dataesr/scientific-paragraphs-categorization huggingface dataset:
Data cleaning First steps of data processing included corrections of misnamed columns or incorrect data formats (e. g. , antibiotics in columns instead of rows). Taking into consideration the study objectives, six variables were deemed essential: year, isolate ID, animal host, bacterial species, antibiotic agent, and MIC/disk diameter value. Entries for which those variables were incorrect, ambiguous, or missing (i. e. , thesaurus not respected) were removed from the dataset (Figure  1 ). Other variables were either considered as optional or could readily be obtained from the data provider. In addition, entries corresponding to combinations of animal species/bacterial species/antibiotic agents outside of the EARS-Vet scope were discarded from the dataset. Semantic errors in the dataset were also looked for.
First, all true duplicated entries (i. e. , exact same rows) were deleted. Then we checked that (i) each isolate ID corresponded to a unique animal species, bacterial species, country and year, and (ii) each antibiotic agent tested on a given isolate had a unique MIC/disk diameter value. When one or two of these properties were violated, the data were either corrected or excluded from the dataset. For each partner, feedback was provided in the form of an individual report on data quality, to facilitate data verification and improve future data extractions.
Combinations of bacterial species-antibiotic agents with no available (T)ECOFF or CA-SFM Vet cut-off were not categorized as WT/non-WT, and were excluded from the analyses. Similarly, MIC data for which the tested concentration ranges were insufficient to allocate WT vs. non-WT categories based on EUCAST (T)ECOFFs were excluded from the analyses. Spanish CLSI disk diffusion data were also excluded from the analyses, since CLSI currently has no epidemiological cut-off values publicly available. To cope with the fact that partners were testing for different antibiotic agents within certain antibiotic classes, and similarly to what is done by EARS-Net, AMR trends analyses were performed at the level of either antibiotic agent or antibiotic categories, the latter grouping together antibiotic agents with similar resistance mechanisms.
2%), swine (13. 1%), cats (6. 5%) and turkeys (4. 3%). Most isolates were E. coli (66. 1% of all collected isolates) followed by Staphylococcus pseudintermedius (14. 0%) (Table  3 ). E. coli was the only bacterial species for which data were provided by all countries, and collected for all animal categories. Detailed distributions of isolates per bacterial species and partner are provided in Supplementary Table  S3. The amount of data was approximately equally distributed across the 5-year targeted period, with each year including from 18. 5 to 21. 1% of the data. Virulence data were provided by four partners (1,850 isolates characterized in total), ESBL/AmpC phenotype confirmation by four partners (140 isolates), and mecA/mecC presence confirmation by three partners (151 isolates) (Supplementary Table  S4 ).
AMR trends analysis Figures 2, 3 depict AMR trends analyses over 2016-2020 for two selected combinations of bacterial species-antibiotic categories, namely E. coli resistant to aminopenicillins and S. pseudintermedius resistant to fluoroquinolones. These were selected to illustrate EARS-Vet results for a diversity of bacteria (Gram-positive and Gram-negative), animal species (livestock and companion animals), critically and non-critically important antibiotic classes, and countries. AMR trends for the 51 other combinations of bacterial species-antibiotic categories of interest with sufficient data and available interpretation criteria are provided in supplementary material (Supplementary Figures  S1-S51 ). Figure  2  shows wide variability in the proportion of E. coli resistant to aminopenicillins, both within and among countries, although with relatively large confidence intervals due to limited number of isolates in certain countries or animal species.
In addition, dairy vs beef production are not always easy to distinguish, since calves born in dairy herds are commonly transferred and later reared for beef production. Similarly, specimen data were difficult to retrieve (except for milk in cattle), and some partners had difficulties to allocate recorded specimen into the EARS-Vet predefined list. Alternatively, EARS-Vet could consider working with a list of predefined infection types (e. g. , respiratory, digestive, urinary, mastitis), to which data could be allocated more easily, hence facilitating data interpretation for the next data collection rounds. While almost the entire scope initially envisaged for EARS-Vet  (Mader et al. , 2022a)  was covered by the data collected, some bacteriaantibiotic combinations were poorly covered; for example, resistance data to carbapenems, which are of primary interest for public health, were hardly ever available, since these antibiotics are not authorized in veterinary medicine, and consequently not routinely tested by veterinary diagnostic laboratories.
The French data were interpreted with national (CA-SFM Vet) epidemiological cut-off values, since CA-SFM Vet disk diffusion standards differ from EUCAST standards. Still, working with EUCAST (T)ECOFFs was not straightforward because several partners worked with narrow concentration ranges that did not necessarily include the EUCAST (T)ECOFF value. For those working with MICs, providing exact MICs (instead of semiquantitative should strongly be recommended for future EARS-Vet data collection. Should these not be routinely available, a representative subset of isolates could be re-tested using MIC panels with a wider range of concentrations. Similar to the EFSA monitoring, EARS-Vet could consider obtaining MIC plates customized to the needs of the network. Tentative concentration ranges to be used for broth microdilution in animal pathogens have recently been proposed  (Teale and Borriello, 2021)  and could be considered for EARS-Vet.
, 2021). In the meantime, alternative strategies are needed. An option that was initially explored as part of this study was to use CLSI breakpoints (CLSI, 2020) to interpret MIC data with no available EUCAST ECOFFs. However, this solution was discarded, since it would imply mixing up clinical breakpoints with epidemiological cut-off values. It also required extrapolating clinical breakpoints across animal and bacterial species, antibiotics, and types of specimens, since CLSI clinical breakpoints are available for only few combinations of interest to EARS-Vet. After extrapolation, CLSI breakpoints only covered an extra 16% of the 148 EARS-Vet combinations not already covered by an EUCAST (T)ECOFF, while 29% of the combinations had no (T)ECOFF or breakpoints available (data not shown). To parallel the EARS-Net approach, another alternative would be to work with EUCAST clinical breakpoints; however, defining these breakpoints for animal pathogens is still work in progress, e.
g. , using data from the European Surveillance of Veterinary Antimicrobial Consumption (ESVAC) project  (European Medicines Agency, 2022). Statistical significance of trends or differences between countries were not assessed at this stage because of concerns about comparability of data from different years. Similarly to the EARS-Net approach, this will be introduced at a later stage, with EARS-Vet progressing step by step toward collection of more comparable data. To conclude, this pilot study provided a proof-of-concept of what EARS-Vet can achieve, and formed a basis to improve future data collection and analysis. The next steps will consist in adjusting data collection tools, addressing key methodological issues (e. g. , lack of ECOFFs and clinical breakpoints), improving harmonization and comparability across countries, and more generally strengthening capacities for AMR surveillance in animal pathogens across Europe.
In the mid-to long-term, EARS-Vet aims to become a sustainable initiative, with regular release of surveillance reports and the development of an online dashboard for dissemination of the results to a large audience, hence complementing the existing pool of data to support evidence-based management of AMR in animals in Europe. For Sweden, data collection and reporting was in part funded by the SvarmPat program. For Switzerland, additional financial support was received from the Food Safety and Veterinary Office (FSVO)  (grant no. 071-40011-65). Other partners participated with their own funding.
PIM2 is strongly induced in B cells engaged in differentiation into plasmablasts CD23-negative preplasmablasts compared to uncommitted CD23positive B cells exhibit strong expression of PIM2 -ahead of the PRDM1 gene in our single-cell RNA dataset and detection of the protein by western blotting -which occurs after release of BACH2 repression  [3]. This upregulation of PIM2 is driven by STAT3 and induced by the cytokines IL-21/IL-10 and IL-6 in plasma cells. Thus, BACH2 repression can be viewed as a gateway to the ultimate commitment of antibody-secreting cells, mediated by an IL2/ERK/ELK1 signaling pathway  [8, 46]  and responsible for the release of both PIM2 and BLIMP1 expression, with PIM2 upregulation preceding BLIMP1 expression (Fest lab, unpublished). These findings suggest that PIM2, independent of BLIMP1, plays a critical role in the differentiation of B cells into plasma cells.
Funding This study was funded by  Programmes labellisés (PGA)   2022 ARC N • ARCPGA2021120004244_4856 ,  Ligue Régionale GO ,  2020 , and an internal grant from the  Hematology Laboratory, CHU de Rennes. M. Haas  has received a doctoral fellowship from  FHU CAMIn, Ligue Contre le Cancer/Comité d'Ile et Vilaine.
Background The Invasive Respiratory Infection Surveillance (IRIS) Consortium was established to assess the impact of the COVID-19 pandemic on invasive diseases caused by Streptococcus pneumoniae, Haemophilus influenzae, Neisseria meningitidis, and Streptococcus agalactiae. We aimed to analyse the incidence and distribution of these diseases during the first 2 years of the COVID-19 pandemic compared to the 2 years preceding the pandemic. Methods For this prospective analysis, laboratories in 30 countries and territories representing five continents submitted surveillance data from Jan 1, 2018, to Jan 2, 2022, to private projects within databases in PubMLST. The impact of COVID-19 containment measures on the overall number of cases was analysed, and changes in disease distributions by patient age and serotype or group were examined.
4  The main aim of the IRIS Consortium is to investigate the incidence of invasive diseases caused by S pneumoniae, H influenzae, and N meningitidis. Invasive infections due to these bacterial species are legally notifiable to public health registries in the majority of countries participating in IRIS. 4  Previously, we reported significant reductions in the incidence of diseases caused by all three bacteria early in the COVID-19 pandemic and showed that these reductions were associated with the timing and stringency of COVID-19 containment measures. [4] [5] [6] [7] [8] [9]  A subset of laboratories also submitted data for cases of disease caused by Streptococcus agalactiae, a major cause of invasive disease across all age groups but especially neonates, and which is not transmitted via the respiratory route.
1,10 S agalactiae was included as a comparator organism to assess the stability of routine disease surveillance during the pandemic. There was no change in the incidence of invasive S agalactiae infections in the early months of the pandemic, suggesting that any disruptions to routine laboratory surveillance during the COVID-19 pandemic were minor and did not explain the observed reductions in diseases caused by S pneumoniae, H influenzae, and N meningitidis. 4  We conducted an expanded prospective analysis of surveillance data for four bacterial species (S pneumoniae, H influenzae, N meningitidis, and S agalactiae) in the 2 years before COVID- 19 (2018-19).
Implications of all the available evidence Future epidemics and pandemics will occur and, although it is essential to understand the pathogen that is directly responsible for the pandemic, we need to understand that overall human health and the transmission of other microbes are also broadly affected by population-level responses to an epidemic or pandemic. Data from IRIS provide evidence of the effects of such public health responses on severe invasive bacterial infections across many countries during the COVID-19 pandemic. and the first 2 years of the pandemic (2020-21). Four countries were added to the IRIS Consortium since our first publication in 2021, 4 which expanded the geographical coverage of IRIS to 30 countries and territories across six continents. We collected data on patient age and bacterial serotype or group to assess epidemiological changes that might have implications for disease burden and vaccination programmes.
Study design and participants For this prospective analysis, national reference and expert microbiology laboratories in 30 countries and territories submitted data on confirmed cases of invasive infections (within a normally sterile site) caused by one or more of the four bacteria under investigation. Australia, Colombia, Greece, and Paraguay joined IRIS after the initial establishment of the consortium. All IRIS-participating laboratories provided national reference data apart from Australia (New South Wales only) and China (one Beijing hospital only). Data were collected for patients of all ages except in South Korea, where only data from patients aged younger than 16 years were available for analyses.
Data collection No patient-identifiable data were submitted to IRIS. In the originating laboratories, bacteria from clinical samples were primarily recovered and identified by standard microbiological culture methods and occasionally by PCR testing. Invasive disease cases identified from Jan 1, 2018, to Jan 2, 2022 (the end of ISO [International Organization for Standardization] week 52 for 2021), were included in the current analyses. The PubMLST suite of databases was used to collect and manage IRIS data, and a private project only accessible to IRIS participants was used for each of the four organisms. At a minimum, information about the specimen sampling date, patient age, and serotype or group was submitted for each case except where data protection rules in a country prevented the submission of data on patient age.
Study data were entered by IRIS participants or the database curators (ABB, KAJ, and DS). Automated data integrity checks were applied before data upload, and all IRIS data were manually checked by the curators for data consistency; any discrepant or missing data were queried and resolved with the submitting laboratory. Google COVID-19 Community Mobility Reports (CCMRs) are anonymised, within-country mobile device location history data that capture the movement of people in six categories, including time spent in workplaces and residential areas. Google CCMR data are calculated as a daily percentage change from the baseline day, which was the median value between Jan 3 and Feb 6, 2020. In our previous study we used Google CCMR data to estimate the week when each country first implemented COVID-19 containment measures.
Time-series analysis and decomposition Case counts were summed by month to generate countryspecific and organism-specific time series for 2018-21. Monthly case totals were used to improve the overall model fit and accommodate the 2020 leap year (ie, 53 weeks). A second time-series analysis was done for   the S pneumoniae, H influenzae, and N meningitidis datasets to account for potential seasonal differences, whereby case counts were pooled by countries residing in the northern or southern hemispheres, respectively. S agalactiae data were only collected from countries in the northern hemisphere.
Interrupted time-series analysis Seasonal autoregressive integrated moving average (ARIMA) models were used to quantify the impact of COVID-19 containment measures on the incidence of invasive bacterial disease, and to generate counterfactual trends with 95% prediction intervals (PIs) for each of the four pathogens. Although it would have been desirable to fit separate ARIMA models by country, the dataset in most countries was too small for this to be implemented, and residual autocorrelation and negative case counts produced after model fitting precluded this approach. ARIMA models took the simplified form of: where p=non-seasonal autoregressive (AR) order, d=nonseasonal differencing, q=non-seasonal moving average (MA) order, P=seasonal AR order, D=seasonal differencing, Q=seasonal MA order, and s=time span of repeating seasonal pattern (number of observations in a year), 12 months.
12, 13  To measure the impact of COVID-19 containment measures, step and slope variables were included as regressors in the final ARIMA models: step (0 before containment measure implementation, 1 thereafter); and slope (0 before containment measure implementation, +1 for each month thereafter). Based on Google CCMR data, the step variable switched from 0 to 1 from March, 2020, onwards for all countries. 4  These changes reflect the change in personal behaviour in response to the pandemic as well as the containment measures initiated across the 30 countries and territories in our cohort. The final ARIMA models were used to produce a counterfactual prediction that assumed the COVID-19 pandemic did not occur, based on disease data from the two pre-pandemic years, which generated a mean monthly case estimate and 95% PI.
Results All 30 countries participating in the IRIS Consortium submitted data on cases of S pneumoniae invasive disease, as represented by bacterial isolates or case reports, or both, submitted to each of the IRIS laboratories. Most countries also submitted invasive disease data on H influenzae (n=24) and N meningitidis (n=21), and nine countries submitted invasive disease data on S agalactiae. Overall, 116 841 cases were analysed: 76 481 in 2018-19, before the pandemic, and 40 360 in 2020-21, during the pandemic. The number of S pneumoniae, H influenzae, and N meningitidis cases during 2020-21 was approximately half the expected number each year compared to pre-pandemic totals, but the number of S agalactiae cases was similar each year (table  1 ). There was an association between the stringency of COVID-19 containment measures implemented in each country and the number of S pneumoniae cases reported to laboratories (ie, as the stringency of containment measures decreased during 2021 in many countries, there were concomitant increases in S pneumoniae cases; figure  1 ).
Similar associations were observed for H influenzae and N meningitidis, but not S agalactiae (appendix pp 3-5). Time-series analyses by northern and southern hemispheres for each of the bacterial species showed an overall reduction in cases of disease caused by S pneumoniae, H influenzae, and N meningitidis, but not S agalactiae, during the pandemic (figure  2 ). Notably, cases due to S pneumoniae, H influenzae, and N meningitidis were increasing by the end of 2021. Google CCMR data were used to assess the point at which there was a step change that precipitated the reduction in cases of disease within each country, which was from March, 2020 (when the pandemic was declared), in both the northern and southern hemispheres (figure  2 ). The data were meta-analysed by hemisphere, the results of which showed a significant reduction in the risk of invasive disease caused by S pneumoniae (RR 0•47; 95% CI 0•40-0•55), H influenzae (0•51; 0•40-0•66), and N meningitidis (0•26; 0•21-0•31) but not S agalactiae (1•02; 0•75-1•40; figure  3 ).
Sensitivity analyses supported the use of the ARIMA model (appendix p 6), which estimated that 36 289 (95% PI 17 145-55 434) cases were averted in these 30 countries during the first 2 years of the pandemic (table  2 ). Data were then stratified by serotype or group and patient age. Among S pneumoniae cases, there were significant reductions in the case count of all major serotypes in 2020-21, although cases of disease due to some serotypes were beginning to increase in the latter months of 2021 (figures 2, 4A). Case numbers in 2020-21 were reduced in every age category and there were no major changes in the overall patterns of disease by age or serotype (figure  4B ). Stratification of H influenzae cases by serotype and patient age showed a reduction in case counts of all serotypes except for serotype b (Hib), which decreased in 2020 and then increased at the end of 2021 (p<0•0001).
Although we assessed data from 2018 to 2021, when taking account of a yearly trend, the necessary seasonal adjustment in the model leads to a loss of a year's worth of data, which affected our sample size. 12  Despite these limitations, strengths of these analyses included the large datasets rapidly contributed by investigators, which spanned 4 years and increased the power of these analyses. Additionally, high-quality data at a national level were made available by accredited reference laboratories undergoing routine audit and data validation practices, which minimised information bias. Selection bias is likely to be minimal because the bacteria under investigation cause diseases that necessitate urgent hospital care, and legislation in most of these countries and territories mandates the reporting of invasive diseases due to one or more of these bacteria.
The infrastructure for the IRIS Consortium is funded by a Wellcome Trust Investigator Award to ABB (grant number 206394/Z/17/Z). The IRIS databases are part of PubMLST, which is funded by a Wellcome Trust Biomedical Resource Grant awarded to MJCM, ABB, and KAJ (grant number 218205/Z/19/Z). The high-performance computing requirements of the data analyses were supported by the Wellcome Trust Core Award (grant number 203141/Z/16/Z) and the NIHR Oxford Biomedical Research Centre. This work was also partially supported by research funding from the Spanish Ministry of Science and Innovation (grant PID2020-119298RB-I00 to JY); research grants from Pfizer (grant 69765907) to Seoul National University Hospital, and from the Korea Disease Control and Prevention Agency (grant 2018F240600) to Seoul National University College of Medicine; research funding from the Torsten Söderberg Foundation, Stockholm County Council, and the Swedish Research Council to the Karolinska Institutet, Sweden; research funding from the German Federal Ministry of Health and the Robert Koch Institute (grant 1369-237) to the National Reference Centre for Meningococci and Haemophilus influenzae; research funding from the Robert Koch Institute, Pfizer, and Merck, to the German National Reference Centre for Streptococci; and research funding from the Greek National Public Health Organization (EODY) to the Greek National Meningitis Reference laboratory.
The authors were deeply saddened by the sudden death of Professor Ulrich Vogel, who contributed to the IRIS Consortium but more importantly was a dear friend and colleague to many of the authors. The authors are grateful to all those working in the clinical microbiology laboratories who have processed and characterised bacterial isolates that contribute to the IRIS Consortium. The authors For the source code see https:// github. com/brueggemann-lab/ iris_ldh_2022.
g. blood-feeding mites were equivalent to nonparasitic mites provided that mite body sizes were similar. Here, we implemented  Hechinger's (2013)  framework by analysing an unprecedently large dataset and parametrizing scaling equations using current knowledge of the biology of a particular host-symbiont system: vane-dwelling feather mites (Acariformes: Astigmata: Analgoidea and Pterolichoidea) from European passerine bird species. Feather mites are ectosymbionts found on almost all birds  (Walter & Proctor 2013). Their entire life cycle is spent on their living hosts, mainly on the wing and tail flight feathers, where they are usually queuing between the feather barbs (i. e. , the primary branches of the feather rachis; Figure  1 ) or next to the rachis  (Kelso & Nice 1963; Choe & Kim 1989; Yamasaki et al.