dataesr/scientific-paragraphs-categorization huggingface dataset:
According to the literature, and the databases Uniprot and Human Protein Atlas, among the 21 upregulated proteins, 7 are already related to diabetes (Table  3  and in detail in Table  S7 ) and 15 are attributed to kidney expression and/or function (Table  3  and in detail in Table  S7 ). Among the 18 downregulated proteins, 7 are related to diabetes (Table  4  and in detail in Table  S7 ) and 14 have documented kidney expression and/or function (Table  4  and in detail in Table  S7 ). The biological functions of the consistently changed glomerular proteins associated with early and late DKD are listed in Table  5. Table  5. Biological function of the most significant consistently changed glomerular proteins associated with early and late DKD. The biological function of the most significant consistently changed glomerular proteins associated with early and late DKD.
Validation of Findings in Late T2D DKD Animal Model and Cross-Omics Validation, Further Confirming Peroxisomal Changes in DKD In order to validate the findings of the glomerular proteome analysis and investigate further if they are also observed in a T2D DKD model, the kidney cortex proteomic profile of db/db mice, a model of T2D DKD, of advanced age (6 months old) was also analyzed. The diabetic stage (late DKD) of the db/db mouse model was verified via biochemical data analysis (Figure  S1 ). Multiple pair-wise comparisons were performed (described below). On average 674 proteins were detected in wild-type mice and 670 in db/db mice (Table  S8 ). A comprehensive list of the identified proteins including the fold change (ratio) and the statistics is shown Table  S9 ; Sheet 1 (db/db kidney cortex dataset).
When comparing the db/db to respective WT data, 33 upregulated proteins (Table  S9 ; Sheet 2), the majority related to glutamine and glutamate metabolic process, and 23 downregulated proteins (Table  S9 ; Sheet 3) mostly related to peroxisomal protein import, were identified. Interestingly, these processes were also observed in early T1D DKD (Section 2). Taken together, these observations suggest that common deregulated processes between early T1D, late T1D and T2D DKD exist. Several of the proteins associated with DKD identified in glomeruli from T1D mice were also found in the kidney cortex from T2D mice (Table  6 ). Specifically, NUDT19, AMACR and PIPOX were downregulated in DKD in both glomeruli and the kidney cortex in T1D and T2D models, respectively (Table  6 ). To further place our findings of deregulated mitochondrial and peroxisomal proteins in the context of published datasets in DKD, kidney transcriptomics data from DKD patients vs.
healthy controls and DKD mouse versus healthy controls were retrieved from the Nephroseq database (7 datasets in total;  [38] [39] [40] ). In addition, a reported single-cell kidney transcriptomic dataset of early human DKD  [41]  was also investigated. Of the reported differentially expressed and 'consistent' (Table  6 ) mitochondrial and peroxisomal proteins, mRNA expression levels were recorded for the three peroxisomal proteins NUDT19, PIPOX and AMACR with a reported decrease in DKD cases versus controls (Table  6 ). The cross-omics analysis highlighted additional glomerular-cortical mitochondrial and peroxisomal proteins (Tables  3, 4 , S10 and S11) in agreement in their change in mRNA and protein levels. In the transcriptomics datasets, mRNA expression levels were also recorded for three mitochondrial proteins (GLS, GLDC, AMT) and five peroxisomal proteins (ACOX1, CROT, EHHADH, AGPS and PECR), all consistently changing in early and late DKD (Tables  3  and 4 ), and three peroxisomal (CAT, EPHX2, DAO) cortical proteins associated with late DKD.
MS Data Processing Raw files were analyzed with the Proteome Discoverer 1. 4 software package (Thermo Fisher Scientific, Waltham, USA), using the SEQUEST search engine and the UniProt mouse (Mus musculus) reviewed database, downloaded on 22 November 2019, including 16. 935 entries. Stringent criteria were used for protein identification and relative quantification as previously established in our lab  [86]. Cysteine carbamidomethylation was used as the fixed modifications and methionine oxidation as the variable modifications. Two missed cleavage sites were allowed, and the precursor and fragment mass tolerance were set at 10 ppm and 0. 05 Da, respectively. The False Discovery Rate was 1% (peptide level). Label free quantification analysis was performed considering the precursor ion area values that were exported from the total ion chromatogram as defined by the Proteome Discoverer 1.
4 software package. Output files from the Proteome Discoverer were processed with an in-house script in the R environment for statistical computing (version 4. 0. 3) as follows: Protein lists were concatenated into a master table. Raw protein intensities for each individual sample were subjected to normalization according to X' = X/Sum(Xi) * 10 6 and only proteins present in at least 55% of the samples in at least one group were further selected for downstream statistical analysis.
Functional Analysis Pathway annotation was performed with the ClueGO plug-in 3. 7. 2 (Cytoscape) using the REACTOME pathway database (updated on 17 February 2020). The same GlueGO plugin was used for the biological function annotation with the addition of the GO-Biological Process EBI-Uniprot GOA database (updated on 17 February 2020). Statistically significant pathways corrected for multiple testing (Benjamini-Hochberg (BH) corrected p-value ≤ 0. 05, two-sided hypergeometric test) were further considered. Results were interpreted based on biological relevance by utilizing only the leading term from each group.
Investigation through Transcriptomics Data Analysis Nephroseq (www. nephroseq. org, accessed on 30 November 2021) was employed for the investigation of the expression of the shortlisted mitochondrial and peroxisomal proteins in existing mouse and human transcriptomics datasets. The list of proteins was uploaded in Nephroseq v4 in the form of EntrezGene IDs. DKD datasets selection was held after application of the filters: Primary Filters > Group > Diabetic nephropathy. The corresponding gene expression was searched in seven available DKD mouse and human datasets observed after filtering, on comparison of DKD vs. Healthy Living Donor groups (Table  S1 ). Only significantly deregulated genes (p < 0. 05) were extracted, and their differential expression was compared with the mitochondrial and peroxisomal deregulated proteins.
Evaluation of Immunohistochemistry Qualitative immunohistochemical evaluation was assessed by two independent observers (HG and DP) blinded to clinical data. The pattern, topography, extension and intensity of staining of all the antibodies under study in both the controls and DKD cases were analyzed. A rough comparison of the expression of each marker between the controls and DKD cases, as well as between cases of different DKD classes was also performed. Staining intensity was also further quantified using ImageJ software as previously described  [85]. Optical density was normalized over the unstained tissue and mean intensity values were estimated. Statistical significance among multiple groups (stages) was confirmed with ANOVA analysis and further pair-wise comparisons were performed with the Student's t-test.
Statistical Analysis of Proteomics Data Statistical significance of continuous variables was defined at p < 0. 05 with the nonparametric Mann-Whitney test. Proteins with p value ≤ 0. 05 and ratio ≥ 1. 5 (upregulated) or ≤0. 67 (downregulated) were considered statistically significant and differentially expressed. Dotplots for the mouse physiopathologic characterization were created with functionality from the ggplot2 and ggpubr R packages. Depicted statistical comparisons correspond to independent Mann-Whitney tests. For the correlation analysis, Spearman's correlation was utilized to assess the relationships of our data to one external proteomics dataset  [37] , using the means of the normalized protein intensity across the samples after transforming them to the natural logarithmic scale.
Funding: This research was funded by the  Hellenic Foundation for Research and Innovation (HFRI)  and the  General Secretariat for Research and Innovation (GSRI) , under grant agreement No  695  ( MolProt-CKD ). The APC was funded by the  Hellenic Foundation for Research and Innovation (HFRI)  and the  General Secretariat for Research and Innovation (GSRI) , under grant agreement No  695  ( MolProt-CKD ).
edulis. The main objectives of this project are to (1) produce hundreds of full-sib families tested in different environments (deep water and foreshore), (2) introduce the most performant families in natural oyster beds showing a decline in their population in order to enhance their dynamics and then contribute to their restoration, and (3) specifically select the most performing families on the foreshore in order to reintroduce this mode of culture for O. edulis. In the present study, an improved chromosome-level assembly of O. edulis genome was developed through a combination of high coverage Pacific Biosciences (PacBio) long-read sequencing, 10X chromium library sequencing, accurate Illumina short-read data and Hi-C library sequencing. We focused our analyses on the diversity of transposable elements through a comparison with other oyster genomes, highlighted some specificities in noncoding RNA diversity and provided the first RNAseq analysis on M.
| Hi-C libraries The Hi-C library construction protocol was adapted from  Lieberman-Aiden et al. (2009)  and  Lazar-Stefanita et al. (2017). Hi-C library was made from a piece of adductor muscle from another individual from the same population and sequenced on a NextSeq 550 apparatus (2 × 75 bp, paired-end Illumina NextSeq). Contact maps were generated from reads using the hicstuff pipeline for processing generic 3C data, available at https:// github. com/koszu llab/hicstuff. The backend uses the bowtie2 (version 2. 2. 5) aligner run in paired-end mode (with the following options: --maxins 5 -very-sensitive-local). Alignments with a mapping quality lower than 30 were discarded. The output was in the form of a sparse matrix where each fragment of every chromosome was given a unique identifier and every pair of fragments was given a contact count if it was nonzero.
| Gene prediction All RNAseq reads obtained from the different organ transcriptomes were quality-trimmed using Trimmomatic (version 0. 36)  (Bolger et al. , 2014)  and mapped to the genome assembly using HISAT2  (Kim et al. , 2019). The alignment information was processed to generate genome-guided transcriptome assembly using Trinity (ver. 2. 8. 4)  (Grabherr et al. , 2011)  and in parallel, de novo transcriptome assembly was also generated by the Trinity software. The genomeguided and de novo transcriptome assemblies were processed by the PASA pipeline (ver. 2. 3. 3) (Haas et al. , 2003)  to generate a training set for optimizing gene prediction parameters in AUGUSTUS  (Stanke et al. , 2006). RNAseq reads were also aligned to the genome using STAR (ver. 2. 7. 1a)  (Dobin et al. , 2013)  to produce hint files of exon and intron information.
Then, de novo gene prediction was performed using AUGUSTUS (ver. 2. 5. 5). The completeness of the predicted gene models was assessed by Benchmarking Universal Single-Copy Orthologs (BUSCO) using metazoa_odb9 database  (Simão et al. , 2015). Gene annotation was made using Trinotate  (Grabherr et al. , 2011). 2. 3. 7 | Noncoding RNA prediction tRNA genes were predicted using tRNAscan-SE v1. 3. 1  (Lowe & Eddy, 1997)  with eukaryote parameters, and rRNA with high conservation were predicted by aligning reads to the Arabidopsis thaliana template rRNA sequences using BLASTN  (Altschul et al. , 1997) , with an e-value of 10 -5. Additionally, INFERNAL  (Nawrocki & Eddy, 2013)  was used to predict miRNA and snRNA genes on the basis of the Rfam database (Griffiths-Jones, 2005).
| LTR-retrotransposons identification We specifically investigated LTR-retrotransposons in four genomes of Ostreidae (O. edulis, C. gigas (GCA_902806645. 1), C. virginica (GCA_002022765. 4), and S. glomerata (GCA_003671525. 1)) and one genome of a Pteriidae species, the pearl oyster Pinctada martensii (GCA_002216045. 1) using a detailed and precise pipeline previously customized for M. mercenaria  (Farhat et al. , 2022). To annotate these LTR-retrotransposons, we first extracted and translated the RT/RNaseH domain from the sequences obtained with LTRHarvest using BLASTx  (Camacho et al. , 2009 ) (e-value <10 -5 ) against an inhouse database of RT/RNaseH  (Thomas-Bulle et al. , 2018)  made from Gypsy Database  (Llorens et al. , 2011). Here, we kept the single sequences and/or a consensus sequence per previously defined cluster and used them in phylogenetic approaches to determine the position of the elements in each clade.
Cladistic analyses were performed on amino acid sequences corresponding to the RT/RNaseH domains of the newly characterized sequences and reference elements. Multiple alignments of these protein sequences were performed using MAFFT  (Katoh et al. , 2018). After manual curation of the alignments, phylogenetic analyses were conducted using neighbor-joining and the pairwise deletion option of the MEGA 5. 2 software  (Tamura et al. , 2011). Using Topali2. 3  (Milne et al. , 2009) , the best-fitted substitution model retained was the JTT model with a gamma distribution. Support for individual groups was evaluated with nonparametric bootstrapping using 100 replicates.
| Repeated sequences annotation Repeated sequences were annotated in the five genomes by running RepeatMasker (http://repea tmask er. org) with the default parameter and the script "Concatenate_sequences. py"  (Thomas-Bulle et al. , 2018). Two different libraries were used: (i) one with only inserts-cleaned consensus from each LTR-retrotransposon cluster detected by LTRHarvest to retrieve putatively missed copies (with corrupted LTRs or deleted); (ii) the second with all other consensus transposable elements obtained with RepeatModeler v2. 0. 1  (Flynn et al. , 2020 ) using REPBASE, version 2017 -01-27 (Jurka et al. , 2005). were collected in GENBANK and data for Ostrea denselamellosa and Ostrea stentina were specifically generated for this analysis.
| Phylogenetic analysis Orthofinder version 2. 4. 1  (Emms & Kelly, 2019)  was run using all proteomes with default parameters. From Orthofinder results, we used the single-copy gene OGs (114OGs) and concatenated the proteins per species to generate the species tree using MAFFT online  (Katoh et al. , 2018)  with default parameters. MEGA version 5. 2 software was used to construct the phylogenetic tree with a bootstrap of 1000 on a neighbor-joining method.
| Differential expression analysis RNAseq reads from M. refringens-infected and -noninfected libraries were checked for quality issues and adapter content with FastQC 0. 11. 7 and cleaning for sequencing adapters, trimming of lowquality bases (minimum mean quality score of 30), and filtering for length were performed with Trimmomatic 3. 3  (Bolger et al. , 2014). Reads were aligned to the Roscoff_O. edulis-V1 genome assembly with Bowtie 2  (Langmead et al. , 2019)  to generate BAM files that were used to generate expression counts with the IdxStats software  (Li et al. , 2009). Raw read counts were first normalized using the FPKM method  (Mortazavi et al. , 2008) , and FPKM values below 5 were eliminated when the gene was present in both conditions. Data were log2 transformed and then imported into R 3.
5. 0 (R Core Team, 2017) for analysis with DESeq2 package  (Love et al. , 2014). The genes with a threshold of false discovery rate (FDR) < 0. 05 and |log2 (fold change)| >1 were defined as differentially expressed genes (DEGs). Functional enrichment analysis for the Gene Ontology term was performed using DAVID software  (Huang et al. , 2009)  using a Benjamini adjusted p-value of. 05.
| RNA prediction Detection of miRNA using INFERNAL and Rfam database allowed the identification of 126 families of miRNAs (e-value <10 -5 ) in the Roscoff_O. edulis-V1 genome. A large variation in the copy number is also detected among miRNA families and a total of 99 miRNA families contains more than one paralog. The mir-148 family is the most represented family with 219 copies followed by the mir-821 family with 106 copies and 23 families exhibiting more than 10 copies (Table  S1A ). The distribution of the 25 most represented miRNA families shows a relatively homogeneous distribution along the chromosomes with a higher frequency observed in chromosomes 5, 6, 7, 8, and 9. Most of the conserved miRNA families identified in other mollusc species  (let-7, lin-4, miR-2, miR-29, miR-87, miR-184, miR-8, miR-10, miR-15, miR-17)  are present in Roscoff_O.
To cluster families from protein-coding genes, proteins from the longest transcripts of each gene from O. edulis and other mollusc species, including Argopecten irradians (SRP174526), A. purpuratus (PRJNA418203), Aplysia californica (GCA_000002075. 2), Biomphalaria glabrata (GCF_000457365. 1), C. gigas (GCA_902 806645. 1), C. virginica (GCA_002022765. 4), Chlamys farreri (in MolluskDB), Elysia chlorotica (GCA_003991915. 1), Modiolus philippinarum (GCA_002080025. 1), P. fucata (PRJNA283019), Lottia gigantea (PRJNA175706), Octopus binaculoides (GCF_001194135. 1), S. glomerata (GCA_003671525. 1), Haliotis discus (PRJNA317403).
Gb genome size estimated by flow cytometry using SYBGREEN labeling (data not shown). Then, the 10× Chromium reads (67× raw coverage) were used by the ARCS v1. 0. 4 (https://github. com/ bcgsc/ arcs, v1. 0. 4) together with LINKs pipeline v1. 8. 5 (https:// github. com/bcgsc/ LINKS, v1. 8. 5) to scaffold and make the Masurca assembly more contiguous. A total of 5417 contigs were obtained for an estimated size of 1028 Mb with an N50 length of 0. 94 Mb. HaploMerger2 software (https://github. com/maple fores t/Haplo Merge r2/relea ses/; Huang et al. , 2017) was used to remove potential duplicated sequences and significantly improved the assembly to 2846 contigs (N50 of 1. 62 Mb). A chromosome-level assembly was then generated using Hi-C data and InstaGRAAL software. The final assembly of the Roscoff_O.
assuming a 1 1 Gb genome size estimated by flow cytometry using SYBGREEN labeling (data not shown). Then, the 10× Chromium reads (67× raw coverage) were used by the ARCS v1. 0. 4 (https://github. com/ bcgsc/ arcs, v1. 0. 4) together with LINKs pipeline v1. 8. 5 (https:// github. com/bcgsc/ LINKS, v1. 8. 5) to scaffold and make the Masurca assembly more contiguous. A total of 5417 contigs were obtained for an estimated size of 1028 Mb with an N50 length of 0. 94 Mb. HaploMerger2 software (https://github. com/maple fores t/Haplo Merge r2/relea ses/; Huang et al. , 2017) was used to remove potential duplicated sequences and significantly improved the assembly to 2846 contigs (N50 of 1. 62 Mb). A chromosome-level assembly was then generated using Hi-C data and InstaGRAAL software. The final assembly of the Roscoff_O.
It is also possible to draw incorrect conclusions from ORA. For instance, the online Metaboanalyst web server provides a suite of tools for metabolomic analysis, including, but not limited to, ORA. 6 These have become justly popular, as they are free to use, available online, integrated with data processing and biostatistical modules, and updated to ensure they remain current. They also provide some opportunities to set parameters that affect the results, opening up the possibility of inadvertently misusing the tools. (NB that this is not an implicit criticism of the team behind Metaboanalyst: individual researchers should take responsibility for their own results, including interpretation. ) We recently published a study of the sensitivity of ORA of metabolomics data to some of the different parameters that can be chosen.
7 A wide range of different factors affect the results (Figure  1 ). First, and Viewpoint pubs. acs. org/est obviously, the choice of database and pathway definitions has a major impact. Second, the precise P value cutoff used for selecting metabolite hits had, unsurprisingly, major effects on the number of significantly enriched pathways. Third, using a background or reference metabolome (i. e. , the total list of annotated metabolites detected in a particular experiment) is critically important: if the background is not taken into consideration, the results tend to be very overoptimistic�the P values obtained are much more significant than they should be. We decided to survey the literature to get an idea of the current practice in environmental metabolomics. We searched for environmental metabolomics papers (Clarivate Web of Science core database, July 7, 2022; searched all fields for "metabolom* or metabonom*", and constrained by Topic = Environmental Sciences, by Document Type = Article, and by Publication Year = 2020-2022) and identified 988 recent papers.
We randomly selected 30 papers from this list (after manually excluding three more reviews that had been incorrectly labeled; the list of papers is given in Table  S1 ) and checked to see what form of PA, if any, was used. Twothirds of the studies (20 of 30) employed PA (two additional studies mapped metabolites to pathways, but without an associated statistical test); all of these (20 of 20) used ORA, although this generally was not specified by name. Two of them used simultaneous enrichment of transcriptomic and metabolomic data, although full details were not given. Fourteen of the studies used Metaboanalyst; one used the R package Mummichog, and seven failed to specify which software was used. With the exception of the study that had used Mummichog, they generally failed in reporting key parameters that affect the outcome.
B Some studies set ad hoc thresholds based on the "pathway impact" statistic provided by Metaboanalyst. It is clear that ORA is being unintentionally misused in environmental metabolomics research, in a fashion that is likely to lead to misleading results. We conclude by making some brief recommendations for using ORA with environmental metabolomics data (see ref 7 for more detail and fuller discussion). (1) Accurately report the analyses carried out. The specific software package/online tool used should be reported, along with all of the parameters, even if they were left as defaults, including the database version and organism used for pathways. Specify what P value cutoff/other parameter was used for selection of metabolite hits for PA, including any correction for multiple testing, and also whether correction for multiple testing was carried out on the output (i.
Data We use two different geomagnetic indices (SYM-H and AL) Iyemori90,Davis66. While the former quantifies the disruptions of the low latitude ring current during a geomagnetic storm, the latter monitors the high latitude westward electroject associated with the impulsive activity of the magnetotail during magnetospheric substorms. Specifically, the use of the high latitude AL index (instead of the more widely studied AE index) is motivated by our intention to investigate the link between geomagnetic storms and magnetospheric substorms, with a special emphasis on the internal magnetotail dynamics. Indeed, as widely reported in the literature, AL is mainly related to the impulsive events taking place in the central plasma sheet (CPS) being representative of the westward electrojet current, which is connected with the closure of the FAC and DP1 ionospheric and substorm current systems  [26].