dataesr/scientific-paragraphs-categorization huggingface dataset:
The same two reviewers independently assessed selected full-paper copies of included publications on the same four criteria for final eligibility. Reference lists of the included papers were not tracked. Regarding the population of interest, if a mixed population was present in the studies retrieved, the minimum proportion of the population of interest in the sample, as defined by the working group (e. g. 80%), was used for eligibility. To assess for possible publication bias or selective reporting of results, the working groups assessed studies identified by trial registries in the WHO and ClinicalTrial. gov databases using the methodology as outlined in the GRADE handbook. 16 From relevant trials identified from these databases, related publications were searched for in the original literature search database using the trial registration number of these relevant trials.
| Data extraction Data were extracted from each included publication that had a controlled study design and summarized in an evidence table. This table included participant and study characteristics, characteristics of the intervention and control conditions, and primary and secondary outcomes. One of the reviewers of the original team of two extracted the data, while the other reviewer checked the table for content and presentation. All members of the working group discussed the data in the evidence tables. Each working group created a PRISMA flow diagram showing the process of selection of papers for the qualitative analysis, and a risk of bias table presenting in detail the risk of bias per included publication.
sign. ac. uk/ assets/sign_grading_system_1999_2012. pdf). 20 Level 1 refers to RCTs and Level 2 refers to case-control, cohort, controlled before-andafter designs, or interrupted time series. The risk of bias was scored for each study as ++ (very low risk of bias), + (low risk of bias), or-(high risk of bias). Additionally, working groups assessed all publications with a controlled study design for quality of reporting using the 21-item scoring system for reports of clinical studies developed by the IWGDF in collaboration with EWMA. 15 To prevent any conflict of interest, reviewers who were one of the authors of any study assessed for inclusion did not participate in the assessment, data extraction, or discussion of publications of that study. They were involved in the working group discussions of the summary of judgements and recommendations to which that study contributed.
| Systematic review of diagnostic procedures We obtained specific methods for the systematic review of diagnostic studies from Brownrigg et al  21  and PRISMA guidelines,  18  and we asked all groups systematically reviewing studies and writing guidelines on diagnostic procedures to follow the methods used in this study. 21 Working groups assessed the methodological quality of the included studies against parameters included in the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool, a consensus quality assessment tool designed specifically for diagnostic accuracy studies. 22 Reviewers extracted data and entered them in a QUADAS data extraction form and calculated positive and negative likelihood ratios for each test in each study. 23, 24.
| CONCLUDING REMARKS With the worldwide diabetes epidemic, it is now more imperative than ever that appropriate action be taken to ensure access to quality care for all people with diabetes, regardless of their age, geographic location, and economic or social status. The IWGDF Guidelines on the prevention and management of diabetes-related foot disease are the result of a rather unique process that over 24 years has become more and more founded in a strong evidence base, with procedures to guarantee consistency, transparency and independency. The evidence base for how to help prevent and optimally manage diabetes-related foot disease is progressively growing, but it remains a challenge how to use this data to optimize outcomes in different healthcare systems, in countries with different resources and different cultures.
the literature search started (https://www. crd. york. ac. uk/prospero/). The literature databases used for each systematic review were PubMed (via Medline), EMBASE (via Ovid SP), the Cochrane database, or both. Each working group devised a search string for each database. Individual working groups could consult a medical librarian to help in devising their search string. Study designs included in the systematic review of interventions were randomized controlled trials (RCTs).
ACKNOWLEDGEMENTS We are grateful to the working group members who have collaborated tirelessly, lending their time, expertise and passion to the realization of the IWGDF guideline project. We would also like to thank the independent external experts for their time to review our clinical questions, outcomes and guidelines. In total, 100+ experts from all over the world contributed voluntarily, representing the many different disciplines involved in care for people with diabetesrelated foot disease, resulting in a unique set of multidisciplinary evidence-based guidelines with a global perspective. In addition, we sincerely thank the sponsors who, by providing generous and unrestricted educational grants for travel and meetings, made the development of these guidelines possible.
STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ The study is designed (randomised, controlled) to provide the highest level of evidence. ⇒ Validated measures will be used: adherence to physical exercise (primary outcome) will be evaluated using the Evaluation of Adherence Rating Scale. ⇒ Adherence to physical activity will also be evaluated qualitatively using video. ⇒ As with all observational data, participant recall data (physical activity, app's log in, etc) are subject to social desirability bias. Open access developed to reduce the deconditioning syndrome, with positive results. 4 8 9 Exercise helps to alleviate CLBP, and people who engage in adequate levels of physical activity have a good prognosis in terms of pain, disability and quality of life. 10 The exercise therapy programmes studied included daily global reconditioning activities aiming to improve some aspects of health-related quality of life in addition to reducing pain and improving function.
Open access Protocol deviations and reasons for withdrawal will be described. Regarding continuous variables, participant characteristics will be compared between randomised groups using the Student's t-test or the Mann-Whitney test if the conditions for the t test are not met. Homoscedasticity will be analysed with the Fisher-Snedecor test. For categorical parameters, between-group comparisons will be performed with the χ 2 test or the Fisher's exact test, as appropriate. All data will be analysed according to intention to treat principles. A linear mixed model will be used to compare the primary endpoint (EARS score) between randomised groups at 6 months post-randomisation. The randomisation group will be evaluated as a fixed effect and session as random effect to consider between-session and withinsession variability.
The statistical analysis plan and subsequent versions will be kept in the study file. The statistical analysis plan may be revised during the study to take into account any changes to the protocol or other changes to the study that may affect the initially planned statistical analysis. Any changes to the statistical analysis plan or protocol analysis will be subject to the approval of the local ethics committee and the funder and communicated to investigators. All analyses will be conducted before the randomisation code is broken, in line with the International Conference on Harmonisation Good Clinical Practice guidelines. Data storage and management will be conducted according to international guidelines relevant to French institutions. All data will be entered using an electronic case report form, and data accuracy will be analysed by the study data manager.
Any adverse event that occurs during the protocol will be reported to the principal investigator. In the event of any negative impact of participating in the study on the participant's health status, the participant will be entitled to compensation in accordance with French regulations. According to the provisions concerning data confidentiality that are available to those responsible for the quality control of biomedical research, all researchers with direct access to the data will take the necessary precautions to ensure the confidentiality of information (participant identification and results). All data collected will be anonymised.
Observations Data were obtained in project 2018. 1. 00618. S, using ALMA band 3 and 7 receivers in Cycle 6 in 2018. Each execution block (EB) was calibrated separately in flux, bandpass, and gain. The polarization calibrations were performed after merging the calibrated EBs of each band. The four basebands were set in TDM mode (64 channels on 1. 875 GHz with resolution 29. 3 MHz). The calibration (bandpass, phase, amplitude, flux) was performed using CASA 8  v5. 4. 0. No self-calibration was performed. The uncertainties of the presented polarization position angle are in the range of 4°-10°. The phase center of the presented images is at (α, δ) = (04:32:30. 37, +17:31:40. 24). We only present polarization results when the polarized intensity is above a signal-tonoise ratio of 3. Table  1  summarizes the observation parameters.
This project is supported by the  Ministry of Science and Technology (MoST) in Taiwan. Y. W. T. acknowledges support through  NSTC  grant  NSTC 110-2112-M-035- and  NSTC 111-2112-M-001-064-. P. M. K. acknowledges support through  NSTC  grant  NSTC 110-2112-M-001-057- and  NSTC 111-2112-M-001-070-. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2018. 1. 00618. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO  and  NAOJ. This work was supported by the  French "Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU  co-funded by  CEA andProgramme National de Planétologie(PNP) of CNRS/INSU co -funded  CNES.
(2020). (g), (h), (i) histograms: (g): Histograms of the angle differences defined in panel (b) for the 0. 9 mm data, color coded for data at r > 1 85 (black) and at r < 1 85 (red; excluding the Aa disk). The curves are the best-fit Gaussians to the data. (h): The same as in (g) but for the 3 mm data and for all radii. (i): Histogram of the angle differences between the detected polarization orientations and the minor axis of the dust ring for data at r < 1 85 at 0. 9 mm.
, 2014). Another major drawback is that ecologists and conservation biologists do not employ the same methods when assessing conservation risk. In this study, we repurposed data from independent studies of genetic variation assessment and camera-trapping surveys of felids to examine whether within-population GD estimates derived from presumably neutral nuclear microsatellite markers are associated with PD across species. Our response variables were the three most often reported measures of GD in population genetic studies: (1) observed heterozygosity (Ho), (2) expected heterozygosity (He) and (3) allelic richness (AR; or the mean number of alleles across loci examined within a population). We used PD data (i. e. , number of individuals per 100 km 2 ) estimated using spatially explicit capture-recapture (SECR) approach, with some exceptions to the lion's (Panthera leo) PD estimates because there were fewer lion studies using this approach  (Braczkowski et al.
| GD data We used within-population GD data collected and as presented elsewhere  (Azizan & Paradis, 2021) , including 10 additional studies published since 2020 found in Web of Science and Google Scholar (n = 175). These additional studies provided data for Geoffroy's cat (Leopardus geoffroyi)  (Bou et al. , 2021) , puma  (Gallo et al. , 2020; Zanin et al. , 2021) , European lynx  (Herrero et al. , 2021) , jaguars  (Kantek et al. , 2021; Zanin et al. , 2021) , snow leopard  (Korablev et al. , 2021; Singh, Singh, Joshi, Chandra, et al. , 2022) , bobcat  (Cancellare et al. , 2021; Miller-Butterworth et al. , 2021)  and leopard  (Singh, Singh, Joshi, Singh, et al. , 2022). Detailed methods for assembling this data set can be found elsewhere  (Azizan & Paradis, 2021). Briefly, we selected GD estimates within a population with at least five individuals, more than 70% of polymorphic loci and measured with a minimum of five presumably neutral and autosomal loci derived from a single set of microsatellite markers.
| PD data PD data were based on those collected elsewhere  (Anile & Devillard, 2018 , 2020) , including 168 studies published since 2018 (n = 479). We scaled capture-recapture (CR) estimates to SECR estimates (either via maximum likelihood or Bayesian) in a single relationship with body mass (or density-mass allometry, DMA) under the assumption that estimates from the two methods are correlated  (Jędrzejewski et al. , 2018; Luskin et al. , 2017). We initially selected PD records estimated using the CR method (n = 41 for 5 species and 19 study sites) and with full mean maximum distance moved (CR-FMMDM) and half mean maximum distance moved (CR-HMMDM) approaches. We first scaled CR-FMMDM estimates to CR-HMMDM, which was the most-used approach for estimating PD before the advent of SECR. We hence built a global linear mixed-model-relating density based on CR-FMMDM (D.
Occasionally, some groups (either from the cluster or country data set) had several GD estimates, leading to unbalanced data, thus we considered these multiple response values as repeated observations (i. e. , same PD value for all GD observations in a specific cluster, instead of using an averaged GD estimate per group). The number of clusters and countries per species is given in Table  S1. The map of the population locations in Figure  1  was generated using ggmap package in R.
| Statistical analyses To identify outliers in the response variables, we used boxplots on all three GD estimates and PD estimates per species (Figure  S1 ). Any data points that fell outside of the 1. 5 × interquartile range (whiskers) were considered outliers. These outliers were hence labelled in both data sets and we assessed the GD-PD relationship with and without outliers (Table  S2 ). We also inspected the relationship of PD  and GD within five species with the greatest number of clusters to check the subsequent effect of these outliers (Figure  S3 ). We performed linear mixed-effects (LME) modelling using the "lme4" package  (Bates et al. , 2015)  with species set as random intercepts to capture between-species variability (i. e. , the effect of explanatory variable was allowed to vary between species).
Generation length data were extracted from  Pacifici et al. (2013) , and body mass data were obtained from  Johnson et al. (2017). We assessed the predictive power of our models by comparing the marginal R 2 between simple and more complex models  (Nakagawa et al. , 2017). The best-fit models were chosen based on the lowest AIC. We used the dredge function in the "MuMin" package  (Bartoń, 2020)  to compare summary statistics of models containing every possible combination of the explanatory variables. In the final data sets, AR, PD, body mass and sample size were log 10 transformed prior to model fitting. All covariates were scaled to have mean = 0 and standard deviation = 1. The p-value for each covariate was computed using the "lmerTest" package  (Kuznetsova et al. , 2017) , and the confidence intervals (95%) of the fixedeffect estimates were calculated using parametric bootstrapping (n = 100).
| RE SULTS Of the 449 studies found during the literature search, we retrieved PD and GD data from a total of 330 studies that corresponded to our selection criteria. These studies were published between 1998 and 2022 and encompassed felid populations across 41 countries (Figure  1 ). We were able to group 38% of PD and GD records from all retrieved records using the K-means clustering method which resulted in 132 groups across 15 species in the cluster data set (Table  S1 ). The joining of PD with GD populations according to the countries where the studies were conducted yielded 72 groups across 18 species, from 683 records of PD (76%) and 484 records of GD (84%; Table  S1 ). A bias towards large-bodied species was evident and there were marked differences in the number of records according to the type of studies among species (Figure  1 ).
F I G U R E 2 Positive relationship between within-population genetic diversity and population density from camera-trap surveys across wild felids using cluster (a-c) and country (d-f) data sets. Regression lines represent model predictions using population density as the only fixed effect, with the slope parameters (β PD ) and its 95% confidence interval (CI) computed using the bootstrap. The median GD and PD values for each species calculated from the data sets are represented by the square shape (see legend in Figure  1    in the Gir Forest were an outlier population with an extreme reduction in genetic diversity compared to African lions. Furthermore, since we included population density of lions estimated using non-SECR framework (e. g. , distance sampling and track surveys) as there were very few PD data that reached our criteria (3 of 17 studies), these outliers might have arisen given the difference in methods used for the estimation of the PD.
Source studies also varied in how loci were chosen, in reporting the GD measures and in providing the raw microsatellite data available. Unfortunately, we found very limited raw genotype data, measures of dispersion for each loci-specific GD estimate, as well as overlapping loci across studies and species. Thus, we were unable to account for the microsatellite and sample size effect in the GD data set beforehand. Nevertheless, in a previous study using the same genetic database  (Azizan & Paradis, 2021) , the effects of sample sizes and number of loci on heterozygosity and allelic richness were significant but weak. Despite the more recent and attractive tools to assess population genetics such as the single nucleotide polymorphism (SNP), microsatellite markers are still relevant and less costly and time-consuming to develop for conservation efforts.
Our study revealed that GD studies were less numerous than PD (116 vs. 214), and 60% of the retrieved records were excluded in the cluster data set due to insufficient overlap or distances between the GD and PD records exceeding species dispersal (Figure  S5 ). The uneven assessment of GD and PD across species and distribution areas further complicated the pairing of these two records for analysis. For instance, while leopard PD has been extensively measured in Africa through 25 studies, only 4 studies have reported their GD using microsatellite markers. The leopard cat (Prionailurus bengalensis), which occurs through most of Asia, provides another example. Although PD records were available in five countries, GD has only been measured in South Korea, where PD data were not available. Conversely, puma GD records in North America (n = 120) greatly exceed the number of PD records (n = 26) and population genetic studies on the Canada lynx (Lynx canadensis) provided 47 GD records, whereas there was only one PD study in the same area which corresponded to our criteria (Table  S1 ).
NTR I B UTI O N S Conceptualization: Amira Azizan, Emmanuel Paradis, Stefano Anile and Sébastien Devillard. Methodology: Amira Azizan, Emmanuel Paradis, Stefano Anile and Sébastien Devillard. Investigation: Amira Azizan and Stefano Anile. Visualization: Amira Azizan and Stefano Anile. Supervision: Emmanuel Paradis and Sébastien Devillard. Writing -original draft: Amira Azizan and Stefano Anile. Writing -review & editing: Clayton K. Nielsen, Emmanuel Paradis and Sébastien Devillard. ACK N O WLE D G E M ENTS We are grateful to T. D. Meeûs for insightful discussions on the population genetic analyses and J. Claude for aid in the statistical analyses. SA is supported by NBFC to the University of Palermo, funded by the Italian Ministry of University and Research, PNRR, Mission 4 Component 2, "From research to business", Investment 1.
| I N T RO DU CT I O N Human respiratory syncytial virus (HRSV, henceforth referred to as RSV) is an RNA virus that in 2019 was estimated to cause 33. 0 million serious respiratory infections in children under 5 years of age annually, resulting in 3. 6 million hospitalisations and 101 400 deaths 1 globally. There is a paucity of data regarding the true burden of RSV in low-and middle-income countries (LMICs). A Global RSV Surveillance Program was therefore launched by the World Health Organization (WHO) Global Influenza Program in 2016 initially in 14 countries across the six WHO regions to establish a global surveillance infrastructure to monitor the incidence and prevalence of RSV, particularly in LMICs, and to provide standardised scientific data to guide the introduction of RSV vaccines or other immunoprophylaxis/treatments once they become available.
| Development of qualitative survey The purpose of the survey was to assess the quality of the WHO RSV surveillance system to ensure the data collected by participating laboratories from all countries met set targets and standards. As such the survey was designed by UK NEQAS and the Reference Laboratory Working Group to collect information on general laboratory practices, amplification platforms, nucleic acid extraction methods, RSV molecular detection and subtyping assays used in the EQA. Survey reporting requirements were used to custom design the UK NEQAS website for reporting of information.
| Matching of detection and subtyping primers to globally circulating RSV strains The CDC detection and VIDRL subtyping primers were matched to the curated database of 1010 RSV-A and 633 RSV-B WGS (Table  3 ). The sequences with the highest number of mismatches over the CDC detection primers/probe was found to be a strain from Jordan (HRSV/ A/Jordan/JOR-D3721/2013, GISAID ID EPI_ISL_2588376, 2013) with four mismatches across the probe and reverse primer. For the VIDRL RSV-A subtyping primers/probes, 18 sequences with a total of three mismatches across the primers/probe were found. For RSV-B, the most divergent sequence was HRSV/B/Nicaragua/NIC-7309-04/2013 (GISAID ID EPI_ISL_2577425, 2013) which had five mismatches across the forward primer and probe. For this strain, none of the mismatches were found to be in the final five nucleotides of the 3 0 of the VIDRL RSV-B primer/probe sequence and can therefore be inferred to be unlikely to significantly impact on primer probe binding.
The CDC detection assay provided for this EQA 7 targets the M gene and the VIDRL assay the L gene. 8 WGS of the isolates used in the RSV EQA 2019-2020 showed no mutations likely to impact on the effectiveness of these primer/probe sets used as part of Phase II. In addition, both the CDC and VIDRL primer/probe sets were matched against the sequences available on GenBank. The highest number of mismatches across probe and primer for the CDC assay was 4, and for the VIDRL typing assay there were samples with 3 and 5 mismatches across the primers/probe for RSV-A and B respectively. Despite the identification of some mismatches, they were judged insufficient to affect the assays' ability to detect the RSV strains in the panel. However, the described mismatches might have slightly impacted assay sensitivity.