dataesr/scientific-paragraphs-categorization huggingface dataset:
The involvement of these cells in GDM is further supported by our celltype specificity results highlighting Nr5a1_Adcyap1 in ARC which project from the VMH  30  and appear to correspond to a similar gene expression pattern to Glu7 31 in our conditional analysis (Supplementary Figure  27 , Supplementary Table  28 , Supplementary Note). In contrast, while pregnancy is characterized by impaired insulin sensitivity and some of our class G genes (e. g. PCSK1 and MTNR1B) affect insulin biosynthesis and secretion  32, 33  , no evidence of a genetic relationship between GDM and insulin levels is observed either in top hits or genome-wide at the current sample sizes. Nonetheless, given the complexity of GDM the limited sample size of our dataset allows us to glean insight into only a few potential mechanisms.
Cohort The FinnGen Study is a public-private partnership project combining data from Finnish biobanks and electronic health records from national registries. The linked national health registers include data on hospital and outpatient visits, primary care, cause of death, and medication records. Approval from the FinnGen Study was received to use the data in the present work. After a 1-year embargo, the FinnGen summary stats are available for download. In this study, we used the results from the FinnGen release R8, which includes data from 342,499 individuals and more than 4500 disease endpoints.
Phenotyping Full details of phenotyping are described in the Supplementary Note. Briefly, clinical endpoints with corresponding dates were constructed for gestational diabetes and related diagnoses for exclusions for all FinnGen participants as described in the Supplementary Note. Temporal phenotyping was then performed to phenotype each pregnancy for presence of glycemic disease and then assign individuals as cases or controls. Beginning with 330,000 pregnancies among genotyped FinnGen participants, we defined a "pregnancy window" of 40 before delivery until 5 weeks after delivery. A pregnancy met inclusion criteria for "gestational diabetes" if it had (I1) gestational diabetes ICD codes occurring in the pregnancy window, (I2) any diabetes codes occurring in the pregnancy window (e. g. for ICD8), or (I3) abnormal blood glucose test results in the Medical birth register, which contains data on the mother's diseases during pregnancy.
Shared variants analysis We applied linemodels package (https://github. com/mjpirinen/linemodels) to summary statistics from T2D GWAS and GDM GWAS. The analysis included 28 lead variants from the GWASes (13 from GDM and 15 from T2D). We classified the variants into two classes based on their bivariate effect sizes. The classes were represented by line models whose slopes were estimated using an EM-algorithm, resulting in values 1. 53 (labelled as class T) and 0. 25 (labelled as class G). For both models, the scale parameters determining the magnitude of effect sizes were set to 0. 2 and the correlation parameters determining the allowed deviation from the lines were set to 0. 99. The membership probabilities in the two classes were computed separately for each variant by assuming that the classes were equally probable a priori.
Cell type specificity analyses To get better resolution on specific cell types, we performed cell-type specificity analyses with high quality single cell murine datasets using FUMA (https://fuma. ctglab. nl/tutorial#celltype; Supplementary Note). First, we identified tissue-level associations with Tabula Muris data 38 identifying significant associations (FDR <. 05) with expression in brain and pancreas after Benjamini-Hochberg multiple testing correction (Supplementary Figure  27 ). We then performed cell-type specificity analyses as previously described  25  , augmenting Tabula Muris with additional high quality scRNA-seq of hypothesized involved brain regions (Supplementary Note). Analysis was performed on genetic summary statistics for both our Gestational Diabetes GWAS and for a recent Type 2 Diabetes European meta-analysis dataset  19.
Funding Statement The  FinnGen  project is funded by two grants from  Business Finland  ( HUS 4685/31/2016  and  UH 4386/31/2016 ) and by eleven industry partners ( AbbVie Inc ,  AstraZeneca UK Ltd ,  Biogen MA Inc ,  Celgene Corporation ,  Celgene International II Sàrl ,  Genentech Inc ,  Merck Sharp & Dohme Corp ,  Pfizer Inc. ,  GlaxoSmithKline ,  Sanofi ,  Maze Therapeutics Inc. ,  Janssen Biotech Inc). This work received the following support: A. E. was a research Scholar supported by  Sarnoff Cardiovascular Research Foundation , R. W. was supported by  R01MH101244 , M. P. was supported by the  Academy of Finland  ( 336825 ,  338507 ), P. T. received support by  NIH/NIA  ( R00-AG062787 ), E. W. received support by the  Academy of Finland Center of Excellence program  ( 352796 ).
Patient Demographics and Clinical Results Each of the four disorders was represented by two international cohorts of bilaterally implanted   S1. Tables  S2-5  provide comprehensive patient-specific information. Disease-wise cohorts were analyzed separately but each underwent equivalent processing steps (Fig. 1 ). Electrode localization via Lead-DBS software, v3. 0 (www. leaddbs. org)  34  confirmed placement to the subthalamic region for all patients (Fig. 2 ). Electric field magnitudes (E-fields) estimated based on patient-specific stimulation parameters were used to determine optimal stimulation targets (DBS Sweet Spot Mapping, Fig. 1a ) as well as tracts (DBS Fiber Filtering, Fig. 1b ) that correlated with maximal symptom improvements. Fiber tracts were primarily defined based on a normative ultra-high-resolution (760 µm isotropic) tractogram of a single healthy participant  35  to identify small differences in anatomical mappings but repeated using three additional normative connectomes.
To account for this, we repeated our analysis on a pathway atlas manually curated by expert anatomists 37 (Fig. 6c ). While this dataset is likely the most accurate atlas of subcortical tracts that currently exists, a potential drawback is its proneness to false negatives (simply because not all fibers of the brain were defined in it). Second, as the atlas is not based on empirical dMRI, small details of tract trajectories in template space may be misaligned. Thus, we replicated analyses using a pathway atlas informed on population based fiber tracking with expert defined pathways  37, 39  which we augmented by inputs to the STN from the entire frontal cortex (DBS Tractography Atlas, v2, Fig. 6d ). Despite clear differences (Fig. 6 ), a comparable rostro-occipital gradient emerged across connectomes, with the same order of motor disorders in sensorimotor and premotor cortices toward associative-limbic OCD connections.
Spatial Scaling of Functional Representations from Cortex to Subcortex The basal ganglia anatomically compress cortical inputs. During this process, a concurrent compression of information is bound to happen  33. Anatomical projections then re-expand on their way back, via the thalamus, toward the cortex. Within this loop, the STN forms one of the smallest nuclei and receives input from the entire frontal cortex  3. As such, it embodies a network node with particularly high compression density. In other words, the STN may incorporate a drastically miniaturized representation of how functionally selective anatomy in the frontal cortex is organized. While the ratio of anatomical scaling carried out by the basal ganglia has been estimated before  53  , the present dataset constitutes a unique opportunity to empirically confirm these estimates in the human.
Fourth, with patient-specific dMRI largely unavailable, anatomical delineations were based on normative connectivity. Undoubtedly, the use of connectivity acquired outside of the patient sample in question introduces limitations regarding the anatomical accuracy of results. Nevertheless, an advantage of the normative connectome principally used here lies in its ultrahigh resolution and excellent signal-to-noise-ratio. This unprecedented quality resulted from a long scanning duration and use of advanced acquisition hard-and software  35  , which is hardly attainable during clinical imaging. Additionally, we replicated our findings with four normative connectomes to probe for differences as a function of dataset. Indeed, our study aimed to derive at a "broad lens" description of functional networks in the average human brain.
DBS Electrode Localization and Electric Field Modeling DBS electrodes of all patients were localized based on default settings in an advanced, state-ofthe-art processing pipeline as implemented in Lead-DBS software, v3. 0 (https://www. leaddbs. org)  40. Lead-DBS being a MATLAB based software, MATLAB R2022b, v9. 13. 0. 2105380 (The MathWorks Inc. , Natick, MA, USA) was used to implement this analysis pipeline. In brief, our approach involved linear coregistrations of postoperative head CT or MRI scans to preoperative T1-weighted images by means of Advanced Normalization Tools (ANTs, http://stnava. github. io/ANTs/)  91. Coregistration results were subsequently corrected for potential intraoperative brain shift via an automatized subcortical refinement module (as implemented in Lead-DBS), but also needed to conform to meticulous visual inspection by two expert users (BH and NL).
Functional Segregation at Tract and Cortical Levels (DBS Fiber Filtering) Model definition (Fig. 1b ): The second part of our analysis stream followed the intention of deriving the organization of neuroanatomy-function pairings i) at the level of hyperdirect prefronto-subthalamic tracts, but also ii) that of their projection sites within the frontal cortex. To understand the relationship between modulation of specific fiber tracts seeding from bilateral E-fields and a given functional effect, we thus employed a previously validated structural connectivity analysis, termed DBS Fiber Filtering 45 (Fig. 1b ), in an adapted form for implementation in (non-binarized) E-fields  46. Structural connectivity was primarily defined by a normative whole-brain connectome, derived from a multi-shell diffusion-weighted imaging dataset at 760 µm isotropic diffusion acquired in-vivo in a single healthy participant over a total duration of 18 scanning hours (Massachusetts General Hospital [MGH] Single Subject 760 µm Connectome)  35.
Visualizations and functional gradient: To elucidate the organizational gradient of fiber projections at the prefronto-cortical level, sweet tract bundles were first converted to voxelized images (tract-density maps). The resulting maps were then smoothed using an 8 mm Gaussian kernel at full width half maximum as implemented in SPM12 (https://www. fil. ion. ucl. ac. uk/spm/) and projected onto the cortical surface of the MNI template using SurfIce software, v. 1. 0. 20211006 (https://www. nitrc. org/projects/surfice). Anatomical correlates of disease-wise projection sites of fiber tracts were then defined based on the Johns Hopkins University (JHU) atlas parcellation  49. Influence of electrode placement: Subsequently, we aimed to scrutinize the relative impact of different model inputs. Besides the choice of a normative connectome, DBS Fiber Filtering results are mainly determined by two major sources of variability across patients: namely, electrode placement and clinical improvements.
Data availability Clinical outcome and imaging data cannot be publicly shared as this would compromise patient privacy according to current data protection regulations. It is, however, available from the principal investigators of the collecting sites upon reasonable request within the framework of a data sharing agreement. The sweet tract atlas and sweet spots of all four disorders are openly available within Lead-DBS software (www. lead-dbs. org). Further, the DBS Tractography Atlas, v2 can be openly downloaded via the Lead-DBS knowledge base (https://www. leaddbs. org/helpsupport/knowledge-base/atlasesresources/normative-connectomes/). The HCP-1,065 diffusion source data  95  used to inform this atlas can be openly accessed via DSI-Studio (https://sites. google. com/a/labsolver. org/brain/diffusion-mri-data/hcp-dmri-data).
The following normative resources have been made openly available by the original authors: the MGH 760 µm Connectome  44  (https://datadryad. org/stash/dataset/doi:10. 5061/dryad. nzs7h44q2) and the Basal Ganglia Pathway Atlas 37 (https://osf. io/mhd4z/). While a processed version of the HCP 33 985 Connectome 30 can be requested from the corresponding authors, source data are freely accessible via the repository of the HCP (https://www. humanconnectome. org/study/hcp-youngadult/document/1200-subjects-data-release).
Proteomics data analysis Raw DIA files were analysed in directDIA mode using Spectronaut Pulsar (v15, Biognosys, Zurich, Switzerland). The data were searched against a species-specific protein database (Uniprot Mus musculus release 2016_01) with a list of common contaminants. The data were searched the following modifications: carbamidomethyl (C) as fixed modification, and oxidation (M) and acetyl (protein N-term) as variable modifications. A maximum of 2 missed cleavages was allowed. The search was set to 1 % false discovery rate (FDR) at both protein and peptide levels. DIA data were then uploaded and searched against this spectral library using Spectronaut Professional (v. 15) and default settings. Relative quantification was performed in Spectronaut for each pairwise comparison using the replicate samples from each condition using default settings, except: Major Group Quantity = median peptide quantity; Major Group Top N = OFF; Minor Group Quantity = median precursor quantity; Minor Group Top N = OFF; Data Filtering = Q value; Normalization Strategy = Local normalization; Row Selection = Automatic; Exclude Single Hit Proteins = TRUE.
Acknowledgments We thank  Albert Compte  for stimulating discussions and very constructive comments on this manuscript. We thank  Claudia Sommer ,  Christin Reißig ,  Marc Raugust , and  Marin Kempfer  for expert technical assistance and for image acquisition and analysis. We would like to thank  Emil Ceanga  for support and very helpful methodological discussions. The authors gratefully acknowledge support from the  FLI Core Facility Proteomics. The FLI is a member of the  Leibniz Association  and is financially supported by the  Federal Government of Germany  and the  State of Thuringia. This work was supported by the  German Research Foundation  ( FOR3004 ;  GE2519/8-1 ,  GE2519/9-1 , to C. G. ; Research Training Group ProMoAge  GRK 2155  to A. O. ), by the  German Federal Ministry of Education and Research  ( 01GM1908B ,  01EW1901  to C.
Data and code availability: Statistical data associated with this study is presented in Supplementary Table  1. All datasets generated during this study are available from the corresponding authors upon reasonable request. Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  70  partner repository. Code associated with the neural network model is publicly available code in ModelDB (https://senselab. med. yale. edu/ModelDB; accession number: 138421). The Matlab code for IRASA method can be found at https://purr. purdue. edu/publications/1987/1.
Estimating accurate high-dimensional transformations remains very challenging, especially in a clinical setting. In this paper, we introduce a multiscale parameterization of deformations to enhance registration and atlas estimation in the Large Deformation Diffeomorphic Metric Mapping framework. Using the Haar wavelet transform, a multiscale representation of the initial velocity fields is computed to optimize transformations in a coarse-to-fine fashion. This additional layer of spatial regularization does not modify the underlying model of deformations. As such, it preserves the original kernel Hilbert space structure of the velocity fields, enabling the algorithm to perform efficient gradient descent. Numerical experiments on three datasets, including abnormal fetal brain images, show that compared to the original algorithm, the coarse-to-fine strategy reaches higher performance and yields template images that preserve important details while avoiding unrealistic features.
Introduction Although the quantitative analysis of anatomical images is an old problem  [1] , to this day it is still a challenging one, especially given that datasets of clinical images are often small in number and large in dimension. Estimating the transformation that warps an object onto another provides an efficient way of quantifying shape differences, which is the cornerstone idea of Computational Anatomy  [2]. When one works with collections of images that are instances of the same anatomical object, the question arises how to faithfully model the mean and variability over these structures. Atlas estimation is a method to achieve such modeling: an estimate of the average shape is given in the form of a template image, which represents the invariants across the population, i. e. shared anatomical features, and the variability is given by deformations from the template space to each subject's space, which express how these common features vary within the population  [2].
It is then straightforward to infer the distance between the deformed template and the target objects and to compute the regularity term with Equation (  6 ). Finally, one can compute the gradient of the cost function with respect to the template, and use a backward integration along time to compute the gradient with respect to the momentum vectors. These steps correspond to the lines 14-18 in Algorithm 1. The algorithm is publicly available as part of the open-source software Deformetrica  [34].
Experiments In this section, we evaluate the benefits of the wavelet reparameterization of the initial velocity fields by comparing our algorithm to the original version  [10]  on three different datasets. The training phase consists in atlas estimation and the test phase consists in registering the estimated template image to a set of new images. Performance of the algorithms is assessed with the relative residual error: R = ∆ j end ∆ 0 with ∆ 0 and ∆ jend and the mean residual value over subjects at iteration 0 and at convergence, respectively. As the residual distance between each image and the deformed template is part of the objective function (Equation (  11 )), we also compute the Structural Similarity Index Metric  [38]  (SSIM) after training and test: SSIM (I 1 , I 2 ) = l(I 1 , I 2 ) × c(I 1 , I 2 ) × s(I 1 , I 2 ) where I 1 and I 2 are the compared images, l is a function comparing the luminance (i.
Handwritten digits In this section, we use images of the digit two extracted from the well-known United States postal database of handwritten digits  [39]. The size of the images is 28 by 28 pixels. We test the original algorithm against the coarse-tofine strategy using an experimental procedure similar to that of Durrleman et al. [10] : atlas estimation is performed using 20 randomlychosen training images and the estimated template image is registered to 10 randomly chosen test images with the same parameters as those used during training. The experiment is repeated five times with different training and test sets, with no intersection between any of the training and test sets. This procedure is reproduced with different kernel widths σ g. Since the five experiments are performed on independent datasets, we use paired Student t-tests to compare performance between the two algorithms.
These observations belie the quantitative evaluation, which shows that the performance of the original algorithm increases with the number of parameters. This discrepancy demonstrates that residual error alone is not sufficient to evaluate the accuracy of the algorithms, as irregular vector fields might be able to match any target image at the cost of producing unnatural deformations. Unlike the original algorithm, the coarseto-fine procedure produces realistic template images, whose quality is preserved when σ g is decreased. Moreover, all reconstructed images are very close to the original ones. Their quality slightly increases with the number of control points: this is most evident for the third and fifth reconstructed subjects, which become more accurate for lower values of σ g. Table  1  Performance (mean ± standard deviation) of the original and multiscale algorithms over five folds of cross-validation after atlas estimation and registration on the dataset of handwritten digits.
Artificial characters In the previous experiment, one can remark that the performance of the algorithms diverge most when a high number of parameters is used. Therefore, one might simply be tempted to employ the original algorithm with a lower number of parameters, as in Durrleman et al. [10]  whose experiments were performed with 36 control points. However, datasets that present a higher amount of details and inter-subject variability may benefit from our coarse-to-fine strategy even when a lower number of control points is used. To confront our algorithm with a more difficult task, we manually designed a dataset of 30 characters. The size of the images is 28 by 28 pixels. We compare our algorithm to the original version using cross-validation: the dataset is randomly split into a training set (24 images) and a test set (6 images).
Fetal brain images To evaluate the performance of our coarse-tofine approach on a dataset of clinical images, we use 30 fetal brain MRIs with agenesis of the corpus callosum acquired in Hopital Trousseau, France  [3]. Gestational ages are comprised between 32 and 34 weeks of gestation (mean = 32. 9 ± 0. 6). Agenesis of the corpus callosum is a developmental anomaly characterized by the total or partial absence of the corpus callosum. It is often associated to anatomical features such as widening of the lateral ventricles. Atlas estimation can help better understand congenital anomalies by providing an insight into how these anatomical characteristics vary together  [3]. However, as abnormal fetal brains may present a wide range of defects, this makes atlas estimation more difficult and prone to errors than with datasets of healthy fetuses.
This reparameterization can easily be translated to other mathematical frameworks  [28; 29]  which model deformations using vector fields. We performed experiments on three datasets of increasing difficulty. Compared to the original algorithm, the coarse-to-fine algorithm yields higher quality templates with better stability, that are able to generalize to unseen images. Not only does our strategy produce images that have a realistic anatomy, but it leads to enhanced preservation of anatomical details, including unusual or abnormal ones. This makes it particularly appropriate for tasks involving high inter-subject variability, specifically clinical images. Some limitations of this algorithm have to be highlighted. Unlike approaches based on a mixture of kernels, our deformation model relies on a single Gaussian kernel.
It is supported by the government with the aim of creating or renovating urban areas and improving local services while increasing economic revenues (Paris Region Institute, 2020) (Fig. 1 ). Considering this context, inundation maps would strongly condition the response to tsunami risk on the short and the long terms for the plain of Martil, and more widely for the northern coast of Morocco. This study analyses the exposure of the plain through the use of inundation maps created with four different methods  (static, hydrodynamic, hybrid and volumetric) , thus reflecting the geological surveys that have been undertaken by the interdisciplinary research program Evaluation of seabed hazards and risk analysis in the Mediterranean Sea (ALARM).