dataesr/scientific-paragraphs-categorization huggingface dataset:
SPHERE  also received funding from the  European Commission Sixth and Seventh Framework Programmes  as part of the  Optical Infrared Coordination Network for Astronomy (OPTICON)  under Grant number  RII3-Ct-2004-001566 for FP6  ( 2004-2008 ), Grant number  226604  for  FP7  ( 2009--2012 ), and Grant number  312430 for FP7  ( 2013--2016 ).
Our results revealed that the mitochondrial compartment is a superoxide overproduction site in DEGS1 patient fibroblasts (Figure  3H ), although we cannot rule out other sources. DEGS1 is localized at the MAMs. In light of these data indicating the malfunction of mitochondrial respiration and dynamics, we set out to revisit the reported localization of DEGS1 at the ER  (26, 27). We first explored the subcellular localization of DEGS1 in control fibroblasts. We performed immunofluorescence costaining with markers for specific cell compartments: ER (calnexin), mitochondria (MitoTracker), MAM (ERLIN2, ACSL4, DRP1 and MFN2), and Golgi apparatus (GM130). We found that DEGS1 was enriched in the ER areas next to mitochondria, colocalizing with MAM-resident proteins. In contrast, we detected less colocalization with MitoTracker or calnexin and essentially no colocalization with GM130 (Figure  4  and Supplemental Figure  3 ).
TAGs are synthesized de novo from diacylglycerides (diacylglycerol [DAGs]) by diacylglycerol O-acyltransferase 1/2 (DGAT1/2), among others. When in excess, DAGs can also be stored in LDs  (41). Notably, our targeted lipidomics data showed a significant increase in DAG levels in DEGS1 patient fibroblasts. Additionally, the levels of its immediate biosynthetic precursor, phosphatidic acid (PA), were increased (Figure  7D ). However, the levels of TAGs in DEGS1 fibroblasts were not significantly increased compared with controls (Figure  7D ). To gain insight into the mechanism behind these alterations, we measured the expression of the genes encoding key enzymes involved in the regulation of DAGs and TAGs. We observed increased mRNA levels of DGAT2 and diacylglycerol kinase alpha (DGKA), which phosphorylates DAGs into PA.
Consequently, we evaluated the levels of the main mammalian PLs, PS, PE, and PC. In DEGS1 patient fibroblasts, PS levels were increased, while the levels of PE were diminished and PC showed a decreasing trend (Figure  7I ). These expected results are consistent with the PL synthesis and trafficking assay reflecting MAM activity shown in Figure  6, B  and C. Taken together, our data indicate that DEGS1 deficiency affects the regulation of TAG metabolism and TAG storage into LDs, as observed in muscle biopsies (Figure  1 , A and D), as well as the regulation of PL homeostasis.
Proteomics analysis Sample preparation, mass spectrometry (MS) analysis, bioinformatics, and data evaluation of MAM samples were performed in collaboration with IDIBELL's Proteomics Platform. Briefly, 10 μg of MAM lysates in lysis buffer were digested with Lys-C and Trypsin. Prior to digestion, samples were reduced and alkylated with DTT and CAA; then the samples were diluted with Tris 0. 1M to reach urea 2 mol/L. Lys-C was added at 1:25 (w/w) (enzyme-to-protein ratio), and protein digestion was carried out at 30°C overnight. Then the samples were diluted again with Tris 0. 1M to reach urea 0. 8 mol/L. Trypsin was added at 1:25 (w/w) (enzyme-toprotein ratio), and protein digestion was carried out at 30°C over 8 hours. Enzymatic reaction was stopped with FA (10% [v/v] final concentration). Digested samples were desalted using C18.
Liquid chromatography-MS (LC-MS) analysis was carried out using an HPLC system (EASY-nanoLC 1000, Thermo Scientific), with a C18 column of 50 cm (EASY-Spray; 75 μm ID, PepMap RSLC C18, 2 μm particles, 45°C) and a 90-minute gradient connected to a Orbitrap Fusion Lumos Mass Spectrometer (Thermo Scientific). The raw data were analyzed using the Proteome Discoverer software suite (version 2. 0, Thermo Fisher Scientific), and the Mascot search engine (version 2. 5, Matrix Science) was used for peptide identification and quantification. Samples were searched against a SwissProt database containing entries corresponding to human (version of January 2018), a list of common contaminants, and all the corresponding decoy entries. Trypsin was chosen as an enzyme, and a maximum of 3 miscleavages were allowed.
Mitochondria morphology (area and sphericity) and Manders' correlation coefficient were quantified using Imaris software, version x64 9. 7. 2. The 3D image reconstruction of patient fibroblasts was also performed using Imaris software. Live-cell imaging. Fibroblasts were seeded in μ-Slide 8-Well High (80806, Ibidi) chambers. Growing fibroblasts were stained with MitoTracker as previously described. We optimized the conditions to avoid photobleaching, decreased the concentration of MitoTracker to 50 nM, and decreased laser intensity to the minimum. 3D images of fibroblasts were acquired using a Leica TCS SP8 STED 3× Laser Scanning Confocal Microscope (LSCM) with an HC PL APO CS2 ×100 oil objective (pixel size = 113 nm). Fluorescence excitation was achieved with a pulsed white-light laser source, and fluorescence emission was collected using a Leica HyD SMD detector.
Images were obtained every 16 minutes for 4 hours and analyzed with Imaris software by means of the disconnected number of counts for automatic quantification of mitochondria units over time. Inner ΔΨm quantification Growing fibroblasts were washed with PBS and incubated with 50 nm tetramethylrhodamine, ethyl ester (TMRE) (Molecular Probes) in prewarmed PBS for 30 minutes at 37°C. Cells were trypsinized, centrifuged at 1,000g for 5 minutes, and resuspended in prewarmed PBS. All samples were captured by a Gallios Analyzer (Beckman Coulter), which recorded 20,000 cells for each genotype tested. FCCP (200 μM for 10 minutes) was used as a positive control. Histograms showing the inner Ψm levels were obtained after gating live cells. The data were analyzed with Kaluza Analysis software, version 2.
5 hours at RT. The staining solution was removed, and the cells were washed twice with distilled H 2 O for 1 minute. Hematoxylin (2 ml per well) was added for 10 minutes to counterstain the nuclei. This was followed by one more rinse with distilled H 2 O for 1 minute. The coverslips were allowed to dry completely at RT and then mounted with 12 ml Mowiol. The Feret's diameter function of ImageJ software was used to determine the LD diameters. This parameter measures the longest distance between any 2 points along the selection boundary and is also known as the maximum caliper. ImageJ software was also used to count the number of LDs per cell. Between 30 and 50 fibroblasts per clone were quantified. Quantitative real-time PCR Total RNA was extracted using the RNeasy Mini Kit according to the manufacturer's instructions.
Acknowledgments We are indebted to the patients who participated in this study. We thank  Cristina Guilera  for her excellent technical assistance and  Edgard Verdura  for variant calling and bioinformatic analysis of whole-exome sequencing data. We appreciate the excellent help and analysis suggestions of IDIBELL's facilities staff:  Saioa Mendizuri ,  Joan Repullés ,  Jaume Boix , and  Cristina Pardo  ( Optical and Electron Miscroscopy Platforms ) and  Jose Miguel Andrés Vaquero  ( Flow Cytometry Platform ). We are grateful to the  "Biobanc de l'Hospital Infantil Sant n de Déu per a la Investigació"  integrated into the  Spanish Biobank Network of ISCIII  for sample and data procurement. We thank the confocal microscopy unit of  Sant Joan de Déu Children's Hospital as part of "Centre Daniel Bravo de Diagnòstic i Recerca en Malalties Minoritàries" (CDB)  for the acquisition of the histochemical images.
Since then, CO 2 Martian ice clouds have been observed by a diversity of methods like imaging, stellar, and solar occultations. Mars Global Surveyor (MGS) and Mars Pathfinder missions in the late 90s were precursors to missions that provided a wealth of data about Mars' atmosphere and clouds. For example, all of MGS Thermal Emission Spectrometer (TES)  (Christensen et al. , 2001) , MGS Mars Orbiter Camera (MOC)  (Malin & Edgett, 2001) , and MGS Mars Orbiter Laser Altimeter (MOLA)  (Smith et al. , 2001)  have allowed the observation of Martian clouds. TES showed the evolution of the aphelion cloud belt  (Hale et al. , 2011) , gave dust and water ice aerosol optical depth, as well as water vapor column density during nearly 3 Martian Years, and provided interannual variability  (Smith, 2004).
Catalog Global Summary The catalog is available alongside this article. It is presented as a text file in which the four first lines describe the parameters given for each cloud. The fifth line gives every parameters a column title to ease the read of the catalog by numerical libraries. All 261,862 MAS are then presented following the exact same scheme. An id number is attributed to every MAS for an easier identification of each of them and is widely used in our plotting program (given with the catalog). We made the choice to reduce the number of parameters for each MAS compared to the raw data so that the catalog can be navigated easily. First three parameters allow finding the MAS in the PEDR data set: the file it comes from and the lines of the first and last returns of the MAS in that file.
(2005). These bMAS could be dust struc tures but are too early in the year to be attributed to the global dust storm that started around L s = 177°. However, their presence may suggest specific precursors for the necessary atmospheric conditions to support a global dust event, particularly given the absence of such features in MY24. Distribution of atmospheric structures at this solar longitudes are compared with the Mars Dust Activity Database results (J. M. Battalio & Wang, 2019)  in Figure  9  bottom panel. Latitude and longitude extend of dust structures are in total agreement with our observations and with the band in the southern hemisphere, allowing us to hypothesize on their compositions. In addition, structures are observed earlier at Solis Planum south border in our observations than in the database, for which dust activity becomes important from L s 180°.
This result is consistent with TES observations  (Smith, 2004 ) that show a sudden increase of dust optical depth starting at L s 210° of MY24, and which is initially more intense in the southern hemisphere. This is also coherent with  Montabone et al. (2015)  observations of Mars Year 24 where a rise of the mean column dust optical depth was seen in this solar longitude interval. Observations in this period are compared with the Mars dust activity database results (J. M. Battalio & Wang, 2019)  for MY24 in top panel of Figure  9. As early as L s 200°, dust activity was observed around Acidalia Planitia longitude, which can also be seen in the corresponding panel of Figure  7  on the same time period. Dust then extends to Arcadia Planitia longitude. Differences occur between L s 238° and L s 253°, when dust activity is negligible in the dust database while still being very present in our observations.
This is probably linked to some limitations on how the MDAD was built (M. Battalio & Wang, 2021). Indeed, dust edges were collected by eye, which becomes difficult whenever haze is too diffuse for the boundary between clear-air and dusty-air to be visible. Our clustering method collects all returns of higher opacity in the atmosphere, making it a good complement to MDAD for distinguishing dust haze and surface. Comparing these two data sets provides us additional observations of the phenomenon, allowing us to conclude on the dust composition of some of the atmospheric structures. Start and end of this rise in dust activity, as well as the longitude at which it takes place at, correspond to the definition of the "A" storm from  Kass et al. (2016) , which is a recurrent regional dust storm happening during Martian Years that are not affected by a global dust storm.
Conclusions The MOLA instrument provides data for 1. 5 Martian Years and has proven being able of detecting cloud signatures coming from the lower atmosphere, up to 20 km. Studying the data set with machine learning methods, and particularly clustering algorithms, bypasses the determination of a stringent limit between clouds and surface laser returns. We show that the MOLA data set is indeed clusterable but also that a certain number of clusters emerges using optimization methods. Clustering provides more detections than previous studies while maintaining a similar seasonal and latitudinal distribution, which confirms the viability of the process. We reduce the chances of having false positive in our atmospheric features cluster by picking the most appropriate number of clusters. We gather consecutive atmospheric returns into structures to keep track of cloud or dust structure sizes.
This way, some areas are revealed in the seasonal distribution as being more favorable for big atmospheric structures. Atmospheric structures are presented in a unique catalog regrouping results from the whole MOLA data set. MOLA cloud observations are compared with two other instruments, MGS TES and MGS MOC, which have also observed clouds in the Martian atmosphere for the same time period. This comparison notably helps confirm assumptions on the composition of atmospheric structures and shows that both water and CO 2 ice clouds and important dust structures are found in the MOLA data set. Development of the aphelion cloud belt and the south polar hood are observed with a large temporal resolution. Because of the important proportion of channel 4 structures and by using both dust activity database and dust optical depth observations, we also showed that MOLA observed an important number of the dusty events that occurred during MY24 and MY25.
Acknowledgments We thank the  Agence National de la Recherche  for funding (project  MECCOM ,  ANR-18-CE31-0013 ) this research. Computational Resources for this project were provided through CICLAD server by the  ESPRI  mesocentre at the IPSL institute. We acknowledge the help of G. A. Neumann whose experience allowed us to understand the data set more easily. Finally, we deeply thank  Michael Battalio  and an anonymous reviewer for their very constructive reviews that helped improving this paper.
Background Plankton seascape genomics studies have revealed different trends from large-scale weak differentiation to microscale structures. Previous studies have underlined the influence of the environment and seascape on species differentiation and adaptation. However, these studies have generally focused on a few single species, sparse molecular markers, or local scales. Here, we investigated the genomic differentiation of plankton at the macroscale in a holistic approach using Tara Oceans metagenomic data together with a reference-free computational method.
Particularly, Tara Oceans data provide a unique dataset from many locations in all oceans worldwide, enabling global approaches to investigate plankton  [49] [50] [51] [52] , but blind spots in terms of taxonomy or function are still an obstacle for further analyses due to the lack of reference genomes or transcriptomes, especially for eukaryotes. The first approach to address this issue relies on the use of metagenome-assembled genomes (MAGs) that enable the retrieval of numerous lineages from metagenomic samples, especially small-sized genomes found in viruses, prokaryotes, and protists  [45, 49, [53] [54] [55] [56]. The second method is single-cell sequencing after flow cytometric sorting, which allows genome reconstruction of small eukaryotic species  [57]. An alternative method for studying plankton population genomics has been proposed based on single nucleotide polymorphisms (SNPs) calling directly from metagenomic data using an assembly-free strategy  [58].
The latter uses DiscoSNP + +  [59] , a SNP calling tool applied directly to raw high-throughput sequencing data without assembly. Its application to Tara Oceans metagenomic data generated 18 million SNPs and a proof of concept of their utility and robustness for population genomics has been demonstrated on the epilagic copepod Oithona nana using its genome as a reference to relocating SNPs  [58]. SNPs can also be directly clustered by species to bypass the use of genome references. To perform this, metaVaR was developed and applied as a proof-of-concept to simulated and real metagenomic datasets  [60]. This approach allows the profiling of the genomic differentiation of several species separately and opens gates for new investigations. Here, we propose to study plankton connectivity from a holistic point of view, using metagenomic data extracted from samples gathered during Tara Oceans expeditions in the Mediterranean Sea, Atlantic Ocean, and Southern Ocean.
Taxonomy and biogeography of 113 plankton species based on reference-free SNPs We used over 18e 6 SNPs called using a reference-free approach generated from 114 metagenomic samples collected from 35 Tara stations (Fig. 1A ). The SNPs were clustered into groups SNPs belonging to the same taxon, and the minor allele frequencies of each SNPs were computed by population (Fig. 1B , Supplementary Table  S1 ). Fig. 1  Clustering of SNPs by taxon from the metagenomic dataset of Tara Oceans. A Worldmap showing the locations of the 35 Tara Oceans stations used in the study. Each circle is divided into four parts, depending on the detection of plankton taxa by SNPs clustering. Grey colour indicates that no species were retrieved. B Pipeline to cluster SNPs by species using metaVaR, with additional statistics by size fraction.
From top to bottom: number of SNPs before and after filtering, number of SNPs clusters detected, and number of plankton taxa finally selected Most of the SNPs harbouring sequences used for taxonomic assignment did not show any signal due to lack of planktonic data in the public databases. However, we assigned 113 taxa showed various lineages spanning all plankton trophic levels with a predominance of Hexanauplia (46 taxa), Bacteria (24 taxa), and Eumetazoa (21 taxa, comprising three Cnidaria and one Echinodea) (Fig. 2A  and B ). Among the Bacteria, we found nine Cyanobacteria, with eight taxa assigned to Synechococcus and one assigned to Prochlorococcus. Other notable eukaryotic taxa include Dinophycea  (5) , Haptophyta (4), Mamiellales (3), Collodaria (2), Ciliophora (2), Cryptophyta (1), and Pelagomonadaceae  (1).
Reference-free approach for non-model species population genomics Thanks to our approach exploiting metagenomic data, the population connectivity was reconstructed for planktonic eukaryote taxa representing the different trophic levels of the epipelagic layer of oceans and enabled a realistic overview of the population structures of marine planktonic species lacking reference sequences. With hundreds of variants per taxon, we drew the silhouette of population structures across four oceanic areas using more markers than previous studies often based on few genetic markers, few samples, and limited to small geographic areas. It must be noted that for each taxon, most sequences did not show any taxonomic signal, an observation already made in other studies using Tara Oceans data  [50, 52]. The level and quality of taxonomic assignment are both due to a lack of references in databases and the short length of the sequences supporting the variants, reducing the chance of matching a reference with an acceptable coverage and having a taxonomic assignment with a high resolution.
Third, in protists, two species corresponding to Mamiellales (6_5_14 and 9_500_10) are respectively located in Tara stations where Bathycoccus prasinos and Bathycoccus spp. TOSAG39-1 were the most abundant (Supplementary Table  S2 ), as described in a previous study using Tara Oceans metagenomic dataset  [65]. Finally, copepods formed the largest group, with a predominance of calanoids over cyclopoids. Numerous copepods were expected considering their high abundance in oceans  [66, 67]  and good representation in the Tara Oceans dataset. A limitation of our approach was the presence of a cluster of SNPs predicted to belong to a single taxon but harbouring extreme pairwise-F ST values, showing that some validated clusters of SNPs may refer to a complex of closely related species, as previously described for the cosmopolitan copepod Oithona similis  [68].
Taxonomic assignment of species Taxonomic assignment of each species was performed using three different methods (Supplementary Figure  S5 ). For the first method, the short sequences supporting the variants (generated by DiscoSNP + +) were mapped on the downloaded NCBI non-redundant database with diamond v0. 9. 24. 125  [92]  using blastx and parameter -k 10, and the results were filtered based on the E-value (< 10 -5 ). The taxonomic ID and bit scores of each match were maintained. A fuzzy Lowest Common Ancestor (LCA) (see https:// github. com/ insti tut-de-genom ique/ fuzzy-lca-module) method was used to assign a taxonomy to each sequence using bitscore as a weight with -r 0. 67 (i. e. taxa covering at least 67% of all bitscores) and -ftdp options. The highest phylogenetic rank was retained as the best assignment for each sequence.
For the second method, the sequences were mapped using the blastn algorithm implemented in diamond on MATOU, a unigen catalog based on Tara Oceans metatranscriptomic data  [50]  , and the last method involved mapping the sequences on the MMETSP transcriptomic database  [93]. The species (or a complex of closely related species) were assigned to the most probable taxon to offer three taxonomic assignment levels, from the most precise to the widest (Supplementary Table  S1 ). The final set of taxa was first grouped into 24 taxonomic groups and finally merged into six reliable wider groups (Viruses, Bacteria, Unicellular Eukaryotes, Copepods, and other animals, and poor classification) (Fig. 2B ).
Lagrangian travel time estimation and environmental data To estimate Lagrangian transport, we used a method based on drifter data  [61]  to compute the travel time of the most likely path between Tara stations back and forth. We used the public database of the Global Drifter Program (GDP), managed by the National Oceanographic and Atmospheric Administration (NOAA) (https:// www. aoml. noaa. gov/ phod/ gdp/), which contains information on drifters ranging from February 15, 1979, to September 31, 2019. We extracted the data for both drogued and undrogued drifters (i. e. , drifters that lost their socks) to maximize the information. No drifters have been observed to exit the Mediterranean Sea through the Strait of Gibraltar. Therefore, to avoid missing data, we arbitrarily added 100 years to the travel times of pathways out of the Mediterranean Sea over the Strait of Gibraltar and added one year to the pathways going into the Mediterranean Sea, based on previous models of surface water  [95, 96].
We used 450 rotations within the method to reduce the reliance on travel times on the grid system used. Two travel times are obtained by the method for each pair of stations, back and forth, resulting in an asymmetric travel time matrix between all possible station pairings. For our analyses, we retained only the minimum of the two travel times. Environmental variables corresponding to the 35 selected Tara stations were extracted from the World Ocean Atlas public database (https:// www. nodc. noaa. gov/ OC5/ woa13/ woa13 data. html) for the period 2006-2013 on a 1° × 1° grid, covering the dates of Tara Oceans expeditions. The following parameters were obtained: temperature (°C), salinity (unitless), silicate (µmol. L -1 ), phosphate (µmol. L -1 ), and nitrate (µmol. L -1 ) (Supplementary Figure  S7 ).