dataesr/scientific-paragraphs-categorization huggingface dataset:
By looking at the pangenome, we found that the core and accessory genes display distinct patterns, characterized by a significantly lower abundance and higher dispersion of accessory gene expression compared to core genes (Figure  2B-C ). Beside this genomic view, we further investigated the transcriptional behavior of genes from a functional perspective. We examined 62 broad-and nonredundant biological process GO slim terms using gene set enrichment analyses (GSEA) based on rankings of the mean expression abundance and dispersion across all genes included in our dataset. Among the 62 GO slim terms, 59 are significantly enriched (FDR < 0. 05) for abundance or dispersion, and are then grouped into three quadrants depending on the direction of the enrichments (Figure  2D , Table  S3 ). Specifically, genes involved in GO terms related to growth and cellular metabolisms show high abundance and high dispersion.
Another well-known adaptive trait in certain wine isolates was several translocations that lead to the overexpression of SSU1, a sulfite pump that confers resistance to sulfur dioxide, a commonly used compound in wine making  31, 32. The overexpression of SSU1 is indeed seen in the Wine/European clade (1. W) in our dataset (Figure  4I-K ). Differential expression analyses highlight transcriptional signatures that are specific to different subpopulations. Interestingly, wild subpopulations appeared to be less differentiated in terms of the transcriptional diversity despite the high level of genetic divergence among these clades. In contrast, domesticated subpopulations exhibit clear signatures that correspond to distinct adaptive processes, notably in various metabolic pathways uniquely selected in different domestication events.
The remaining 5,372 SNP-eQTL are distant and trans-acting. Overall, local SNP-eQTL are less frequent, representing ~26% of the total set of eQTL detected, which is consistent with previous findings based on linkage mapping across a large segregant panel in a yeast biparental cross  6. The trans eQTL detected are uniformly distributed across the genome, with ~41% of eQTL (2,206 out of 5,372) impacting only one trait (Figure  6A ). The most significant eQTL hotspot was mapped to the CTT1 gene, encoding for a cytosolic catalase T, which is associated with 251 expression traits and was previously found to be an eQTL hotspot in stress conditions  33  (Figure  S7C ). Contrasting to previous observations in a yeast cross  6  and recent findings in a C. elegans population 5 , the set of trans eQTL detected in our large dataset are not biased toward a few hotspots with extreme pleiotropic effects (Figure  S7C ).
Calculating mean expression abundance and dispersion In total, 944 out of the 969 isolates in the final dataset were present in 1,011 isolates characterized previously  20. For these isolates, the pangenome annotations in terms of the presence and absence of a given gene in each isolate are available. We used this set of isolates and their expression levels to calculate the mean expression abundance and dispersion. The abundance corresponds to the mean expression levels for all isolates where the gene is annotated as present. The dispersion is calculated as the mean absolute deviation using the following formula: 1 𝑛 # |𝑥 ! -𝑥| " !#$ Where 𝑛 is the number of strains that carries the gene, 𝑥 ! is the expression level in log2(TPM+1) for the ith isolate and 𝑥 is the mean log2(TPM+1) for all samples for a given gene.
Subpopulation specific differential expression analyses The variance stabilized expression dataset comprised of 6,116 genes was used to perform subpopulation specific differential expression analyses using DEseq2  44. Each subpopulation was compared to the remaining isolates using the expression matrix and annotated isolate information. Around 10% of the isolates in our dataset were haploids with defined mating types. To eliminated the effect of mating type specific expression signature, we incorporated the mating type information in the design model as a covariate. Significant differential expressions were determined using the Benjamini-Hochberg adjusted P-value less than 5%, corresponding to a 5% false discovery rate (FDR). Due to the imbalanced subpopulation sizes, the FDR cutoff alone is biased toward detecting more differential expression with small effect sized in larger subpopulations.
Gene set enrichment analyses Gene set enrichment analyses (GSEA) were performed for gene-level abundance and dispersion analyses (Figure  S1 ), co-expression module over-representation (Figure  S3B ) and for differential expression sets (Figure  S3C ). GSEA on expression abundance/dispersion was performed using the fgsea R package  51. Annotation of the GO slim terms was obtained from the SGD database. To reduce redundancy, we used the rrvgo R package and calculated the similarities between each annotated term in a pairwise manner using the "Resnik" method, and removed terms that are at least 70% overlapping with another term. We then performed GSEA using the fgsea() function, the pathways corresponding to the reduced GO slim terms and a size limit on the terms between 50 and 600. In total, 100,000 permulations were performed.
Data availability All sequencing reads are available in the European Nucleotide Archive (ENA) under the accession number PRJEB52153. The 1002 Yeast Genome website -http://1002genomes. u-strasbg. fr/files/ -(RNAseq) provides access to: -Datafile S1: final_data_annotated_merged_04052022. tab -Datafile S2: GWAS_combined_lgcCorr_ldPruned_noBonferroni_20221207. tab.
Description of the occurrence data Thirty-eight marine species or genera were selected for the proof of concept that is described in the present article. They include large pelagics, elasmobranchs, turtles, sea mammals, and two species of marine birds (cf. table  1 ). These taxa may be replaced or complemented with others in the future. Presence data for these taxa were downloaded from the GBIF. This database contains species occurrences that are free to access and download, which is essential to reproducibility. Some flawed data is unavoidably present in the database, but small errors in the geographical coordinates are not a problem as the oceanographic landscape that we consider has limited precision due to environmental data resolution. We removed points located on the continents, and no other filtering was conducted on geographical precision.
So the geographical distribution that we obtained is not the one corresponding to the accessory ANK gene. In TOC1 promoter, the 2. 2 kb insertion identified in RCC1613, RCC1615 and RCC4752 encodes a Methyltransferase-like protein (AdoMTase METTL24 IPR026913 IPR029063) of 320 aa with 41. 7% amino acids identity to the predicted Ostreococcus lucimarinus CCE9901 protein (XP_001422352). Searches were performed at high stringency in OGA (Expect threshold 1e-300) so that only the presence of the near-identical sequences was retrieved. TOC1 and AdoMTase sequences were not strictly co-occuring. At one station located near Chile (arrow Figure  2 ), TOC1 sequences were abundant but no AdoMTase was recorded which suggests that in this particular area the Bathycoccus genomes are almost devoid of AdoMTase.
Surprisingly, it was possible to also find AdoMTase sequences not associated to Bathycoccus TOC1 sequences indicating that this accessory gene can be found in other unknown microorganisms suggesting possible horizontal gene transfer. Most AdoMTase hits were found near the equator at temperature higher than 25°C. As a control, the co-occurrence of TOC1 and CCA1 (Bathy06g4380) sequences was complete (Supplemental Figure  1 ). Transcription of the gene encoding this AdoMTase was confirmed in the metaT database at OGA (data not shown). In conclusion, two large INDEL comprised additional genes and these should be considered as structural rearrangements rather then simple INDEL. These accessory genes could potentially be beneficial for adaptation.
A phylogenetic tree further supported this dichotomy (Figure  4 ). OGA MetaG database was interrogated with TIM4222 and TIMA8 and each retrieved a single hit, respectively OGATIM4222 and OGATIMA8 confirming the existence of the 2 isoforms of TIM world-wide (Figure  4B ). OGATIMA8 has a marked abundance for high latitudes and cold temperatures in the Northern hemisphere while OGATIM4222 is more widely distributed. Based on the results described above, the Banyuls isolates were classified in eight MLG (Table  3 ). MLG 1 and MLG 2 represent respectively 53% and 29% of the population. Since the number of isolates from each sampling date was not identical (Supplemental Table  2 ), this percentage may not be entirely representative. However the presence of MLG 1 and 2 in five out of nine independent samplings rules out a bias due to experimental cloning during aisolation, Figure  3 ).
The time of sampling is indicated as week of the month (e. g. Oct-01 = 1 st week of October) to facilitate the comparison between years. The complete dataset is presented in Supplemental Figure  3  and Supplemental Table  3. The initiation of the bloom was analysed during two consecutive years (Figure  5 ). In 2019, the presence of Bathycoccus was detected in the third week of November with a clear predominance of the 200 bp allele of yrdC promoter and the presence of both TIMa and TIMb. In 2020, Bathycoccus was detected in October, was barely detected or absent in November and reappeared in December, consistent with the recording the decrease of abundance of picophytoplankton by flow cytometry (Supplemental Figure  2 ). In 2020, the allelic ratios of 200/400 bp of the yrdC promoter were clearly different from those in 2019 and TIMb was not detected.
Similarly, temporal succession of two genetically distinct sub-populations was observed during the bloom of the haploid Alexandrium dinoflagellate in Gulf of Maine  (Erdner et al. , 2011). Finally, in addition to eight Bathycoccus prasinos MLG (B1), we identified two other Bathycoccus species, including the previously described species Bathycoccus calidus (B2) and a yet not described Bathycoccus B3 species that we name B. catiminus. Our results clearly demonstrate that under a single Bathycoccus 18S ribotype are hidden at least three distinct species. This emphasises the necessity of isolating new Bathycoccus to design interspecific markers that could be used on environmental samples and metagenomic datasets in order to understand the dynamics of Bathycoccus blooms and the adaptation of this cosmopolitan genus in the world ocean.
Our studies on three successive blooms in Banyuls showed that the ONT designed markers are capable of determining the dominant allelic variants and their perennial occurrence. Similarly these INDEL markers could be used as query on metagenomic databases for a wider and large analysis of Bathycoccus populations. Variations in allele frequencies were observed for three consecutive years, raising the question of the nature of the highly reproducible yearly re-occurrence of Bathycoccus in the bay of Banyuls. Seasonal blooms may result either from re-activation of "dormant/survivor" cells from the water column (whose genetic fingerprint will determine the genetic profile of the next bloom) or by yearly de novo fertilisation by cells carried by the north Mediterranean current along the gulf of Lion.
Acknowledgments We are grateful to the captain and the crew of the  RV 'Nereis II'  for their help in acquiring the samples. Additional ONT were performed with the help of  Christel Llauro  and  Marie Mirouze LGDP. The authors acknowledge the ISO 9001 certified IRD itrop HPC (member of the  South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper (URL: https://bioinfo. ird. fr/http://www. southgreen. fr). The work was financed by a internal  LOMIC Microprojet  and the  ANR Climaclock 2020-2024. We are thankful to  Marie-Line Escande  and the platform.
Current methods for single-molecule orientation localization microscopy (SMOLM) require optical setups and algorithms that can be prohibitively slow and complex, limiting the widespread adoption for biological applications. We present POLCAM, a simplified SMOLM method based on polarized detection using a polarization camera, that can be easily implemented on any wide-field fluorescence microscope. To make polarization cameras compatible with single-molecule detection, we developed theory to minimize field of view errors, used simulations to optimize experimental design, and developed a fast algorithm based on Stokes parameter estimation which can operate over 1000 fold faster than the state of the art, enabling near instant determination of molecular anisotropy. To aid in the adoption of POLCAM, we developed open-source image analysis software; a napari plugin for visualization of high-dimensional diffraction-limited polarization camera datasets, and a website detailing hardware installation and software use.
We validated and characterized our method using samples with known structures; single fluorophores immobilized on cover glass, in polymer, and lipid bilayer-coated glass beads labeled with membrane dyes. We next performed SMOLM on amyloid fibrils in vitro and on the actin network of fixed mammalian cells. To demonstrate that a polarization camera can also be used for polarized diffraction-limited microscopy, we imaged the actin network of COS-7 cells, and the membrane of live human T cells interacting with an antibody-coated cover glass. Our approach can be easily implemented by switching the regular camera on any single-molecule fluorescence microscope for a polarization camera. The camera used in this work is supported by the popular image acquisition software Micro-Manager  (47) , and we provide image analysis software in the form of MATLAB applications for single-molecule and diffraction-limited image analysis and real-time image processing and rendering during acquisition and a napari  (48)  plugin for processing multi-dimensional diffraction-limited polarization camera image datasets.
d) Illustration of a silica microsphere (5 µm diameter) coated using a lipid bilayer (DPPC + 40% cholesterol). e) Diffraction-limited image of a cross-section at a z-plane in the middle of a lipid bilayer-coated silica microsphere labeled using the membrane dye Di-8-ANEPPS. f) POLCAM SMOLM reconstruction of a cross-section of a silica microsphere acquired through PAINT with the Nile red-derivative NR4A. Each localization is drawn as a rod with a direction indicating the estimated in-plane angle φ. g) An experimental bias curve for the estimation of φ generated using a PAINT dataset such as the one shown in panel f. The column of points at the right side of the plot (at assumed true φ = 90 • ) is due to a physical imperfection of the lipid coating on the bead. The clusters of points around (-90,90) and (90,-90) are due to the fact that φ plus an integer multiple of 180 • remap to the same orientation.
Experimental accuracy and precision. To measure the experimental accuracy of orientation estimation, silica microspheres with a diameter of 5 µm were coated with a lipid bilayer (DPPC containing 40% cholesterol, Fig  2d ) as in  (25, 29)  and labeled using different membrane dyes. Polarization-resolved diffraction-limited images were acquired using bulk labeling concentrations of different membrane dyes (Di-8-ANEPPS, DiI, Nile red, and the Nile red derivative NR4A (  55 )). As expected, the emission dipole moments of Di-8-ANEPPS and Nile red orient perpendicular to the membrane surface, and DiI orients parallel to the membrane surface (Fig. 2e , Supplementary Fig. S13 ). NR4A and Nile red were used to collect a PAINT (Point Accumulation for Imaging in Nanoscale Topography (4)) dataset of a lipid-coated microsphere to generate an experimental accuracy curve for the in-plane angle φ (Fig.
2f-g ). Experimental orientation estimation and localization precision curves as a function of the number of detected photons were generated using single Alexa Fluor 647 (AF647) dyes immobilized in poly(vinyl alcohol) (PVA). The data points in figure 2i are the measured standard deviation on the repeated measurement of the orientation of an AF647 molecule. At 500 detected photons (the default lower threshold on photon number that is used in all datasets), we achieve an experimental in-plane angle precision σ φ of 7. 5 • (Fig. 2i ), i. e. the upper bound on σ φ. The precision converges to a lower bound of 1 • at higher photon numbers (Fig. 2i ). The experimental localization precision at 500 detected photons is 25-30 nm and converges to 5 nm at high photon numbers (Fig. 2j ). The same curves generated using simulations with a realistic noise model largely agree with the experimental data (Supplementary Fig.
3c-e ). All orientations are well sampled without any obvious photo-selection, resolving similar fibrils in all directions (Fig. 3c-e ). In addition, no obvious systematic artefacts are observed at known feature sites, for instance at cross points between overlapping fibrils (58) (Fig. 3e ). The narrow distributions of individual fibrils can be extracted (Fig. 2f-i ) where the standard deviations of these distributions range between 7 • and 9 • , which approaches the expected precision for this dataset (Fig. 2i , ∼ 5 • at 1000 detected photons). This indicates that the width of this distribution can not be explained by measurement precision alone, and must partly be due to Nile red molecules binding at a range of orientations that are not exactly parallel to the fibril axis. While a range of avgDoLP and photon values were extracted using POLCAM (Fig.
3j ,k), we apply lower limit filtering thresholds to ensure high-accuracy results; (i) at least 500 detected photons (applied to all single-molecule data presented in this work), and (ii) avgDoLP > 0. 4 when φ-colorcoded data is shown, as localizations with avgDoLP < 0. 4 are too rotationally free to estimate a meaningful orientation. An example TAB-PAINT dataset is included in the supporting information (Dataset 2). Taken together, these data demonstrate that POLCAM can accurately measure the in-plane orientation of single dyes and generate high-quality orientation-resolved super-resolution images. dSTORM imaging of actin in fixed HeLa cells. Next, to demonstrate the versatility of POLCAM in a complex environment, we performed orientation imaging in fixed cells. The actin network of HeLa cells was labeled using phalloidin-Alexa Fluor 488 (AF488) and phalloidin-Alexa Flour 647 (AF647) and imaged using dSTORM.
4c-d ) using a colormap where more orientationally random areas appear in white (avgDoLP < 0. 4) and the more ordered appear colored. Analysis of the distributions of the avgDoLP revealed an ordered subset of localizations (Fig. 4e ) (25 % for AF488 and only 5 % for AF648). By filtering these localizations using this empirically determined threshold (avgDoLP > 0. 4), a refined POLCAM image of actin in cells (Fig. 4f ) can be generated that excludes localizations that are too rotationally free to generate an accurate φ estimate (Fig. 4g ). The resolution of the super-resolved images was measured using FRC (Fourier Ring Correlation (59)) to be 42 nm for the AF488 dataset and 26 nm for the AF647 dataset (Fig. 4f ). The difference in FRC resolution is likely explained by the higher signal-to-noise ratio of the AF647 dataset.
A complete comparison of the algorithms can be found in Supplementary figures S16-S20. Figure  5f  shows super-resolution reconstructions generated by the two algorithms of a subset (200×200 pixels and 10,000 frames and ∼ 15 localizations/frame) of the phalloidin-AF488 dSTORM dataset from Fig. 4g. Although both algorithms generated similar-looking reconstructions, zooming in on specific regions (Fig. 5g ) seems to confirm that the dipole PSF-fitting algorithm produces a slightly higher resolution image showing sharper, more pronounced features. A spatial comparison of the φ estimates shows that the widths of the φ distributions generated by the dipole PSF-fitting algorithm are slightly more narrow (Supplementary Fig. S21 ). On a typical workstation (see Methods for system specifications), the intensity-only algorithm is > 750-3700 times faster (2 minutes total processing using centroid localization, and 10 minutes using least squares rotated asymmetric Gaussian fitting) compared to the dipole PSF-fitting algorithm (∼ 127 hours total processing).
Therefore, the dipole PSF-fitting algorithm is recommended if the complete 3D orientation (φ, θ) needs to be estimated, or if the dataset contains dense blinking with overlapping PSFs. This relative computational expense of SMOLM based on dipole PSF-fitting has limited the widespread feasibility of higher throughput experiments. If knowledge of φ and the rotational mobility proxy avgDoLP is sufficient, the fast intensity-only algorithm is recommended. Live diffraction-limited polarization microscopy. The combination of instrumental simplicity and fast computation makes POLCAM compatible with real-time image processing. We have taken advantage of this advance in two ways: a standalone application for on-the-fly processing and rendering (POLCAM-Live), and a napari plugin (napari-polcam) for the open-source multi-dimensional image viewer napari  (48).
This package makes the method much more accessible to the bio-imaging community. We present two SMOLM analysis algorithms for POLCAM; 1) a fast algorithm based on Stokes parameter estimation and simple intensity measurements for robust in-plane angle estimation compatible with high-throughput data collection, and 2) an unbiased 3D orientation estimation algorithm that fits a dipole PSF model. Due to the computational cost of the PSF-fitting algorithm, it is more suited to the analysis of small datasets. A diffraction-limited polarization colormap (HSVmap) can be generated and displayed in real-time during experiments, for which we provide the standalone software POLCAM-Live. In theory, the intensity-only SMOLM algorithm is also fast enough to be compatible with real-time processing, as a 200×200 pixel image (10×10 µm) with ∼ 15 localizations takes about 10 ms to process, which is less than typical camera exposure times.
Single-molecule data analysis was performed using the MAT-LAB application POLCAM-SR, which includes tools for localization, filtering, drift correction, and data visualization. The source code and installer are available on GitHub at https://github. com/ezrabru/POLCAM-SR. Diffraction-limited data analysis was performed using custom MATLAB scripts (MATLAB R2022a, Mathworks) and a napari (48) plugin called naparipolcam for processing and visualization of multi-dimensional polarization camera datasets. The source code and installation instructions for naparipolcam are available on GitHub at https://github. com/ezrabru/ napari-polcam. Refer to Supplementary Note. for a detailed description of the image analysis pipelines.
ACKNOWLEDGEMENTS The authors thank the  Cambridge Advanced Imaging Centre (CAIC, University of Cambridge)  for the use of a microscope for the dSTORM experiments, and the mechanical workshop ( Yusuf Hamied Department of Chemistry, University of Cambridge ) for building custom parts of optical setups used in this work. We thank  Dr. Aleks Ponjavic  and  Dr. Joseph Beckwith  for critical reading of the manuscript. We thank  Ms. Laila Elfeky  for testing POLCAM software.
CODE AVAILABILITY All code used in this work is available on GitHub. The source code and installer for the PolCam-SMOLM and PolCam-Live software are available at https:// github. com/ezrabru/POLCAM-SR and https://github. com/ezrabru/ POLCAM-Live, and the source code for RoSE-O-POLCAM is available at https: //github. com/Lew-Lab/RoSE-O_polCam. The napari plugin for visualization of high-dimensional diffraction-limited polarization camera dataset is available at https: //github. com/ezrabru/napari-polcam.