dataesr/scientific-paragraphs-categorization huggingface dataset:
We selected these thresholds for partitioning the dataset in order to maximize the power to select the informative features. Stratified random sampling  (23)  based on the outcome variable was undertaken in order to preserve the distribution of the liver fat categories in the two feature selection and model generation sets. We selected LASSO, as a non-linear data reduction tool might lead to overfitting owing to the high dimensionality of omics data. LASSO was conducted with package glmnet in R  (24)  with a ten-fold cross-validation step for defining the  parameter that results in the minimum value for the mean square error of the regression model. Feature selection using LASSO was undertaken in each omics dataset (genetic, transcriptomic, proteomic and metabolomic) using 70% of the available data (models 5-18).
For the genetic dataset, we first performed a genome-wide association study (GWAS) prior to LASSO in order to identify single-nucleotide polymorphisms (SNPs) tentatively associated with liver fat accumulation (P<5 × 10 78 ). LASSO was then applied to these index variants for feature selection in 70% of the study sample. The individual SNP association analysis was conducted with rvTests v2. 0. 2  (25) , which applies a linear mixed-model with an empirical kinship matrix to account for familial relatedness, cryptic relatedness, and population stratification. Only common variants with minor allele frequency (MAF) greater than 5% contributed to the kinship matrix. SNPs were selected out of the 623 SNPs with p-values < 5 × 10 78. For the transcriptomics, 93 genes were selected out of 16,209 protein-coding genes.
Model training and evaluation The remaining 30% of the data was used to develop the binary prediction models for fatty liver (yes/no) with selected features used as input variables. We utilized the Random Forest supervised machine learning method, which is an aggregation of decision trees built from bootstrapped datasets (a process called 'bagging'). Typically, two-thirds of the data are retained in these bootstrapped datasets and the remaining third is termed the 'out of bag' dataset (OOB), the latter of which is used to validate the performance of the model. To avoid over-fitting and improve generalizability, five-fold cross-validation was done for resampling the training samples and was repeated 5 times to create multiple versions of the folds. The number of trees was set to 1000 to provide an accurate and stable prediction.
DISCUSSION Using data from the IMI DIRECT consortium, we developed 18 diagnostic models for early-stage NAFLD. These models were developed to reflect different scenarios within which they might be used: these included both clinical and research settings, with the more complex (and less accessible) models having the greatest predictive ability. The models were successfully validated in the UK Biobank, where data permitted. Overall, the basic clinical variables proved to be stronger predictors of the fatty liver than more complex omics data, although adding omics data yielded the most powerful model, with very good cross-validated predictive ability (ROCAUC=0. 84). NAFLD is etiologically complex, rendering its prevention and treatment difficult, and diagnosis can require invasive and/or relatively expensive procedures.
Thus, noninvasive and cost-effective prediction models with good sensitivity and specificity are much needed. This is especially important because if NAFLD is detected early, treatment through lifestyle interventions can be highly effective  (35). However, simple NAFLD is usually asymptomatic and many patients only come to the attention of hepatologists when serious complications arise  (36). To date, several prediction models have been developed to facilitate the diagnosis of steatosis (thoroughly reviewed elsewhere (  11 )). FLI is the most well-established and commonly used index, initially developed using ultrasound-derived hepatic steatosis data  (30). The FLI yielded similar predictive performance in the diabetic and nondiabetic cohorts of IMI DIRECT (ROCAUC ~ 0. 75). Though commonly used for liver fat prediction, the FLI has a similar discriminative ability as waist circumference alone  (37).
Using data from IMI DIRECT, we explored the predictive ability of genetic, transcriptomic, proteomic and metabolomic data from the blood for the diagnosis of NAFLD. The top twenty features of each omic model are presented in the S7-13 Figs. The details of the LASSO selected features are summarized in the Supporting Information (Excel file). Reassuringly, several of the features that ranked highest have been previously described for their association with liver fat content or closely related traits; this includes PNPLA3 gene variants  (40, 44) , fetal liver tyrosine kinase-3 (FLT3) transcripts  (45) , IGFBP1  (46) (47) (48)  and Lipoprotein lipase (Lpl) (49) proteins, and the metabolite glutamate  (50). In the analysis of the targeted metabolites, phosphatidylcholines, including PC. aa. C32, PC.
The linear Lasso method was used to minimize overfitting that can occur with high-dimensionality data, while Random Forests was used to identify non-linear associations where data structure allowed. Heavy alcohol consumption is a key determinant of fatty liver but is unlikely to be a major etiological factor in IMI DIRECT owing to the demographics of this cohort. Nevertheless, a further limitation of this analysis is that alcohol intake was self-reported and may lack validity. To address this limitation, we removed all self-reported heavy alcohol consumers from the UK Biobank cohort and undertook sensitivity analyses, but this did not materially affect the results. A further consideration for future work is the impact lifestyle and medications are likely to have on the prediction of NAFLD.
applying any statistical procedures for feature selection. Model 1 includes six nonserological input variables: waist circumference, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), alcohol consumption and diabetes status. Model 2 includes eight input variables: waist circumference, BMI, TG, ALT, AST, fasting glucose (or hemoglobin A1C (HbA1c) if fasting glucose is not available), alcohol consumption and diabetes status. Model 3 includes nine variables: waist circumference, BMI, TG, ALT, AST, fasting glucose, fasting insulin, alcohol consumption and diabetes status. The three clinical models along with FLI, HSI and NAFLD-LFS indices were applied to the non-diabetic and diabetic cohort datasets separately, as well as in the combined dataset; the ROCAUC results are presented in Fig 3.
An overview of the prediction models' performance metrics for all of the fatty liver indices in the IMI DIRECT and UK Biobank datasets Validation in UK Biobank and IMI DIRECT Liver fat data were available in 4617 UK Biobank participants (1011 with >=5% and 3606 with <5% liver fat). Of these individuals, 4609 had all the required variables to replicate the clinical model 1. To perform model 2 either with fasting glucose or HbA1c, 3807 participants had data available for a complete case analysis. Given the limited availability of variables in the UK Biobank dataset, only models 1 and 2 of the NAFLD prediction models we developed could be externally validated. To facilitate this validation analysis, the Random Forest models developed in the IMI DIRECT cohorts were used to predict the liver fat category (fatty vs.
ACKNOWLEDGMENTS We thank  Mattias Borell  for developing, logistical support and advice related with the web interface. We thank all the participants and study center staff in IMI DIRECT for their contribution to the study. We thank all the participants in the  UK Biobank. This research was conducted using the UKBB Resource (application ID:  18274 ). For the proteomics analyses, we thank the entire staff of the  Human Protein Atlas , the  Plasma Profiling facility at Science for Life Laboratory  and in particular  Elin Birgersson ,  Annika Bendes , and  Eni Andersson  for technical assistance.
non-fatty) for the UK Biobank participants. The performance of FLI and HSI was also tested in the UK Biobank cohort. We validated both models 1 and 2 in the UK Biobank cohort with a similar ROCAUC as seen in the IMI DIRECT dataset. The ROCAUCs were 0. 71 (95% CI= 0. 69, 0. 73), 0. 79 (95% CI= 0. 77, 0. 80), and 0. 78 (95% CI= 0. 76, 0. 79), for model 1 and model 2 (with fasting glucose or with HbA1c), respectively. The FLI had a ROCAUC of 0. 78 (95% CI= 0. 76, 0. 80), which is similar to model 2. The HSI yielded a ROCAUC of 0. 76 (95% CI= 0. 75, 0. 78).
The MAPT study The present study used data of the Multidomain Alzheimer Preventive Trial (MAPT). The MAPT Study (registration: NCT00672685) was a randomized controlled trial (RCT) aiming to assess the effects of multidomain interventions (nutritional and physical activity counselling, and cognitive training), omega-3 supplementation, or their combination on cognitive function over 3 years. The trial found no effect of these interventions on a composite cognitive score. Participants were additionally followed for 2 years. MAPT methods and procedures have been previously described  (16) (17) (18). The trial respected the Declaration of Helsinki and was approved by the ethics committee in Toulouse (CPP SOOM II). Written consent was obtained.
Standardized method for data source use For each subject, demographic data (age, sex, place of living) and clinical data were considered at baseline, during the follow-up period, and up to 1 year following the date of frailty occurrence. This 1-year period was decided because the discovery of physical frailty frequently led to additional investigations allowing the diagnosis of underlying causes. Considering clinical data, we focused on: past and current comorbidities at baseline ; all available clinical assessments: cognitive status assessed by the Mini-Mental State Examination (MMSE), functional status assessed by the Short Physical Performance Battery (SPPB), nutritional status assessed by the Body Mass Index (BMI), psychological status assessed by the Geriatric Depression Scale (GDS) and physical frailty assessed by Fried criteria ; the intervening medical events (e.
Results Among the 1679 subjects enrolled in MAPT Study, 91 were excluded because of missing data on frailty and 51 because they were frail at baseline. Among the 1537 non frail patients at baseline, 195 (12. 6 %) became frail after a mean period of 32. 5 (±18. 5) months. In the first phase of the classification process, 127 subjects (65%) were classified: 46 subjects (36%) as "frail related to diseases", and 81 (64%) as "age-related frailty". Sixty-eight (35%) were unclassified and reconsidered in the second phase of the classification process. During the second phase, 7 (10%) unclassified participants were classified as "frailty related to diseases", 1 (1%) as "age-related frailty" and 60 (79%) as "frailty of uncertain origin". At the end of the process of classification, 82 subjects (42%) were classified as "age-related frailty", 53 (27%) as "frailty related to diseases" and 60 (31%) as "frailty of uncertain origin".
Our study is a secondary analysis of the MAPT study, thus, it was not specifically designed to test our hypothesis about the origin of physical frailty. Lastly, the lack of clinical data due to retrospective analysis of the clinical files could have impacted the classification process, leading to an overestimation of frailty with uncertain origin. In order to properly evaluate main causes of incident frailty, it would be important to apply the classification procedures prospectively. For this reason, we are implementing the identification and description of age-related frailty and frailty related to diseases in the on-going translational cohort of the INSPIRE Project  (32) (33) (34) (35).
Fund (ERDF) (Project number:  MP0022856 ). This study received funds from Alzheimer Prevention in Occitania and Catalonia ( APOC Chair of Excellence -Inspire Program ). The MAPT study was supported by grants from the  Gérontopôle of Toulouse , the  French Ministry of Health  ( PHRC 2008 (PHRC , 2009)),  Pierre Fabre Research Institute  (manufacturer of the omega 3 supplement),  ExonHit Therapeutics SA, Avid Radiopharmaceuticals Inc. and in part by a grant from the  French National Agency for Research called "Investissements d'Avenir " no. ANR-11-LABX-0018-01. The promotion of this study was supported by the  University Hospital Center of Toulouse. The data sharing activity was supported by the  Association Monegasque pour la Recherche sur la maladie d'Alzheimer (AMPA)  and the  INSERM-University of Toulouse III UMR 1027 Research Unit.
Elucidating the structure of neuronal networks provides a foundation for understanding how the nervous system processes information to generate behavior. Despite technological breakthroughs in visible light and electron microscopy, imaging dense nanometer-scale neuronal structures over millimeter-scale tissue volumes remains a challenge. Here, we demonstrate that X-ray holographic nano-tomography is capable of imaging large tissue volumes with sufficient resolution to disentangle dense neuronal circuitry in Drosophila melanogaster and mammalian central and peripheral nervous tissue. Furthermore, we show that automatic segmentation using convolutional neural networks enables rapid extraction of neuronal morphologies from these volumetric datasets. The technique we present allows rapid data collection and analysis of multiple specimens, and can be used correlatively with light microscopy and electron microscopy on the same samples.
We combined a highly focused and brilliant hard X-ray nano-probe  (da Silva et al. , 2017) , an advanced nano-positioning system, cryogenic imaging, new sample preparation protocols, and enhanced phase retrieval approaches to achieve significant improvements in image quality. We show that individual neurons can be manually traced through XNH datasets of central and peripheral nervous tissue of the fruit fly Drosophila melanogaster and mouse. In addition, we use a 3D U-NET convolutional neural network (CNN) to automatically segment dendrites and axons from the XNH data. Moreover, we demonstrate that XNH is compatible with cell type-specific genetic labeling techniques as well as post-hoc EM of the same samples. These results demonstrate that XNH is a versatile volumetric imaging modality for biological microscopy, which can be combined with LM and EM in correlative workflows to accelerate our understanding of neuronal networks.
XNH imaging of central and peripheral nervous systems To explore the potential of XNH for imaging neuronal morphologies, we imaged samples of mouse cortex as well as the brain, ventral nerve cord (VNC), and leg of adult Drosophila. We varied the reconstructed isotropic voxel size (edge length) from 30 nm to 120 nm, yielding FOVs (edge length of square cylindrical volume) from 60 µm to 240 µm (96 µm to 384 µm with extended FOV, see Supplementary Table  1 ) for each dataset. Sample sizes ranged from whole Drosophila brains (300 µm × 200 µm × 700 µm) to blocks of mouse cortex (1 mm × 0. 5 mm × 2 mm). Samples were prepared using standard protocols for EM  (Hua et al. , 2015; Zheng et al. , 2018)  and mounted onto aluminum cylindrical pins for imaging (Fig. 1a , inset). XNH imaging was conducted at the nano-imaging beamline ID16A at the European Synchrotron (ESRF).
For image acquisition, the samples were positioned downstream of the focal spot of a highly brilliant X-ray beam (Fig. 1a ). After traversing the sample, the propagating beam generated self-interference patterns, (i. e. holograms) that were recorded on a lens-coupled CCD detector about 1. 2 m downstream of the sample (Fig. 1b ). For each dataset, four tomographic scans (rotations of the sample over 180º) were recorded at different focus-tosample distances (Fig. 1a ), and processed together via a phase retrieval algorithm to obtain angular phase maps of the sample (Fig. 1c )  (Cloetens et al. , 1999; Mokso et al. , 2007; Yu et al. , 2018). Lastly, a 3D image volume of the tissue was generated from the angular phase maps by tomographic reconstruction using filtered back-projection  (Mirone et al.
, 2014). Figure  1d -e shows a tomographic slice and a 3D rendering of an XNH scan capturing the central brain of an adult Drosophila (120 nm voxels) (Supplementary Video 1). At this resolution, large individual neuronal processes can be resolved (Fig. 1d , inset, Fig. 1e , orange tracts). Figure  1f  shows a volume rendering of a large FOV XNH scan from mouse cortex (100 nm voxels). In this dataset, we can trace large dendrites, in particular the apical dendrites of pyramidal neurons. Figure  1g  shows a virtual slice from a high-resolution mouse cortex scan (30 nm voxels) (Supplementary Video 2). At this resolution, many ultrastructural features are resolved including mitochondria, endoplasmic reticulum, dendrites, myelinated and unmyelinated axons (Fig. 1g , insets). To quantify the spatial resolution of XNH image volumes, we utilized Fourier Shell Correlation (FSC)  (Harauz and van Heel, 1986).
Correlative X-ray imaging with EM and genetic labeling To verify that XNH images faithfully reproduce the ultrastructure of biological samples, we collected thinsections of samples after XNH imaging and imaged the same regions at higher lateral resolution with EM. We found that corresponding EM and XNH image data from the Drosophila leg nerve (50 nm pixels, Fig. 2a , Supplementary Video 3) and mouse cortex (100 nm pixels, Fig. 2b ) have very similar characteristics, with membranes and mitochondria appearing as the most prominent dark features. Most of the neurites in the EM image are also resolved in XNH, and only the smallest processes are not resolved with XNH. We also verified that fine ultrastructure features, such as membranes, synaptic vesicles and post-synaptic densities, are preserved in the EM images after XNH imaging  (Fig.
Millimeter-scale XNH imaging of a Drosophila leg at single-neuron resolution To image large regions with complex topology at high resolution, we stitched multiple, overlapping XNH scans to image the majority of a Drosophila leg (coxa, trochanter, femur, and tibia segments) and the associated first thoracic (T1) neuromere in the VNC (Fig. 3a, b ). This dataset consisted of 12 individual scan volumes at pixel sizes between 50 and 200 nm, including a length of the main leg nerve totaling more than 1. 4 mm (Fig. 3c, d , Supplementary Table  2 , Supplementary Video 4). Because each XNH scan takes about 4 hours to acquire, this entire dataset was collected in less than 2 days. The 12 scans were aligned together to produce a continuous dataset encompassing the leg and the neural circuit in the VNC that controls this leg's movements (Fig.
Reconstruction of Individual Neuron Morphologies Until now, tracing of individual neuron morphologies from X-ray image data has been only possible through sparse labeling  (Fonseca et al. , 2018; Mizutani et al. , 2013; Ng et al. , 2016; Shahbazi et al. , 2018). However, our results suggest that XNH image volumes contain sufficient signal to noise and spatial resolution to resolve and reconstruct dense populations of neurons without specific labeling. To test this, we reconstructed individual motor and sensory axons in high resolution scans of the Drosophila VNC (50 nm pixels, Supplementary Video 3) via manual skeletonization, a process where human annotators trace a wire-frame model of the neuronal processes  (Saalfeld et al. , 2009; Schneider-Mizell et al. , 2016). A total of 108 neurons were seeded from their axons in the front leg nerve and were traced into the VNC (Fig.
5b, c ). During the subsequent agglomeration step, voxels are first grouped into supervoxels using a watershed algorithm, which are then progressively merged into larger objects based on the predicted affinities along the contact area of adjacent supervoxels. The merging procedure is stopped at a user-defined threshold, giving rise to the final segmentation (Fig. 5d ). To assess whether automated segmentation can be used with XNH data to reconstruct neuronal morphologies, we applied automated segmentation to an XNH image volume encompassing the majority of a T1 neuromere and part of the main front leg nerve (Fig. 5e , Supplementary Video 5). Generally, the morphologies of the segmented neurons match the corresponding manual reconstructions, demonstrating that automated segmentation can generate dense single neuron morphologies from XNH data.
However, in some instances, different voxels from the same neuron are erroneously labeled with different neuron IDs (split error) or two different neurons are erroneously combined into a single neuron ID (merge error). Such errors are also present in segmentations based on EM data, and are usually corrected via human proofreading. To determine how accurate the automated segmentation is, we compared it to manual (voxel-wise) segmentation for small test volumes and quantified split and merge errors using clustering similarity metrics (Fig. S2a , Supplementary Table  3 ). Comparing these error scores with published metrics from EM segmentation studies suggests that this automated segmentation workflow works similarly well with XNH data as with EM data and can be used to rapidly and accurately segment neurons.
Neuron segmentation from XNH data We demonstrate that XNH enables individual neuronal morphologies to be reconstructed in their dense circuit context. Here, we adapted reconstruction approaches developed originally for large-scale EM. Because XNH images appear qualitatively similar to EM images (Fig. 2 ), adapting these techniques was relatively straightforward. While XNH imaging currently cannot resolve the smallest neuronal branches (< 50 nm), reconstruction of a neuron's large-diameter processes often clearly indicates its cell type  (Schneider-Mizell et al. , 2016). Indeed, we were able to identify pyramidal neurons in mouse cortex (Fig. 1g ) and to differentiate sensory and motor neurons in the fly VNC (Fig. 4e ). The larger voxel size of XNH data relative to EM data offers several practical advantages.
Larger voxels produce substantially smaller image files for a given tissue volume, which can be analyzed without specialized hardware infrastructure necessary for large-scale EM datasets. Furthermore, manual and automated analysis can be completed more rapidly, considering the reduced complexity of the task. Indeed, we deployed CNNs for automated segmentation in XNH volumes on timescales of a few hours (~ 0. 1 s/μm 3 XNH compared with ~ 10 s/μm 3 for ssTEM).
Outlook XNH is complementary to and compatible with both LM and EM imaging. XNH offers superior resolution with denser labeling compared to most LM techniques, and can image samples too thick for LM without tissue clearing. XNH achieves lower resolution than volumetric EM, however, offers advantages in speed, convenience, and data handling. Since physical thin-sectioning is not necessary, XNH can image specimens that are difficult to cut reliably (such as a whole Drosophila leg). XNH image data also requires no section-to-section alignment, eliminating a step that continues to be a computational challenge for large-scale EM datasets. By filling a gap between the resolutions of LM and EM, XNH can enable multiscale characterization of neural circuits that depend on both long-range connections and local computations, such as the Drosophila motor system and mammalian cortex.
Acknowledgements The authors acknowledge  Jan Funke  for providing code and assistance with automated segmentation;  Julio da Silva  for providing code and assistance for Fourier Shell Correlation measurements;  John Tuthill  and  Tony Azevedo  for discussions and advice regarding Drosophila motor systems;  Norbert Perrimon ,  Matt Pecot , and  Haluk Lacin  for providing fly lines;  Rick Fetter  and  Andrew Thompson  for sample preparation advice;  Rachel Wilson  and  Hannah Somhegyi  for discussion and advice;  Thedita Pedersen  for preprocessing and alignment of image data;  Lia DeCoursey ,  Rholee Xu , and  Thedita Pedersen  for neuron tracing; and  Jimin Shin ,  Wei-Wei Lou ,  Julie Han ,  Mingguan Liu ,  Yumin Hu , and  Rholee Xu  for manual annotation of ground truth segmentation for CNN training.