dataesr/scientific-paragraphs-categorization huggingface dataset:
The API package (pacfish. api yellow module) can be used to facilitate the integration of adapters for conversion from arbitrary file formats into the IPASC data format. To create a conversion adapter, a Python representation of (1) the binary data, (2) the acquisition metadata dictionary, and (3) the device metadata dictionary needs to be implemented. The adapter must provide at least the minimal metadata as defined in the Metadata Attributes section. An option to add additional custom metadata items is also provided. The core classes (pacfish. core green module) represent the metadata and data structure in Python. Each metadatum is described with specific device tags defining the name, data type, necessity and SI unit (if applicable), and setting a value constraint. Basic metadata constraints have been implemented to avoid accidental typos within the values field (e.
g. only positive numbers larger than zero are applicable for acquisition wavelengths). If the value is not within the constraints a TypeError is raised. Metadatum-specific functions enable easy addition of the values for the specific metadata field. The quality control functionalities (pacfish. qualitycontrol blue module) ensure the correctness of the conversion into the IPASC format: a completeness checker tests that all metadata are being called and a consistency checker ensures that all metadata are within their constraints. An automatically-generated output report gives a humanreadable summary of the quality control checks and ensures that the likelihood of conversion mistakes are minimised. To assess the Device Metadata, the detector and illuminator positions can be represented in a 3D coordinate system as visual control (Fig.
Using the API to work with data in the IPASC format: This listing includes Python code showcasing how to use PACFISH to read data in the IPASC format, to unpack metadata relevant for image reconstruction, and to write data to the hard drive. 1 import pacfish as pf 2 3 # Loading data from the hard drive 4 pa_data = pf. load_data ( " path/to/ hdf5file. hdf5 " )   The upper image shows a linear transducer with two illumination slits at either side as described in  [19]. The lower image is a representation of the MSOT inVision 256-TF (iThera Medical GmbH, Munich, Germany).
Using the custom adapter to access/convert photoacoustic data: 1 from pacfish import write_data 2 from custom_module import CustomAdapter 3 from pacfish import quality_check_pa_data Sample data in the IPASC format can be found on Zenodo, 10 and application examples can be accessed on Github. 11 Fig. 5  shows the Zenodo sample data, displaying the device visualisation, simulated time series data, and reconstruction result of a conventional back projection algorithm  [20]  for four different sample data sets with different detection geometries.
Discussion We have presented the IPASC data format for PAI data storage. The IPASC format is designed to store raw time series data with associated metadata as HDF5 files. To facilitate the use of the IPASC data format, a Python-based software tool (PACFISH = Photoacoustic Converter for Information Sharing) has been created and tested by members of the DAM working group; it is provided to the PAI community to use and further develop. PAI systems can have vastly different illumination and detection geometries, including linear array, cylindrical, and hemispherical designs. For any hardware configuration, the scanning protocol can also vary (full-scan vs. composite-scan). While the IPASC format is designed to accommodate all needs, limitations remain. First, the IPASC data format does not yet support the inclusion of data from bimodal systems, such as combined photoacoustic and ultrasound systems.
Future implementations should account for these hybrid system types, as they become increasingly common (cf. e. g. [21, 22] ), especially in clinical settings. There are also limitations associated with the use of the underlying data format HDF5, such as the risk of corruption or potential performance issues when dealing with large datasets  [23]. Whilst these drawbacks are currently outweighed by the advantages of HDF5 -such as its flexibility, organisation, open access, and its capacity to store large annotated datasets within a single file -the limitations should be kept in mind to ensure safe use of the format. The IPASC data format is designed to accommodate raw time series measurements with all relevant metadata. Storing raw data is preferable to storing reconstructed images, as reconstruction can lead to image artefacts and loss of information.
For reconstructed images, compatibility with the DICOM format is desirable, as it is the most widely used format in management of medical image data. The IPASC industry board, which includes vendors from commercial PAI systems, is currently working towards integrating photoacoustic image data into DICOM. As the DICOM tags will be based on the metadata naming convention presented here, IPASC hopes to enable appropriate mapping between the two formats and to ensure their compatibility. The PACFISH Python tool is still under development and is currently being tested by members of the photoacoustic community and by vendors to assess its applicability in an industrial setting. Adapters to other programming languages, such as MATLAB, could be added to increase the accessibility of the PACFISH within the user community.
Moreover, the direct integration of PACFISH into software tools relevant for numerical forward modelling, such as k-Wave  [24]  or Monte Carlo eXtreme (MCX)  [25]  would be beneficial. Our focus in the near future will be to widen the adaptation of the IPASC format for both the vendor and research communities. Besides implementing additional adapters to proprietary formats, programming languages and software tools, we would like to make established image reconstruction algorithms compatible with the IPASC format to streamline post-processing pipelines using the format. A further aim is to create an open-access database that includes data held in the IPASC format, to facilitate reproducibility studies and structured evaluation and benchmarking of reconstruction algorithms. By introducing the IPASC format, we hope to facilitate technological advancement, foster community collaboration, and ultimately accelerate clinical translation and adoption of this modality.
For tag SNPs that were intergenic, missense and nonsense variants in both genes flanking the tag SNP were examined. We identified three missense variants in three genes present in Europeans (minor allele frequency (MAF) >5% in Europeans from the 1000 Genome Project, Database of Single Nucleotide Polymorphisms (dbSNP), current build 154, released 21 April 2020) that were nominally associated (P < 0. 050) with liver fat content in the DHS (Table  1 ). Among these, we found two variants robustly associated with FLD and circulating triglycerides, namely the Glucokinase regulator (GCKR) rs1260326 (P = 0. 007) and Transmembrane 6  0. 12 0. 02 Of 32 loci identified by a previous genome-wide association study on circulating triglyceride levels, three missense variants with a MAF >5% in Europeans were nominally associated with hepatic triglyceride content in the DHS cohort.
Of these three variants, one was associated with a decrease in hepatic fat content (rs71519934, beta = -0. 02), and two were associated with increased hepatic fat content (rs1260326, beta = 0. 03 and rs58542926, beta = 0. 12). The association was tested by linear regression analysis adjusted for age, gender and the top four principal components of ancestry. N0, number of individuals homozygote for the major allele; N1, number of individuals heterozygote; N2, number of individuals homozygote for the minor allele; rsID, reference SNP identification. a MAF in the overall DHS cohort. In the DHS, rs71519934 was found as rs7003060, that is, in complete linkage disequilibrium with rs71519934 (D′ = 1, r 2 = 1). b MAF EUR refers to 1000 Genome data reported in the dbSNP (current build 154, released 21 April 2020), except for rs71519934 where the frequency was estimated in white British participants from the UK Biobank 50,000 exome sequencing data because data were not available in dbSNP.
3 ). To seek independent replication of the association between the PSD3 variant and liver fat content, we examined white British participants (n = 10,970) from the UK Biobank with liver fat content measured as magnetic resonance imaging (MRI)-derived proton density fat fraction (PDFF). In this population-based study, genetic data on the rs71519934 dinucleotide substitution were not available, therefore we used the rs7003060 which is in complete linkage disequilibrium (D′ = 1, r 2 = 1) with the rs71519934 in Europeans. We did not find any association between the PSD3 minor allele and lower liver fat content in the UK Biobank (Table  3 ). However, because genetic variations affecting FLD have a robust gene-environment interaction, with BMI amplifying their effect  25  , we examined the differences in liver fat content among the PSD3 rs7003060 genotype after stratification for severity of overweight/ obesity as measured by BMI.
a-h, Psd3 GalNAc-ASO treatment did not affect body weight gain (a) but reduced liver cholesterol (b) and cholesteryl ester (c), plasma AST (d), total cholesterol (e) and LDL cholesterol levels (f). There were no effects on plasma triglyceride (g) or HDL cholesterol (h) levels. Hepatic mRNA was quantitated by digital gene expression profiling as described in the Methods and expressed as transcripts per million. i-n, Psd3 GalNAc-ASO treatment had no effect on hepatic Srebp1-c (i) but significantly reduced hepatic Acc1 (j), Fasn (k), Scd1 (l), αSma (m) and Ccl2 (n) mRNA expression levels. Data are presented as mean ± s. d. a-h, Two-sided P values were calculated by one-way ANOVA Kruskal-Wallis non-parametric tests followed by Dunn's multiple comparisons tests. Multiple comparisons were performed by comparing each group with the control group (Ctr GalNAc-ASO).
Among these variants, TM6SF2 rs58542926 and GCKR rs1260326, two well-known genetic variants associated with the entire spectrum of FLD as well as with circulating triglycerides  15, 24  , were associated with increased liver fat content. The other variant, namely the rs71519934 (found as rs7003060 that is in complete linkage disequilibrium (D′ = 1, r 2 = 1) in the PSD3 gene, was associated with lower liver fat content. In the LBC, the rs71519934 had an overall MAF of 33%. When we stratified the cohort based on the centre of recruitment, we found that the rs71519934 had a higher frequency in individuals from Finland (MAF 48% and 42% in the two subgroups from Finland) than in participants from Italy (MAF 26% and 28% in the two subgroups from Italy; Supplementary Table  8 ) reflecting reference data (Exome Aggregation Consortium database).
In addition, a total of 323 individuals from a fourth and independent Finnish centre  39  have been included (supplementary material)  [40] [41] [42]. In the current study, only individuals with complete data available for both PSD3 genotype and liver histology were included (n = 1,951). The diagnosis of NASH was based on the presence of steatosis with lobular necro-inflammation and ballooning or fibrosis. Disease activity was assessed according to the NAS; fibrosis was staged according to the recommendations of the FLD clinical research network  28. Scoring of liver biopsies was performed blind by pathologists unaware of the patient's genotype and status. The study was approved by the ethics committees of the Fondazione IRCCS Ca' Granda (Milan), Palermo University Hospital (Palermo), Northern Savo Hospital District in Kuopio (Finland), and the ethics committee of the Hospital District of Helsinki and Uusimaa (Finland).
UK Biobank. The UK Biobank is a large cohort study comprising more than 500,000 adults (aged between 40 and 69 years at recruitment) who visited 22 recruitment centres throughout the UK between 2006 and 2010. Both the phenotypic and genotypic data used in the current study were obtained from the UK Biobank under Application Number 37142. For the current study, we restricted our analysis to a subset of unrelated white British participants from the UK Biobank, after further removal of individuals with more than ten putative third-degree relatives, a mismatch between self-reported and genetically inferred gender, putative sex chromosome aneuploidy, withdrawn consent and those who were identified by the UK Biobank as outliers based on heterozygosity and missingness  43, 44. We used liver MRI-derived PDFF data (data field 22,436).
Genotyping. All participants from the DHS were previously genotyped for the variants using an Illumina Infinium HumanExome BeadChip  12, 14. Participants from the LBC and from the central European independent replication cohort were genotyped by TaqMan 5′ nuclease assays (Life Technologies). The allelic discrimination probe for PSD3 rs71519934 was not commercially available. A custom assay for this variant has been designed (supplementary material). Nine individuals denoted as AC homozygotes (n = 3), CT homozygotes (n = 3) and AC/CT heterozygous (n = 3) by the TaqMan assay were Sanger sequenced with consistent results (Supplementary Fig. 2 ). UK Biobank participants were genotyped using two highly similar UK BiLEVE or UK Biobank Axiom arrays (>95% overlap). Genotyped data were then imputed based on the 1000 Genomes Phase 3, UK10K haplotype and Haplotype Reference Consortium reference panels  47.
3. 6. 1, MATLAB R2020b (academic license). For descriptive statistics, data are shown as the mean and s. d. or the median and interquartile range (i. q. r. ) as appropriate. Categorical traits are shown as numbers and proportions. For continuous traits, P values were calculated by linear regression under an additive genetic model unadjusted or adjusted for age, gender and BMI. Non-normally distributed traits were log-transformed before being entered into the model. For categorical traits, P values were calculated by the chi-squared test or by binary logistic regression adjusted for age, gender and BMI using IBM SPSS statistics v. 27. Differences in PSD3 and NAT2 expression levels in human tissues were evaluated by the Mann-Whitney non-parametric test (for comparisons between healthy individuals and those with FLD) or by linear regression analysis (for comparisons among genotypes).
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy All data associated with this study are present in the paper or the Supplementary Information. For UK Biobank, all individual-level phenotype/genotype data are accessible via a formal application to the UK Biobank http://www. ukbiobank. ac. uk. Antisense oligonucleotides associated with this study can be made available, upon reasonable request, to academic researchers under a material transfer agreement with AstraZeneca and Ionis Pharmaceuticals.
Acknowledgements We thank  Julia Kozlitina  at  University of Texas Southwestern Medical Center, Dallas, TX  for the analysis of the DHS data. This work was supported by a project grant from the  Swedish Research Council (Vetenskapsradet (VR) ,  2021-005208 ) (S. R. ), the  Swedish state  under the Agreement between the Swedish government and the county councils (the ALF agreement,  SU 2018-04276 ) (S. R. ), the  Swedish Diabetes Foundation  ( DIA2020-518 ) (S. R. ), the  Swedish Heart Lung Foundation  ( 20200191 ) (S. R. ), the  Wallenberg Academy Fellows  from the  Knut and Alice Wallenberg Foundation  ( KAW 2017. 0203 )(S. R. ), the  Novonordisk Project grants in Endocrinology and Metabolism  ( NNF20OC0063883 ) (S. R. ),  AstraZeneca Agreement for Research , and Grant  SSF ITM17-0384  (S.
R. ),  Swedish Foundation for Strategic Research  (S. R. ), the MyFirst Grant AIRC n. 16888,  Ricerca Finalizzata Ministero della Salute   RF-2016-02364358  (L. V. ),  Ricerca Corrente Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico  (L. V. ) and the  European Union  ( EU) Programme Horizon 2020  (under grant agreement no. 777377 ) for the project  LITMUS-'Liver Investigation: Testing Marker Utility in Steatohepatitis ' (L. V. ).
Data availability statement All data associated with this study are present in the paper or the Supplementary Information. For UK Biobank, all individual-level phenotype/genotype data are accessible via a formal application to the UK Biobank http://www. ukbiobank. ac. uk. Antisense oligonucleotides associated with this study can be made available, on reasonable request, to academic researchers under a material transfer agreement with AstraZeneca and Ionis Pharmaceuticals. Owing to study participants' privacy data protection, the RNA-seq data of the liver biopsies of the LBC can only be made available on request to the corresponding authors for collaborative projects. Bulk RNA-seq data of the primary human hepatocytes are deposited in the NCBI SRA under the BioProject identifier PRJNA778044.
All other data are available from the authors on reasonable request. The following online databases have been used: Database of Single Nucleotide Polymorphisms (dbSNP) (https://www. ncbi. nlm. nih. gov/snp/); Ensembl (https://www. ensembl. org/index. html); Exome Aggregation Consortium [ExAC] database (http://exac. broadinstitute. org). Source data are provided with this paper.
The objective of our study was to develop an original bioinformatics pipeline aiming to exploit the currently available metagenomic data for the characterization of genomic differentiation of protists in the marine ecosystems. To address this issue, we focused on the Mediterranean Sea, which is an ideal location to study population genomics (i. e. a semi-enclosed marginal sea characterized by tortuous coastlines, with several environmental gradients despite a highly dynamic circulation,  Ayata et al. , 2018) , on three a priori abundant planktonic protistan species in this area  (de Vargas et al. , 2015). We gathered reference sequences and metagenomic data previously published for which genomic differentiation could be highlighted at the species scale and tentatively explained by external drivers, such as geography, environmental conditions and oceanographic circulation.
cordata  (Zingone et al. , 1999). While transcriptomes were used for P. calceolata and P. cordata (RCC969 and RCC1383, respectively), a reference genome was used for B. prasinos (RCC1105). The high gene density of B. prasinos genome  (Moreau et al. , 2012)  was assumed to limit the impact of intergenic regions in the analysis (Supporting Information S7). Given the size range of these organisms, we focused on the 0. 8-5 μm size fraction in the TO data, consisting of 13 metagenomics samples available from the surface layer (accession number PRJEB4352,  Carradec et al. , 2018 ; Supporting Information S1), containing on average 185 million sequence reads. We built a bioinformatic pipeline to extract single nucleotide polymorphisms (SNPs) from metagenomic sequences in comparison to reference sequences (here genome or transcriptome).
Calculations of geographic, environmental and oceanic distances Firstly, the latitude and longitude of sampling (metadata from PANGAEA;  Pesant et al. , 2015)  were used to compute geographic distances among pairs of stations (i. e. minimal distances between two stations without crossing the lands). Secondly, each TO sample was associated with its hydrological and biogeochemical environment based on geographic coordinates and depth (TO metadata,  Pesant et al. , 2015). The environmental variables extracted from Medatlas-II climatologies  (Fichaut et al. , 2003)  included surface temperature ( C), surface salinity (PSU) and surface concentrations of ammonium (mmol. m À3 ), oxygen (ml. l À1 ), nitrate (mmol. m À3 ), nitrite (mmol. m À3 ), phosphate (mmol. m À3 ), silicate (mmol. m À3 ) and chlorophyll a (mg.
m À3 ). A PCA was performed on these normalized and standardized variables  (Legendre & Legendre, 2012)  for a total of 22 TO stations (13 stations corresponding to our metagenomic sample and nine additional TO stations). The environmental distances between pairs of stations were computed as their Euclidean distances in the PCA space (using only significant axes based on the Kaiser-Guttman criterion) and the stations were clustered using hierarchical clustering. Thirdly, physical transport by ocean circulation was estimated with Lagrangian model simulations to compute oceanographic distance between stations. As no assumption about mechanisms of dispersal and underlying model settings for each organism could be established, data from two types of existing models were used to assess Lagrangian transport in the Mediterranean Sea.
The first Lagrangian dataset was a product of  Berline et al. (2014)  providing the mean connection time (MCT, in days) for each pair of stations. The second dataset was obtained by performing simulations of the model of  Ser-Giacomi et al. (2015)  based on the Lagrangian flow network approach. The dataset corresponded to a connectivity matrix estimating the probability of connection (PC) for a particle leaving a station to end up in another station in a given time period. To estimate PC over time, three dispersal durations were used (3, 6, and 12 months) and averaged to integrate dispersal characteristics at various temporal scales (seasonal, biannual, and annual circulation). As Lagrangian matrices are asymmetric, we chose the maximum PC and the minimum MCT as oceanographic distance for each pair of stations.
8-5 μm size fraction of the Tara Oceans (TO) dataset, consisting of 13 metagenomic samples collected at the surface layer from 13 stations in the Mediterranean Sea (Figure  1 , accession number PRJEB4352,  Carradec et al. , 2018 ; see Supporting Information S2 for details). We mapped the metagenomic reads (on average 185 million reads/sample) on reference assemblies and obtained horizontal and vertical coverages between species (Figure  2 ; percentage of the reference covered by at least one read and mean number of reads aligned at each position of the reference, respectively). B. prasinos and P. calceolata displayed higher horizontal coverages than P. cordata despite their longer reference sizes (maximal horizontal coverage, B. prasinos: 98. 1%, P. calceolata: 68. 2%, F I G U R E 1 Geographic location of the 13 stations sampled during the Tara oceans for metagenomic analysis.
prasinos and for P. calceolata than for P. cordata (min-max mean vertical coverage, B. prasinos: 0. 085-17. 6 X, P. calceolata: 0. 119-7. 2 X, P. cordata: 0. 51-2. 47 X). B. prasinos showed highest coverages at stations 5 and 6, located in the Western part of the Mediterranean Sea (Alboran Sea). SNPs were detected from aligned and filtered reads (Experimental procedures, Supporting Information S2). B. prasinos, P. calceolata and P. cordata showed different total numbers of SNPs over reference size ratio (respectively, 3. 4, 51. 4 and 0. 5 SNPs/Mb). In average, only 9. 26%, 9. 67% and 18. 74% of the total number of SNPs were observed in each station for B. prasinos, P. calceolata and P. cordata, respectively. Moreover, a filtering on the vertical coverage led to the removal of SNPs (i. e. SNPs having between 4 and μ + 2σ vertical coverage were kept; Experimental procedures, Supporting Information S2).