dataesr/scientific-paragraphs-categorization huggingface dataset:
Reconstruction of the Secretome Linked to Tenderness and Identification of Secreted Proteins in Plasma We propose for the first time a repertoire of secreted proteins related to tenderness. As predicted by bioinformatics, these proteins could be secreted through different pathways. From the aggregated dataset, 11. 5 % of the proteins were predicted as secreted proteins through conventional-and 7. 7% through alternative pathways. This is consistent with the report that 10-15 % of the human proteome is likely to be secreted through conventional and UPS secretory pathways  [69, 70]. However, although the bioinformatics reconstruction of the secretome with ProteINSIDE could identify secreted protein thanks to prediction algorithms, it did not enable to distinguish between proteins secreted into the surrounded extracellular fluid and proteins secreted into the bloodstream  [67]  Noteworthy, by overlapping the repertoire of predicted secreted proteins with a curated non-exhaustive bovine plasma atlas, we depicted 24% of them as putative plasma proteins.
This result fits with the report by  [71]  that 31% of the secreted proteins of the human proteome are found in the plasma. However, the lower proportion of the secreted proteins in plasma in our dataset may be explained by the fact that our plasma atlas was very less that the 10,000 human proteins detected in serum/plasma curated from >500 published studies  [70]. This suggests that by using a more complete plasma bovine atlas, we would increase by many the repertoire of secreted proteins in plasma. The semantic analysis of the enriched GO Biological Process associated with the repertoire of predicted secreted proteins in plasma (Table  4 ) revealed 6 associated biological pathways, linked to "cell adhesion", "apoptotic process", "endocytosis", "response to oxidative stress", "hydrogen peroxide metabolism", and "lipid metabolism".
Supporting this hypothesis, we found that 36 % of the proteins were found in an atlas of vesicular proteins and 11 % in the exosome atlas. Therefore, we propose for the first time that EVs and exosome may be a possible reservoir of biomarkers for tenderness. We have identified 13 EVs proteins and two exosomal proteins in the dataset of hypothetically secreted proteins in plasma. Unexpectedly, we also found three vesicular proteins and two exosomal proteins (including the GAPDH protein in common) in the dataset of conventionally and unconventionally secreted proteins in plasma. Similarly,  [70]  also reported that proteins containing signal peptides that are secreted by the ER-Golgi pathway are also detected in extracellular vesicles. They suggested an unknown mechanism of sorting secreted proteins into these vesicles.
Parameters of Inclusion All of the articles related to cattle meat tenderness were reviewed and curated based on the relevance and significance of the results. Only, molecular data related to the meat tenderness of Bos taurus and Bos indicus were conserved. Protein data could come from individual data. Only data with significant correlation of genetic polymorphism with tenderness, or differential abundances of transcripts or proteins according to tenderness as declared by the authors, were kept to build a meat tenderness aggregated dataset. A study associated with one GEO Dataset reporting transcriptomic data was analyzed with GEO2R (https://www. ncbi. nlm. nih. gov/geo/geo2r/) that enabled to compare two groups of samples according to tenderness. The differentially abundant transcripts between tenderness groups were included in our study.
Protein Identifiers Standardization Protein ID and gene symbols were converted into the corresponding Gene Name identifiers (GN), as unique identifiers by use of three tools: Retrieve/ID Mapping tool of the Uniprot database (The UniProt 24), the Protein Identifier Cross-Reference service 25 and/or the ProteCONVERT tool of the ProteINSIDE web interface 26. Last conversion from ID to GN in February 2018.
Gene Ontology In order to identify biological pathways associated with the aggregated dataset, Gene Ontology (GO) analysis was performed with the ProteINSIDE webservice (http://www. proteinside. org) [109]  The GO enrichment analyses were achieved in the Human species in order to extend and promote GO interpretations because the bovine annotations remain limited. Only the Biological Process (BP) were considered. The Benjamini Hochberg (BH) adjusted P-values were considered to establish lists of significant enriched pathways in each dataset as compared to the whole genome. The GO_BP overview was carried out only with annotations with p-values < 0. 001, minimum of annotated proteins ≥ 2. A table of the GO_BP overview was constructed in a semantic SimRel similarity-based Scatterplots with p-values associated to GO terms using REVIGO web tool (http://revigo.
Prediction of the Secreted Proteins In order to identify putatively secreted proteins belonging to the aggregated dataset, we used ProteINSIDE, a free web tool (http://www. proteinside. org) [109]  that enables retrieving biological information from public databases in a single query. The secretion prediction module of ProteINSIDE runs a local version of SignalP 4. 1. From the sequences of input ID proteins, it looks for signal peptide type sequences. The program also checks if proteins are related to a secretory function by looking for GO secretion annotation terms. The aggregated dataset was submitted to a computational prediction of proteins secreted using "custom analysis", "bovine species", "signal P" and "increase cleavage site sensitivity (D-cutoff 0. 34)" parameters (version of Database 1.
2. 11, CBS signal-P 4. 1 software, May 2018). To declare proteins as "predicted secreted proteins", we used the following criteria. (1) File tab "Secreted Protein", Signal-P score > 0. 5 and Target-P score ≤ 2 to identify the proteins predicted as secreted through a signal-P sequence and/or a transmembrane domain (TM) (named "conventional predicted secreted proteins"). (2) File tab "other secreted protein", Target-P score ≤ 3 with GO term associated to identify the proteins predicted as secreted through an unconventional pathway of secretion (or UPS) without signal-P (named "UPS predicted secreted proteins")  [72, 111]. The conventional-and UPS-predicted secreted proteins were merged in a single repertoire referred to as predicted secreted proteins. All of the proteins not identified as predicted secreted proteins were "the remaining proteins" (aggregated data minus secreted proteins).
Prediction of Plasma Location In order to search for the proteins that may be found in the plasma, we compared protein lists using VIB / UGent (http://bioinformatics. psb. ugent. be/webtools/Venn/). The comparisons were performed between the repertoire of predicted secreted proteins and a "Bovine Plasma proteome Atlas" (BPA, n = 1101 plasma proteins, which were merged from publications  [67]  and experimental data (Supplementary Data 3). Similarly, a comparison between the remaining proteins and the BPA was performed to detect hypothetically secreted proteins and found in plasma.
Network Analysis and Protein-Protein Interactions In order to enrich the list of putative plasma proteins, we used the academic Cytoscape open source software ® (Version 3. 7. 2, https://cytoscape. org/) [112]  with the Psicquic plugging web service (https://apps. cytoscape. org/apps/psicquicuniversalclient, up to date, 2017-12-17)  [113]. The parameters for network analysis were "MINT database", "human species". The proteins that interact with proteins within our dataset were named "interactors". For representation, the 13 predicted secreted proteins in plasma (conventional and alternative pathways) are shown in purple ellipses. The 44 hypothetically secreted proteins in plasma are shown in pink ellipses. The green rectangle refer to interactor identify using the MINT Cytoscape analysis.
Dataset Descriptors Four datasets were generated in this study. The aggregated dataset merged from three individual lists related to beef tenderness, namely a "proteomic dataset", a "transcriptomic dataset", and a "genetic dataset" The aggregated dataset (named "full compiled atlas") was deposited as ". xls" files at the French INRA public repository (Portail Data INRA, data. inra. fr) hosted by Dataverse. org and is directly available at  [63]. In addition, the two repertoires generated by reconstruction of the secretome were named the "predicted secreted proteins in plasma" and the "proteins hypothetically secreted in plasma". Finally, the merged of the "predicted secreted proteins in plasma", the "proteins hypothetically secreted in plasma" and the "interactors" putatively found in the plasma" generated the final list of candidate plasma proteins proposed by this study as putative low-invasive candidates for meat tenderness in beef cattle.
We report all of the Biological Process associated with the Gene Ontology annotations identified with a significant p-values (pvalue < 0. 001) and associated with minimum of two proteins. This GO Tablewas obtainedusing REVIGO (semantic SimRel measure) including GO terms and p-value parameters. ID Gene Name: Proteins identified as related with tenderness within each Gene Ontology group. Enrichment in Dataset (%): Percentage of enrichment within the dataset. Enrichment in genome Database (%): Percentage of enrichment without the genome Database used by the ProteINSIDE algorithm analysis. (">" GO term): GO term included in up-GO term by removing redundant GO terms.
differentiation We report all of the Biological Process associated with the Gene Ontology annotations identified with a significant p-values (pvalue < 0. 001) and associated with minimum of two proteins. This GO Tablewas obtainedusing REVIGO (semantic SimRel measure) including GO terms and p-value parameters. ID Gene Name: Proteins identified as related with tenderness within each Gene Ontology group. Enrichment in Dataset (%): Percentage of enrichment within the dataset. Enrichment in genome Database (%): Percentage of enrichment without the genome Database used by the ProteINSIDE algorithm analysis. (">" GO term): GO term included in up-GO term by removing redundant GO terms.
GO Term Description ID Gene Name Enrichment in Dataset (%) Enrichment in Genome Database (%) p-value Adjusted Inflammatory Response GDI2 ASAH1 PNP HSP90AA1 PGM1 PSMC2 PKM MPO PLAC8 HSPA1A GO:0043312 neutrophil degranulation PRDX6 PGAM1 CCT8 ATP8B4 CLEC12A ALDOA HSPA1B 6. 14 5. 17 1. 98 × 10 -22 SERPINA3 HSPA6 GSTP1 HBB HSPA8 DNAJC3 ATP11ADGAT1 ADA CASP3 SOD1 NPPC PPARG LOX ENO3 VAV3 ABCG5 GO:0042493 response to drug LGALS1 CENPF AQP1 4. 91 5. 39 2. 53 × 10 -18 ACTC1 PNP CTNNB1 KCNK3 SST FABP3 LDHALCK SLC6A9 ABCA12 VDAC2 CACNA1C SLC25A12 ABCG5 ANKH ITPR1 VDAC1 GO:0055085 transmembrane transport PSMB2 SLC6A20 HCN1 KCND2 5. 16 2. 9 3. 38 × 10 -14 SLCO3A1 SLC39A11 TRPM3 PSMC2 SLC9A9 AQP1 SLC9A7SLC25A48 GO:0098869 cellular oxidant detoxification PARK7 APOE ALB GSTP1HBB TXN PRDX3 PRDX6 1. 97 50 3.
Computational Processing The processing of sequencing reads was as described in (S. S. Shah, Hartono, S. , Chédin, F. and Heyer W-D, 2020). The Gargamel pipeline (available at https://github. com/srhartono/footLoop) allows user to map reads, assign strands, call single molecule D-loop footprints as peaks of C to T conversion, perform clustering on peaks, and visualize the data. The distribution of D-loop lengths and position were analyzed as in (S. S. Shah, Hartono, S. , Chédin, F. and Heyer W-D, 2020).
Quantitation and Statistical Analysis Quantitation of gels was done using ImageJ software. Statistical tests were performed as indicated for each assay. 2D ). On the right is a SYBR gold stained gel of the D-loop reactions. The D-loop reactions were performed with either the ds98-607 or the ds98-607-78ss substrate. The concentration of Tid1-KR is as indicated. (B) Quantitation of D-loops from the gel in (A). (C) ATPase assay measuring rate of ATP hydrolysis with Tid1-KR added prior to Rad54. The order of additions followed the brown arrows on the left with 5 min incubations between each addition. All reactions were in presence of 3kb dsDNA (6 µM nt) added before Rad51. '+' indicates that 10 nM Rad54 or Tid1-KR was used, unless otherwise indicated (in nM) by a numeric value (for details, see materials and methods).
Mean ± SD (n = 2 or 3). (D) ATPase activity measured with Rad54 added prior to Tid1-KR. Again, the order of additions is as per the black arrows on the left, with 5 min incubations between each addition. All reactions were in presence of 3kb dsDNA (6 µM nt) added before Rad51. '+' indicates that 10 nM Rad54 or Tid1-KR was used, unless otherwise indicated (in nM) by a numeric value. Mean ± SD (n = 3). (E) ATPase activity of Rad54 measured with Tid1-KR titration on dsDNA, in absence of Rad51. 10 nM Rad54 was added to 3kb dsDNA (6 µM nt) reaction containing the specified relative concentration of Tid1-KR (for details, see materials and methods). Mean ± SD (n = 3). ). The haploid data is adopted from  (Piazza et al. , 2019b). (D) DLC signal obtained from the DLC assay performed in haploid and diploid yeast transformed with plasmids.
pEV, pTid1 and pTid1-KR indicates yeast transformed with an empty vector, a vector containing GST-Tid1 or GST-Tid1-KR under a galactose promoter respectively. The DLC signal was normalized to the sample containing empty vector. The DLC signal was measured 2 hrs post DSB induction with galactose. Mean ± SD (n = 2). 197-78ss  substrate and a linear donor. Note that a smaller fraction of reads with footprints was observed for the ds98-197-78ss substrate due to its lower D-loop formation efficiency  (Wright & Heyer, 2014b). (E) Table summarizing the total number of reads containing a footprint as 'peak' and the total number of reads analyzed as 'total' for each strand. '% D-loops/% Peak' indicate the percentage of reads containing a footprint. The data represents a cumulation from > 3 independent replicates.
ACKNOWLEDGEMENTS We thank members of the  Heyer laboratory  for stimulating discussions. We especially thank  Diedre Reitz  and  Shih-Hsun  for providing valuable feedback on the manuscript. We also thank members of the  Chedin laboratory , in particular  Lionel Sanz  for providing AMPure beads and  Maika Malig  for technical help. We are grateful to  Jim Haber  for sending strains. We also thank the  DNA core technologies  at  UC Davis  and the  UC Berkeley Genomic Facility  for providing PacBio sequencing services. This research used core services supported by  P30 CA93373  and was supported by  NIH  grants  GM58015  and  CA92276  to  W. -D. H. and  NIH  grant  GM120607  to FC.
Supplementary Table  S2  online documents the trials factorial design and content of the input packages. The trials were managed by farmers and designed by researchers who provided the inputs, and guidance on practices, as is the case in much of other recently published research. The researchers selected participants non-randomly by asking community members to nominate five farmers, thought to be good farmers who would be interested in participating in the trials. This follows a common procedure for trials, and leads to a selected sample of farmers, as in cases where the technicians directly select farmers based on prior interactions with certain farmers (another common procedure). One of our central hypotheses is that these methods of selecting farmers tend to generate data from a group that are systematically unrepresentative of the village as a whole (as confirmed by differences in skills and socio-economic characteristics in Supplementary Table  S3  online).
Source: authors calculation, using Stata version 16 (www. stata. com). a ratio above two (i. e. above the rule-of-thumb threshold). And once all the adjustments are accounted for, the input package is only found to be profitable for 47%. The relatively thick left tail of the soybean yield increment with the agronomic calculations is indeed a first indication that many farmers were not benefitting from the input package, even when the average value-cost ratio was high, and this becomes even more apparent once the different adjustments are made. Overall, the results show that the systematic ex-post adjustments commonly used in the literature are not sufficient to palliate the differences between the two approaches. That said, the results for value-cost ratios and profits come with the caveat that they do not account for labour costs, as doing so would require imposing additional assumptions that in the context of smallholder family farms can be hard to defend.
Agronomists and contact persons were trained for standardized application of the forms. Observations of the agronomists and contact person were averaged across all visits and all seasons, separately for trial and non-trial parcels, to create indices capturing weed absence, Striga absence, and quality of land preparation. After averaging the grades, they were rescaled so that 1 corresponds to the highest possible score and 0 to the lowest possible score (hence Striga and weed prevalence were inversed). While the amount of weed and Striga could also result from inherent plot conditions, most of those are factored out in the comparison of the trial parcel with the neighbouring parcel. We use these data sources to measure observed management. A farmer survey was conducted prior to the first season, and again after each season.
At baseline, detailed information regarding the farmer's skills were collected, which are aggregated in separate standardized indices for cognitive, non-cognitive and technical skills, following methods explained in detail in  55. Data on asset ownership were also collected and aggregated in a standardized wealth index using principal component analysis. The units of the skill and wealth indices correspond to standard deviation changes. We also collected data on whether the main farmer in the household (who was to be responsible for the trial) was a woman and was the household head. Data were also collected regarding plot characteristics, allowing to characterize both the parcels selected for the trials, and all other parcels cultivated with annual crops, with questions capturing subjective soil quality (on a scale from 1 to 5), inclination (scale from 1-3), area (in acres), distance to homestead (in minutes), past crops and past practices used (fertilizer, manure, erosion control)-see Supplementary Table  S3  online for the specific questions.
After each trial season, parcel-level and farmer-level data were collected to characterize input use and practices on trial and non-trial parcels. Data were collected for all cereal and legume parcels cultivated by the farmer. We use this data to obtain alternative proxies of management, by constructing variables capturing whether the farmer harrowed the parcel at least twice, weeded the parcel at least twice, and did gapping or thinning to adjust for uneven germination after planting. As management practices can differ by plot, and to reflect the best management practices farmers themselves chose for their own plots, management practices for the non-trial parcels are coded based on the best practice observed among all cereal and legume parcels for each farmer. If anything, the positive differences in Supplementary Table  S3  online hence should underestimate differences between trial and non-trial parcels.
Statistical analysis. Trials were repeated for 3 consecutive seasons, to gain power and avoid results being excessively subject to weather variations in a single season. The yield increments are obtained by taking the difference between the yields in the paired treated (with the input package) and control plots. Supplementary Table  S15  online displays the yield by subplot averaged over the 3 seasons, for all farmers together and separately for community selected and randomly selected farmers. When data are missing for a given season because the trial was not planted (typically because of refusal from the farmer to continue the experimentation in his/her field), we predict the value of the missing season before averaging the three seasons. The prediction uses the average value of the yield increments during the seasons for which observations for that plot are available and adjusts it for the seasonal variation.
In this paper, we present the a-TDEP post-process code implemented in the Abinit package. This one is able to capture the explicit thermal effects in solid state physics and to produce a large number of temperature dependent thermodynamic quantities, including the so-called anharmonic effects. Its use is straightforward and require only a single ab initio molecular dynamic (AIMD) trajectory. A Graphical User Interface (GUI) is also available, making the use even easier. We detail our home made implementation of the original "Temperature Dependent Effective Potential" method proposed by Hellman et al. [1]. In particular, we present the various algorithms and schemes used in a-TDEP which enable to obtain the effective Interatomic Force Constants (IFC). The 2 nd and 3 rd order effective IFC are produced self-consistently using a leastsquare method, fitting the AIMD forces on a model Hamiltonian function of the displacements.
As already highlighted by other authors, this method can be applied successfully to go beyond QHA and capture explicit thermal effects  [50, 51, 52, 53, 54, 55]. If a large number of equations and definitions are already published in the literature, few details are available concerning the implementation of this method. In the following, we propose to give them in the particular case of our home-made implementation in the Abinit code  [56]. In the first section, we present the TDEP method as it is exposed in the literature. Then, we detail its implementation in the ABINIT code and focus on the algorithms used in practice. In the following, we describe all the capabilities of the a-TDEP code ; i. e. all the thermodynamic quantities that can be computed. Finally, we show the results obtained for three prototypical materials : Si, Zr and U.
a-TDEP inside the Abinit package The algorithms and all the thermodynamic quantities detailed in the previous sections have been implemented in the Abinit package. If the a-TDEP code is present in Abinit since the Version 8. 6 (released on November 3, 2017), the current described version is implemented in the Version 8. 12. As deeply integrated with Abinit, it follows all its coding rules. It uses Fortran2003 language and makes use of data structures close to a Object-Oriented Programming. Thanks to the structures and features already implemented in Abinit, many functionalities are directly taken from it for easiness, reliability and robustness. Regarding parallelism, even if Abinit has several levels of parallelism  [71]  with both the Message Passing Interface (MPI) and OpenMP (threads), a-TDEP does not use parallelism yet.
14. [root]nbcoeff-phij. dat/[root]nbcoeff-psij. dat show how the number of IFC coefficients are reduced (for each shell and each symmetry) at the second and third order, respectively. 15. For reliability and robustness, a-TDEP benefits from the full Abinit test farm. Abinit already includes more than 1200 tests that enables not only a strong guarantee on the results but also on portability of the code. a-TDEP takes advantages of all this pre-existent framework and includes its owns tests on several materials, geometry and physical quantities, including those presented in the following. This makes the code safer and more reliable. To help the user, an external open source tool is also available. This one is called qTdep and is interfaced with the present version of a-TDEP. Its use is very simple.