dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  C. Richardson  for IT support,  K. Betteridge  ( ICR Light Microscopy Facility ) for assistance with fluorescence microscopy,  M. Ranes  and  K. Chabowska  for cloning of the initial constructs for stable cell lines,  R. Knight  for support of insect cell culture,  S. Hearnshaw  for biophysics support,  J. Sandall  and  L. Winsor  for infrastructure management,  I. Gutsche  for discussions on EM,  T. Daviter  for biophysics advice,  S. Taylor  for  HeLa Flp-In T-Rex cells , and members of the S. G. team for helpful discussions and feedback on the manuscript. Work in the laboratory of S. G. has been funded by  Cancer Research UK , initially through a  Career Establishment Award  ( C47521/A16217 ), then through a  Programme Foundation Award  ( C47521/A28286 ), by the  Wellcome Trust  through an  Investigator Award  ( 214311/Z/18/Z ), by The  Lister Institute of Preventive Medicine  through a  Lister Institute Research Prize Fellowship , and by  The Institute of Cancer Research.
Discussion The multimodal anatomical approaches of this study indicate that the human forniceal commissure is much more delicate than previously described and presented in anatomical textbooks. This finding is consistent with the observed phylogenetic trend of a reduction of the forniceal commissure in NHP compared to nonprimate eutherian mammals. This anatomical redimensionalization of the forniceal commissure permits a critical reinterpretation of previous studies and serves to tailor future investigations on the morphology, function, and pathophysiological role of this structure. Virtual fiber dissection based on 7 Tesla diffusion-weighted imaging dataset of 178 participants did not identify interhemispheric connections between the crura fornicis. Previous tractography literature has been inconclusive about the visualization of a forniceal commissure: while some studies of the fornices did not describe commissural fibers  30, [32] [33] [34]  , others did describe a forniceal commissure  31, 35, 37, 38.
Considering the spatial proximity between fibers of the splenium and the forniceal commissure, as histologically demonstrated in our study, there is a risk of misattributing splenial fibers as fibers of the forniceal commissure in tractography studies. The tractographic phenotype of the forniceal commissure in studies achieving putative detection  31, 35, 37, 38  , resembles the visual appearance of rostrocaudal splenial fibers, but not that of histologically detected forniceal commissure fibers in our study. The absence of evidence in our study, however, might not be the evidence of absence. Previous reports indicated that fibers crossing the hemispheric midline have a different diameter  39. In this case, the algorithm might not be able to trace commissural connections accurately. While the Human Connectome Project 7 Tesla data is a high-field high-resolution dataset offering high quality in vivo data (1 mm) for studying connectional anatomy in the living human brain, future studies might benefit from using submilimeter resolution datasets.
Reasons which rendered a macroscopic morphometric analysis inadmissible were the small dorsoventral extent of the structures bordering macroscopic measurement accuracy and the high variance of their extent along the rostrocaudal axis. And third, while we opted for a high resolution (HCP 7 T) in vivo analysis of the forniceal commissure, future studies might benefit from using submiliter resolution dataset to reinvestigate the fornix. The emergence of new datasets that combine postmortem and in vivo measurements might be a promising avenue toward mapping discreet anatomical structures  62, 63.
Methods This study was approved by the local ethical review board of the Canton of Zurich, Switzerland (KEK ZH 2016-00957). Nomenclature. As outlined above, the nomenclature of the structure examined in this study is complex for historical reasons. We used the descriptive terms psalterium and forniceal commissure. The psalterium was defined as a triangular subsplenial membrane extending between the crura fornicis. The term forniceal commissure was reserved for interforniceal fiber tracts. We chose to adhere to this nomenclature as the term dorsal hippocampal commissure can be considered misleading given the absence of fibers originating from the hippocampus proper in NHP and the absence of an identifiable ventral hippocampal commissure in humans. Imaging datasets. Structural connectome data were downloaded from http:// www.
bcblab. com/BCB/Opendata. html. This publicly available dataset was derived from the diffusion-weighted imaging dataset of 178 participants acquired at 7 Tesla by the Human Connectome Project Team (http://www. humanconnectome. org/)  64. The Human Connectome Project 7 Tesla data is a high-field high-resolution dataset that is unique in its resolution to investigate connectional anatomy in the living human brain at 1 mm isotropic. Demographic cohort data are shown in Table  2. The scanning parameters have been described previously  64, 65. In brief, each diffusion-weighted imaging consisted of a total of 132 near-axial slices acquired with an acceleration factor of 3  66  , isotropic (1. 05 mm3) resolution and coverage of the whole head with a TE of 71. 2 ms and with a TR of 7000 ms. At each slice location, diffusion-weighted images were acquired with 65 uniformly distributed gradients in multiple Q-space shells  67  and 6 images with no diffusion gradient applied.
This acquisition was repeated four times with a b-value of 1000 and 2000 s/mm-2 in pairs with anterior-to-posterior and posterior-to-anterior phase-encoding directions. The default Human Connectome Project preprocessing pipeline (v3. 19. 0)  68  was applied to the data  69. The susceptibility-induced offresonance field was estimated from pairs of images with diffusion gradient applied with distortions going in opposite directions  70  and corrected for the whole diffusion-weighted dataset using TOPUP  71. Subsequently, motion and geometrical distortion were corrected using the EDDY tool as implemented in FSL. In vivo fiber dissection. We performed whole-brain deterministic tractography in the native diffusion-weighted imaging space using StarTrack (https://www. mrstartrack. com). A damped Richardson-Lucy algorithm was applied for spherical deconvolutions  71, 72.
A fixed fiber response corresponding to a shape factor of α = 1. 5 × 10-3 mm2 s -1 was adopted, coupled with the geometric damping parameter of 8. Two hundred algorithm iterations were run. The absolute threshold was defined as three times the spherical fiber orientation distribution of a grey matter isotropic voxel and the relative threshold as 8% of the maximum amplitude of the fiber orientation distribution  73. A modified Euler algorithm  74  was used to perform the whole-brain streamline tractography, with an angle threshold of 35°, a step size of 0. 5 mm and a minimum streamline length of 15 mm. We co-registered the structural connectome data to the standard MNI 2 mm space using the following steps: first, whole-brain streamline tractography was converted into streamline density volumes where the intensities corresponded to the number of streamlines crossing each voxel.
Second, a study-specific template of streamline density volumes was generated using the Greedy symmetric diffeomorphic normalization (GreedySyN) pipeline distributed with advanced normalization tools (ANTs)  74, 75. This provided an average template of the streamline density volumes for all subjects. The template was then co-registered with a standard 2 mm MNI152 template using flirt as implemented in FSL. This step produced a streamline density template in the MNI152 space. Third, individual streamline density volumes were registered to the streamline density template in the MNI152 space template and the same transformation was applied to the individual whole-brain streamline tractography using the trackmath tool distributed with the software package Tract Querier 76 using ANTs GreedySyn.
This step produced a whole-brain streamline tractography in the standard MNI152 space. Two examiners (KA, SJF) reviewed the alignment through visual inspection and ensured its match with the MNI152 template. The fornix was dissected manually by two examiners (KA, SJF) in every individual dataset using Trackvis (www. trackvis. org). The placement of the regions of interest (ROIs) and regions of avoidance (ROAs) was guided by the high resolution T1-weighted image (Supplementary Fig. 1 ). We used an atlas  38  based single spherical ROI around the body of the fornix, which was extended around the crus fornicis of each side, to avoid missing any commissural fibers. ROAs were defined for the corpus callosum and the anterior commissure, additional exclusion regions were placed anterior to the septum pellucidum and posterior to the splenium of the corpus callosum.
Percentage overlay maps. The resulting reconstructions were binarized and converted to fiber density maps to generate percentage overlay maps using an inhouse matlab script (Matlab R2021b). Results were visualized with FSL (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSLeyes). Cadaver specimens. 17 human brains were obtained from healthy body donations to the Anatomical Institute of the University of Zurich, Switzerland. In Switzerland, body donations are fully anonymized beyond the confirmation of the absence of any neurological pathology, therefore no demographic data were available for the human specimens. Sheep brains for comparative macroscopic and histological post mortem studies were derived from autopsy specimens at the Animal Hospital Zurich from previously healthy female Swiss Alpine sheep (age 2-4 years).
Acknowledgements This study was supported by the Prof. Dr. med. Karl und  Rena Theiler-Haag  foundation (grant Nr. :  F-86101-26-01 ), the  Horizon2020 Marie Skłodowska-Curie program  (SJF; Grant agreement No. 101028551 ), and the  Donders Mohrmann Fellowship  (SJF). Many thanks to  Ahmad Beyh  for discussions.
Data availability The tractography data (Package X) is freely available as a preprocessed dataset from http://www. bcblab. com/BCB/Opendata. html. Individual dissections and the percentage overlay map of the fornices are available from https://neurovault. org/collections/12108/. A 3D model of the fornix is available for digital exploration and 3D printing at https:// www. thingiverse. com/stephforkel/designs.
(2013) , a short list of EBVs that are standardized, recognized, and responsive to natural and anthropogenic drivers will allow for uptake of high-quality, comparable and scalable biodiversity data by an array of endpoint users. Similar to Essential Climate Variables (ECVs;  Bojinski et al. , 2014) , EBVs should be relevant, sensitive to change, generalizable, scalable, feasible, and have substantial accessible databases available  (Schmeller et al. , 2018). EBVs must consider the fundamental characteristics of biological systems, and methods for collecting and analyzing observation data. EBVs should be applicable to all life forms, at multiple scales, and in all ecosystems. The implementation of EBVs allows for data to be synthesized into products such as indicators (e. g. the Species Habitat Index, https://geobon.
It is now timely to do so. Over the last 50 years, genetic data production and methods have advanced rapidly. Genetic data were first collected in the form of isozymes and allozymes from the 1970s to early 1990s, followed by DNA sequences, microsatellites and other technologies from the late 1980s to the present day, and large-scale next generation sequencing assessing genome-wide variation starting in the 2000s and increasing in use to the present. Detailed overviews of the temporal trends, applications and integration of the various types of molecular markers can be found in  Schlötterer (2004) ,  Ouborg et al. (2010) ,  Putman & Carbone (2014) ,  Garner et al. (2016 ), Garrido-Cardenas, Mesa-Valle & Manzano-Agugliaro (2018) , and  Leigh et al. (2021). Genetic composition has been assessed for thousands of species globally with increasingly high-resolution population-level genetic data sets available at decreasing costs  (Vranckx et al.
go-fair. org/ fair-principles/;  Wilkinson et al. , 2016) ], applying data schemes like the Minimum Information about any (x) Sequence (MIxS, https://gensc. org/mixs/) devised by the Genomics Standards Consortium, and establishing highly searchable metadatabases (Sections III and IV). Accessible and affordable genomic-level data, and digitization and curation of older genetic data (Sections IV and V), provide a pathway towards operational Genetic EBVs. In parallel, all the aforementioned advances have recently favored the emergence of a new field named 'macrogenetics', which aims at understanding patterns and drivers of genetic variation across large spatial, temporal, and/or taxonomic scales [hundreds to thousands of species  (Blanchet, Prunier & De Kort, 2017; Leigh et al. , 2021) ]. The fastgrowing field of macrogenetics is paving the way for Genetic EBV deployment (see Section IV.
g. natural defenses against pests). In short, genetic diversity underlies processes upon which all other biodiversity depends. Genetic diversity data can be observed directly through the analysis of genetic markers (e. g. DNA sequences, microsatellite fragment data, single nucleotide polymorphisms (SNPs), methylation patterns, etc. ) in individuals sampled through geographic space and time. Genetic observations are often summarized as allele frequencies at the population level (analogous to species abundances in an ecosystem;  Hu, He & Hubbell, 2006) , or through summary statistics measured at various hierarchies (individual, population/metapopulation, lineage, or species level; Fig. 1 ). Such hierarchies can be characterized as alpha diversity (population-level measures at one location), beta diversity (differentiation between locations in space or time), and gamma diversity (across the system of populations).
Note that molecular genetic tools such as DNA sequences are also used to gather observations for taxonomic diversity at and above the species level (e. g. phylogenetics and systematics). While genetic data within a species are applied to the Genetic EBV class, data above the species level are appropriate for the Species or Community EBV classes. Molecular genetic data may also be collected for genetic forensics and environmental DNA (eDNA) assessments. Targeted (e. g. species-specific) and metabarcoding eDNA tools can infer species presence using eDNA detected in water, soil, and air samples  (Goodwin et al. , 2019; Bal azs et al. , 2020)  and are well suited to provide data for the Species Population EBV classes; but note, only within-species data are relevant for the Genetic EBV class. (3) Aims and structure of this review In this review we elucidate the major stages and gaps on the route towards enabling large-scale standardized observations of genetic variants.
Section II defines Genetic EBVs, explains their calculation and meaning, and assesses how each Genetic EBV meets the criteria of  Schmeller et al. (2018) : relevance, sensitivity to change, generalizability, scalability, feasibility, and available data. Section III explains the workflow for transforming, aggregating, harmonizing and archiving genetic data underlying Genetic EBVs, including publishing data and metadata standards in interoperable formats for integration with large data sets. In Section IV, we discuss methods and models for interpolation and extrapolation, translating genetic data or proxies for genetic data into patterns of change across space and time, and looking towards EBV development for global application. In Section V, we Biological Reviews (2022) 000-000 © 2022 The Authors.
, 2010; Tallmon et al. , 2010). As opposed to the current state of genetic diversity, N e informs inferences on future genetic diversity changes and is highly suited for monitoring of conservation management actions. The previous EBV of breed/varietal diversity was integrated into the more comprehensive Genetic Differentiation EBV. We examine how each EBV reflects the essential characteristics of genetic composition, genetic health, and genetic viability of populations and species. We explain the biological features that each represents, discuss how to interpret change, and review assumptions and cautions. The proposed list should be applicable for wild, semi-wild or managed, and domesticated species, and covers the forces that underpin genetic variation (Section II. 1a). A key point is that although each EBV summarizes a different aspect of genetic composition, all Genetic EBVs can typically be calculated from a single data set originating from DNA samples collected from individuals in a target population.
Identifying adaptive loci under selection is challenging  (Hoban et al. , 2016) , but methods are advancing to account better for loci under selection  (Hohenlohe, Funk & Rajora, 2021). Various correlation methods can be used to identify putative adaptive loci, often through interactions with simple environmental data  (Rellstab et al. , 2015; Ahrens et al. , 2018). Alternatively, loci with a particular, known function (identified with a reference genome and/or field and laboratory experiments) can be analyzed, although such data are available for fewer species. Nonetheless, these loci (assuming sufficiently large SNP datasets, good ecological knowledge and a sequenced, annotated genome) are useful to map local adaptations, model adaptive responses, and determine the number and degree of differentiation among 'population units' the required first step for evaluating Genetic EBVs.
, 2021) , so interpretation should be directed by the type and number of genetic markers and the number of populations/individuals analyzed (see Section V. 2 on data set parameter selection). Alternatively, an integrated framework for measuring diversity, such as Hill numbers, could be explored for unifying different measures of Genetic EBVs, and to investigate variation at different EBV classes across multiple scales  (Sherwin et al. , 2017; Gaggiotti et al. , 2018). (3) Genetic differentiation EBVs: number of units and their connectivity Genetic differentiation is defined as a divergence in genetic composition (specifically the frequencies of alleles) between multiple populations of the same species, caused by natural selection, genetic drift, and restricted gene flow (Section II. 1a). This is a measure of beta diversity.
It is important to note that measures of genetic differentiation are usually based on a priori, or at times a posteriori, stratification of samples representing prospective units. As such, these units can only represent hypotheses of genetic structuring, which may not correspond to all underlying patterns of differentiation. Thus, this EBV is measured under the assumption that The data from the polymorphic sites (single nucleotide polymorphisms; SNPs) in the sequence can be summarized as a matrix of genotypes for each locus (L1-L4). When these loci are bi-allelic SNPs, the data can be summarized as the Minor Allele Countthe number of occurrences of the least frequent allele at that locus, a convenient summary format for certain statistics and models. A Genetic Diversity EBV for evenness, such as observed heterozygosity (H o ), can also be summarized for each individual (rows), or by locus (columns), as illustrated for the SNP matrix.
Estimates of inbreeding have one major advantage over diversity and differentiation: estimates made with a given method are easy to compare across studies, as they should not be dependent on the type of DNA markers used, as long as the data set has sufficient statistical power. (5) Contemporary effective population size EBV Effective population size (N e ) is an established fundamental parameter for genetic biodiversity monitoring. In a population, N e quantifies the amount of genetic change that is occurring and will occur in the future as a result of genetic drift. N e determines the equilibrium level of genetic diversity that a population can maintain and thus the long-term genetic diversity in a population  (Leffler et al. , 2012; Ellegren & Galtier, 2016). N e is the most important parameter for assessing the ability of populations to adapt to rapid environmental change and the likelihood of inbreeding depression and other reactions to perturbations  (Hoban, 2014; Tenesa et al.
For example, the genome sequence of a single member of the Wrangel Island population of wooly mammoth (Mammuthus primigenius), the last remaining population before the species went extinct, showed 20% less heterozygosity and 28 times more runs of homozygosity (a metric of inbreeding) than a more ancient, healthy population  (Palkopoulou et al. , 2015). This is not a unique characteristic of genetic composition data, as data for other EBV classes can be obtained from past relics (e. g. population sizes from fossil abundance, community composition from pollen in sediment). However, the amount of information in genetic data from even one individual can provide high-resolution, reliable inferences on past states and changes that are probably unmatched by other historical evidence. Another unique aspect of genetic data is that a single genetic or genomic data set can be used for calculating many (or all) Genetic EBVs (assuming sufficient sampling of individuals).
III OBTAINING AND ARCHIVING GENETIC COMPOSITION DATA AND METADATA (1) Obtaining genetic data The steps in generating Genetic EBVs include collection of biological samples, laboratory work, computational processing of raw genetic data, analysis/calculation, publishing, archiving, modeling and/or synthesis (Fig. 4 ). Collecting genetic data begins with a biological samplesome remnant of a living or preserved individual, including 'invasive' sampling (direct handling of the organism to obtain a sample) or 'non-invasive' sampling (from hair, faeces, shed skin, etc. ) (Fig. 5 ). Samples may be obtained from extant populations, biological collections, or sub-fossils (ancient DNA). Molecular biology techniques are used to analyze DNA sequences extracted from the sample. These data can be presented as sequences of DNA nucleotides, the allele 'states' at a locus for each individual, and/or as allele frequencies in populations, from which genetic statistics are derived (Fig.
2 ). DNA storage, extraction, sequencing and quality control are discussed briefly in Appendix S3. Data sets are often considered 'genetic' (tens of genetic markers, usually without a reference genomeas for most legacy data sets) or 'genomic' (thousands to millions of markers, often with a reference genomeas for many current data sets). Resolution and precision of EBV values are higher for the latter. Genetic composition can be reliably described for both data types, although reliable estimates of relatedness and inbreeding are best measured using genomic data sets. The growing size and complexity of genomic data sets creates the need for a corresponding growth in computer storage and power (i. e. supercomputers) and statistical programs to allow for EBVs to monitor global genetic biodiversity comprehensive analyses.
Advancing the necessary supporting components, such as computing infrastructure and bioinformatics, will facilitate data accessibility and application. As for many biodiversity observations, genetic composition data can be obtained from different types of scientific studies (e. g. assessing the conservation status of important species; testing ecological hypotheses; stock assessments in forestry or fisheries; taxonomy; forensic investigations). The spatial extent of studies is highly variable; sampling often focuses on a specific region but sometimes includes a species' entire range. It is critical that samples are collected in an observational design that considers the expected data product, such as prediction of possible future change under different scenarios; nonetheless numerous studies use opportunistic and biased sampling.