dataesr/scientific-paragraphs-categorization huggingface dataset:
In several systems, this is known to cause problems with retrieving updates or resolving problems in a timely manner. For example, in the Netherlands animal population data retrieved from the Central Board of Statistics were not always in line with animal population data collected by livestock sectors. Also updates in animal population data occurred after the annual report on AMU in the Dutch livestock sectors, leading to corrections in AMU figures after publication of the report. In Belgium, the SANITEL database for identification and registration of food producing animals does not contain animal numbers of all animal categories monitored in the AMU data collection systems. Furthermore, for several farms, SANITEL data are found to be incomplete or not up-to-date. Additional data can be collected to allow for more detailed analysis and refined AMS.
For example, use by animal production stage or type, the weight at treatment, indication(s) for treatment, and/or the prescribing veterinarian, type of administration (e. g. , treatment, metaphylaxis, and prophylaxis) etc. However, the requested input should be of relevance to the analyses that will be carried out. Requesting too much or too detailed information will result in an unnecessarily high workload for the data provider and might lead to a subsequent lack of engagement. It is also important to consider whether the data requires transformation prior to calculating AMU. For example, data on use of feed mixed with antimicrobials can be obtained directly by requesting the amount of premix delivered/mixed into the feed, or by requesting the amount of medicated feed delivered while also providing information on the concentration of premix in the medicated feed.
Besides a lack of clarity around which species was treated, this is one of the main limitations of sales data. This is particularly true for some highest priority CIAs for human medicine, such as third and fourth generation cephalosporins, the use of which should be reduced in veterinary medicine. When basing the analysis on the PDDA, automatic data input is only feasible if prescriptions are digitalized, which may require additional investment. Moreover, if the prescription does not specifically mention the dose in, for example, mg of active substance per kg bodyweight or mg of active substance per animal, the number of PDDA will require the data to be transformed first, particularly if an in-feed or in-water regimen is prescribed. The UDDA can be calculated from administration data (treatment written/electronic treatment logbooks) and will, by definition, yield the most accurate reflection of the AMU on the farm.
Depending on individual country regulations, working with the UDDA will often require the farmer/attending veterinarian to have a role in data input, either as a sole provider of the use data or for validating deliveries or prescriptions provided by another party (vet, feed-mill, etc. ) based on what was actually used on farm. Count-based data, which are comparable to UDDA-based data in terms of outcome, can be determined from prescriptions, deliveries, as well as administration data and need input from farmers on what was really used for how many days and in how many animals. If the intention is to be able to cross-check farm-level data with sales data on a national level, which are almost always mass-based, it must be possible to deduce the used mass of antimicrobials from the farm-level AMU data.
e. , the time interval during which AMU is assessed, the result will be a treatment incidence  (33). The period at risk of treatment should correspond to the animal population at risk and to the period during which the numerator data are collected. As an example, if considering AMU over 1 year, the corresponding period at risk should be 1 year. In Denmark and Belgium 100 animal-days are used which is a proxy for the percentage of time an average animal is treated or the percentage of animals that are treated daily with one substance. In the Netherlands, the number of days an animal was treated in a year is calculated, which might be particularly useful for animals, such as dairy or beef cattle, with a longer production cycle.
Benchmarking Benchmarking of AMU refers to the comparison of a party's AMU with the AMU of similar parties (the reference population), given that AMU for all parties is quantified in a comparable manner. To the authors' knowledge, benchmarking is currently carried out-or planned to be carried out as soon as good quality data are available and a methodology is developed-in 12 countries, encompassing 20 AMU monitoring systems (Tables  1, 2 ). Most existing AMU monitoring systems benchmark farmers; three systems benchmark veterinarians (Table  1 ). Benchmarking is performed with a certain frequency (for example, twice a year) and takes AMU within a certain time interval (for example, the preceding year) into consideration. The shorter the production cycle of the animal species or animal category for which the AMU is benchmarked, the greater the relevance of a high benchmarking frequency.
In Denmark, the threshold values have been revised five times since they were launched in 2010  (21). Furthermore, a differentiated benchmarking strategy was implemented in 2016, with certain antimicrobial classes being weighted with higher factors in the quantification of AMU in this country. An advantage of dynamic benchmarking is that it is more difficult for the benchmarked parties to circumvent prudent usage policies by strategic changes in their AMU to comply with the threshold values. Dynamic values ensure that constant pressure is sustained for parties to keep reducing their AMU. However, this might have a discouraging effect, as reducing AMU does not necessarily mean that a party will reach a level below the threshold, because the threshold may have been lowered as well. A challenge of dynamic benchmarking is that data validation needs to be finalized and the data need to be fixed before benchmarking is applied.
Summarizing reports, typically not containing individual results, are more useful for a wider audience. The latter reports focus on general trends, achieving policy targets, comparing AMU among different animal species, categories, or other subgroups, etc. Several countries have been publishing reports of antimicrobial sales data for many years, increasingly including AMU data and sometimes AMR data as well [examples from countries in the AACTING network  (21, 22, 29, (41) (42) (43) (44) ]. Reports for regulators can fall in either of the two categories, i. e. , the individual party and the anonymous summary reports. In Belgium, yearly summarizing reports, without individual farm results, are also made available for some quality assurance schemes. Regardless of the purpose of data collection and the report type, the ownership of the data and their confidentiality must be defined and strictly adhered to.
Any communication of results to third parties must be approved by the data owner. Summary results such as general trends by sector can, however, generally be published. Data ownership by the government is another option. In terms of policy making and auditing, reporting of farmspecific results should be periodic, i. e. , at pre-defined times and for all farms. In the Netherlands, the "SDa" reports analyses on AMU to regulators, policy makers and the general public, while the quality systems report, in parallel, to their farmer members. Private quality assurance systems of the monitored livestock sectors report results and provide feedback to individual farmers about their AMU. Broiler farmers, for example, receive a report every 3 months in which several aspects of their AMU are compared to sector averages.
DISCUSSION This overview shows a very large variety across systems, especially at the level of analysis (choice of numerator, denominator and indicator). As AMU reduction figures for several countries have proven the principle of implementing farm level monitoring might be a more defining factor for success than the actual methods used. Furthermore, each system has its limitations, perhaps not so much in design as in execution: the results (calculated AMU) largely depend on the degree to which the data were provided to the system(s) correctly and in a timely manner. Hence, it could be argued that aiming for harmonization across systems is not only unfeasible but also unnecessary. Nonetheless, a desire to compare, essentially between similar sectors in different countries, does exist  (13).
As a secondary effect, these guidelines might be a step toward more harmonized approaches for farm-level AMU monitoring. It should be recognized that there is potential for more harmonization. Currently, a lack of harmonization is one of the factors that limits comparisons of AMU between farms within countries and comparisons of farm-level AMU data between countries, even though such comparisons are important for improving AMU practices  (25, 51). More harmonized approaches to monitoring AMU at farmlevel will improve understanding, communication, and sharing knowledge regarding AMU monitoring and benchmarking. Yet, even if using harmonized usage indicators, comparing systems in different countries might be seriously limited by variations in health and husbandry conditions, for example, the production cycle length of animals, availability of medical products, and regulations.
It remains the responsibility of the parties aiming for, or performing any, comparisons of systems to carefully address the differences between them, which will not be resolved by choosing a uniform indicator. Apart from bringing forward the usefulness of a specific type of indicator for monitoring farm-level AMU, the AACTING guidelines had the aim of providing useful information as how to organize the data collection, what to take in consideration when choosing a methodology for analysis, principles of benchmarking, and critical factors for reporting. The rationale for many of the points raised in the guidelines is provided in this overview. It is therefore advised to jointly consider both documents. In conclusion, quantifying AMU at the farm-level represents an important step toward AMS, as mirrored in the ESVAC vision for 2016-2020.
| Statistical analysis Statistical analyses were performed with a commercially available software (XLstat software). Data were expressed as median and range for continuous variables and as proportion for categorical variables. Clinical response to aerosolized delivery of gentamicin was studied on 2 criteria: the proportion of dogs cured after 4 weeks and the median duration of treatment until clinical cure. Both criteria were studied depending on several factors including: age less than 6 months, breed (brachycephalic vs nonbrachycephalic dogs), acute presentation (cough for less than 2 weeks), presence or absence of co-infection by M. cynos or other bacterial species, presence or absence of alveolar pattern on thoracic radiographs, history of previous steroid treatment, concomitant oral antimicrobial treatment during gentamicin protocol, use of cage vs facial mask, concomitant abnormal airways conformation at diagnosis, total cellularity of BALF (TCC less or more than 1000 cells/μL), severity of neutrophilic inflammation (less or more than 50% neutrophils in BALF) and finally, type of protocol (use of diluted or undiluted gentamicin).
Bioinformatics analysis The genome sequence of M. bovis reference strains HB0801 (GenBank accession no. CP002058) was retrieved from the GenBank database. Transposon insertion sites were mapped on the chromosome of M. bovis using the basic local alignment search tool for nucleotides (BLASTn; https://blast. ncbi. nlm. nih. gov/Blast. cgi?PAGE_TYPE=BlastSearch) and Geneious prime software (https://www. geneious. com/academic/). Predicted functional domains were identified with BLAST protein (https://blast. ncbi. nlm. nih. gov/Blast. cgi?PAGE=Proteins). Protein alignments were performed using ClustalW multiple sequence alignment programs (https://www. ebi. ac. uk/Tools/msa/clustalw2/) combined with ESPript 3. 0 software (http:// espript. ibcp. fr/ESPript/ESPript/). Metabolic pathways were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.
MS was performed in positive ion mode. Survey scans were acquired at a resolution of 70,000 with a m/z ratio of 200 and a ranging scan range of 300-1800 m/z. The resolution for MS was set to 17,500 at m/z = 200. The generated data were identified and quantified by using the MaxQuant quantitative proteomics software package (Max Planck Institute of Biochemistry, Am Klopferspitz, Germany) as described previously  [56]. Each protein group was set up with 3 biological replicates and the average LFQ intensities were compared as ratios for the comparison of altered expressed proteins. Differences were considered to be significant when p < 0. 05 while difference � 2 folds. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [57]  partner repository with the dataset identifier PXD017374.
Statistical analyses Statistical analysis were performed using Student's t-test for one comparison and one-way ANOVA for multiple comparison for data in normal distribution, while Mann-Whitney U test for one comparison with SPSS software (SPSS, Inc. , Chicago, IL, USA). Student's t-test and Mann-Whitney U test was performed in selected mutants, one-way ANOVA was used in cell colonies, M. bovis growth and protein expression level, the significance among groups was evaluated by using Post Hoc test (Tukey's test). Significance values obtained were presented as p � < 0. 05, p �� < 0. 01, p ��� < 0. 001 and "ns" represented non-significance (p > 0. 05). The numerical data used in all figures are included in S1 Data.
This work was supported by the  National Nature Science Foundation of China (NSFC)  [31772745; https://isisn. nsfc. gov. cn/egrantweb/], Wuhan international science and technology cooperation program  [2017030209020258; http:// kjj. wuhan. gov. cn/html/index. html], Special Fund for Chinese Agricultural Research System (Beef/ yaks)  [ CARS-37 ; http://www. beefsys. com/],for the development of new antimicrobials against multidrug-resistant pathogenic mycoplasma species. Special Fund for National Distinguished Scholars in Agricultural Research and  Technical Innovative Team  and the  Fundamental Research Funds for the Central Universities  [2013QC001], and financial supports from the  INRAE  and  ENVT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The orientation of the mTn is indicated in parenthesis. c CDSs found disrupted in M. bovis mutants are indicated by the mnemonic codification in the GenBank database. d For each CDS, the relative position and orientation of the mTn are indicated. e Conserved domains were identified using the Basic Local Alignment Search Tool (https://blast. ncbi. nlm. nih. gov/Blast. cgi). DUF: Domain of unknown function. https://doi. org/10. 1371/journal. ppat. 1008661. t001.
Therefore, CD38 is an appealing tumor-associated antigen for targeted therapy; and monoclonal antibodies against CD38, such as daratumumab, have shown efficacy in the clinical treatment of relapsed and refractory multiple myeloma (RRMM) and newly diagnosed MM patients  (11) (12) (13). Isatuximab is an immunoglobulin G (IgG)1 monoclonal antibody that targets a specific epitope on CD38  (14). The Phase 3 ICARIA-MM trial (NCT02990338) evaluated isatuximab, in combination with pomalidomide and dexamethasone, in patients with RRMM who had received at least two prior lines of treatment, including lenalidomide and a proteasome inhibitor  (15). The addition of isatuximab to pomalidomide and dexamethasone significantly improved progression-free survival and was well tolerated  (15). The ICARIA-MM study has led to the approval of isatuximab in the US, EU, Switzerland, Canada, and Australia, in combination with pomalidomide and dexamethasone, for the treatment of adult patients with MM who have received at least two prior therapies including lenalidomide and a proteasome inhibitor  (16) (17) (18) (19).
The fixed cells were pelleted by centrifugation and then washed twice with 200 µl of 1× permeabilization buffer (BioLegend) before staining with fluorochrome-conjugated antibody of interest in 100 µl of permeabilization buffer overnight in the dark at 4 • C. After washing twice, the cells were resuspended in 100 µl 1× PBS and 0. 1% BSA for analysis by flow cytometry (FACSCanto II, Becton Dickinson, Franklin Lakes, NJ). The data were analyzed using FlowJo software (FlowJo LLC, Ashland, OR).
Surface Receptor Density Determination Surface densities of CD38, CD46, CD55, and CD59 receptors were quantitatively determined using QIFIKIT (Agilent Technologies, Inc. , Santa Clara, CA) per manufacturer's instructions. Briefly, ∼0. 5 million test cells were incubated in 100 µl of PBS with a mouse primary antibody specific for the target receptor or an isotype control antibody (10 µg/ml) on ice for 30 min. After washing three times with Ca 2+ -and Mg 2+ -free PBS, primary antibody-bound cells or the calibration bead mix were incubated with the FITC-conjugated detection antibody for 30 min on ice. Fluorescence was measured by flow cytometry using a FACSCalibur (Beckton Dickinson) with CellQuest Pro software (v5. 2) (BD Biosciences). A calibration curve was constructed by plotting the fluorescence intensity of the individual bead populations against the number of mouse antibody molecules on the beads.
To understand the CD38 expression in patients with MM, we analyzed immune cells from healthy donors and MM patients from an ongoing study (NCT04045795). Results for CD38 receptor density measurement demonstrated that the majority of immune cells from the bone marrow of patients with MM have similar levels of CD38 expression compared with peripheral immune cells from healthy donors with the exception of B cells and NK cells, which exhibit a trend of elevated CD38 receptor density (Figures  1B, C ). We further analyzed the expression of CD38 on myeloma cells from 323 MM patients enrolled in isatuximab clinical trials. Twothirds of patients showed at least 10-fold higher CD38 receptor density than monocytes and NK cells from healthy individuals. Only 16 (5. 0%) patients had CD38 expression <50,000 molecules/cell (Figure  1D ).
Different Mechanisms Mediate Isatuximab-Induced Cytotoxicity Against MM Cells and DLBCL Cells To better understand the mechanism of isatuximab-mediated cytotoxicity, we assessed the activity of different isatuximabmediated cytotoxic pathways in 22 MM and 14 DLBCL cell lines in which we also quantified CD38 receptor density. Isatuximabmediated ADCC was observed in 7/15 tested MM cell lines and 4/5 tested DLBCL cell lines. All the ADCC-sensitive cell lines displayed high CD38 receptor density (>100,000 molecules/cell) (Figure  2A ). Similarly, these ADCC-sensitive MM cells, with one exception, were sensitive to isatuximab-mediated ADCP, while not all ADCC-sensitive DLBCL cells were responsive to isatuximab-mediated ADCP (Figure  2B ). When isatuximabmediated CDC was analyzed among the same MM and DLBCL cell lines, only one MM cell line, LP-1, and one DLBCL cell line, SUDHL-8, were susceptible to isatuximab-triggered cell lysis (data not shown).
Therefore, blockade of IL-6 or PD-1/PD-L1 may have the potential to improve the therapeutic efficacy of isatuximab in the treatment of MM. Interestingly, lenalidomide downregulates PD-L1 on primary MM cells, which might enhance NK cell function in MM patients  (25). Altogether, these findings have provided a rationale to explore PD-1 blockade to enhance isatuximab-induced ADCC of MM cells. Currently, a Phase 1/2 study in RRMM patients is ongoing to evaluate the safety, pharmacokinetics, and efficacy of isatuximab in combination with the anti-PD-1 antibody cemiplimab in RRMM patients (ClinicalTrials. gov: NCT03194867). In addition to cell-cell contact-mediated immunosuppression, immunosuppressive cytokines such as TGF-β also play a critical role in dampening NK cell function  (26). Using MM cell lines, we demonstrated that both recombinant TGF-β1 and TGF-β produced by MM cells suppressed the cytolytic activity of NK cells and isatuximabinduced ADCC.
ACKNOWLEDGMENTS The authors would like to thank  Sandrine Macé  ( Sanofi Research and Development, Vitry-sur-Seine, France ) for her contribution to the MM patient bone marrow immune cell data and the comparison with peripheral immune cells from healthy volunteers. Medical writing support (including development of a first draft and subsequent drafts in consultation with the authors, assembling tables and figures, collating author comments, copyediting, fact checking, and referencing) was provided by  Beatrice Tyrell , DPhil, at  Aspire Scientific (Bollington, United Kingdom ), and funded by  Sanofi , Cambridge, MA, United States.
et al. , 2016 ). There were no notable differences between autologous LDGs and PMNs in the total NE protein content (Figure  4a ). Although SLPI levels were variable in leukocytes from different donors, they were likewise found to be comparable in autologous PMNs and LDGs on average (Figure  4b ). Overall, these data suggest that the stronger staining of LDGs for NE and weaker staining for SLPI, in comparison to PMNs, cannot be attributed to the difference in cellular content of NE and SLPI. Using an activity-based spectrometric assay, we next determined the enzymatic potential of NE in lysates of LDGs and PMNs sorted from psoriasis patients. On average, the LDGs and PMNs were found to contain similar levels of catalytically active NE, as indicated by the hydrolysis of a widely used chromogenic, synthetic peptide substrate, MeO-SucAAPVpNA.
Flow cytometry For the staining for NE and SLPI, the PMNs and PBMCs were first fixed with 3. 7% formaldehyde and permeabilized with 0. 1% saponin in phosphate buffered saline. The cells were incubated with primary antibodies-polyclonal rabbit-anti-NE IgG (Athens Research and Technology, Athens, GA) and/or monoclonal-biotin-mouse-anti-SLPI (Abcam, Cambridge, United Kingdom) followed by secondary antibodies; allophycocyanin-F(ab)2 goat-antirabbit IgG (Jackson Immunoresearch, West Grove, PA) and phycoerythrin-or allophycocyanin-streptavidin (eBioscience, San Diego, CA or BD Pharmingen, San Jose, CA). Where indicated, the cells were stained without the fixation or permeabilization step. The same primary Abs for NE and SLPI were used across all the assays, unless otherwise indicated. Flow cytometry was performed on an LSRII (BD Biosciences, San Jose, CA), and the data were analyzed using the DIVA and FCS Express software.
SUPPLEMENTARY MATERIALS AND METHODS Low-density granulocytes (LDGs) and polymorphonuclear neutrophils (PMNs) immunophenotyping Peripheral blood mononuclear cells and PMNs isolated from psoriasis patients were stained with the following directly-conjugated monoclonal mouse antihuman antibodies: anti-CD15, CD14, CD11b, CD66b, and CD10 (all from BioLegend, San Diego, CA); anti-CD62L (eBioscience, San Diego, CA), anti-fMLPR (Miltenyi Biotec, Bergisch Gladbach, Germany), or mouse isotype controls. Flow cytometry was performed on an LSRII (BD Biosciences, San Jose, CA), and the data were analyzed using the DIVA and FCS Express software.
Lines indicate the mean value for each data set; n ¼ 10 donors in each group. ***P < 0. 001, ****P < 0. 0001; two-way matched ANOVA; Sidak post hoc test. (d) Cell-sorted PMNs and LDGs were harvested on poly-L-lysine coated slides and incubated for 3 hours. Cells were then stained for NE (red), counterstained with Hoechst to detect DNA (blue), and examined by fluorescence microscopy. One representative dataset of at least three experiments is shown. Bar ¼ 10 mm. ANOVA, analysis of variance; LDG, low-density granulocyte; MFI, mean fluorescence intensity; PBMC, peripheral blood mononuclear cell; PMN, polymorphonuclear neutrophil; SLE, systemic lupus erythematosus.