dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene Ontology pathway enrichment and clustering The GO terms aiming to investigate the potential functions and molecular mechanisms were evaluated for 3 datasets; the entire 124 proteins (Table  S3 ), the 64 proteins (cut-off ≥2, Table  S4 ) and the 33 robust list of protein biomarkers (cut-off ≥4, Table  S5 ) using the webbased ProteINSIDE tool (http://www. proteinside. org/). Using this tool, a list of the Top40 GO enrichment terms (P-value, Benjamini-Hochberg < 0. 05) were generated against human orthologs Fig. 2. Categorization of the 124 identified proteins. A) Ranking of the 124 putative protein biomarkers of beef tenderness, regardless of the animal category, by the number of identifications across the 28 proteomicbased studies. The proteins identified ≥4 times across all of the studies (n = 33) are in a white box and in bold font, those identified 3 (n = 12) or 2 times (n = 19) are in a grey box and those identified one time only (n = 60) are in a yellow box.
(caption on next page) to take advantage of the most complete annotation available. The ProteINSIDE tool relies mainly on GO enrichment tests among the Biological Process (BP), Molecular Function (MF) and Cellular Component (CC) categories. As an additional process, Metascape® was utilized to distribute the 64 proteins among the animal categories, based on Circos plots which display overlap and functional connections between the genes, hence allowing the creation and linking of different modules based on the enriched ontology term (Fig. 5A ). The known and potential targets based on the Top20 enriched terms generated by the pathway and process enrichment analyses from Fig. 4  and across animal categories were compared and displayed by means of hierarchical heatmap clustering (Fig. 5B ). This GO analysis suggests that "muscle contraction", "ATP metabolic process", "muscle structure development", "leucocyte activation involved in immune response" and "oxidative stress" are associated with beef tenderness determination, regardless of the animal category (Fig.
ProteQTL interrogates a public library of published QTL in Animal QTL Database (https://www. animalgenome. org/QTLdb) that contains cattle QTL and association data curated from published scientific articles. As a result, 18 QTLs were identified for tenderness score (n = 4: PYGM, VDAC2, TNNT3 and GSTP1) and shear force (n = 15: PYGM, ALDH1A1, CA3, EEF1A2, TNNC1, ATP5F1B, HSPA9, PGM1, LDHA, HSPA8, MYL3, MYL6B, CSRP3, MYL2 and UQCRC1) at different chromosomes (Chr) (Table  3 ). PYGM on Chr. 29 was common to the two beef tenderness traits (Table  3 ). Overall, eleven chromosomes grouped the 18 proteins (genes) with 10 in three chromosomes that are: Chr. 29 contains 5 genes (GSTP1, TNNT3, PYGM, LDHA and CSRP3), Chr. 22 contains 3 genes (TNNC1, MYL3 and UQCRC1) and Chr. 5 contains ATP5F1B and MYL6B.
Gender: Overlap and distribution of the proteins with cut-off ≥2 The comparison of the different protein lists from the 64 proteins with cut-off ≥2 among animal categories: bulls (n = 57), steers (n = 43), cows (n = 33), heifers (n = 23) and bulls + steers (n = 19), was performed using Venn diagrams (http://bioinformatics. psb. ugent. be/webtools/Venn/); while the intersection was visualized by an Upset plot (Fig. 6 ) generated by Intervene (https://intervene. shinyapps. io/ intervene/). Upset plots are a more intuitive and superior to Venn diagrams for visualizing intersecting sets. Proteins were ordered and shown with their corresponding colors following their corresponding biological pathway. From this Upset, we can see that 3 proteins, ACTA1, MYH7 and CKM, were common across all datasets and animal categories (Fig.
7 ) was computed using "Compute pI/ Mw tool" at the ExPASy server (https://web. expasy. org/compute_pi/) for the entire proteins based on bovine UniprotIDs. Although the number of proteins is small, the distribution visually illustrates the overlap of the main biological pathways identified, especially for "energy metabolism" and "muscle contraction, structure" pathways. In addition, the plot illustrates the bimodality pattern of pI distribution (marked by two horizontal brackets in Fig. 7 ), a common feature described in different proteomes  (Wu et al. , 2006) , with low fractions of proteins with pIs close to 7. 4. This multimodal character of pI distribution is likely associated with the need for different pI values depending on subcellular localization  (Schwartz et al. , 2001). This tendency was also illustrated despite our small dataset where the proteins from the "energy metabolism" pathway have a pI in the range of 6.
Discussion The current integromics meta-analysis aggregated data from proteomics-based experiments with the aim of identifying robust biomarkers of beef tenderness. To the best of our knowledge, this is the first meta-analysis that constitutes a list of 124 proteins from 28 proteomics datasets as DAP or correlated with tenderness traits of the Fig. 3. Interconnectedness of beef tenderness protein biomarkers. Protein-protein interaction (PPI) network linking the shortlisted 64 protein biomarkers (cut-off ≥2 times) from the 28 proteomic-based studies (see Table  1 ). The interaction map was generated from the web-based search STRING database (https://string-db. org/). Default settings of confidence of 0. 6 and 4 criteria for linkage: Co-occurrence, experimental evidence, existing databases and text mining were used.
The different analyses performed above evidenced that key pathways and proteins were identified very often based on the inclusion criteria of cut-off ≥4 from at least four different datasets. Such screening has allowed proposing a robust panel of the top 33 proteins (insert Fig. 2A ) that belong to 5 interconnected biological pathways. Of this panel, 12 proteins were from muscle contraction and structure, 9 were from energy metabolism, 8 were HSPs, 3 were oxidative stress proteins and one protein belongs to the fifth pathway grouping proteins involved in the regulation of cellular processes, binding, apoptosis and transport. Worthy to note, several of these proteins are multifunctional and some are importantly involved in apoptosis. Generally speaking, the difference in abundance of any given protein between tough and tender samples of longissimus muscle can result from one of two causes, and the interaction between them.
Actin (ACTA1): the most frequently identified biomarker Actin is the second most abundant myofibrillar protein of muscle. In our database, actin was found to be predominantly a positive biomarker of beef tenderness (Table  1 ). The role of cytoskeleton in apoptosis is well known and actin has been considered an important hallmark  (Atencia, Asumendi, & García-Sanz, 2000). Actin has been described as the first protein targeted by executor caspases  (Kemp et al. , 2010; Ouali et al. , 2013) , leading to a progressive upward trend of the appearance of the major 30-32 kDa fragment, along with other lower molecular weight fragments, in post-mortem tissue  (Lana & Zolla, 2016; Laville et al. , 2009; Morzel et al. , 2008). The 30 kDa fragment has long been described as a marker for meat tenderization and is generally considered to be a breakdown product of troponin-T, which is associated with the actin filament and is discussed in section 3.
Of the 5 HSPs members, we retrieved two subfamilies only: three small HSPs (HSPB1, HSPB6 and CRYAB) and the large HSP70s sub-family with 4 proteins (HSPA1A, HSPA1B, HSPA8 and HSPA9). YWHAE, identified in 4 studies, is a mediator of signal transduction. It is noticeable to see that no member of the HSP60, HSP90, and HSP100 was identified to be related with beef tenderness, which contrasts to beef color  (Gagaoua, Hughes, et al. , 2020). From the dataset of the 33 robust protein biomarkers, the GO term "protein folding chaperone" for the molecular function GO:0044183 ranked third, highlighting the importance of this pathway in meat determination (Table  S5 ). In the following, we briefly describe the roles that this superfamily of proteins could play in post-mortem muscle, although several aspects yet to be understood.
Further, marked changes occur in the mitochondrial proteome of the postmortem Longissimus muscle compared to other muscles  (Zhai et al. , 2020) , and large HSPs may help maintaining the mitochondrial membrane potential and ATP levels, thus participating in energy production machinery  (Daugaard et al. (2007). Interestingly, the three HSPA1A, HSPA1B and HSPA8 are all grouped in the GO term "ATP metabolic process", GO: 0046034 for the 64 (Table  S4 ) and 33 proteins datasets (Table  S5 ). HSP70 proteins are further involved in apoptotic pathways by sequestering pro-apoptotic factors such as Bcl-2  (Jiang et al. , 2011; Picard & Gagaoua, 2017) , hence impacting the cascade of meat tenderizing process  (Ouali et al. , 2013; Picard et al. , 2017). The other large HSP member, HSPA9, is different from the others as it is not heat-inducible.
Biomarkers from the oxidative stress pathway The origin of oxidative damage in muscle are the mitochondria, which produce ROS through oxidative phosphorylation, involving different components of the muscle cells and leading to lipid oxidation, DNA and cell damage, protein oxidation and turnover  (Domínguez et al. , 2019; Ouali et al. , 2013; Sierra & Olivan, 2013). A plethora of cellular antioxidant defense mechanisms, comprising both enzymatic and non-enzymatic scavenger agents, are involved to maintain ROS at harmless levels, hence protecting muscle proteins  (Picard & Gagaoua, 2017; Sierra & Olivan, 2013)  or interacting with them  (Malheiros et al. , 2019) , thereby playing a role in meat tenderization. This meta-analysis identified 11 proteins from this pathway (Table  1 ), from which five were reported in at least 2 datasets (PARK7, PRDX6, SOD1, MSRA and GSTP1).
This is easier said than done, owing to the number of operational and technical challenges that arise when investigating proteoforms, matrisome and degradome. Finally, this integromics study revealed the power of data integration, and has led to the identification of a robust panel of 33 proteins: 12 proteins from muscle structure and contraction pathway (ACTA1, MYH1, MYL1, TNNT3, MYH7, MYBPH, FHL1, MYLPF, TNNT1, CAPZB, TNNC1 and TNNI2); 9 proteins from energy metabolism pathway (CKM, ENO3, ENO1, GAPDH, PGM1, TPI1, MDH1, ALDOA and PKM); 8 proteins from response to stress pathway (HSPB1, HSPB6, HSPA1A, CRYAB, HSPA8, HSPA1B, HSPA9 and YWHAE); 3 oxidative stress proteins (PARK7, PRDX6 and SOD1) and one protein (CA3) from other pathways and involved in cell detoxification. This panel of 33 biomarkers among others warrant further exploration and validation as universal biomarkers of beef tenderness.
Acknowledgements Dr. Mohammed Gagaoua  is a  Marie Skłodowska-Curie Career-FIT Fellow  under the number  MF20180029. He is grateful to the funding received from the  Marie Skłodowska-Curie  grant agreement No. 713654  and the support of  Meat Technology Ireland (MTI ) a co-funded  Industry/Enterprise Ireland  project ( TC 2016 002 ). He further conveys special thanks to  Mr. Peter Mooney  from  Innovation@Dawn at Dawn Meats Group  for his great support and collaboration, and acknowledges  Daniel Ekhlas ,  Teagasc Food Research Centre Ashtown , for his help in drawing the schematic views of the sarcomere and energy metabolic pathways.
Joseph Otting, of Nevada, to be Comptroller of the Currency; and The Honorable Randal Quarles, of Colorado, to be a Member of the Board of Governors of the Federal Reserve System. "Mr. Otting brings a particular expertise and understanding of our banking system from a long career in financial services. I am confident that Mr. Otting will bring strong leadership to the OCC" and "Mr. Quarles has a wealth of government and private sector experience dealing with both domestic and international financial markets. He is no stranger to public service, having previously served in multiple top posts in the Treasury Department". To fulfill the objective of the study, we hand-collect data for US Commercial Banks and biographical information of Congressional Committee Chairmen from the period 2000-2015.
Sample, data and Variable selection The working sample of the present analysis consists of a fully hand-collected dataset including information for U. S. Commercial banks for the years 2000q1 -2015q4. Financial characteristics of banks are obtained by the 031/341 Call-report files available from the Federal Financial Institutions Council (FFIEC) and Chicago Fed website. The sections below provide an overview of the construction procedure of the final working sample, the variables included in the analysis, and their descriptive statistics. Table  1  provides a list of the variables. [Insert Table  1  Around Here]   et al. (2016)  show that such actions do not have an impact on a bank's financial condition.
Enforcement action variables Finally, our preliminary results show that these types of actions are not associated with political influence. In order to match the enforcement action data to the Call Report Data, we follow two steps. First, in cases where the unique identifier number of the punished institution (cert or rssd9001) is available, this is directly matched with the one provided by the Call Report data. Second, if this information is not provided, we match the name, city and state of each institution. Since Call Report data are provided on a quarterly basis, enforcement actions are also matched quarterly. For example, if Bank XX received an enforcement action during January 1st-March 31st, then this information is assigned to the 1st quarter. In the case where a bank received multiple types of enforcement actions during a given period; the most severe type is retained.
We collect voting information from the Federal Elections Commission data on Elections and Voting 26  and manually match this information to each elected official of our sample. Second, we collect information for the total dollar amount of contributions received by each elected official in our sample. Our decision to employ this measure is driven by the fact that campaign contributions are considered to be an indirect measure of a politician's power  (Berry and Fowler, 2017). To tailor the investigation around our research question, we focus on contributions particularly from the Financial Services industry and use the ratio of the sum of contributions received from the financial sector as a fraction of the total contributions received by a candidate. We choose this measure to specifically observe the level of "aggressiveness" of financial institutions in approaching politicians 27.
Political influence, committee membership and sub-committee chairmanship The analysis presented so far has focused on the role of the Chairmen. In this sub-section we extend our analysis to examine the role of other members of the Committees. We obtain information on members of the Senate's Committee on Banking, Housing and Urban Affairs and the House of Representative's Financial Services Committee from 103rd to the 114th Congress from the Charles Stewart III and Jonathan Woon data set 29. Due to the fact that it is very likely for more than one committee member to be elected from a particular state, we aggregate the information from the Charles Stewart III and Jonathan Woon data set and 28 We also explore the potential effect that political party affiliation has on enforcement action likelihood.
We implement two approaches. First, we estimate the baseline model using a dummy variable representing the chairman's political party (i. e. democrat or republican). Second, we explore political party alignment among the chairmen and president. The findings show no significant relationship and thus are not reported for brevity. They are, however, available upon request. 29 Please find further information on the data and related studies at the Charles Stewart Congressional data page http://web. mit. edu/17. 251/www/datapage. html. construct a dummy variable (Committee member ), which takes the value of 1 if a bank is headquartered in a state where there is at least one elected official that serves as member in one of the two Committees of interest, and the value of 0 otherwise. Additionally, we explore the potential influence of officials serving as Chairs in subcommittees.
The results obtained do not provide any evidence on this matter. However, it should be noted that out of the 360,000 observations in the sample, there are only 12,857 cases were this variable takes the value of 1, and it takes the value of 0 in all other cases. Furthermore, out of the 12,857 cases, there are only 22 cases that correspond to an enforcement action. Thus, the insignificance could possibly be due to these particular characteristics of the sample, the results should be treated with caution, and this issue should be explored further. To conserve space, we do not tabulate the results, however they are available upon request 33 We do not split the sample on above/below average sub-samples, on the basis of the number of States where a bank operates, as this would result in having a considerable low number of enforcement actions in SNL Financial database.
g. Layman, 1997)  , as well as members of the US Congress (e. g. Fastnow et al. , 1999). In the context of our study, religiosity could potentially influence the behavior of the politician in order to "serve" the beliefs of his or her potential voters. However, what is important to highlight is that this would not necessarily reflect the beliefs or ideas of the politician. In order to test the above, we collect information from the "Churches and church membership files of American religion data archive (ARDA)" 39. In particular we retrieve data on religion that are made available from surveys conducted in 2000 and 2010. We follow  Adhikari and Agrawal (2016)  and obtain estimates for intermediate years by linearly interpolating the decennial data. We make use of state-level version of data for consistency with the set-up of our working sample.
C. elegans strains and maintenance Strains were maintained under typical conditions as described  (Brenner, 1974). CRISPR-CAS9-genome edited mTagBFP2, mNeonGreen and mScarlet-tagged proteins were generated at the Biologie de Caenorhabditis elegans facility (Universite Lyon 1, UMS3421, Lyon, France). The strains used in this study are listed in Table  S1. The sequence of alleles are available from the Dryad Digital Repository  (Gregoire, 2021) : dryad. qrfj6q5hh.
In vivo confocal imaging in C. elegans For in vivo imaging, C. elegans larvae were mounted on a 10% agarose pad in a solution of 100 nm polystyrene microbeads (Polysciences) to stop worm movement. Embryos were mounted on a 2% agarose pad with a mix of bacteria and M9 medium (localization) or M9 only (live imaging). Single confocal slices of the anterior intestinal cells or stacks of the whole intestine were performed on adults/larvae and whole embryos, respectively, using a Leica SP8 (Wetzlar) equipped with a 63×, 1. 4 NA objective (LAS AF software) or a super-resolution Zeiss LSM880-Airyscan (Oberkochen) equipped with a 63×, 1. 4 NA objective (Zen Black software). For the image in Fig. 3D , young adult animals were quashed to let the intestine out and have a large imaging surface. Quantitative recording of the apical localization of brush border markers was performed on the Leica SP8 microscope using the photon-counting function of HyD hybrid detectors and image accumulation (Fig.
FRAP FRAP experiments were performed using the Zeiss LSM880-Airyscan on a rectangular region of interest (ROI) of 1. 3 µm width crossing the apical plasma membrane with 100% 488 nm laser power, 10-20 iterations, and recovery was measured every 30 s for 10-15 min. Post-FRAP images were analyzed using the Fiji software. The mean fluorescence intensity of the bleached ROI was normalized for photobleaching by recording the intensity of the same ROI on a non-bleached region and cytoplasmic background was subtracted on each frame. Finally, the percentage recovery was calculated on each timeframe by comparing the normalized signal intensities with the mean of two time points before bleach. Curve fitting was performed with one-phase association non-linear regression analysis using GraphPad Prism 9 software.
Quantification TEM micrographs were analyzed using Fiji software and were representative of all the sections observed. Three to five independent worms at each developmental stage were examined in five to ten different sections along the antero-posterior axis. Lumen perimeter was measured around the apical membrane. In total, 32 (2-fold), 36 (3-fold), 31 (4-fold), 26 (L1), 22 (L2/3) and 20 (L4/young adults) lumen perimeters were measured. Microvilli length was measured from the tip to the point where their base intersected with the apical pole. Microvilli width was measured at mid-height. In total, 61 (2-fold), 83 (3-fold), 89 (4-fold), 60 (L1), 62 (L2/3) and 92 (L4/young adults) length and width measurements were performed. Microvilli density was defined as the number of microvilli per unit length of lumen perimeter.
Acknowledgements We thank  Marc Tramier  and  Steṕhanie Dutertre  for their advice on fluorescence quantification and super-resolution imaging, respectively, as well as  Matis Soleilhac  for the initial analysis of expression patterns. We also thank  Verena Göbel ,  Michel Labouesse ,  Junho Lee  and  Ronen Zaidel-Bar  for strains,  MaïtéCarre-Pierrat  ( Biology of Caenorhabditis elegans Facility, Universite Lyon 1, UMS3421 ) for generating CRISPR strains, as well as  Ceĺine Burckléand Guillaume Halet  for helpful discussions. Some strains were provided by the  CGC , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ;  University of Minnesota, USA ). Imaging was performed at the  Microscopy Rennes imaging Center (MRiC, Biosit, Rennes, France ), a member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ).
We captured variation across the phenotypic spectrum, extending beyond disorder. Our study focused on traits that have been assessed using continuous measures in multiple cohorts from the international GenLang network (www. genlang. org), together with several public datasets that have data available for the relevant phenotypes, matched to genome-wide genotype information. Five quantitative traits were identified for which phenotype data could be aligned across different cohorts, to yield sufficiently large sample sizes for GWAS: word reading, nonword reading, spelling, phoneme awareness and nonword repetition. Univariate GWAS metaanalyses were performed to identify genetic variation influencing these traits and to model the genetic overlaps between them. For comparative purposes, a GWAS meta-analysis for performance IQ was also performed in the same dataset.
A total of 22 cohorts, aggregated by the GenLang consortium combined with several publicly available datasets, provided data for one or several of these traits (Supplemental Table  1  and 2, Supplemental Figure  1  and 2 ). The cohorts connected in the GenLang network have either been originally ascertained through a proband with language/reading disorder (DLD/SLI or dyslexia) or were sampled from the general population; all cohorts include quantitative phenotypic data gathered via validated psychometric tests. Some of the samples are birth cohorts, and some involve family or twin designs. The phenotype data were collected across an array of different ages, test instruments and languages (primarily English, and also Dutch, Spanish, German, French, Finnish, and Hungarian). We reduced heterogeneity of assessment age by excluding individuals over 18 years of age (except for three cohorts, see Supplemental Methods), and, where phenotype data were available from the same participant at multiple ages, by choosing the age that matched best with the assessment ages of the largest cohort(s).