dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank  Aurelien Hees  and  Peter Wolf , as well as  Quentin Baghi ,  Jean-Baptiste Bayle ,  Valerie Domcke ,  Nikolaos Karnesis ,  Jonathan Gair , and  Angelo Ricciardone  for fruitful discussions. O. H's work was supported by the  Programme National GRAM of CNRS/INSU  with  INP  and  IN2P3  co-funded by  CNES. O. H. and M. L. gratefully acknowledge support from the  Centre National d' Études Spatiales (CNES). M. M. gratefully acknowledges support by the  Deutsches Zentrum fur Luft-und Raumfahrt (DLR)  with funding from the  Bundesministerium fur Wirtschaft und Technologie (Project Ref. No. 50 OQ 2301  based on work done under Project Ref. No. 50 OQ 1801 ). M. P. 's work was partially funded by  STFC  grant  ST/T000791/1  and by the  European Union 's  Horizon 2020 Research Council  grant  724659  Massive-Cosmo ERC-2016-COG.
However, microfluidic single-cell dispenser systems have made it possible to analyze non-culturable microbial cells, including CPRs (Figure  1 )  [11]. Center for Biotechnology Information (NCBI) database  [26]. CPRs are also sometimes referred to as "Patescibacteria", since more than 74% of their genomes belong to this superphylum or phylum  [27, 28]. Nevertheless, the distinction between superphylum and phylum has not yet been clearly described for this radiation. Moreover, as there is no proposed formal name for this division, the misuse of "Patescibacteria" as a synonym for CPR is confusing when addressing a superphylum/phylum particularity without generalizing it to the whole CPR group. CPRs, also referred to as "ultramicrobacteria" or "nanobacteria", have a reduced genome (around one megabase pair) and are small in size (100 nm to 300 nm), with a cell volume of 0.
A dramatic increase in biological data has significantly expanded the scale of the microbial community, and new species are regularly being identified. CPRs include more than 70 phyla, with around 11,000 genomes, most of which are incomplete and considered as metagenome-assembled genomes (MAGs) in the National Center for Biotechnology Information (NCBI) database  [26]. CPRs are also sometimes referred to as "Patescibacteria", since more than 74% of their genomes belong to this superphylum or phylum  [27, 28]. Nevertheless, the distinction between superphylum and phylum has not yet been clearly described for this radiation. Moreover, as there is no proposed formal name for this division, the misuse of "Patescibacteria" as a synonym for CPR is confusing when addressing a superphylum/phylum particularity without generalizing it to the whole CPR group.
Genomes of CPR have shown fewer open reading frames (ORFs) than other bacteria, which indicates a correlation between bacterial genome size and the number of encoded proteins (Table  1 )  [45]. Furthermore, based on an analysis of a massive metagenomic dataset, CPR genomes were found to harbor diversity-generating retroelements (DGRs)  [46]. DGRs are mobile elements that diversify DNA sequences by introducing nucleotide substitutions at specific locations, thereby creating different protein-encoding genes  [47]. They were found to be abundant in microorganisms with small genomes and episymbiotic lifestyles, such as CPRs  [48]. Their function expands ligand specificity for different cellular binding proteins involved in attachment with host cells  [28]. This process could accelerate the evolution of CPRs to exploit potential hosts in different environments.
Defense Mechanisms CPRs harbor enzyme-encoding genes with antibiotic resistance (AR) activity  [64]. In 2021, an exhaustive in silico analysis using a new adapted strategy of AR gene research was applied to CPR genomes (n = 4062; all CPR genomes available on 12 September 2020 in the NCBI database) (Figure  1 ). According to the NCBI Conserved Domain Database (CDD) search, these AR genes have the functional domain necessary to inactivate the antibiotic and to protect or alter its target. This analysis has revealed a rich reservoir of 30,545 AR genes, with 89 enzyme activities in all CPR superphyla (Candidatus Parcubacteria, Candidatus Microgenomates, Candidatus Saccharibacteria, unclassified Patescibacteria group, Candidate division WWE3 (Katanobacteria), Candidatus Peregrinibacteria, Candidatus Berkelbacteria, Candidatus Dojkabacteria, Candidatus Doudnabacteria, Candidatus Gracilibacteria, Candidatus Absconditabacteria, Candidate division Kazan-3B-28, and Candidatus Wirthbacteria, with an average of 7.
Funding: This work was supported by the  French Government  under the " Investissements d'avenir" (Investments for the Future ) programme managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence Alpes Côte d'Azur and European funding (FEDER (Fonds européen de développement régional) PRIMMI (Plateformes de Recherche et d'Innovation Mutualisées Méditerranée Infection) ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Human skin explants This ex-vivo study was performed on 24 abdominal skin explants which were collected from surgical residue of post-bariatric surgery for 4 different human subjects (patient data available in supplemental Data S1) after obtaining informed consent from subjects, and in full respect with the Declaration of Helsinki and the article L. 1243-4 of the French Public Health Code. BIO-EC Laboratories (Longjumeau, France) possesses an authorization from the Bioethics group of the general director services of the French research and innovation ministry (registered under nDC2008542) to use human skin from surgical waste since 5th May 2010. Explants were dimensioned into 3 cm diameter cylinders with a 2 cm thickness by BIO-EC Laboratories. All 3 layers of skin tissue (epidermis, dermis, hypodermis) were present.
Image processing 3D tomographic reconstruction was achieved using the NRecon ® software (Brucker™). Each slice of the 3D-images were loaded into Matlab ® (Mathworks™). The two first 3D-images were acquired while the pump had not started to deliver, so they could be used as reference of a free-from-insulin tissue. The first image was used as reference for all uneven acquired images, and the second for all even images. Indeed, due to the change in rotation direction between even and uneven images, there was a slight angle tilt between even and uneven images. On these two first 3D-images, the cylindrical volume corresponding to the inserted cannula in the tissue was manually identified. This corresponding volume was then automatically removed on all following images acquired during the injection.
For each processed 3D-image, the 2D-slices were loaded into the Matlab ® environment. The corresponding slices in the free-from-insulin image were subtracted, in order for most of the remaining signal to be the injected insulin and contrast agent mixture. It should be noted that tissue displacement due to the infused volume was minimal, and did not require any image registration step to perform this subtraction. However, should this method be used for larger infused volumes, image registration should be considered before performing the subtraction to ensure alignment of matching tissue structures. Then, in order to extract insulin depot properties, a linear transformation was performed and a binary image was obtained by using the 0. 95 quantile of pixel values as a threshold, allowing to remove noise due to the previous subtraction.
Data Generally, Cramér-Rao bounds do not need real data to be calculated, as mentioned in Section 1. In  [64] , Cramér-Rao Bounds are calculated using a simulated set of water parameters, for different sensor noise characteristics. However, the use of realistic parameters provides practical information in the context of scene analysis. This work aims to investigate those bounds for real water products. The products of the inversion of two hyperspectral images of the same area that is located in the south of France are used as input parameters to calculate the BCR and the BCRB for respectively DESIS and PRISMA sensors.
Satellite Images The DESIS and the PRISMA hyperspectral satellite data are used in this study. The DESIS sensor is characterized by a spatial resolution of 30 m, a spectral range from 400 nm to 1000 nm and a spectral sampling interval of 2. 5 nm. The PRISMA sensor is characterized by a spatial resolution of 30 m, a spectral range from 400 nm to 2500 nm and a spectral resolution of 10 nm. DESIS image was acquired on 13 June 2021 and PRISMA image on 14 August 2021. Atmospheric correction was operated from the Rayleigh reflectance and the atmospheric transmittance that were simulated using the atmospheric model 6S and using default values of Mid-latitude summer atmospheric model for Rayleigh correction  [74]. We did that because no measurements of Sea Level Pressure and Ozone over the study area were available at two different dates (13 June 2021 and 14 August 2021).
The optical thickness of the aerosols has been measured using the data provided by the nearest AERONET ground station. The aerosol reflectance was subtracted from the top-of-atmosphere radiance using the black pixel method for pixels located offshore  [75, 76]. Such a method relies on the strong light absorption by seawater in the near infrared, which thus leads to derive the atmospheric radiance from the satellite measurements. Two bands in the near infrared (NIR) are required to extrapolate the aerosol reflectance from the near infrared to the visible part of the spectrum. Since the study area consists of clear waters (not turbid), the use of the black pixel method is relevant. In this work, PRISMA and DESIS wavelengths were both ajusted to a 10 nm resolution, and the considered spectral range was [420 nm-700 nm] for both sensors, because of the high absorption caused by the seawater in the NIR.
Water Column Bio-Optical Parameters and Seabed Composition The water column parameters {C CHL , C CDOM , C SPM , H} and the seabed composition coefficients {a m , m = 1. M} were obtained by inverting the atmospherically corrected reflectance from DESIS and PRISMA images in the spectral range [420 nm-700 nm]. The inversion procedure was achieved by minimizing the Euclidian distance between the measured reflectance spectrum and the simulated one, using the lsqcurve f it ® MATLAB optimisation algorithm and Equation (1). The parameters were each optimized in a fixed range. The main constituents of the seabed are sand, Posidonia, and brown photophilic algae. Consequently, three mixing coefficients a 1 , a 2 , a 3 (see Equation (  4 )), are considered. The values of the retrieved geophysical products {C CHL , C CDOM , C SPM , H, a 1 , a 2 , a 3 } are used as input parameters for the calculation of the CRB and BCRB, for each pixel and for each satellite image.
The DESIS (resp. PRISMA) covariance matrix is calculated on a rectangular area corresponding to the low values of the ALCL weighted map. It can be seen in Figure  4  that the estimated noise covariance is slighty higher for PRISMA than for DESIS. The mean value of the diagonal of the covariance matrix is 2. 28E -7 for DESIS data and 2. 39E -7 for PRISMA. This small difference can be due to the sensor SNR, but also to the sea surface effects and atmospherical conditions, since the dates of acquisition were not the same.
Results for PRISMA Data Using the Inversion Domain D p 3-20 A second set of experiments was made for PRISMA image, to impose a more restrictive inversion domain and tighter constraints for the inversion. In the following, the inversion domain D p 3-20 is defined as the nonoutsiders pixels in the domain z ∈ [3 m-20 m]. The inversion bounds are set to [0 m-20 m] for H. The mean values of the retrieved parameters are given in Table  4.
Bounds for inversion [0-30] [0-5] [0-5] [0-5] [0-1] [0-1] [0-1] The main conclusions on the water quality remain the same as for the PRISMA image: the suspended matters (SPM) concentration is moderate, while low concentrations of Chlorophyll and organic dissolved matters are retrieved. However, the mean value of the retrieved SPM are higher (0. 70 g. m -3 for DESIS and 0. 21 g. m -3 for PRISMA). For the bottom cover, a little difference is noticeable with PRISMA results, the estimated sand cover is higher (0. 65) and the Posidonia coverage is lower (0. 30) for PRISMA data. It could be due to the use of the colonized Posidonia spectral signature which is more similar to sand spectra than fresh Posidonia spectral signature (used to invert PRISMA data). The square root of the mean CRB (resp. BCRB) obtained for the DESIS image are presented in Table  8  for the data in D 3-30.
The CRB and BCRB also depend on the set of wavelengths used. Other sources of error could be examined, such as the spatial resolution. In Section 2. 1, where the data are presented, it is shown that the depth increases rapidly with the distance from the beach (Figure  2 ). The spatial resolution is 30 m for both PRISMA and DESIS images. The depth slope, which is calculated between the pixel #1 and the pixel #4 of the depth profile shown Figure  2 , leads to a variation of 2. 3 m depth per pixel (square of 30 m × 30 m of the scene) in mean. However, in the pixelwise inversion procedure developed to obtain the vector parameter Λ, the depth is assumed to be constant over one pixel. If the depth varies inside a single pixel, there could be a confusion between depth (H) and bottom composition (a m ).
RE is 23% (resp. 34%) for PRISMA data and RE is 16% (resp. 23%) for DESIS data. DESIS data seem to be more robust than PRISMA data for the deepest areas; however, the RMSE and RE errors are very dependent on outliers pixels, which can be masked or not in the inversion procedure. Therefore, it is not possible to draw any final conclusion about the comparison of the PRISMA and DESIS sensors performances.
Comparison between CRB N and BCRB N For the depth estimation, as previously mentioned, there is no significant difference between CRB 1 and BCRB 1. This is because the inversion of one parameter is not challenging in the context of the noise of PRISMA (and DESIS) data and shallow clear water. For such a case, the introduction of a priori knowledge does not change the accuracy of the estimate. Of course the expected RMSE error of estimation, as well as the CRB N and the BCRB N , depend on the number of unknown parameters N that should be estimated simultaneously. The differences between CRB N and BCRB N appear for three parameters unknown, and increase with the number of retrieved parameters (6 parameters). The CRB N become unstable and diverge for the higher depths and the higher number of parameters.
Conclusions and Perspectives In this paper, a Bayesian Cramér-Rao Bounds approach has been proposed for assessing the water column parameters estimation from hyperpsectral satellite data acquired over a coastal environment. Two sets of satellite data have been used, namely DESIS and PRISMA data. For both cases the RMSE empirical error between ground truth and retrieved bathymetry was compared to the standard Cramér-Rao Bounds and to the Bayesian Cramér-Rao Bounds, for the case where up to six water colum and bottom parameters should be retrieved by the inversion algorithm. The standard CRB approach was shown to go beyond the empirical error for the most challenging retrieval configurations such as those which imply retrieving various water parameters and bathymetry simultaneously. The Bayesian BCRB approach that takes into account the inversion constraints applied to the retrieved parameters, is proved to be more robust than CRB, lower than the empirical error and to vary in parallel with it in the most challenging cases.
Results General characterization of biallelic variants in PNPLA8. We identified  12 individuals  showing neurological manifestations from 10 unrelated families with biallelic variants in PNPLA8 (Figure  1A ). The age of the patients at the last assessment ranged (removed due to medRxiv policy) of age (Table  1 ). We identified 12 variants, of which 10 were novel. Of the 12 variants, four were nonsense, four were frameshift, three were intronic variants near an exon-intron boundary, and one was a missense variant. According to the population-based variant frequency data, such as Single Nucleotide Polymorphism database  (24) , 1000Genomes  (25) , jMorp  (26) , and Genome Aggregation Database  (27) , the frequency of all of the variants was less than 0. 001%, suggesting a strong purification effect due to disease pathogenicity.
Regarding the impact on splicing, c. 1625+3_1625+6del identified in the patient from Family 1 (Patient 1) was assessed using patient blood-derived lymphoblastoid cell lines. Sequencing of each separated transcript by TA cloning revealed that c. 1625+3_1625+6del resulted in the skipping of exons 6-8 due to aberrant splicing (Supplemental Figure  1, A  and B ). Due to the lack of patient-derived cells, we could not experimentally assess the splicing impact of c. 2075-2A>G in Patient 2 and c. 1684-2A>G in Patient 5. However, in silico analysis with SpliceAI  (28)  predicted that c. 2075-2A>G and c. 1684-2A>G results in the loss of a splice acceptor site with a ∆ score of 0. 97 and 0. 93, respectively (high specificity cutoff: 0. 8). Collectively, 11 out of 12 individuals carried the combination of nonsense, frameshift, and splice-site variants, which suggests LoF in PNPLA8.
To examine which cell types are involved in the reduced expansion of the SVZ in PNPLA8 KO cerebral organoids, we quantified the number of bRGCs and bIPs in the SVZ at 12 weeks of culture. The number of PAX6 + HOPX + bRGCs in the SVZ were significantly reduced in PNPLA8 KO cerebral organoids compared with WT cerebral organoids (Figure  4, A  and B ). In contrast, the number of TBR2 + bIPs was similar between WT and PNPLA8 KO cerebral organoids (Figure  4, C  and D ). These data suggest that the reduced abundance of bRGCs mainly contributes to the reduced size of SVZ in PNPLA8 KO cerebral organoids. We hypothesized that the reduced number of bRGCs could result from three possible mechanisms; the reduced proliferation of bRGCs, the increased apoptosis of bRGCs, or the decreased generation of bRGCs  (41, 42).
0 × 10 6 reads per sample. These reads were mapped to more than 2. 0 × 10 4 reference genes by RNAseq analysis. Thus, we successfully obtained assessable spatial gene expression data from cerebral organoids. Principal component analysis (PCA) showed distinct expression profiles between Control and Patient 1 samples (Figure  6C ). To characterize gene expression profiles, we detected differentially-expressed genes (DEGs) from the data. As shown in the heatmap of DEG clustering, there were 104 up-and 43 down-regulated genes in Patient 1, compared with the Control (Supplemental Figure  7A , Supplemental Table  2 ). The DEGs were analyzed using Cytoscape-based BiNGO software for enrichment analysis  (53). This analysis revealed three significantly enriched biological processes, such as "translation", "translational elongation" and "regulation of neuron differentiation" (Figure  6D ).
These results suggest that patient aRGCs exhibited a premature shift towards differentiating divisions. Next, we performed gene set enrichment analysis (GSEA) and determined significantly different expressions of gene sets from the mSigDB database  (54) (55) (56). Among the acquired data, we found that gene sets related to "Neocortex basal radial glia-up" and "Human neocortex" were much less enriched in Patient 1-derived cerebral organoids (57) (Figure  6, E  and F ). These results support our notion that loss of PNPLA8 impairs bRGC-mediated neurogenesis by altering the fate specification of aRGCs. We next created an enrichment map to visualize GSEA results using the Gene Ontology (GO) tool as a gene-set source  (58). This visualization confirmed the enrichment of gene sets related to neuron development (e.
We next analyzed pathways involved in the neurodevelopmental defects of PNPLA8-deficient patients using the transcriptomics data. The Pathway Interaction Database, a collection of curated pathways focusing on signaling and regulatory pathways rather than metabolic processes or generic mechanisms like transcription and translation (64), revealed "lysophospholipid pathway" as the top-ranked enrichment in Patient 1-derived cerebral organoids (Figure  7, A  and B ). Within the dataset, LPAR2 was significantly enriched in Patient 1, whereas LPAR1 was not enriched in Patient 1 (Figure  7C ). Lysophosphatidic acid (LPA) acts as a signaling molecule through its distinct G protein-coupled receptors (LPAR1-6) with diverse effects during brain development  (65, 66). Specifically, LPAR2 is thought to be involved in neural differentiation, whereas LPAR1 is likely to mediate various responses, such as proliferation  (66, 67).
Methods Additional details can be found in Supplemental Methods. Patients. Fourteen individuals from 12 unrelated families from around the world were identified through international collaboration and data sharing. Although Family 6 and 7 in this study had been previously reported without details  (20, 79) , we included these patients with unreported clinical and genetic data. Additionally, we reviewed the clinical features of previously reported patients (20-23) (Table  1 ). Physical features of weight and head circumference were converted to standard deviation scores, corrected for gender, age, and gestational age  (80, 81). Clinically-acquired brain images were available for eight patients. Among them, only low-resolution photographs of brain MRIs were available for Patient 7 and Patient 10.
On the 14th day, culture media was replaced with 'organoid culture media 2'. The cerebral organoids were cultured for up to two months. The culture media was replaced every one to three days. After one month, the organoids were cut into two to three pieces with a scalpel under a stereo microscope every week to prevent cell death in the central portions  (92). Images of the organoids were acquired using an inverted optical microscope (CKS53; Olympus, Japan). The organoid size was measured based on the surface of the organoids using ImageJ and FIJI software. Generation of iPSC-derived NPCs. Generation of NPCs from iPSCs was performed as previously described with modifications  (44, 94). Briefly, 188,000 cells were seeded into EZsphere 24-well plates (about 400 cells per micro well) and cultured in 'induction media' for the first six days with replacement every two days.
After three PBS washes, samples were incubated in secondary antibodies and DAPI, which were diluted in Blocking-One. After PBS washes, samples were mounted with Fluoromount-G. For BrdU staining, sections were treated with 2N HCl at 37°C for 20 min, followed by three PBS washes before permeabilization. Images were obtained with a SpinSR10 confocal microscope and cellSens software (Olympus, Japan), and an A1RS + confocal microscope and NIS Elements software (NIKON, Japan) with 10×, 20×, or 40× magnification. Quantification of images was performed using FIJI software. Primary and secondary antibodies are listed in SupplementalTable 3. Spatial transcriptomic analysis. Cerebral organoids were fixed in 4% paraformaldehyde in PBS at RT for 10 min, followed by 30% sucrose in PBS at 4°C for 24 h. The organoids were transferred into Tissue-Tek O.