dataesr/scientific-paragraphs-categorization huggingface dataset:
Subjects Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. ucla. edu), for up-to-date information, see www. adniinfo. org. The data were analysed anonymously, using publicly available secondary data from the ADNI study, therefore no ethics statement is required for this work. From the ADNI database, a preliminary dataset containing AD and normal subjects were downloaded first while developing the method  [22]. From this first dataset, we selected the subjects who had at least 2 scans with at least 200 days apart. This resulted in 90 AD patients and 54 control subjects. Later we expanded the dataset to have similar number of controls as patients and added the MCI group. From the downloaded subjects, we included the first about 90 subjects who fulfilled the inclusion criteria, namely having at least two MRIs with at least 200 days apart.
Measurements of Atrophy Following the recommendation of Yushkevich et al. [26] , we performed an unbiased symmetric deformation field estimation to measure the hippocampal atrophy rate. ANTS software (www. picsl. upenn. edu/ANTS) was used to compute the symmetric deformation field (SyN method) between MRI_acq1 and MRI_acq2 using a normalised cross-correlation metric. The displacement of each voxel in MRI_acq1 was written in the WarpX, WarpY, and WarpZ images of the deformation fields. The parameters of our deformation-based morphometry included isotropic 2 mm (FWHM Gaussian kernel) image smoothing. Finally, for each vertex of the hippocampal surface (MRI_acq1), the dot product between the normal and the deformation field defined the signed displacement of the vertex (in mm) (Fig. 1B ). The resulting values (divided by the duration between acquisition MRI_acq1 and MRI_acq2) represent the atrophy rate in mm/ year.
Region of Interest Analysis To further test for differences between pMCI and sMCI, we performed a Region of Interest (ROI) analysis of the atrophy rate. The ROIs were used to increase the sensitivity to distinguish patients with stable MCI from progressive ones. We defined the ROIs as clusters of vertices with significantly higher atrophy rates in AD than in control subjects; which make them independently defined from the MCI dataset. A total of four ROIs were defined both in the right (R1R-R4R) and left (R1L-R4L) hippocampi. R1 is the cluster of vertices in the medial side of the hippocampal head, R2 corresponds to the cluster in the medial side of the body, R3 represents the cluster in the lateral side of head, whereas R4 contains the clusters along the lateral side of the hippocampal body and tail.
The ROIs therefore do not correspond to a specific hippocampal subfield derived from the high-resolution atlas (see section Hippocampal subfields). Within each region, we computed the average atrophy rate for each MCI patient. The stable and progressive MCI groups were compared statistically using Wilcoxon test (significance level was adjusted for the number of ROIs with Bonferroni correction). Receiver Operating Characteristic (ROC) analysis was then performed using the R package pROC version 1. 4. 4  [28].
Although the difference in hippocampal volume had the same trend in many studies previously, the volume itself differed substantially. Groups, using the ADNI database of patients with AD and MCI and healthy elderly controls, reported hippocampal volumes in AD varying between 1600  [35]  and 3000 mm3  [20]. In the present study, the hippocampal volume of AD patients was about 2000 mm3. The remarkable difference among studies might derive from the segmentation of the template hippocampus used to extract the structure in each individual. There are different recommendations for hippocampal segmentation which include different amount of the subiculum and the tail of the hippocampus  [38]. It can also differ how far anterior the hippocampus is segmented inferior to the amygdala. Here we followed the recommendations of Franko et al.
Importantly, more and more studies measure the atrophy using a longitudinal MRI dataset, allowing the visualisation of the hippocampal atrophy within each single subject over time  [15, 19, 21, 32, 42]. This method does not require a template hippocampus computed by averaging of healthy subjects' brains, therefore it is more robust for individual variability in the hippocampal shape. However, the methods could be biased by asymmetric deformation mapping  [26, 48] , which could explain the contradicting findings in the previous studies. We therefore examined the hippocampal atrophy performing an unbiased symmetric deformation field estimation. One of the first studies comparing the hippocampal atrophy rate in patients with AD to that in healthy subjects was performed by Thompson et al. [19].
Acknowledgments The authors are indebted to  T. E Cope  for comments on earlier versions of the manuscript. Data used in preparation of this article were obtained from the  Alzheimers Disease Neuroimaging Initiative  (ADNI) database (adni. loni. ucla. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. ucla. edu/wp-content/uploads/how_to_ apply/ADNI_Acknowledgement_List. pdf.
Summary of plant traits included in the handbook The range of values corresponds to those generally reported for field-grown plants. Ranges of values are based on the literature and the authors' datasets and do not always necessarily correspond to the widest ranges that exist in nature or are theoretically possible. Recommended sample size indicates the minimum and preferred number of individuals to be sampled, so as to obtain an appropriate indication of the values for the trait of interest; when only one value is given, it corresponds to the number of individuals ( = replicates); when two values are given, the first one corresponds to the number of individuals and the second one to the number of organs to be measured per individual. Note that one replicate can be compounded from several individuals (for smaller species), whereas one individual cannot be used for different replicates.
2001; Arneth et al. 2010 ). However, current-generation DGVMs do not yet incorporate continuous variation in plant traits among plant species or types  (Cornwell et al. 2009). Next-generation models could benefit from the incorporation of functional traits and syndromes that are simple and general enough to be assessed at the regional and global scales, and yet informative enough to relate to biogeochemical dynamics, dispersal and large-scale disturbance  (Ollinger et al. 2008; Stich et al. 2008; Doherty et al. 2010; Harrison et al. 2010; Ma et al. 2011). As a consequence of this surge of theoretical and practical interest, there has been a rapid expansion of large regional and global trait databases (e. g. Díaz et al. 2004; Wright et al. 2004; Kleyer et al. 2008; Cornwell et al. 2008; Chave et al.
2009; Paula et al. 2009; Baraloto et al. 2010a; Zanne et al. 2010; Fortunel et al. 2012; Patiño et al. 2012). The TRY Initiative  (Kattge et al. 2011 ; see Box 1) is compiling a communal worldwide database of plant traits, an unprecedented step in improving the capacity of the scientific community to access and utilise plant-trait information. In this context, standardisation of protocols applicable under a wide range of situations and geographical contexts becomes even more important. In this manual, we consider plant functional traits to be any morphological, physiological or phenological feature, measurable for individual plants, at the cell to the wholeorganism level, which potentially affects its fitness (cf. McGill et al. 2006; Lavorel et al. 2007; Violle et al. 2007)  or its environment  (Lavorel and Garnier 2002).
We give only brief ecological background for each trait, with a short list of references with further details on significance, methodology and existing large datasets. The main section of each recipe contains a brief, standardised protocol, and under the heading Special cases or extras, we give pointers to interesting additional methods and parameters. Readers can find complementary methods and additional discussions and comments in specific associated web pages (see Box 1). Specific citations have not been included in the recipe descriptions. We hope that the authors of relevant publications (most of them cited at the end of each recipe) will understand this choice, made for clarity and brevity, and in full recognition of the important contribution that each of them and many additional studies have made to the theory and measurement procedure for each trait.
This new handbook both updates theory, methods and databases covered by its predecessor  (Cornelissen et al. 2003) , and provides protocols for several additional plant functional traits, especially for organs other than the leaf. It has better coverage of (1) measurements important in less studied biomes and ecosystems,  (2)  floras with special adaptations and (3) plant functions related to carbon and nutrient cycling, herbivory, water dynamics and fire. We hope that the focus on practical techniques and streamlined trait recipes will help this handbook become a useful reference in laboratories and in the field for studies around the world. We strongly invite users to share their experiences with us about both general issues and specific details of these protocols (see  Box 1) , so that the next edition will be an even better bed-side table companion.
How to record? Growth form is a hierarchical trait assessed through field observation or descriptions or figures or photographs in the literature. Because we are classifying types along a continuum, intermediate forms, between the categories recognised here, may be encountered, as well as occasional unique forms lying outside any of these categories. References on theory, significance and large datasets:  Cain (1950) ;  Ellenberg and Müller-Dombois (1967) ;  Whittaker (1975) ;  Barkman (1988, and references therein) ;  Rundel (1991) ;  Richter (1992) ;  Box (1996) ;  Ewel and Bigelow (1996) ;  Cramer (1997) ;  Lüttge (1997) ;  Medina (1999) ;  McIntyre and Lavorel (2001). More on methods:  Barkman (1988, and references therein).
For vegetation types with dense undergrowth that makes the measurement of H max difficult, there are modified versions of the equation above; they involve the use of a pole of known height that must be placed vertically at the base of the tree. References on theory, significance and large datasets:  Gaudet and Keddy (1988) ;  Niklas (1994) ;  Hirose and Werger (1995) ;  Thomas (1996) ;  Westoby (1998) ;  Kohyama et al. (2003) ;  King et al. (2006) ;  Poorter et al. (2006 Poorter et al. ( , 2008)) ;  Moles et al. (2009). More on methods:  Korning and Thomsen (1994) ;  Thomas (1996) ;  Westoby (1998) ;  McIntyre et al. (1999) ;  Weiher et al. (1999).
Clonal growth may fulfil more than one of these functions, in which case it may not be possible to distinguish between them. In some cases, the functional nature of clonal growth may be simply (4) unknown or not evident, in which case it may be recorded as such. References on theory, significance and large datasets: De Kroon and Van Groenendael (1997);  Klimeš et al. (1997) ;  Van Groenendael et al. (1997) ;  Klimeš and Klimešová (2005) ;  Knevel et al. (2003) ;  Klimešová and Klimeš (2007). More on methods:  Böhm (1979) ;  Klimeš et al. (1997) ;  Van Groenendael et al. (1997) ;  Weiher et al. (1998) ;  Klimeš and Klimešová (2005).
Bear in mind that the size, structure and behaviour of herbivores vary enormously, so the degree of (4) Intermediate or high density of hard, sharp spines >5 mm long; plant causes strong pain when hit carelessly. (5) Intermediate or high density of hard, sharp spines >20 mm long; plant may cause significant wounds when hit carelessly. (6) Intermediate or high density of hard, sharp spines >100 mm long; plant is dangerous to careless large mammals, including humans. protection provided by spine mass, size and distribution can be determined only with reference to a particular kind of herbivore. When selecting the most meaningful measurement/s of spinescence, always consider what herbivores are relevant. References on theory, significance and large datasets:  Milton (1991) ;  Grubb (1992) ;  Cooper and Ginnett (1998) ;  Pisani and Distel (1998) ;  Olff et al.
An indicator of branching architecture, called apical dominance index (ADI), is obtained by dividing the number of ramifications by the total length of the branch in metres. The value of ADI can vary between zero (no branching) to >100 m -1 (extremely ramified). References on theory, significance and large datasets:  Horn (1971) ;  Pickett and Kempf (1980) ;  Strauss and Agrawal (1999) ;  Enquist (2002) ;  Archibald and Bond (2003) ;  Cooper et al. (2003) ;  Staver et al. (2011). More on methods: Fisher (1986).
For these species, the conductivity of the sapwood (and its decline with sapwood age) can be quantified by placing the cut end of the shoot into a fairly strong solution of a dye, such as eosin, and allowing the foliage to transpire in air, and after 10-20 min, cutting a crosssection of the stem a few centimetres above its cut end and measuring the dye-stained area. References on theory, significance and large datasets: Chiba (1991);  Eamus and Prior (2001) ;  Maherali and DeLucia (2001) ;  Mäkelä and Vanninen (2001); McDowell et al. (2002) ;  Preston and Ackerly (2003) ;  Addington et al. (2006) ;  Buckley and Roberts (2006) ;  Maseda and Fernández (2006) ;  Wright et al. (2006) ;  Cornwell et al. (2007) ;  Litton et al. (2007). References on meta-analysis: Mencuccini (2003).
Note that some reports of differences in RMF across resource gradients are potentially confounded by failure to account for allometry and size (see References on theory, significance and large databases below in the present Section). Additionally, RMF does not directly translate to a high soil resource-uptake rate. Lower allocation to roots may well be compensated by higher specific root length (see Section 5. 1) and by higher uptake rate per allocation to root mass, length or surface area. The RMF can best be used for comparative purposes if measured for plants of similar mass. Alternatively, if plants are harvested of a range of mass, allometries can be used to estimate RMF for plants of a given size. Care should be taken to harvest all the roots (see Section 5), despite the difficulty of separating roots from soil, particularly fine roots.
Special cases or extras (1) Storage organs and root fractioning. RMF should in theory include everything that is plant-developed (so not including mycorrhizae!). However, particular studies can subdivide specific fractions for specific purposes (i. e. fine roots, coarse roots, crowns, rhizomes (for grasses), tap roots (in trees)) to evaluate the relative proportions of each in relation to each other and/or to above-ground biomass. References on theory, significance and large databases:  Evans (1972) ;  Grime (1979) ;  Aerts et al. (1991) ;  Elberse and Berendse (1993) ;  Veneklaas and Poorter (1998) ;  Aerts and Chapin (1999) ;  Reich (2002) ;  Sack et al. (2003) ;  Poorter et al. (2012).
Special cases and extras (1) Succulents and halophytes. Many salt-tolerant succulents are halophytes and occur only in saline environments; expression of the traits described above can depend on the actual salinity of the plants' soil. We, therefore, suggest measuring soil salt concentrations (as described under Selective root cation uptake above, within the present Section) to accompany trait measurements. Several other salt-related habitat descriptors are also relevant, e. g. elevation and duration of daily marine inundation (if any) in salt marshes or on beaches, and location relative to the high tide mark visible as a litter belt, or white patches on the soil surface, indicating salt crystals in dry areas. References on theory, significance and large datasets:  Flowers et al. (1977 Flowers et al.
(4) Smooth curves. In the case of frequent (small) harvests, a special technique can be applied, in which polynomial curves are fitted through the data. This is an art in itself! References on theory, significance and large datasets: Evans (1972);  Grime and Hunt (1975) ;  Kitajima (1994) ;  Cornelissen et al. (1996) ;  Walters and Reich (1999) ;  Poorter and Nagel (2000) ;  Poorter and Garnier (2007) ;  Rees et al. (2010). More on methods:  Evans (1972) ;  Causton and Venus (1981) ;  Hunt (1982) ;  Poorter and Lewis (1986) ;  Poorter and Welschen (1993) ;  Cornelissen et al. (1996) ;  Rees et al. (2010).
The summed proportion of live and dead fine fuels (foliage and woody of <6 mm) may be the best correlate of overall surface area-tovolume ratio. (4) Fuel bulk density (= fuel weight/fuel volume) and canopy compactness (ratio of fuel volume to canopy volume) have also been used to characterise heat conductivity, mainly at the population and community levels. Furthermore, high litter fall and low decomposition rate will increase the combustibility of the community. References on theory, significance and large datasets:  Mutch (1970) ;  Rothermel (1972) ;  Bond and Midgley (1995) ;  Bond and Van Wilgen (1996) ;  Schwilk and Ackerly (2001) ;  Gill and Zylstra (2005) ;  Scarff and Westoby (2006) ;  Cornwell et al. (2009) ;  Pausas et al. (2012). More on methods:  Papió and Trabaud (1990) ;  Stephens et al.
(5) Epiphyte load, which in some cases can be species-specific but generally is not, can hugely influence water interception of rainfall and fog, and probably stemflow (increasing water retention on the host plant and modifying water fluxes). Wherever epiphyte load is important, it should be accounted for, e. g. as a covariate. Epiphyte load can be assessed as mass, as percentage cover or as counts (for large epiphytes). Then, their interception and retention properties can be quantified as described above. References on theory, significance and large datasets:  Skinner et al. (1989) ;  Brewer et al. (1991) ;  Puigdefábregas and Pugnaire (1999) ;  David et al. (2005) ;  Martorell and Ezcurra (2007). More on methods:  Aston (1979) ;  Herwitz (1985) ;  Veneklaas et al. (1990) ;  Meyer (1994) ;  Domingo et al.
Specific leaf area is a function of leaf dry-matter content (see Section 3. 3), and L th (see Section 3. 4). Both components can contribute to SLA to different degrees, depending on the habitat and the plant group in question. In cool-temperate herbaceous datasets, low SLA of slow-growing species tends to be related to high leaf dry-matter content, more than to high leaf thickness. When woody perennials are dominant, L th can be equally influential. Some species that normally grow in deeply shaded, and thus presumably resource-limited, micro-habitats (e. g. Oxalis) have a high SLA and low L th. In areas with severe soil-nutrient limitations, slow-growing plants with sclerophyllous leaves (with thick epidermal walls and cuticle, abundant sclerification, high ratio of crude fibre to protein) are common.
Cutting leaves into smaller pieces may facilitate flattening. In both cases, LA can be measured with image analysis software. Freely downloadable programs are e. g. Leafarea (A. P. Askew, University of Sheffield, UK, downloadable from the Nucleo DiverSus toolbox, see Box 1) or, for more complex analyses including other plant organs, ImageJ (from the US National Institutes of Health; http:// www. nih. gov/, accessed 22 February 2013) and GIMP (from the GNU Project; http://www. gnu. org/, accessed 22 February 2013). Transform images to the HSV colourspace for a better separation of leaf and background. Additional details on image processing can be found at Prometheus Wiki (see Box 1). After the area measurement, put each leaf sample in the oven (petioles and laminae either separately or in the same envelope, according to the objective of the study, see Special cases or extras below in the present Section), ideally at 70 C for at least 72 h, or at 80 C for 48 h (avoid higher temperatures); then determine the dry mass.
There is also emerging evidence that leaf width contributes more positively than does the area of the whole leaf to the expression of canopy dominance. (5) Leaf number per node. Leaf size is a compromise between functional and resource-use efficiency. Plants are modular in construction and, as a result, these functions can be partially uncoupled. Species with alternate, opposite and whorled leaves frequently co-exist and leaf dry mass or area multiplied by the number of leaves per node provides additionally a crude estimate of the size of each growth module. This may in extreme cases be 10 times the value of a single leaf. References on theory, significance and large datasets:  Raunkiaer (1934) ;  Parkhurst and Loucks (1972) ;  Givnish (1987) ;  Cornelissen (1999) ;  Ackerly et al. (2002) ;  Westoby et al.
Special cases or extras (i) Needle leaves. For needle leaves that are circular in crosssection, average L th can be quickly estimated as Diameter Â p/4 (equivalent to cross-sectional area divided by cross-section width). Still, because needle leaves typically taper towards the leaf tip, several measurements would normally need to be made. References on theory, significance and large datasets:  Clements (1905) ;  Givnish (1979) ;  Parkhurst (1994) ;  Enríquez et al. (1996) ;  Knapp and Carter (1998) ;  Smith et al. (1998) ;  Wilson et al. (1999) ;  Green and Kruger (2001) ;  Niinemets (2001) ;  Díaz et al. (2004). More on methods:  Witkowski and Lamont (1991) ;  Garnier and Laurent (1994) ;  Shipley (1995) ;  Wright and Westoby (2002) ;  Vile et al. (2005) ;  Poorter et al. (2009) ;  Hodgson et al.
This should be carried out immediately before shaking and centrifuging. References on theory, significance and large datasets: Zinke (1962);  Marschner (2012) ;  Finzi et al. (1998) ;  Cornelissen et al. (2006 Cornelissen et al. ( , 2011)) ;  Freschet et al. (2010). More on methods:  Cornelissen et al. (2006 Cornelissen et al. ( , 2011)).
Special cases or extras (1) Leafless and heterophyllous plants. See Section 3. 1. References on theory, significance and large datasets:  Chapin (1980) ;  Field and Mooney (1986) ;  Lambers and Poorter (1992) ;  Aerts (1996) ;  Cornelissen et al. (1997) ;  Grime et al. (1997) ;  Reich et al. (1997 Reich et al. ( , 2010)) ;  Aerts and Chapin (1999) ;  Wright et al. (2004). More on methods:  Allen (1989) ;  Anderson and Ingram (1993) ;  Horneck and Miller (1998) ;  Temminghoff and Houba (2004).
Special cases or extras (1) Leafless plants. See Section 3. 1. (2) Leaf-tissue toughness or leaf-specific toughness. This interesting additional parameter of leaf strength can be obtained by dividing F t , W s or F p by the (average) thickness of the leaf sample. Measurement of L th (in mm) for this purpose is most practically carried out with a calliper, immediately after measuring the leaf-sample width and before placing it in the measuring apparatus. References on theory, significance and large datasets: Coley (1988);  Choong et al. (1992) ;  Grubb (1992) ;  Turner (1994) ;  Wright and Vincent (1996) ;  Cornelissen et al. (1999) ;  Lucas et al. (2000) ;  Díaz et al. (2004) ;  Read and Stokes (2006) ;  Kitajima and Poorter (2010) ;  Onoda et al. (2011). More on methods:  Hendry and Grime (1993) ;  Darvell et al.
Special cases or extras (1) Leafless plants. If photosynthetic tissues do not die and fall off as separate units, follow Method 2 (above) for specific zones of the photosynthetic tissues, as specified above for monocotyledons. References on theory, significance and large datasets:  Chabot and Hicks (1982) ;  Coley (1988) ;  Reich et al. (1992 Reich et al. ( , 1997 Reich et al. ( , 2004)) ;  Aerts (1995) ;  Westoby et al. (2000) ;  Wright et al. (2002 Wright et al. ( , 2004)) ;  Poorter and Bongers (2006). More on methods:.