dataesr/scientific-paragraphs-categorization huggingface dataset:
SPECIMENS Vertebrae stemming from the collections of major German natural history museums, the Koninklijke Maatschappij voor Dierkunde of Antwerp, and the Natural History Museum of London were included in this study. All analyzed specimens appeared to be skeletally mature (indicated by epiphyseal fusion). One Camelus bactrianus (SMF 25542) specimen lacked complete epiphyseal fusion in the vertebrae, but long bones of this specimen clearly indicated an adult individual and it was included in the analysis. No vertebrae showed any obvious pathologies. Both sexes were sampled indifferently; wild-caught animals were preferred but zoo animals were also included in the study to compose a larger dataset. The sample includes vertebrae from 37 extant ruminant and camelid species and one extinct giraffid.
DATA ACQUISITION Three-dimensional surface models of specimens were acquired using either microcomputed tomography (µCT) or photogrammetry (PH). All surface models are available on the public database MorphoMuseum (https://doi. org/10. 18563/journal. m3. 129;  Müller et al. 2021). For µCT (YXLON FF35 CT scanner, Museum für Naturkunde, Berlin), resolution ranged from 56 to 196 µm depending on the size of the vertebrae. The raw data obtained by the CT scanner were further edited with Fiji plug-ins for ImageJ (version 1. 51k;  Schneider et al. 2012 , Schindelin et al. 2012 ). Scans were cropped to reduce the amount of data and the contrast was increased before being saved as 16-bit binary tiff stacks (image sequences). The created tiff stacks were imported into Amira (Thermo Fisher Scientific, version 6.
0. 0), a software for visual data analysis  (Stalling et al. 2005) , and 3D bone surface models were created using the software's segmen-tation editor. The number of polygons was reduced to 1,000,000 consistent for all specimens. For PH, high-resolution images were taken using a Canon EOS 1200D digital camera with 18-55 mm standard zoom lens. Specimens were fixed to a flat surface using modeling clay, and photos were taken from around the specimen. Afterward, the vertebra was turned upside-down and the process was repeated. The images (about 70 images per specimen) were uploaded in Agisoft Metashape (version 1. 5. 2), an image-based 3D modeling software creating 3D objects from still images  (Rcljić et al. 2019). Using the commands "align photos," "build dense cloud," and "build mesh," surface models were generated.
The two resulting models (e. g. , top and bottom aspects of the vertebra) were loaded into MeshLab (version 1. 3. 4 beta;  Cignioni et al. 2008)  and merged using the "alignment" function. Vertebrae C7 and T1 of S. giganteum were digitized at the Structure and Motion Laboratory of the Royal Veterinary College, London, using PH. Vertebrae of one specimen of G. camelopardalis (KMDA M-10861) were digitized at the Department of Veterinary Science, University of Antwerp, using a surface laser scanner. Both datasets were made available to us prior to the study. To test whether the digitizing method has an influence on the results, two models of the same vertebra (C7 Vicugna vicugna SMF 94752) were created using µCT and PH and were included in a geometric morphometrics analysis with other specimens.
The two models plot extremely close to one another in the morphospace (see SI 4). Hence, errors introduced into the analysis by the method of model creation were neglected in our analysis. Comparative data for neck and overall vertebral column lengths were not available in the published literature for most of the species. We therefore determined relative neck length. Relative neck length was the ratio between occiput to cervicothoracic boundary distance and overall occiput-tailhead distance (SI 5). This measure was derived from lateral aspect photos of the species found on the internet with lengths measured using Im-ageJ  (Schindelin et al. 2012).
PHYLOGENETIC COMPARATIVE METHODS Because closely related species share a recent common history, the data cannot be considered as independent  (Harvey and Pagel 1991). Phylogenetic relationships from a molecular tree of  Toljagic et al. (2018)  were used. The time-calibrated tree was pruned to the 37 extant species that were sampled for our analyses. The resulting tree was used in all following phylogenetically informed analyses. Phylogenetically informed analyses were performed using the software R version 3. 6. 3 (respective packages are indicated below) (R Development Core Team, 2019). The fossil S. giganteum was considered the sister taxon to the extant giraffe (G. camelopardalis) based on  Danowitz et al. (2015a). The "phylomorphospace" function in the R package "phytools"  (Revell 2012 ) was used to project the phylogenetic tree into the morphospace resulting from the PCA  (Sidlauskas 2008).
A phenogram using the Procrustes distance between C7 and T1 was built with the function "phenogram" from the R package "phytools"  (Revell 2012)  to visualize morphological disparity in relation to phylogeny. The phylogenetic signal in univariate data (i. e. , neck length, ROM) was estimated using Blomberg's K  (Blomberg et al. 2003 ) with the function "phylosig" in the R package "phytools"  (Revell 2012 ). The degree of phylogenetic signal in the multivariate data (i. e. , Procrustes shape variables) was estimated using the multivariate version of Blomberg's Kstatistic (K mult : Adams 2014) with the function "physignal" in the R package "geomorph"  (Adams et al. 2017). It allowed us to test whether the same trait properties are present in related taxa more frequently than expected by Brownian motion  (Blomberg et al.
2003). Distantly related, putative convergent taxa fall closer to one another in morphospace than to their relatives  (Stayton 2006). To test for convergent evolution in vertebral shape, we used the R package "convevol"  (Stayton 2017). Morphospace was defined as the first three PC axes of the shape analysis for this analysis (about 87% of total variance explained); subsequent PC axes each contribute less than 5% of the variance. The function "convrat" quantifies the degree of convergence providing the measure C 1  (Stayton 2015). C 1 represents the proportion of the maximum distance between the lineages that have been brought together by subsequent evolution. It ranges from 0 (i. e. , not convergent at all) to 1 (i. e. , fully convergent)  (Stayton 2015). The significance of convergence (as quantified by 'convrat') was evaluated using the "convratsig" function (500 simulations).
351, P = 0. 005). By comparison, we did not find evidence for considerable convergent evolution of T1 morphology of long-necked species (C 1 = 0. 108, P = 0. 464). T1 of the giraffe plots clearly apart from the other T1 examined in this analysis and falls within the 95% confidence ellipse of the cervical vertebrae. It is the only thoracic vertebra not falling into the 95% confidence ellipse of our dataset. Noticeably, C7 of Bison bonasus (the European bison) plots within the 95% confidence ellipse of T1 and is the only C7 falling out of the C7 95% confidence ellipse in the PC1-PC2 scatter plot. PC3 and PC4 reveal shape alterations in the length of the vertebral arch (PC3) and the height of the vertebral body (PC4) (SI 9). Overall shape differences between C7 and T1 are reflected in Procrustes distances between these two bones of each species (Fig.
CONFIRMS "FUNCTIONAL ELONGATION HYPOTHESIS" To test the "functional elongation hypothesis," the cervicothoracic vertebral morphology of cetartiodactyl species was quantitatively examined using 3D geometric morphometrics and phylogenetically informed comparative methods. Functional significance of differences in the morphometric data was further evaluated in terms of mobility (ROM). Using geometric morphometrics, subtle shape differences between vertebrae can be quantified  (Arnold et al. 2016; Randau et al. 2017; Böhmer 2017; Jones et al. 2018; Arnold 2020 ). C7 and T1 of the giraffe show striking morphological differences to their respective counterparts in other cetartiodactyl species. C7 of the giraffe differs from C7 of the okapi and most other species in our dataset by a shorter spinous process and relatively larger vertebral body.
Both are characteristics of more anterior cervical vertebrae  (Solounias 1999; Danowitz and Solounias 2015; Gunji and Endo 2016). However, similar C7 morphology evolved in all long-necked taxa of our dataset (see below). In mammals, C7 is commonly referred to as "vertebra prominens" because of its long spinous process  (Cramer and Darby 2014) , but this is not present in the giraffe. Another difference to general mammal C7 morphology concerns the transverse foramen and ventral tubercles  (Solounias 1999 ). These characteristics are present in C7 in the giraffe, but not in C7 of other mammals. This may have functional implications considering the neck musculature. The cervical longus colli muscle (the cervical part of the major neck flexor) originates from the ventral tubercles of the cervical vertebrae.
Gunji and Endo 2016). These characters potentially contribute to the extreme elongation of the giraffe neck by mobilizing the anterior-most part of the thoracic vertebral column. In line with our expectation, our detailed anatomical analysis demonstrated homeotic shifts in C7 and T1 (SI 12). In addition, our functional analysis demonstrated pronounced mobility in comparison to other ruminants and camelids, especially in terms of dorsoventral flexion/extension. The giraffe exhibited the largest dorsoventral ROM of our dataset. Together, our analysis thus corroborates the "functional elongation hypothesis" of the giraffe neck-giraffes indeed are pushing the boundary. The aberrant morphology of T1 may be related to other functions than mobility. For example, aberrant shape of the giraffe's T1 may also be related to a stabilizing function.
2020)  and perissodactyls  (Jones 2016). This C7 morphology is accompanied by a more vertical posture of the neck in giraffe  (Danowitz and Solounias 2015)  and the gerenuk  (Gunji and Endo 2019) , but not within the Camelidae, and seems to have evolved convergently within the long-necked lineages. However, T1 of the non-giraffid ruminants and camelids analyzed in our dataset are inconspicuous and do not differ from species with shorter necks, whereas T1 of the giraffe clearly does. C7 of the European bison plots outside the 95% confidence ellipse of C7 and within that of T1. The shape of C7 of the bison thus resembles the shape of a thoracic vertebra (long spinous process and short vertebral body; Fig. 4B ). Shorter and wider verte-brae may help absorb high compressive forces during impacts of ramming behavior (Vander Linden and Dumont 2019).
The large spinous process helps to accommodate the enormous withers in bison. Besides the "cervicalized" T1 of the giraffe, we therefore suggest a "thoracalized" C7 of the European bison. The thoracalized shape of the bison's C7 is accompanied by one of the smallest cumulative ROM between C7 and T1 of our dataset. Thus, this characteristic may be associated with an increased stabilization of the cervicothoracic transition in these short-necked and heavy-headed species displaying ramming behavior (cf. Watson et al. 2009). We observed one species of the Tragulidae, Tragulus javanicus (the kanchil), showing a similar specialization in T1 with regard to the rib facets when compared with the giraffe (Fig. 4C ). T1 of T. javanicus possesses an isolated facet beneath the transverse process and lacks the cranial costal facet.
ACKNOWLEDGMENTS We thank  K. Jones  and an anonymous reviewer as well as the editors for constructive critique of earlier versions of this article. We wish to thank the following curators and collection managers for access to museum collections:  C. Funk  and  D. Wilborn  (ZMB);  S. Merker  (SMNS);  E. Baermann  (ZFMK);  K. Krohmann  and  I. Ruf  (SMF); and  A. H. van Heteren  and  M. Hiermeier  (ZSM). We thank  F. Mielke  and  J. MacLaren  (both  Department of Veterinary Sciences, University of Antwerp, Belgium ) for scanning the vertebral series of giraffe specimen "Dana," donated by the  Koninklijke Maatschappij voor Dierkunde Centre for Research and Conservation, Antwerp, Belgium , for educational and scientific purposes. Further, we thank  C. Basu  and  J. R. Hutchinson  (The  Royal Veterinary College, Structure and Motion Laboratory, Hatfield, United Kingdom ) for sharing 3D surface models of Sivatherium specimens.
We are indebted to  K. Mahlow  and  M. Kirchner  for their dedication and endless hours of CT scanning. We thank all CERVICOTHORACIC  TRANSITION IN CETARTIODACTYLA members of the "Nyakatura lab for Comparative Zoology"  for inspiring and motivating discussions. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 8900809-EDDI (Marie-Skłodowska Curie fellowship  awarded to CB). JAN acknowledges the support by the  German Research Foundation  (grant number  EXC 2025 ).
Dietary Assessment At recruitment, participants were asked to complete a validated semiquantitative food frequency questionnaire (FFQ) that was developed in the European Prospective Investigation into Cancer and Nutrition (EPIC) study  [15] , and a 24-h dietary recall (24hR), which is designed to assess energy and nutrient intake. The FFQ (covering a 12-month period) and the 24hR were administered by trained personnel. Both FFQ and 24hR were completed by 281 participants (125 females and 156 males). Of the 281 dietary assessments, 66 with missing clinical information were excluded from the analysis and 215 subjects (94 females, aged 23-77 years, and 121 males, aged 24-78 years) having a complete dataset of both dietary assessment and clinical parameters were considered. After misreporting evaluation (see Section 2.
Energy and Nutrient Intake and Misreporting Energy and nutrient intakes from all foods and beverages were calculated using national and international databases and literature information. Dietary information by 24hR was used to corroborate energy and food intakes provided by the FFQ. Daily energy intake was derived for each subject. Daily intake of total available carbohydrates, sugars, total fat, saturated fat, and unsaturated fat was expressed as percentage of daily energy intake (%EI). Intake of protein, dietary fibre, and alcohol was reported as g per day. Based on the protocol developed by the European Food Safety Authority (EFSA) [16], energy misreporting was assessed as the ratio of reported energy intake (EI) to estimated basal metabolic rate (EI:BMR) according to the Goldberg method  [17]  modified by Black  [18].
Statistical Analysis Data were stratified by gender. All clinical parameters were classified as normal  (1)  or abnormal (2) according to their overlap with the recognised normal ranges (Table  S2 ). The distribution of clinical parameters was evaluated using the D'Agostino-Pearson test. Student's t-test for normally distributed data and Mann-Whitney U test for non-normally distributed data were used to compare the general characteristics of the study population grouped by gender. All statistical analyses were conducted using the Python programming language, using custom scripts and the sklearn package  [25]. A predictive model for each clinical parameter was computed using all dietary variables via a ridge regression framework  [26]. To simultaneously reach the best prediction performances while learning which sets of dietary macronutrient intakes (variables) were the most important for each predicted clinical outcome (target), a multivariate model was applied.
The absolute value of the regression coefficients β is related to the univariate effect of a given dietary variable (x) j on a given clinical marker (y), while the sign of coefficients is not directly interpretable as it would have been in a normal ordinary least squares (OLS) solution. Before regression, data were standard scaled. All the ridge models computed were cross-validated to optimise the parameter α through 5-fold cross-validation. Train and test subsets were extracted to maintain the proportion between recruiting centres to minimise the possible confusion factor tied to dietary habits of the country of origin. To represent the statistical dependence between the rankings of dietary variables and clinical outcomes, correlation heatmaps were also computed using the Spearman rank correlation coefficient  [29] , which measures how well the relationship between two variables or targets can be described using a monotonic function.
Of note, none of the enrolled volunteers was a heavy smoker (≥5 cigarettes per day); this minimising the confounding effect and making a stratification based on these lifestyle characteristics impossible. As well, based on exclusion criteria, none of enrolled subjects had a high level of physical activity (≥5 h of physical activity per week). Specific information on physical activity was collected using the international physical activity questionnaire (IPAQ) only for volunteers who accepted to participate in the sub-study of the trial, so we could not use those data in the regression model. Anyway, collected IPAQ confirmed that physical activity was low-moderate.
Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit.
The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data. Cluster analysis aims to partition unlabeled data into homogeneous groups, such that two instances are similar if they belong to the same cluster, and dissimilar otherwise. Although this unsupervised machine-learning task is often considered in the context of either continuous or categorical datasets, this task remains challenging when dealing with "heterogeneous" or "mixed" data, i. e. with both types of variables. As previously emphasized, clustering of mixed data is challenging because it is difficult to directly apply mathematical operations to both types of feature variables 1.
mass index), physical examination (both categorical and ordinal variables) and laboratory or imaging findings (often continuous variables). Note that among laboratory variables, omics data are increasingly available today. Such heterogeneity urges for ways to guide users and clinical practitioners in choosing appropriate clustering approaches for heterogeneous clinical datasets in order to achieve efficient phenomapping of patients in various clinical settings. Discretization and dummy-coding are some of the simple and intuitive solutions to obtain a homogeneous dataset containing only categorical data on which classical techniques can be applied. However, this approach may introduce distortion in the original data and may consequently lead to increased bias  2. Fortunately, a wide range of clustering algorithms has been specifically developed to deal with mixed data.
The present study aims to assess the performance of clustering strategies for mixed data in both simulated and real case scenarios. In the first group of scenarios, virtual populations with available mixed variables were generated on which a benchmark of clustering techniques was conducted. The same techniques were subsequently applied to a real-life dataset from the EPHESUS randomized clinical trial  4  to illustrate the importance of choosing the appropriate clustering technique. As our focus was to test tools readily/easily available to clinical researchers, we therefore restricted our analysis to "off-the-shelf " tools readily available from the R software (R Core Team), that cover only a portion of all available methods for clustering heterogeneous data.
There is presently no official R package for this algorithm, but the source code and the instructions for its installation can be found on Gitlab (https ://gitla b. inria. fr/kdall eau/uetcp p). Each of the previous dissimilarity matrices can then be incorporated into one of the two following clustering algorithms: Partitioning Around Medoids (PAM) and Hierarchical Clustering (HC). Partitioning around medoids (PAM). The PAM method 7 builds a partition by affecting observations to the closest "medoid", i. e. the best representative subject of its cluster. The algorithm is composed of two steps: one for building the current clustering similarly to the K-means (BUILD phase), and another to improve the partition toward a local optimum (SWAP phase). The minimization criteria is the Total Deviation (TD): S � x, y � = 1 m   q � j=1 � 1 - � � x j -y j � � Range � j � � + p � j=q+1 s � x j , y j �   where (m 1 ,.
Kamila. The Kamila algorithm 2 is a model-based adaptation of the k-means for managing heterogeneous datasets. The sample of continuous variables is assumed to follow a mixture distribution with arbitrary spherical clusters (where the density of the data is only dependent on the distance to the center of the distribution). This assumption is less restrictive than those from Mixmod or LCM (see below). Categorical variables are supposed to be sampled from a mixture of multinomial variables. Factors are also assumed to be conditionally independent given the clusters to which they belong. The Kamila algorithm begins with a set of centroids for the continuous variables and a set of parameters for the categorical variables. For continuous variables, the Euclidean distance with the closest centroid is computed.
This set of N minimal distances is used to estimate the mixture distribution of continuous variables. For categorical variables, the probabilities of observing the data given the cluster are computed. The log-likelihood of the sum of these two components is then used to find the most appropriate cluster for each subject. Based on this temporary partition, the centroids and the parameters are updated to best represent the clusters. These steps are repeated until the clusters are stable. Finally, multiple runs of this process are performed with different initializations, and the partition maximizing the sum of the best final likelihoods is retained. The R package kamila is a direct implementation of this technique by its authors. Clustering by mixture modeling (Mixmod). Clustering by mixture modeling was proposed a number of years ago  11  , although powerful computers are needed to realize its full potential.
Latent class model (LCM). This method, implemented by its authors in the VarSelLCM R package  13  , is another type of mixture modeling quite similar to Mixmod but, in addition, it can also determine whether a variable is useful for clustering, as well as the optimal number of clusters. If the j-th variable is relevant (i. e. , its distribution differs significantly across clusters), it is labeled with ω j = 1 and belongs to. If j is irrelevant (i. e. its distribution is similar across clusters), it is labeled with ω j = 0 and belongs to 's complementary, i. e. c. Let ω = ω 1 ,. , ω p be the binary vector of the role of the p variables, and let m = (G, ω) be the resulting model. For an observation x i , the probability density function of the mixture distribution is: In LCM, the variables are assumed to be independent within clusters.