dataesr/scientific-paragraphs-categorization huggingface dataset:
Clearly, this is far from an ideal situation, which impedes maximal value extraction from public data, especially in the increasingly interesting usecase of large-scale data reuse. Additionally, due to the lack of detailed information at the raw le level, it becomes impossible to generate complete SDRF les for the analyzed projects, as crucial information is too often missing, resulting in invalid SDRF les. The only alternative in these cases is to attempt to contact the original submitters for additional information. Obviously, all these issues could be easily avoided by capturing the metadata in the standardized SDRF format. We therefore developed a user-friendly Streamlit application, called lesSDRF, to greatly facilitate and streamline annotation of proteomics data sets in the SDRF format prior to submission to (public) repositories.
Streamlit is a Python framework that simpli es the creation of interactive data science applications. lesSDRF requires no installation and is easily accessible through a web browser. To ensure a seamless user experience, we have integrated several ontologies or CVs, including PRIDE CV  30  , PSI-MS  31  , Cell ontology (CL)  32  , HANCESTRO  33  , EFO (Experimental Factor Ontology)  34  , and NCBITaxon  35  ontologies, ontologies as well as Unimod for protein modi cations 36. These are downloaded locally to prevent any lags in connecting to the Ontology Lookup Service (OLS)  37. The application is structured in ve intuitive steps that can guide users through the annotation process. The rst step involves species selection, and a default SDRF le is automatically opened where raw le names can be entered.
The second step entails uploading a local metadata le that can be mapped to the SDRF format columns. The metadata are checked for ontology compliance in the relevant columns, and the SDRF le is lled in with the provided data. In the third step, users can add labeling information (e. g. TMT), which generates new rows based on the labels' channels. The fourth and fth steps involve the entry of both required and additional columns using ontology terms. Users are given two options to choose correct ontology terms: a tree-wise representation, or an autocomplete search function. Once the appropriate ontology terms are selected, these are automatically added to the SDRF le. To minimize errors, users have the option of selecting multiple ontology terms from a drop-down menu. lesSDRF is available via https://compomicslessdrf-home-2rdf84.
We believe this is in the best interest of users as it reduces the likelihood of confusion by having too many options, while still resulting in a valid SDRF le. Additionally, restricting the available columns to those that adhere to ontology standards further ensures data quality. Should users require the addition of a new column that is not available within the app or that requires a free text input, they can simply download the resulting SDRF le into a spreadsheet software, such as Excel, and manually add the required column, following SDRF guidelines. Lastly, lesSDRF currently only supports one factor value per SDRF le. To avoid confusion concerning biological and technical replicates, the user will need to create multiple SDRF les if multiple factor values are required.
This integration will be twofold. Prior to submission, we aim to provide SDRF les generated by the app with a unique hash code that can be recognized in the PRIDE submission system. This will enable users to skip several steps in the submission process, as the metadata will be completely validated through the SDRF inclusion. For post-submission annotation of existing data sets, we plan to incorporate lesSDRF into the ongoing data set annotation effort on GitHub  27  , allowing users to upload partially annotated SDRF les into the app. Upon completion of the SDRF using the app, the nal and validated SDRF will automatically be pushed to PRIDE and incorporated into the corresponding project. This incorporation of lesSDRF into the PRIDE pipeline not only marks a signi cant step towards better metadata management and annotation, but also sets a potent example for other popular tools in the eld, such as MaxQuant, Proteome Discoverer and Mascot.
Indeed, we hope that these tools will follow suit, by incorporating SDRF export in their pipeline, thus facilitating the distribution of metadata and further increasing the re-usability of proteomics data. By working together as a community, we can promote accurate and comprehensive metadata annotation of proteomics experiments, leading to greater insights and scienti c advances. However, no application can x inaccurate annotation if users fail to take initiative to do so in a correct and su ciently detailed manner. Indeed, accurate metadata annotation should be considered an essential aspect of good scienti c practice, as the increasing potential of the reuse of public data is increasingly evident. Indeed, accurate metadata provision upon data submission will ensure that your experimental data will likely become one of your most productive legacies.
Material and methods Text mining effort extract metadata from public data sets in the PRIDE database, we employed manual text mining using ontology terms obtained from the OLS. A group of seven 3rd year Biotechnology students selected a random sample of projects from PRIDE in the context of a general data reprocessing effort. This resulted in the annotation of 356 proteome projects along with their accompanying research article. Of these projects, 264 had associated open-access research articles in PubMed Central that were retained for further analysis. Moreover, we removed 23 protein cross-linking experiments that did not t with the original objective of the reprocessing study, resulting in 241 projects for further metadata annotation. Metadata categories included biological metadata (species and tissue/cell line) and technical metadata (protease, labeling technique, instrument, fragmentation type, number of missed cleavages, precursor ion mass tolerance, fragment ion mass tolerance, variable modi cations, and xed modi cations).
Acknowledgments We would like to extend our gratitude to the following people who participated in the manual annotation process:  Surya Gupta ,  Akanksha Negi ,  Sourav Das ,  Pragya Srivastava ,  Diksha Gupta ,  Anushka Pandey ,  Amisha Kumari ,  Manshaa Chaudhary  and  Dr Meenu Singh. T. C. received funding from the  Research Foundation Flanders (FWO)  [ 1S57123N ]. K. G. and L. M. acknowledge funding from the  European Union 's  Horizon 2020 Programme  ( H2020-INFRAIA-2018-1 ) [ 823839 ]. L. M. acknowledges funding from the  Research Foundation Flanders (FWO)  [ G028821N ][  G010023N ] and from  Ghent University Concerted Research Action  [ BOF21/GOA/033 ]. J. A. V. and Y. P. R. acknowledge funding from  Wellcome Trust  [grant number  223745/Z/21/Z ],  BBSRC  [grant numbers.
Need assessment The first stage was to quantify the number of RUB needed to cover the activity of the OR, the number of storage locations and the opening and delivery times of the sterilisation department. The number of blades needed was extracted from the annual orders for SUB using CPAGE 1 software v. 208. 010. Because the COVID-19 pandemic has slightly affected the surgical activity of our establishment in 2020, consumption data for the years 2019, 2020, and the first quarter of 2021 were analysed to obtain the average consumption of each size of blade in each sector in the OR. Healthcare services and obstetrics rooms were excluded from the analysis due to the logistical complexity of ensuring timely return from the sterilisation department. A multidisciplinary meeting was held with anaesthetists, anaesthetic nurses, pharmacists specialised in medical devices, and sterilisation and hygiene department staff to assess blade allocations by sector and flow.
Ecologic assessment The ecological impact was evaluated using the LCA method with SimaPro 1 v9. 2. 0. 1 software. The LCA is a quantitative method based on multi-criteria environmental analysis. It considers all life phases of a product or a process/activity, i. e. , raw material extraction, product manufacturing, transport and distribution, use and end of life. The LCA method covers seven axes, i. e. , global warming (climate change impact) (kg CO 2 eq), land occupation (m 2 year crop eq), human toxicity and environmental toxicity (aquatic and terrestrial ecosystems) (kilogram equivalent of 1. 4-dichlorobenzene: kg 1. 4-DCBeq), depletion of mineral resources (nonrenewable resources) (kilogram of copper equivalent: kg Cueq), depletion of fossil resources (non-renewable resources) (kilogram of oil equivalent: kg Oileq), and depletion of water resources (m 3 of water).
Reprocessing RUB reprocessing was modelled with washing and steam sterilisation. For washers, WD290 TM (Belimed) and autoclaves S1000'08 TM (Matachana), the consumption of energy, water and detergents was quantified based on the suppliers' data sheets. The maximum number of blades per washer and autoclave load was determined as 360 blades for the washers and 720 blades for the autoclaves.
Initially, we tested a large range of concentrations and found that the induction of IL-6 was observed from 100 ng of spike protein in all tested donors (Figure  2B ). IL-6 kinetics analysis was performed on HBD PBMC tested for HERV expression using 500 ng of the spike protein. Results showed a significantly increased IL-6 production starting from 15h after exposure in PBMC culture supernatants of all donors, including HERV ''non-responding'' ones (Figure  2C ). IL-6 production was not linked with possible endotoxin contamination as the treatment with the endotoxin inhibitor polymyxin B did not diminish the spike-induced IL-6 secretion (Figure  2D ). Thus, data demonstrated that spike-induced HERV activation occurs very early after exposure, before the release of IL-6 and only in a subgroup of healthy PBMC donors, therefore independently from IL-6 production.
HERV-W ENV protein is expressed at the surface of T lymphocytes from COVID-19 patients and correlates with the detection of soluble HERV-W ENV hexameric protein in plasma We next analyzed the levels of HERV-W ENV expressed on the membrane of PBMC from hospitalized COVID-19 patients and compared them with the detection of the soluble protein in plasma samples available in parallel from a recruitment series of 21 patients (Figure  3 , patients #28 to #48). Biological and clinical data are presented in https://doi. org/10. 17632/3v4hfxv4w8. 1. (D) To exclude an effect of endotoxin possibly present in the recombinant protein buffer, the PBMCs of 3 HBDs were exposed either to 10 ng/mL of LPS or to 2 mg/mL of recombinant spike, in combination or not with 12. 5 mg/mL of polymyxin B (PMB). IL-6 was then quantified in the culture supernatants by ELISA.
Expression of SARS-CoV-2 and HERV-W ENV in tissues from autopsies of acute COVID-19 patients To better understand the extent of HERV-W ENV expression in SARS-CoV-2-infected patients, we next analyzed the expression of this protein in tissues from COVID-19 patients (Figures  5, 6 , and S4). Postmortem tissue samples from lung, heart, gastrointestinal tract, nasal mucosa, and brain were obtained from patients deceased from severe acute forms of COVID-19 (clinical data are provided in https://doi. org/ 10. 17632/hmz5sm67rb. 1). Following the absence of HERV-K ENV detection in plasma of COVID-19 patients (Figure  S3 ), despite positivity for HERV-W ENV (Figure  4C ), we performed immunohistochemistry (IHC) staining with an already validated anti-HERV-K ENV monoclonal antibody targeting a highly conserved epitope.
Antibody solutions were prepared in the previously described saturation buffer. After three washes in PBS 1X, cells were incubated during 1 h with secondary antibodies mix containing 1 mg/mL goat anti-mouse Alexa Fluor 488 (ThermoFisher Scientific, A11029), 1 mg/mL donkey anti-rabbit Alexa Fluor 647 (ThermoFisher Scientific, A31573) and DAPI ½000 (Sigma, D9542) diluted in the previously described saturation buffer. Finally, cells were washed three times in 1X PBS and mounted using the Fluoromount-G mounting medium (Southern Biotech, 0100-01). Images were acquired on NIKON Eclipse TS2R microscope and analyzed on ImageJ software.
HERV-W ENV expression was revealed using Streptavidin FITC conjugate (eBioscience, 11-4317-87). Background noise was assessed using mouse isotype control (mIgG1-FITC, Miltenyi Biotec 130-113-199) and Streptavidin FITC conjugate (eBioscience, 11-4317-87). Stained cells were acquired on a BD LSR Fortessa (BD Biosciences). Fluorochrome emissions from the pool of antibodies were compensated using Ultra-Comp eBeads plus Compensation beads (Invitrogen, 01-3333-42). Data were analyzed with FlowJo software (v. 10). supernatants using the IL-6 ELISA assay kit BD Opt. EIA human IL-6 ELISA set (BD, 555 220) and GloMax plate reader (Promega, GM3000).
HERV-W ENV and HERV-K ENV antigen detection was analyzed on the Wes device using SimpleWestern technology an automated capillary-based size sorting and immunolabeling system (ProteinSimpleTM) as previously described (Charvet et al. , 2021). The following primary antibodies were used: anti-HERV-W ENV mAb GN_mAb_ENV01 (-W01) (20 mg/mL) and anti-HERV-K ENV mAb GN_mAb_ENV-K01 (30 mg/mL) (GeNeuro). Signal quantification was made by calculating the complete Area Under the Curve (AUC) defined by the curve (peak) above the baseline of the electropherogram, within the apparent molecular weight range of HERV-ENV deglycosylated antigen in this capillary matrix (350-450 KDa), using the Wes platform Compass TM software (ProteinSimple, USA). Because this AUC also accounts for non-specific background signal within capillaries in this high molecular weight range, a cut-off of specificity for the quantification interval corresponding to the protein detection is defined from the background signal generated using initially isotype control mAb and subsequently by the same type of samples from negative healthy controls, to take into account the inter-individual variation that generates this background signal with a given technique and to determine a statistical threshold for specific positivity.
Primary antibodies were incubated overnight at 4 C and secondary antibodies were incubated during 45 min at RT. Revelation was performed using AEC kit (Vector, SK-4200) following supplier indications. Nuclei were counter-stained during 3 min with Harris hematoxylin (filtrated and 3-fold diluted) before a quick rinse in water. Slides were mounted using Fluoromount (Southern Biotech). IHC and IF slide observation and image acquisition were performed on NIKON Eclipse TS2R microscope and analyzed using ImageJ software.
ACKNOWLEDGMENTS We acknowledge World Reference Center for Emerging Viruses and Arboviruses (WRCEVA) and UTMB investigator,  Dr. Pei Yong Shi , for kindly providing recombinant NeonGreen virus based on 2019-nCoV/ USA_WA1/2020 isolate and VirPath from the  CIRI, Center International de Recherche en Infectiologie, Lyon, France  ( B. Lina ,  A. Pizzorno ,  O. Terrier , and  M. Rosa-Calatrava ), for providing us with the BetaCoV/ France/IDF0571/2020 virus. We also acknowledge  Dr. Vinca Icard  responsible for the  BioCovid collection at the ''Hospices Civils de Lyon (HCL)'' and the ''Center de ressources Biologiques (CRB) '' of the HCL such as Healthy and Covid-19 donor's samples supplier. We acknowledge the contribution of the cytometry CELPHEDI/AniRa facility depending on  SFR Biosciences  ( UAR3444/CNRS ,  US8/Inserm ,  ENS de Lyon ,  UCBL ) and more particularly Sebastien  Dussurgey  for his help for cytofluorometry analyses.
We acknowledge the contribution of the  Etablissement Francais du Sang Auvergne -Rhone-Alpes. We acknowledge  Dr. Sandrine Levet  for her kind participation to the manuscript illustration and discussion and  Drs Fabienne Venet ,  Said Mougari , and  Magali Mazelier  for the help in the initiation of the study. This work was supported by  ANR-CoronaPepStop  ( ANR-20-COVI-000 ),  Fondation de France  to  BH, ANR-COVERI  ( ANR-21-CO15-0007 ) to HP and by funding from the  European Union  '' Horizon Europe'' research and innovation program  under the grant agreement No  101057302  (HERVCOV).
org/10. 17632/rc74sdgksk. 1 Anti-CD14-BV421 Donors (HBD) https://data. mendeley. com/ BD Biosciences are available from Mendeley Data 563743 ; RRID:AB_2744289 Anti-CD19-APC-H7 datasets/rc74sdgksk/1 BD Biosciences 560252 ; RRID:AB_1645468 Anti-CD19-APC-Cy7 Clinical data: COVID-19 patient cohorts from BD Biosciences Additional supplemental items 557791 ; RRID:AB_396873 https://doi. org/10. 17632/3v4hfxv4w8. 1 Anti-CD68 datasets/3v4hfxv4w8/1 Lyon and Zaragoza https://data. mendeley. com/ Abcam are available from Mendeley Data ab955 ; RRID:AB_307338 Anti-CD31 Clinical data: Brain Necropsies -Mexico city Abcam Additional supplemental items ab28364 ; RRID:AB_726362 https://doi. org/10. 17632/hmz5sm67rb. 1 Anti-Iba1 collection https://data. mendeley. com/ Wako are available from Mendeley Data 019-19741 ; RRID:AB_839504 Anti-N-SARS-CoV-2 datasets/hmz5sm67rb/1 NOVUS NB100-56576 ; RRID:AB_838838 Anti-N-SARS-CoV-2 Experimental models: Cell lines SinoBiological 40143-T62 Anti-S-SARS-CoV-2 Monkey VERO E6 cell line SinoBiological ATCC 40590-T62 CRL1586 ; RRID:CVCL_0574 Goat anti-Mouse-HRP Human Calu-3 cell line Abcam ATCC ab6789 ; RRID:AB_955439 HTB-55 Goat anti-Mouse-Alexa488 Oligonucleotides ThermoFisher A21202 Goat anti-Rabbit-HRP Abcam ab6721 ; RRID:AB_955447 Goat anti-Rabbit-Alexa555 HERV-W ENV forward primer HERV-W ENV reverse primer ThermoFisher In house In house GTATGTCTGATGGGGGTGGAG A32732 CTAGTCCTTTGTAGGGGCTAGAG Bacterial and virus strains HERV-K ENV forward primer In house CTGAGGCAATTGCAGGAGTT SARS-CoV-2 HERV-K ENV reverse primer BetaCoV/France/IDF057½020 In house GISAID Accession ID: EPI_ISL_411218 GCTGTCTCTTCGGAGCTGTT Biological samples N-SARS-CoV-2 forward primer In house AAACATTCCCACCAACAG Healthy blood donor's PBMCs, N-SARS-CoV-2 reverse primer ''Etablissement Francais du Sang'' In house CACTGCTCATGGATTGTT sera and plasma ACE2 forward primer of Lyon (France) In house TCCATTGGTCTTCTGTCACCCG ''Lyon cohort'' COVID-19 donor's ACE2 reverse primer biobank of ''Hospices Civils In house AGACCATCCACCTCCACTTCTC RESOURCE AVAILABILITY PBMCs, sera and plasma B2M forward primer de Lyon'' (France) CellCarta TTACTCACGTCATTCAGCAG B Lead contact ''Zaragoza cohort'' COVID-19 and healthy blood donor's B2M reverse primer B Materials availability B Data and code availability PBMCs, sera and plasma GAPDH forward primer ''Biobanco del Sistema de Salud de Aragon'' (Spain) CellCarta CellCarta GATGGATGAAACCCAGACAC CACCCACTCCTCCACCTTTGAC d EXPERIMENTAL MODEL AND SUBJECT DETAILS Non-COVID-19 paraffin tissue slides biobank of ''Hospices Civils GAPDH reverse primer CellCarta AGACCATCCACCTCCACTTCTC B Study design Software and algorithms de Lyon'' (France) B Ethical approval COVID-19 paraffin tissue slides Compass B Cells B Antibodies ImageJ National Institute of Respiratory ProteinSimple/Biotechne Diseases in Mexico City (Mexico) Schneider et al.
Procedures Prior to the interview, eligible caregiver participants completed a self-report electronic survey that included demographic and clinical characteristics questions. Clinician participants completed an interview only and responded to demographic questions as part of the interview. Study data were collected and managed through REDCap  (Research Electronic Data Capture; Harris et al. , 2009 Harris et al. , , 2019)). Interviews were completed between June 2021 and March 2022, and were conducted using Zoom teleconference software; interviews were audio-recorded to supplement notes. All study materials (e. g. , consent, survey, flyer) were available in English, German, Italian and Spanish, and interviews were conducted in the participant's preferred language. All study procedures were reviewed and approved by the University of Washington Institutional Review Board.
For example, a carefully tuned implementation for z ∈ R in Arb uses m = min( √ n, 8+0. 2 max(0, p-4096) 0. 4 , 60) for n > 50, and m = 1, 2, 4 or 6 for smaller n. The polynomial f can be constructed using repeated multiplication by linear polynomials, resulting in a triangular scheme for the coefficients. It can also be constructed using binary splitting (applied to a rising factorial in the ring Z[X]), but for the degrees that will be encountered in gamma function computation, the triangular scheme suffices (it should be implemented using machine words when n and m are small enough that overflow cannot occur). The scalar operations k f k z k can be implemented using optimized code for dot products  [Joh19b]. If z is a truncated power series, the inner operations can be viewed as a matrix multiplication; the algorithm is related to the Brent-Kung algorithm  [BK78]  for power series composition.
19 We can clearly achieve arbitrary performance goals if we drop this constraint, but enormous lookup tables have their own drawbacks. Even machine-precision implementations tend to be frugal about lookup tables in order to minimize code size and avoid cache misses. In IEEE 754 binary64 arithmetic, Γ(x) could in principle be covered entirely with low-degree piecewise polynomial approximations on the part 0 < x 171. 63 of its positive domain where it is finite, but we are not aware of any mainstream libm implementation that follows this approach. 20 For log Γ(z), the expansions at z = -n are not Laurent series but generalized series expansions with leading logarithms. requiring the values γ, ζ(2), ζ(3),. and exponentiation of a power series. Explicitly, this leads to the recurrence (n -1)a n = γa n-1 -ζ(2)c n-2 + ζ(3)c n-3 -.
Therefore, the approach implemented was based on the paired difference between the free and total toxin content of shellfish as a first step to assess the potential presence of acetylated forms of PnTX-G, and to select the most useful samples for more in-depth investigations. At the same time, we also processed the data for the other toxins quantified from the same hydrolyzed extracts, even though these conjugated forms have not been reported so far. * < LOQ: comprised between the limit of detection (0. 13 µg/kg) and the limit of quantification (0. 4 µg/kg). PnTX-G was quantified in all collected samples, with an increase in summer and a decrease in autumn. Maximum values were observed in July, with 467. 5 µg/kg free toxin, and 475. 2 µg/kg total toxin. The levels measured for free toxins were very close to the levels obtained for total PnTX-G.
0 m/z, AGC target 2 × 10 5 (min AGC target of 8 × 10 3 ) and max IT 100 ms. A top N of 10 was retained with an intensity threshold of 8 × 10 4. For PRM acquisitions (instrumental method A. 3), an MS 2 resolution of 70,000 FWHM, an AGC target of 2 × 10 5 , a max IT of 240 ms, an isolation window of 2. 0 m/z and max IT of 240 ms were defined. For both methods, normalized collisions of 35 and 42 were selected for PtTXs and portimine-B, respectively. Instrument control was handled by a computer equipped with Thermo Q Exactive version 2. 9 Build 2926, Xcalibur version 4. 1. 50 and TraceFinder version 4. 1-EFS (Thermo Fisher Scientific). Acquired data were processed with Freestyle version 1. 8. 51. 0 and Compound Discoverer 3. 30. 550 (Thermo Fisher Scientific).
Understanding the thermal behavior of lakes is crucial for water quality management. Under climate change, lakes are warming and undergoing alterations in their thermal structure, including surface and deep-water temperatures. These changes require continuous monitoring due to the possible major ecological implications on water quality and lake processes. With the scarcity of long-term in situ water temperature datasets, we present a regional long-term water temperature dataset (LakeTSim: Lake Temperature Simulations) produced over 401 French lakes by combining numerical modelling and satellite thermal data. The dataset consists of daily epilimnion and hypolimnion temperatures for the period 1959-2020 simulated with the semi-empirical OKPLM (Ottosson-Kettle-Prats Lake Model). We also describe this model and its performance.