dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , approximately 91% of known acanthocephalan species are not yet barcoded. In addition, the development of a comprehensive database of reference sequences, derived from accurate identifications of vouchers based on morphological grounds, is critical for OTU identification in metabarcoding approaches. It is complementary to the deposition of type, morphological and molecular voucher specimens in recognized, permanent and publicly accessible repositories, concomitantly to local repositories. This practice of "biobanking"  [12]  has steadily increased in recent years, with researchers depositing hologenophores (i. e. , the acanthocephalan specimen from which molecular data was generated) or paragenophores (i. e. , conspecific specimen vouchers collected from the same hosts, at the same time and locality as the molecular specimen) in a num-ber of international repositories.
Unfortunately, the deposition of additional molecular vouchers (i. e. , whole worms, or parts thereof, preserved in 95-100% ethanol) to natural history collections that possess biobank repositories is not standard practice among acanthocephalan researchers. Therefore, we strongly advocate the use and deposition of morphological vouchers of molecular specimens and cross-referenced molecular vouchers as common practice in future acanthocephalan molecular studies, to ensure future taxonomic or ecological studies and the generation of new molecular data, including DNA barcodes. Acanthocephala requires an open access database and the systematic deposition of molecular and morphological vouchers in curated museums, and an increased effort in barcoding.
Metabarcoding could therefore be used as a monitoring tool of acanthocephalans in a wide range of habitats, to detect new acanthocephalan-host associations, while reducing the need for complex host necropsies for monitoring purposes and for well trained taxonomists. However, it calls for an increased effort to generate DNA barcode data and identification in acanthocephalans, because currently only a minority of described acanthocephalan species has been sequenced (see above). Obtaining new data on the intermediate hosts of acanthocephalans (more generally on their life cycle) is one thing, but sharing this knowledge in an open way is another. There is a need for an open database including information on host range (definitive, intermediate or paratenic), but also morphological and molecular information (see above), for several reasons.
First, it would make it easier to share this knowledge in the community of researchers on acanthocephalans. Second, at an applied level, parasites can have a significant impact on commercial aquaculture, by lowering growth, reproduction and survival rates of infected hosts  [155]. Therefore, any advance in understanding the life cycle of parasites can help control them. Third, this kind of information is important for trophic-network ecologists. Such a database should be part of or connected to the diversity database (see above) to collate information on DNA barcodes (BINs), OTUs and morphological characteristics, with ecological features including host range (definitive, intermediate or paratenic) and habitat. The data already available in books (e. g. ,  [87] ), review papers  [27, 67] , or in existing databases  [16]  should be transferred to an open online database and updated.
tereticollis in continental Europe  [11, 124]. The value of a database to centralize important updates on host range and/or acanthocephalan identifications (or misidentifications) is also exemplified for freshwater fish in North America. Since the major treatise by Hoffman, the 2nd edition of which was published in 1999  [78] , there have been many changes to species identifications and subsequent works on genera such as Neoechinorhynchus, as mentioned above, but there has been no centralized document or resource to publish these changes  [152] , which should be done in the future.
Considering the present-day issues of biodiversity conservation and ecosystem stability/resilience in the face of anthropogenic changes, research on acanthocephalans should not lag behind because of a lack of appeal and funding. We hope to have convinced our readers of the excellent opportunities provided by acanthocephalans as a model system to many fundamental and applied aspects of the evolution and ecology of host-parasite interactions. Tools necessary to collectively improve our effectiveness and visibility include the enhancement of cross-referenced molecular voucher specimens in museum collections together with morphological vouchers, and the creation of an online open database gathering molecular barcoding, morphospecies and ecological (life-history and life cycle) data. This will contribute to promoting and coordinating research efforts in integrative taxonomy and in the ecology and evolution of the Acanthocephala.
Acknowledgements. The 10th Acanthocephalan Workshop took place in Dijon, France, from August  28  to August 31, 2022. We wish to thank  Isaure De Buron ,  Halima Jmii Chine ,  Liang Li ,  Anna Marková ,  Bahram Sayyaf Dezfuli , and  Rui-Jia Yang  (participants in the workshop) for interesting and stimulating discussions leading to the identification of future perspectives in the research on  Acanthocephala. We would also like to thank two anonymous reviewers for their feedback on previous versions of the manuscript. Financial support for the workshop was provided by the hosting  Institution/ Laboratory :  Université de Bourgogne  ( BQR2022 ) and Biogéosciences, UMR 6282 CNRS uB.
The virus has spread rapidly in the Global North and has been detected in more than 100 countries as of 30th January 2023. In response, the WHO released standardized case definitions  (13)  and two separate forms for case investigation (CIF) and case reporting (CRF)  (14). The CIF is meant for in-depth epidemiological investigations while the CRF is a minimum dataset capturing the key epidemiologic parameters of monkeypox cases. Currently, the WHO mandates member states to submit the CRF for probable and confirmed cases under Article 6 of the IHR 2005  (13). Given the possibility of differences in the implementation of WHO recommendations based on previous experiences, we henceforth, undertook the present study whereby we investigated how different countries adopted WHO's Mpox guidelines and recommendations in terms of case definitions and case notification.
Methods In the present study, we investigated data from 32 countries. These countries were included based on the following criteria: (i) most affected countries (the countries with the highest Mpox caseload); (ii) publicly available information on Mpox case definitions; (iii) release of such definitions by the country's competent authority and (iv) access to the information. We additionally, excluded countries that are considered endemic and/or had been known historically to be the source of outbreaks. Accordingly, we included 17 out of 20 of the most affected countries along with 15 other countries with relatively low Mpox caseloads. The other three countries in the top 20 most affected countries-Nigeria, Democratic Republic of Congo, and Ecuador were not included due to historically known outbreaks in the first two and no case definition information found for the latter.
Information regarding Mpox case definitions for suspected, probable, confirmed, and discarded cases were collected and gathered from online public sources. Additionally, we collected information regarding which of the Mpox cases are required to be notified to the Health Officers/National Reporting Systems (NRS) by healthcare practitioners. For the data that was not available in English, for the following languages, the data was validated by the authors (native language speakers)-Dutch, French, Greek, Portuguese, Spanish, and Turkish. For other languages, we used bi-directional online language conversion (using Google Translate)-first from the language of the official document to English and then in the reverse direction (for accuracy). A list of sources used for each of the investigated countries along with full case definitions and their translations are provided in Supplementary File.
In fact, most countries have adopted the guidelines based on the principles of ALARA (as low as reasonably achievable), something seen also during the COVID-19 pandemic  (8). For example, the United States included human contact with infected animals (or products from such animals) as a criterion in the definition of a suspect case. The WHO states that this criterion is for countries with known ongoing zoonoses. However, past experience with Mpox outbreaks in the United States might have prompted such inclusion. Case data aggregating databases such as Our World in Data (OWID; available from https://ourworldindata. org), European Centers for Disease Control and Prevention (ECDC), John Hopkins University, WHO, and others have all highlighted the discrepancies in their datasets, most of which could be traced to incomplete data collection at source and intra-and inter-country heterogeneity in guidelines, infrastructure, and indicators  (16).
Study design and settings A retrospective ecological study in a French teaching hospital was conducted between January 2014 and December 2019, using the ConsoRes software (a tool for monitoring data on AMC and AMR provided by the French Public Health Agency). The Nimes University Hospital has 1773 beds, including 46 for critical care, 24 for haemato-oncology, 235 for surgery and 190 for long-term. During the study period, between 41 300 and 50 100 inpatients per year were admitted and the average days of hospitalisation was 55 200 days/month and increased each year up to 57 500 days/month in 2019.
Antimicrobial consumption Data on AMC were extracted monthly from the pharmacy software. The AMC was determined by counting the number of antimicrobials delivered in care units and expressed in defined daily doses (DDD)/10 0 0 inpatient-days according to the ATC/DDD classification  [17]. The focus of the study was on the consumption of penicillin extended-spectrum (J01CA), penicillin and beta-lactamase inhibitor (PBI) (J01CR), anti-pseudomonal activity penicillin and betalactamase inhibitor (AAPBI) (J01CR05), cephalosporin (J01D), carbapenem (J01DH), monobactam (J01DF), fluoroquinolone (J01MA), aminoglycoside (J01GB) and sulfamethoxazole-trimethoprim (J01EE01). Consumption of fourth-generation cephalosporin (J01DE01) was not pooled with other cephalosporins because of its broader spectrum of activity and was studied separately.
Statistical analysis All statistical analyses were performed with R software (version 4. 2. 0). P -values < 0. 05 were considered statistically significant. Time series analyses were managed with the Forecast package  [18]. AMC trends were evaluated by linear regression and Cochran-Armitage tests were used for AMR. Autoregressive integrated moving average (ARIMA) models were used to describe E. coli resistance to several antimicrobial classes. DR models were used to study the relationship between the use of several antimicrobial classes and resistance to a chosen antimicrobial class. The Linear Transfer Function (LTF) was used to link the output series (antimicrobial resistance) to the input series (the use of several antimicrobials) considering possible time lags (delay of antimicrobial effect on AMR acquisition).
Exploiting this knowledge, we developed a cell permeable and highly selective ACE2targeting peptide, NACE2i, which spans and targets the ACE2 NLS motif. NACE2i successfully abolishes nuclear translocation of ACE2 and viral replication in human cell lines infected with SARS-CoV-2  4. The nuclear (n)ACE2-induced transcriptional program is an early event critical for SARS-CoV-2 replication. Here, we extend these in vitro studies and address the impact of targeting the nACE2 pathway with NACE2i in the golden Syrian hamster model of SARS-CoV-2 infection, which closely mimics mild, resolving COVID-19 disease in humans  12. We show that targeting the nACE2 pathway significantly inhibits viral replication and prevents early lung inflammation and pathology associated with COVID-19. Using digital spatial analysis, we identify a transcriptional signature associated with SARS-CoV-2 infection and NACE2i-induced relief of inflammatory lung pathology.
a Using NLS prediction software (NLStradamus, Revision r. 9), an NLS motif (italics) and critical NLS residues were identified (underlined) at the ACE2 C-terminus. A peptide inhibitor targeting this region was designed (red). AlphaFold docked the NLS region of ACE2 in the same location observed in the crystal structure  4  , demonstrating accessibility between the transmembrane region and the cytoplasmic NLS. b Electrophoresis mobility shift assay confirming the IMP1α and ACE2 interaction via the C-terminal domain. The ACE2 C-terminal domain is FITC-labeled. n = 2 experimental replicates. c Microscale thermophoresis and fluorescence polarization to assess NACE2i inhibition of binding between IMP1α and ACE2. n = 3 experimental replicates, KD shown represent the mean ± SD. d Cells immunostained with NFACT1, C-Rel, ACE2, pPKC-zeta, Nurr1/Nur77, HDAC1 or NFKB p50.
Triplicate data were normalized and fitted to a single inhibition curve using GraphPad Prism (GraphPad Software, La Jolla, CA). b Representative immunofluorescence images of lung section from hamsters (n = 3 animals) stained with KRT5 (grey), MUC5AC (green), H3K27ac (magenta), and ACE2 me (red) (scale bar 20 µm). Arrows indicate KRT5 + cells with ACE2 me expression. c Representative immunofluorescence images of lung sections from SARS-CoV-2-infected hamsters treated ± NACE2i (n = 3 animals/group). Lung tissues were stained for ACE2 (green) and ACE2 me (red). DAPI (blue) was used to stain nuclei. Arrow indicated the cells enriched with nuclear ACE2. Scale bars are indicated in white. d Bar plots of the population dynamics of nuclear ACE2 + and ACE2 + ACE2 me+ cells (n = 10 FOVs from 3 animals, Ctrl; n = 16 FOVs from 3 animals, NACE2i).
Proximity ligation assay The Duolink proximity ligation assay was used with PLA probe anti-mouse PLUS (DUO92001), PLA probe anti-rabbit MINUS (DUO92005), and the Duolink In Situ Detection Reagent Red kit (DUO92008) (Sigma-Aldrich). Cells were fixed, permeabilized, and incubated with primary ACE2 antibodies targeting the NLS region and IMPα1 (staining conditions are shown in Supplementary Table  4 ). Cells were processed according to the manufacturer's instructions. Finally, coverslips were mounted onto slides and examined as above. Image segmentation and analysis were carried out using QuPath open software (version 0. 4. 3) for bioimage analysis  64  with a custom script. An analysis workflow was developed with the following steps: (i) threshold-based cell detection using DAPI staining to create a mask for detected nuclei along with a 5 µm cell expansion setting to denote the cell cytoplasm boundary; and (ii) use of the QuPath subcellular detection module to detect the Duolink-positive spots in the Alexa Fluor 555 channel in each cell.
Histopathology assessment Formalin-fixed hamster lung tissues were embedded in paraffin, and sections were stained with hematoxylin and eosin. Slides were read by a veterinary pathologist blinded to the group allocation. Lung histology scores for overall lesion extent, bronchitis, alveolitis, vasculitis, interstitial inflammation, and pneumocyte hyperplasia were assessed using a 0-4/0-5 scale, and scores for each lung were summed to obtain the total histopathological score  19. Viruses SARS-CoV-2 isolate hCoV-19/Australia/QLD02/2020 (QLD02; GISAID Accession ID EPI_ISL_407896) was initially isolated from nasopharyngeal aspirates from a patient and inoculated into Vero E6 cells. Subsequently, passage 2 of these samples were kindly provided by Queensland Health Forensic and Scientific Services, Queensland Department of Health, and stocks were generated in Vero E6-hTMPRSS2 cells as described in ref.
Virus TCID 50 determination in the lungs Two hours before testing, Vero E6/TMPRSS2 cells were plated in 96well plates at a density of 25,000 cells per well in a volume of 200 μL of complete growth medium (DMEM with 10% fetal calf serum). Cells were infected with serial dilutions of the day 2 lung homogenate (triplicate) for 1 h at 37 °C. Fresh medium was then added for 72 h. After cell infection, the MTS/PMS assay was performed according to the manufacturer's protocol (#G5430, Promega, Madison, WI). Briefly, after discarding 100 μL of supernatant, 20 μL of MTS/PMS reagent was added to the remaining 100 μL supernatant. After 4 h, plates were read using an ELISA plate reader and the data recorded.
Protein targets were localized by digital pathology laser scanning microscopy. Single 0. 5 μm sections were obtained using ASI Digital Pathology. ASI Digital Pathology characterizes both the fluorescence intensity, similar to normal immunofluorescent imaging protocols, but also provides the ability to count the population of immunopositive or immunonegative cells. This allows the study of population dynamics using custom algorithms and an automated stage. It also allows the imaging and counting of large numbers of cells to achieve statistical power. The microscope uses a 40x or 100x oil immersion lens running ASI software. Final images were obtained by averaging four sequential images of the same section. Digital images were analyzed using automated ASI software (Applied Spectral Imaging, Carlsbad, CA) to determine the distribution and intensities automatically with automatic thresholding and background correction of the average nuclear, cytoplasmic, or total fluorescence intensity, allowing for the specific targeting of expression of proteins of interest.
Digital images were also analyzed using ImageJ software (ImageJ, NIH, Bethesda, MD) to determine the total cell fluorescence or cell surface only fluorescence for non-permeabilized cells. Appropriate controls were used for all experiments including primary only control, secondary only control, and ACE2 me blocking peptide control. ImageJ with automatic thresholding and manual selection of regions of interest (ROIs) was used to calculate the Pearson's co-efficient correlation (PCC) for each pair of antibodies. PCC values: -1 = inverse of co-localization, 0 = no colocalization, +1 = perfect co-localization. Tukey's post hoc test or Welch's t-test was used to determine significant differences between datasets. The Plot-Profile feature of Fiji-ImageJ was used to record the fluorescence intensity of antibody-stained histone PTM targets and DAPI intensity along a line through selected images plotted with the mean ± SEM.
Opal PerkinElmer Tyramides (Opal 520, Opal 570, and Opal 650) were used for visualization. The Aperio FL Fluorescence slide scanner was used as a whole slide scanner to image lung FFPE tissue sections to compare staining of custom ACE2 me antibody and ACE2 (bulk/commercial antibody). Appropriate controls were used for all experiments including no antibody controls, primary only, or secondary only controls as well as thresholding for positive signals. QuPath analysis software (version 0. 4. 3) 64 was used to characterize the expression of ACE2 and ACE2 me. At least 5 bronchiolar regions were selected per tissue section (with at least 1000 cells counted) for each group (day 2, day 5, and control, i. v. NACE2i, i. p. NACE2i) using QuPath automatic positive cell selection for DAPI. The mean fluorescence intensity was then determined for positive cells for both ACE2 and ACE2 me expression.
Differential expression analysis was performed by comparing the control and NACEi2-treated tissues within bronchi or alveoli using linear mixed-effects models with the R package lme4. Volcano plots were generated with a threshold of fold-change (FC) at 1. 5 on a log2 scale and p-value 0. 05 for a significant cut-off. To identify the biological role of differentially-expressed genes (DEGs), both Gene Set Enrichment Analysis (GSEA) with the R package fgsea and Reactome pathway enrichment analysis with the R package ReactomePA were performed.
Cell type deconvolution analysis of NanoString DSP data Cell type deconvolution analysis was performed using R package SpatialDecon  68. The Q3 normalized count matrix from NanoString DSP data and the safeTME  68  immune cell profile matrix were used as the query and reference input, respectively, to reveal cell type abundance within each of the selected ROIs. Cell type proportion for each selected ROI was visualized using a stack bar plot, where ROIs were grouped by tissue region (bronchioles vs. alveoli) and experimental condition (control and NACE2i-treated samples). In addition, given the nonnormal distribution of the data, the Wilcoxon test was performed to compare each cell type proportion in controls against NACE2i-treated ROIs. The distribution of each cell type and p-value for the comparisons were visualized in box plots.
Image registration analysis of IF and NanoString DSP data The Python package SimpleITK 69 was used for image registration. Both IF images and NanoString images were first downscaled 5x to make the registration more computationally efficient. The DAPI channel in the IF image was cropped and rotated to the same capture area and orientation as the NanoString image and was used as the moving image (query image). NanoString images were converted to grayscale images to transform the pixel data dimension consistent with the IF image and was used as the fixed image (target/reference image). After centralizing the two images, rigid affine transformation was applied for shearing, shifting, and scaling the moving image to align with the fixed image at the same lower resolution as the initial step. Finally, the nonrigid B-spline transformation was applied on affine initialization to refine the local alignment.
Statistics and reproducibility In all experiments, analyses were performed by applying appropriate statistical tests as detailed in the methods and in the figure legends. The number of biological samples is stated in each figure legend. No statistical method was used to predetermine sample size. All animals were sacrificed at the end of experiments and organs were collected and examined in one go. Two-sided paired/unpaired t-tests, the Kolmogorov-Smirnov test, or Welch's tests were performed for two group comparisons. One-way or two-way ANOVA with Tukey's post hoc test or mixed-effects model tests were performed for multiple comparisons. No data were excluded from the analyses. Statistical analysis and data plots were performed using GraphPad Prism software (La Jolla, CA, version 9. 3. 1).
Acknowledgements We are grateful to all participants in this study. We thank the " Oncodesign team"  for the hamster in vivo experiments. We would like to thank the  QIMR Berghofer Scientific Services  for their invaluable assistance in this work, particularly the core histology service for their extensive optimization of antibodies in this study for hamster tissue and the core microscopy facility for assistance with data analysis. We thank  Nigel Waterhouse  ( Microscope Facility, QIMRb ) for his assistance with the ANDOR WD Revolution super-resolution microscopy and microscopy data analysis. We thank  Melissa Caspar  ( Metagene/ASI ) for her assistance with the ASI Digital Pathology system. We thank  Jyothy Raju ,  Pauline Crooks ,  Laetitia LeTexier , and  Archana Panikkar  for PBMC preparation.
Primers and probes. The primers and the probes (TaqMan) have been synthesized by Applied Biosystems and purified by high-performance liquid chromatography when labelled (Table  8 ). The β2-megaglobulin gene (β2MG) was used as an internal control of DNA extraction. The targeted sequences of the 18 S rRNA gene of P. falciparum, P. vivax, P. malariae, P. ovale and P. knowlesi (Accession number M19172. 1, U93233. 1, M54897. 1, KF696363. 1 and PKNH_0320900 respectively) were cloned into five different pEX-K4 plasmids (Eurogentec, Belgium) as Internal Quality Control (IQC) to continuously monitor the performances of the methods. IQC were used at a concentration of 1000 copies/µl in order to obtain a Ct around 30. This value has been chosen to observe potential drifts of the real time PCR from one PCR to another.