dataesr/scientific-paragraphs-categorization huggingface dataset:
DKT is interpretable, which allows us to understand underlying disease mechanisms, and can also predict the future evolution of subjects at risk of diseases. We apply DKT on Alzheimer's variants and demonstrate its ability to predict non-MRI trajectories for patients with Posterior Cortical Atrophy, in lack of such data. This is done by fitting DKT to two datasets simultaneously: (1) the TADPOLE Challenge  [6]  dataset containing subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with MRI, FDG-PET, DTI, AV45 and AV1451 scans and (2) MRI scans from patients with Posterior Cortical Atrophy from the Dementia Research Centre (DRC), UK. We finally validate DKT on three datasets: 1) simulated data with known ground truth, 2) TADPOLE sub-populations with different progressions and 3) 20 DTI scans from controls and PCA patients from our clinical center.
Data Acquisition and Preprocessing We trained DKT on ADNI data from the TADPOLE challenge  [6] , since it contained a large number of multimodal biomarkers already pre-processed and aggregated into one table. From the TADPOLE dataset we selected a subset of 230 subjects which had an MRI scan and at least one FDG PET, AV45, AV1451 or DTI scan. In order to model another disease, we further included MRI scans from 76 PCA subjects from the DRC cohort, along with scans from 67 tAD and 87 age-matched controls. For both datasets, we computed multimodal biomarker measurements corresponding to each brain lobe: MRI volumes using the Freesurfer software, FDG-, AV45-and AV1451-PET standardised uptake value ratios (SUVR) extracted with the standard ADNI pipeline, and DTI fractional anisotropy (FA) measures from adjacent white-matter regions.
For every lobe, we regressed out the following covariates: age, gender, total intracranial volume (TIV) and dataset (ADNI vs DRC). Finally, biomarkers were normalized to the [0,1] range. Results on synthetic data in the presence of ground truth (Fig. 2 ) suggest that DKT can robustly estimate the trajectory parameters (MAE < 0. 058) as well as the subject-specific time-shifts (R 2 > 0. 98). While some errors in trajectory estimation can be noticed, these are due to the informed priors on the model parameters in order to ensure identifiability and convergence of parameters. λ 0 0 = (1, 0. 3, -4, 0) λ 1 0 = (1, 0. 2, 6, 0) Synthetic PCA: λ 0 1 = (1, 0. 3, 6, 0) λ 1 1 = (1, 0. 2, -4, 0) Fig. 2 : Comparison between true and DKT-estimated subject time-shifts and biomarker trajectories. (top-left/top-middle) Scatter plots of the true shifts (yaxis) against estimated shifts (x-axis), for the 'synthetic AD' and 'synthetic PCA' diseases.
Validation on DTI Data in tAD and PCA We further validated DKT by predicting unseen DTI data from two patient datasets: 1) TADPOLE subjects with a different progression from the training Fig. 3 : Estimated trajectories for the PCA cohort. The only data that were available were the MRI volumetric data. The dynamics of the other biomarkers has been inferred by the model using data from typical AD, and taking into account the different spatial distribution of pathology in PCA vs tAD. subjects, and 2) a separate test set of 20 DTI scans from controls and PCA patients from the DRC -full demographics are given in the supplementary material. To split TADPOLE into subgroups with different progression, we used the SuStaIn model by  [3] , which resulted into three subgroups: hippocampal, cortical and subcortical, with prominent early atrophy in the hippocampus, cortical and subcortical regions respectively.
56 ± 0. 18 0. 50 ± 0. 21 0. 32 ± 0. 24 Multivariate 0. 73 ± 0. 09 0. 45 ± 0. 22 0. 71 ± 0. 08 -0. enabled the estimation of quantitative non-MRI trajectories in a rare disease (PCA) where very limited data was available. To our knowledge, this is the first time a multimodal continuous signature is derived for PCA, as the only other longitudinal study of PCA only computed atrophy measures from MRI scans  [10]. Our work has however several limitations, which can be addressed in future research: 1) to account for population heterogeneity, DKT can be easily extended to include subject-specific effects; 2) improved schemes for biomarker allocation to agnostic units can take connectivity into account, or derive it from the data automatically; 3) DKT can be further validated on more complex synthetic experiments with a range of datasets generated with different parameters.
Generation of synthetic dataset We tested DKT on synthetic data, to assess its performance against known ground truth. More precisely, we generated data that follows the DKT model exactly, and tested DKT's ability to recover biomarker trajectories and subject time-shifts. We generated the synthetic data as follows, using parameters from Table  2 : -We simulate two synthetic diseases, "synthetic PCA" and "synthetic AD" -We define 6 biomarkers that we allocate to agnostic units l 0 and l 1 (Table  2  top) -Within each agnostic unit, we define the parameters {θ 0 ,. , θ 5 } corresponding to biomarker trajectories within the agnostic unit. -For each disease, we define the parameters λ corresponding to trajectories of dysfunction scores. -We then sample data from 100 synthetic AD subjects and 50 PCA subjects with β i as given in Table  2  bottom using the model likelihood (Eq.
2 from main paper). For each subject, we generate data for 4 visits, each 1 year apart. Trajectory parameters Biomarker allocation l0 : {k0, k2, k4}, l1 : {k1, k3, k5} Agnostic unit l0 θ0 = (1, 5, 0. 2, 0), θ2 = (1, 5, 0. 55, 0), θ4 = (1, 5, 0. 9, 0) Agnostic unit l1 θ1 = (1, 10, 0. 2, 0), θ3 = (1, 10, 0. 55, 0), θ5 = (1, 10, 0. 9, 0) "Synthetic AD" λ 0 0 = (1, 0.
Parameters used for synthetic data generation, emulating the TAD-POLE and DRC datasets. 6. 3Demographics of test setsThe cohort from the Dementia Research Centre UK used for validation consisted of 10 subjects diagnosed with Posterior Cortical Atrophy, with a mean age of 59. 4, 40% females, as well as 10 age-matched controls with a mean age of 59. 3, 50% females. For the validation on TADPOLE subgroups, we used applied the SuStaIn model on TADPOLE to split the population into three subgroups with different progression: hippocampal, cortical and subcortical subypes with prominent atrophy in the hippocampus, cortical and subcortical areas respectively. The resulting subgroups had the following demographics:.
Acknowledgements This work was supported by the  EPSRC   Centre For Doctoral Training in Medical Imaging  with grant  EP/L016478/1  and in part by the  Neuroimaging Analysis Center  through  NIH  grant  NIH NIBIB NAC P41EB015902. Data collection and sharing for this project was funded by the  Alzheimers Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ). The Dementia Research Centre is an ARUK coordination center.
Cell Culture Human umbilical vein endothelial cells (HUVEC) were purchased from PromoCell (Heidelberg, Germany) and grown in endothelial cell growth medium (PromoCell) supplemented with gentamycin (50 µg mL -1 ) and amphotericin B (50 ng mL -1 ). For all experiments, we used HUVECs pooled from up to four donors, which did not exceed passage 4. Human embryonic kidney (HEK) 293 cells (ATCC CRL-1573) were grown in DMEM medium (Invitrogen, Cergy Pontoise, France) supplemented with 10% fetal calf serum (FCS), 100 IU mL -1 penicillin, and 100 µg mL -1 streptomycin (Invitrogen, Cergy Pontoise, France). C166 mouse endothelial cells (accession number CRL-2581) and LLC1 mouse lung cancer cells (accession number CRL-1642) were grown in DMEM medium (Invitrogen, Cergy Pontoise, France). Media were supplemented with 10% fetal calf serum (FCS), 100 IU mL -1 penicillin and 100 µg mL -1 streptomycin.
mRNA Sequencing For sequencing, RNAs from tumor sorted endothelial cells from Tie2-CreERT2;PPARβ/δ mice treated with Tamoxifen or vehicle were used (n = 4 each). RNA sequencing and data analysis was performed by Novogene, Beijing, China. Briefly, RNA quality was monitored on 1% agarose gels. RNA purity was checked using a NanoPhotometer. RNA concentration was measured using the Qubit ® RNA Assay Kit in a Qubit ® 2. 0 Flurometer (Life Technologies, CA, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext ® UltraTM RNA Library Prep Kit for Illumina ® (NEB, USA) following manufacturer's instructions.
Library quality was assessed on the Agilent Bioanalyzer 2100 system. Library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. Clean reads were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. HTSeq v0. 6. 1 was used to count the reads numbers mapped to each gene. Fragments per kilobase of transcript sequence per millions base pairs sequenced (FPKM) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of the two groups was performed using the DESeq R package (1. 18. 0). Resulting p-values were adjusted using the Benjamini and Hochberg's approach. Genes with an adjusted p-value < 0. 05 were assigned as differentially expressed.
Bioinformatics The maximum scoring subnetwork was calculated with the runFastHeinz function from the R BioNet package  [21]. The p-values obtained from the differential expression were assigned to each node (gene) of the networks. The following networks were analyzed: FULL-Mouse (18 January, 2019) from signor database (http://signor. uniroma2. it/), HumanCyc metabolic pathways (http://humancyc. org/), NCI PID, Complete Interactions (http://www. cancer. gov), Biogrid (https://thebiogrid. org/), HCOP Mouse PCNet  [22] , ConsensusPathDB (http://cpdb. molgen. mpg. de/). All networks were downloaded from http://www. ndexbio. org [23]. Sub-network visualizations and analyses were done with Cytoscape  [24]  and pathway cluster analysis at http://impala. molgen. mpg. de/ [25]. Prediction of PPAR, responsive elements in differentially expressed genes was done using the oPOSSUM3 software at http://opossum.
Funding: This research was funded by " Fondation ARC pour la recherche sur le cancer ", grant number n • PJA 20161204650 (N. W. ),  Gemluc  (N. W. ), and  Plan Cancer INSERM ,  Fondation pour la Recherche Médicale  (K. -D. W. ). S. D. was supported by a PhD fellowship from the  China Scholarship Council.
Thereby, when the two tracks were mosaicked we enforced continuity across the swath. As will be evidenced in the validation results that follow, no noticeable slope trend is observed as a function of cross-track distance of the mosaicked GLISTIN-A swaths. Unfortunately, due to budget limitations and aircraft logistics it was not possible to have coordinated validation flights. However over the Hubbard, and Nabesna lidar data was identified. Hubbard and Nabesna were imaged by GLISTIN-A on 25 April 2013 and by the University of Alaska Fairbanks (UAF) lidar 21 May 2013, and 19 June 2013 respectively. Columbia glacier was imaged by GLISTIN-A twice on 24 April and 27 April 2013. As an example of the GLISTIN-A imagery, Figure  2  shows an optical image from Google Earth next to a relative power mosaic generated from the two overlapping GLISTIN-A swaths posted at a horizontal resolution of 3 m.
Validation The University of Alaska Fairbanks (UAF) has flown lidar altimetry on glaciers in Alaska in 2013 as part of the NASA's Operation IceBridge mission. The laser scanner was a Riegl LMS-Q240i, which has a sampling rate of 10,000 Hz and laser wavelength of 905 nm. The UAF altimetry data are comprised of a series of point measurements on the surface of glaciers recorded from an aircraft. Each point is derived from a pulsed laser range measurement combined with aircraft Global Positioning System Inertial Measurement Unit (GPS/IMU) positioning and orientation measurements. The footprint on the ground of the laser shot points is on the order of 20 cm in diameter with a vertical precision of about ±30 cm and the swath width covered by the shots is typically of 500 m. For our comparisons, the lidar shots are rasterized on the 3 × 3 m GLISTIN grids (UTM projection).
Multiple lidar shots per grid pixel are averaged. Both datasets are converted to the same elevation datum (WGS84). A B Figure  3  shows the height map mosaics generated from the GLISTIN-A data with the lidar tracks overlaid. The lidar was flown along the center of the glacier including the both forks as seen in Figure  3. A nearly two months after the GLISTIN-A acquisition (GLISTIN-A 25 April 2013 vs. lidar 10 June 2013). The time-separation is unfortunate and a source of error in the comparison. Therefore, the differences in elevation are not only reflecting instrument accuracy but also surface elevation change and glacier displacement. Even so, Figure  3A  shows the radar and the lidar compare very favorably. The mean difference is 0. 8 m and the standard deviation is 1. 7 m. Note that no trending is observed across the GLISTIN-A mosaic implying that if there were a slope the mosaicking process has effectively removed it, and that the mean is well within the expected range (better than 1-2 m absolute).
Validation Results For OMG, GLISTIN-A's role is to quantify the mean elevation changes as well as changes in glacier front positions to enable volume change estimates of over 200 marine terminating glaciers. Changes in the glacier slope are also an important dynamic quantity that will be observed annually by OMG's GLISTIN-A campaigns. The OMG requirements of 3 m vertical accuracy at 100 m spatial resolution over an 8 km swath can be readily met by GLISTIN-A  [8]  but because any residual trends (tilts) are systematic and present as a slope error, they could be problematic or create confusion in interpretation. In the Alaska flights, we planned completely overlapping swaths and used near-range data to remove any residual trends. However, this effectively doubles the flight time required and adds additional processing complexity and so was dismissed as an option for OMG due to costs and aircraft availability.
Furthermore, after assessing requirements and assessing performance of existing data, the OMG science team determined that additional calibration to detrend data would not be performed as part of the OMG data delivery. Here we use airborne lidar altimetry data to validate the OMG GLISTIN-A height data and quantify the error statistics. For validation purposes in Greenland, we identified overlapping tracks of the NASA Wallops Airborne Topographic Mapper (ATM) lidar  [5]. The ATM was flying data collections for Operation IceBridge (OIB), but these flights were not coordinated with the OMG campaign. In 2016, 35 of the 70 GLISTIN-A lines had overlapping OIB ATM data. As an example, Figure  6  shows a comparison of an ATM line with GLISTIN-A where the ATM path flown along Zacharae glacier. ATM flew on 9 May 2016 approximately 1.
However, as is the case here, at the lower elevations where the marine terminating glaciers approach sea-level the impact of crevasses and objects movement is not zero mean and must be excluded (as is the instance in Figure  6. We now turn our attention to identifying any residual systematic trends as a function of the cross-track distance, x, in the single-pass data. As mentioned in Section 2. 2 and detailed by Equation (  1 ), residual systematic errors can manifest as a quadratic bias that propagates across the swath. Because the stability of the instrument parameters no quadratic trending of the height measurements with cross-track distance was expected nor observed, but rather any observed cross-track trend are linear (refer Figure  1 ). In the comparison of Greenland single-swath (strip-map) data with ATM, because we assume the ATM to be our validating data set, the vertical height error is defined as ATM measured height-GLISTIN-A measured height.
Similarly Figure  9  shows the results for the 2017 data. Since both the ATM and GLISTIN-A flew more extensively in 2017, we were able to find a lot more lines for comparison, 46 in total. The upper plot shows biases at nadir that are still bounded within the 3 m requirement and consistent with 2016. The biases are -0. 32 ± 0. 95 m. The middle plot reveals residual slopes of 0. 1 ± 7. 6 millidegrees. The bottom plot shows the height errors, When propagated to 10 km cross-track distance (~8 km swath), and in all instances the 3 m accuracy requirement is easily met. We conclude that the GLISTIN-A data meets the OMG science requirements. For OMG, these data will be used to compute volume changes near the glacier termini that are driven by speed-up or slow-down of the glaciers, in other words, dynamic volume changes.
Estimating Yearly Volume Change The primary science objective for OMG is to relate ocean changes to ice changes around perimeter of Greenland. For glaciers that reach the ocean and interact with it, ocean driven changes are expected to manifest as thinning or thickening that is concentrated near the glacier front  [6, 16, 17]. To estimate these changes, swath data from subsequent years will be differenced and averaged over dynamically changing areas. Since the trend-like biases described in the previous section will not be reduced with area averaging, it is important to quantify the impact of such tilt-biases in year to year comparisons. To quantify this, we compare glacier height change signals for two typical glaciers, relative to biases estimated by comparison with co-located ATM observations.
Discussion This paper has presented validation results and systematic accuracy assessments of the GLISTIN-A system. When concurrent overlapping swaths with opposite viewing directions were utilized as in Alaska, no significant cross-track trends were observed since self-consistency and detrending was enforced during the mosaicking process and generated without tie-points or supporting data from other instrumentation such as lidar. For OMG, the luxury of fully overlapping swaths was not financially or logistically feasible. In the absence of ground-control points, an overall bias up to 1-2 m (absolute) and slight trending may remain. Despite these residual systematic trends the 2016 and 2017 validations easily met the OMG requirements. Even without externally-derived correction, dynamic interannual glacier volume changes are readily quantified with reasonable errors.
GLISTIN-A Data The GLISTIN-A 2013 Alaska data was collected at the conclusion of a Principal Investigator lead engineering development to demonstrate success of the system upgrades in meeting measurement requirements over glaciers and ice. As the system was not yet "operational" and the data calibration was still at the development and assessment stage, that data was not staged on the UAVSAR website (unlike the OMG and all contempory GLISTIN-A acquisitions). However, the Alaska data presented in this paper can be accessed on request to the corresponding author.
GLISTIN-A Data The GLISTIN-A OMG data is readily available on the UAVSAR data archive https://uavsar. jpl. nasa. gov. The data base is searchable by year, flight line identifier and/or location. The tables in Appendix A itemize each individual line identifier for which ATM data was used to compare with GLISTIN-A. For comparison with ATM, first all the ATM L2 data were pre-sorted to find all that overlapped the swath coverage of the GLISTIN-A lines. All overlapping "platelets" were saved in files with a name indicating which GLISTIN-A line(s) the data overlapped. In order to assess any trends in the cross-track relative to the flight direction, GLISTIN-A height files in "sch" coordinates were used, that is, those with a suffix "hgt. sch". The sch coordinate system is a "radar-relative" cartesian coordinate system where the coordinates are defined relative to a linear flight track approximation (where "s" is along-track, "c" is cross-track, and "h" is height relative to the WGS84 ellipsoid).
A comprehensive description of the sch coordinate system can be found in  [15]. After identifying all the areas of overlap the spatial topography of the each line was assessed individually identifying the glaciers of interest and regions which were likely to have significant movement (e. g. , the glacial outflow regions and sea-ice). Due to the time separation of acquisitions these regions were excluded using a spatial filtering criteria. Comparison with ATM and/or spatial imagery would typically confirm need to exclude those regions (as illustrated in Figure  6 ). The specific filtering can be reconstructed if the initial overlap sorting to filter the ATM data is replicated. This code (MATLAB) and line-based criteria can be furnished upon request.
ATM Operation Icebridge Data The ATM data is available at the NSIDC. Reference to the data is  [14]. In the case of this comparison the only fields used were location (latitude, longitude) and height. For locations (lat/lon) that overlapped a GLISTIN-A swath we simply identified the 10 × 10 window of 3 m pixels centered around the ATM platelet mid-point and averaged these pixels (i. e. , a 30 m × 30 m average). The L2 ATM slope fields were not used although they could be applied to refine the assessment to a planar comparison. However once the areas that were obviously dominated by surface movement the error bars across the swath were assessed for every line (as per Figure  7 ) and found to be meter level and below and able to reveal the trends with confidence. Two independent assessments using different methodologies as noted in this paper concurred with the results within the error bounds.
Conclusions This paper has presented the first validation results of the operational GLISTIN-A mm-wave single-pass interferometric SAR over glaciers in Alaska and Greenland. First results used lidar data collected by the University of Alaska, Fairbanks for two Alaska glaciers: Nabesna and Hubbard. Despite several months of separation between the GLISTIN-A and the lidar acquisitions, the data sets compared very favorably. In these instances, mosaics of the radar data had been generated from consecutive fully overlapping swaths with overlapping viewing geometry. In doing so continuity is enforced and residual tilts effectively cancel. Differences observed between the radar-derived topography and the lidar measurements were found to agree well with no residual trends observed for both Hubbard and Nabesna glaciers.
Furthermore comparison of mosaics collected with a 3 day separation, indicated excellent system stability with a negligible (~20 cm) mean difference between the two, with crevasse movement dominating the variance. In the case of the Ocean's Melting Greenland mission, the luxury of fully overlapping opposite-viewing swaths was not an option due to flight-time limitations. For these datapoints, we have assessed and quantified where possible residual systematic biases. Our validation assessment used ATM lidar data collected as part of Operation Ice-Bridge. The time of separation between acquisitions was generally weeks to months and thus areas of comparison were spatially filtered for relatively static regions, generally away from the lower outflow areas. Our assessment of 2016 and 2017 OMG data confirmed our prediction that any residual systematic trends would manifest as a small linear characteristic (as quadratic error terms were predicted by design and measurement to be negligible) propagating across the swath.
Resultant bias and slope terms were found to lie within requirements for OMG, but for more exacting science requirements the calibration could be further improved by applying tie points where such ancillary data is available. Under OMG, GLISTIN-A data for over 90 per-cent of Greenland's marine terminating glaciers has been collected for four successive years and will now be used to assess interannual ice volume-changes of those glaciers.