dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we present a large, curated database of tetrapod veterinary necropsy records, including 16,049 individual animals across 292 species of animals, encompassing reptiles, birds, amphibians, and mammals. Because necropsies typically are diagnosed with "neoplasia" which includes both benign and malignant tumors, we developed a terminology dictionary to distinguish benign from malignant neoplasms in the necropsy reports. We calculate and analyze both neoplasia prevalence as well as malignancy (cancer) prevalence. Only a subset of benign neoplasms evolve into cancers over a lifetime, so neoplasia prevalence is always greater than or equal to malignancy prevalence. We also tested for age bias in the animals that died with neoplasias or cancers.
Life History Analyses of Neoplasia Prevalence Evolutionary life history theory provides a framework for understanding the tradeoffs governing species' survival and reproduction  19, 20. Life history theory can be used to explain how species level traits shape organismal cancer risk based on trade-offs between investment in somatic maintenance (e. g. , cancer suppression) and reproduction or growth. Several smaller studies have shown that specific life history traits can serve as prognostic indicators of neoplasia prevalence in animals managed under human care  17, 21. We tested for relationships between life history factors and neoplasia or malignancy prevalence, controlling for phylogenetic relatedness, and weighting species data points by the number of necropsies in our dataset. In contrast to previous studies  [16] [17] [18]  , we found an increase in neoplasia prevalence with increases in body mass (2.
Because somatic mutations are responsible for causing many cancers  [23] [24] [25]  , we hypothesized that species with higher somatic mutation rates should have higher cancer prevalence. Somatic mutation rates were available for 9 of the species in our dataset, but even with that limited set, there is a clear positive association between somatic mutation rates and neoplasia prevalence (Figure  4B , pgls p = 0. 0059). The trend is similar for somatic mutation rates and malignancy prevalence but it is not quite statistically significant (S59, pgls p = 0. 087).
Analysis of Veterinary Necropsy Records We collected necropsy records with permission from 99 zoological institutions, aquariums, and other facilities that house animals under managed care. Gross necropsies had all been conducted by veterinary pathologists who specialize in nondomestic species and neoplasia was identified histologically by board-certified pathologists. Cases where suspect neoplasms were not examined histologically were excluded from the dataset. We used a terminology dictionary (ST5) to distinguish benign from malignant neoplasms based on the diagnoses in the necropsy reports. We excluded neonatal records to reduce bias from high levels of neonate and infant mortality that is common in many species. Because only common names were recorded for most records, we developed a tool, kestrel, to translate common names into scientific names which is available at https://pkg.
go. dev/github. com/icwells/kestrel. All of the institutions that provided prior approval for the use of their data in these analyses are Association of Zoos and Aquariums (AZA) accredited. AZA accreditation encourages the institution to perform a necropsy on all animals that die under their care to determine cause of death and to monitor morbidity and mortality of each species. Furthermore, each institution had IACUC approval with the Exotic Species Cancer Research Alliance (ESCRA) and the Arizona Cancer and Evolution Center (ACE) for the use of their deceased animal's records of animals with neoplasia for use in this study. Previous analyses included both necropsies for animals diagnosed with neoplasia and animals that were still alive  18. In this study, we restricted our analyses to only necropsies -for both cancer and non-cancer diagnosed animals, because alive animals may harbor undetected cancer or might be eventually diagnosed with cancer, thus skewing estimates in cancer prevalence.
Testing for relationships with life history factors We extracted data for maximum lifespan, adult body weight, basal metabolic rate, gestation length, litter size, time to sexual maturity, and growth rate from PanTHERIA  38. We used a weighted phylogenetic regression to control for non-independence of phenotypes (e. g. neoplasia prevalence) in closely related species. We report the phylogenetic signal, lambda, for each analysis, along with the p-value and R 2. A single phylogenetic tree encompassing the three clades was collected from timetree. org. We pruned the tree to the 292 species in our data set using the setdiff and keep. tip/drop. tip functions in the APE R package. Estimates for neoplasia and malignancy prevalence are more accurate in species with more necropsies. To address the differences in number of necropsies, and to limit the noise from prevalence estimates based on few individuals, we weighted the species data points by the square-root of the number of necropsies records we have.
Our R code for all analyses and figures included in this manuscript is freely available at https://github. com/zacharycompton/cancerAcrossVertebrates. git. In addition, we only analyzed species for which we had at least 20 necropsy results (previous studies had used 10 16 or 20  17, 18  for the lower bound number of individuals). The main pglsSEyPagel analyses were done with all species together, including mammals, sauropsids and amphibians. In the analyses of litter size and gestation time, we also tested for a relationship with neoplasia prevalence in mammals alone. We carried out a total of 28 pglsSEyPagel analyses. To control for multiple testing, we used a false discovery rate (FDR) of 10%.
11μM, 0. 33μM, and 1μM). Each condition was tested in triplicate in three separate experiments. Cell proliferation and apoptosis were measured by real-time fluorescence microscopy (IncyCyte, Sartorius) at two-hour intervals for three days. Apoptosis was measured using a fluorescent cell death marker, Annexin V Dye (Sartorius). Images were processed and analyzed using IncuCyte software. The number of dead or dying cells were identified by counting Annexin V positive cells. In addition, cell count overtime was calculated using IncuCyte cell-by-cell software. To measure response to radiation-induced DNA damage, cells were irradiated with one of four doses: 0Gy, 0. 4Gy, 2Gy, and 10 Gy. Radiation dose was delivered using an RS-2000 X-Ray Irradiator (Radsource). Cells were then seeded in 96-well plates in cell growth media containing Annexin V Dye (Sartorius).
These animals were generally protected from predators, provided with veterinary care, had different diets and exercise from their wild counterparts, many lived in an urban environment, and interacted with different species and microbes than a free-ranging animal. It is striking to us that four of the species with the lowest prevalence of neoplasias in our dataset, the gray squirrel, the common dormouse, the striped grass mouse, and the common field vole are all from wild, urban populations. These necropsies come from the London Zoo which has a policy of performing a necropsy on any animal they recover that dies on its grounds, not just the animals under its care. This is a hint that cancer may well be less common in the wild, although this observation may be dependent on the species and their wildlife habitat  61.
Statistical analysis Graphs, histograms, curves, and standard statistical analyses were designed using GraphPad Prism 6x software. Fisher tests were done with R (version 4. 3. 0) using the RStudio interface (RStudio 2023. 03. 0 Build 386). Kaplan Meyer survival curves and Cox univariate and multivariates analyses were done using the R Survival package (URL: https://github. com/therneau/survival).
Chronic lymphocytic leukemia patients can be separated into two groups according to Sm m-3'RR recombination We analyzed counts of Sm-3'RR recombination (Sm-3'RRrec) in DNA samples collected at CLL diagnosis from 47 patients. Blood tumor infiltration was over 90% circulating lymphocytes in 42 of 47 (89%) cases and over 98% in 37 of 42 cases (79%) (Online Supplementary Figure  S1A ). Comparison of junction counts was performed with results obtained from DNA from PBMC of nine HV. As negative controls, we used the Jurkat cell line and naïve B cells sorted from PB of healthy donors (n=2), in which no Sm-3'RRrec junctions could be detected (data not shown). Even at low levels, Sm-3'RRrec was found at comparable levels in both HV and CLL (Online Supplementary Figure  S1B ), and was undetectable in only three of 47 (6.
While levels of surface Ig were comparable between the two groups (data not shown), coding Cm transcripts were also increased in Sm-3'RRrec High CLL (Figure  4C ). Altogether, our results suggest abnormal intratumoral IGH remodeling activity in the Sm-3'RRrec High CLL group. In contrast with those of class switch recombination, structural features of Sm m-3'RRrec junctions harbor an activated B-cell imprint and discriminate both Sm m- 3'RRrec Low and Sm m-3'RRrec High chronic lymphocytic leukemia In addition to being unique, joint structure is differently shaped according to the DSBR machinery. In B cells, DS-BR occurs mainly through NHEJ and, to a lesser extent, through Alt-EJ pathways for IGH recombination. 22, 23  The joint structure of each single Sm-3'RRrec and CSR junction can be determined by alignment to reference sequences.
Acknowledgments The authors would like to thank  Dr. Villéger  at the  CRBioLim  and  Dr. Vallejo  and the  INSERM CIC 1435, Dupuytren Hospital, Limoges, France , for providing human samples. We thank  Dr. Jeanne Cook Moreau  ( UMR CNRS   7276   INSERM   1276 ) for careful English editing.
One μl of peptide mixture diluted in solution A (5% v/v acetonitrile and 0. 1% formic acid) was injected. Peptides were concentrated on Acclaim PepMap100 C18 pre-column (5μm, 300μm i. d. × 5mm) (ThermoFisher Scientific), then were separated on a C18 Acclaim PepMap100 C18 reversed phase column (3μm, 75mm ID × 500mm) (Thermo-Fisher Scientific), using a linear gradient (5-40%) of solution B (75% ACN and 0. 1% formic acid) using a flow-rate of 250nL min-1. LC runs were acquired in positive ion mode with MS scans from m/z 350 to 1,500 in the Orbitrap mass analyzer with a 70,000 resolution for MS and 35,000 resolution for MS/MS. Raw files from in-gel digestion were analyzed using Proteome Discoverer™ (Thermo Scientific, version 2. 2). SEQUEST algorithm was used for database searches with the UniProtKB/ Swiss-Prot reviewed Homo sapiens database (Homo sapiens, 2021_03, Sequences: 20,386) and NLRP6 sequence NM_138329.
A search in the gene data base (https://www. ncbi. nlm. nih. gov/gene) revealed the existence of a predicted NLRP6 isoform X1 (XP_016872742. 1) of 730 amino acids, devoid of PYD, which could match in term of molecular weight (S3 Fig). This truncated form could theoretically be recognized by the ABGENT polyclonal antibody we used (ABGENT AP13529a) since the immunogen is an epitope localized just upstream of the NACHT domain, still present in the predicted truncated form of NLRP6 (S3A and S4A Figs). To specifically test this hypothesis, we first performed western-blotting experiments with other commercial antibodies directed either against an epitope localized in the PYD (Abiocode R3097-1) which should not react with the 90kDa doublet, or in the LRR domain (Abiocode R3097-3), which should (S4 Fig).
In support to the notion that the anti-NLRP6 could cross-react with a HSP90 protein, a sequence alignment using the LALIGN software (https://www. ebi. ac. uk/Tools/psa/lalign/) revealed that the first 10 amino acids of the synthetic peptide used as immunogen by ABGENT share 50% identity (90% similarity) with HSP90 alpha, and the last 20 amino acids 25% identity (83. 3% similarity) (S1 File).
4G2 (BioXCell), and 100 µg/mL of polyclonal rat, mouse, and Armenian Hamster IgG (Innovative Research Inc. ) in FACS buffer and stained with the antibodies described in the antibodies and reagents table for 30 min in the dark at 4°C. Cells were then washed in FACS Buffer before data acquisition. For intracellular staining, cells were first stained extracellularly as described above. Cells were washed in PBS and fixed with fixation buffer for 20 min at 4°C. Cell permeabilization and intracellular staining were realized with permeabilization/wash buffer (Biolegend) following the manufacturer's instructions. Cells were then resuspended in FACS buffer before acquisition All flow cytometry acquisitions were realized using a Becton Dickinson 5 lasers LSR Fortessa X-20 and data analysis using Flowjo vX (Treestar, BD Biosciences).
Statistical analysis Two-tailed Student's unpaired t-tests were used to compare the differences of one variable between two groups when distributions were Gaussian and Mann-Whitney U tests for non-parametric distributions. When more than two groups were compared, one-way ANOVA coupled with ad hoc multiple comparisons post-tests, as indicated in the figure legends depending on the distribution of the values in each group, were used. Statistical analyses were realized using Prism v9. 4 software (Graphpad).
Acknowledgments This work was supported by the  Fondation pour la Recherche Médicale (FRM)  [grant #  EQU201903007794 ] to N. C. , the  French Agence Nationale de la Recherche (ANR)  [grants #  ANR-19-CE17-0029 BALUMET  to N. C. and  ANRPIA-10-LABX-0017 INFLAMEX ], the  Ministerio de Economía y Competitividad y Fondo Europeo de Desarrollo Regional  ( RTI2018-101105-B-I00 ) to J. H. , by the  Centre National de la Recherche Scientifique (CNRS) , by  Université Paris-Cité  and by the  Institut National de la Santé et de la Recherche Médicale (INSERM). We acknowledge the expert work from the members of the animal core facility (I. Renault and  S. Olivré ), the flow cytometry core facility ( G. Gautier ,  J. Da Silva  and  V. Gratio ) and the imaging facility ( S. Benadda ) of the  Centre de Recherche sur l'Inflammation  ( INSERM UMR1149 ), and the help from  O.
To account for contamination, the authors removed operational taxonomic units (OTUs) that were detected in more than 50% of procedural controls, and then identified remaining contaminants in silico (using the decontam R package). They found that most fetal samples were microbiologically similar to negative controls (labelled as 'other meconium'; n = 25), but that some samples, dominated by Lactobacillus (six samples) or Micrococcaceae (nine samples), had distinct bacterial profiles. The authors also detected low amounts of total bacteria by quantitative PCR (qPCR), fluorescent in situ hybridization (FISH), scanning electron microscopy (SEM) and culture (as discussed below). Several of the study's conclusions have been challenged by de Goffau et al. 45  , who reanalysed the publicly available data and found no evidence for a distinct bacterial profile in the subset of samples with matched procedural controls, and concluded that the positive findings were caused by a sequencing batch effect (indicative of contamination) and further contamination during culture  45.
The placenta mediates communication between the fetus and the mother and is a potent immune organ that protects the fetus. Historically, the placenta has been considered sterile (defined here as free from living microorganisms), but in 2014 a complex but low-biomass placental microbiome was detected by DNA sequencing. The proposed placental microbiome showed some similarity with sequencing data of microbial communities of the oral cavity  15. Contamination controls were not included in this study, and subsequent evaluation of the work found that most of the genera detected were also common contaminants  26, 36, 38, 54. Several detected taxa, such as Gloeobacter, a genus of photosynthetic cyanobacteria, appeared biologically implausible as a component of a putative placental microbiome  24, 55.
Acknowledgements T. B. receives funding from the  Deutsche Forschungsgemeinschaft (German Research Foundation  no. BR2925 10-1 & PL241 16-1 ). F. D. B. is funded by  AI045008 ,  AI120489 ,  R33HL137063 ,  CA219871 ,  AI139240 , and the  PennCHOP Microbiome Program. J. D. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  ERC-2017-AdG no. 788191-Homo. symbiosus). W. M. d. V. and A. S. are supported by the  Academy of Finland  (grants  1308255  and  1325103 ). A. M. E. is funded in part with Federal funds from the  National Institute of Allergy   Q23 Q24 Q20  Nature | www. nature. com| 11 and  Infectious Diseases (NIAID) ,  National Institutes of Health, Department of Health and Human Services , under grant number  U19AI110818  to the  Broad Institute.
M. A. E. is funded through grants  R01HD102318 ,  R01HD098867  and  R01NR014784. S. C. G. -V. was funded through a  Peter Hans Hofschneider Professorship  provided by the  Stiftung Molekulare Biomedizin. M. G. G. and D. M. S are funded by the  Canada Research Chairs Program. L. J. H. is supported by  Wellcome Trust   Investigator Awards   100974/C/13/Z  and  220876/Z/20/Z  and by the  Biotechnology and Biological Sciences Research Council (BBSRC)   Institute Strategic Programme Gut Microbes and Health   BB/R012490/1  and its constituent projects  BBS/E/F/000PR10353  and  BBS/E/F/000PR10356. M. W. H. has received funding from the  ERC  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 101019157 ). S. L. has received funding from the  ERC  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement 852600  Lacto-Be ).
F. S. is supported in part by  Science Foundation Ireland. G. C. S. S. acknowledges funding from the  Medical Research Council (UK ;  MR/K021133/1 ) and the  National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (Women's Health  theme). D. M. S. is funded by the  Canadian Institute for Health Research  and the  Canada Research Chairs Program. A. W. W. receives core funding support from the  Scottish Government's Rural and Environment Science and Analytical Services (RESAS). M. Y. is supported by the  Azrieli Faculty Fellowship.
However, in order not to reproduce errors from the past in anti-inflammatory strategies, this novel approach would benefit from identifying groups of patients presenting with a high level of immunosuppression. Since there is no clinical sign of immunosuppression, such stratification should be based on immune biomarkers. Consensus now exists for considering low monocyte expression of human leukocyte antigen-DR expression (mHLA-DR) as the reference surrogate to diagnose sepsis-induced immunosuppression  (3). Thanks to standardized measurement, a clinical decision-making threshold has been proposed, that is, mHLA-DR less than 8,000 antibodies bound per cell (AB/C), and used to stratify sepsis patients in a successful pioneering immunostimulation study  (6). Several ongoing clinical trials also based patient stratification on the same threshold (NCT02361528, NCT04990232).
IPP Model for the Identification of Samples With mHLA-DR Less Than 8,000 AB/C. All samples for which both mHLA-DR and IPP measurements were available were used in the analysis. A machine learning approach was implemented to predict immune status (i. e. , immunosuppressed or immunocompetent), based on a linear regression that used a space dimension reduction: the partial least square-discriminant analysis (PLS-DA) regression. This classification method can simultaneously manage a large number of predictors, even if they are highly intercorrelated, which would hinder the identification of the features primarily associated with the outcome. Data were divided into a train set containing 1,221 samples (70%) to build the model and a test set containing the 510 remaining samples (30%) to assess the performance.
For this purpose, we used the threshold (i. e. , < 8,000 AB/C) that has been previously used in different clinical studies for treating patients with the most severe immunosuppression  (6). In addition, this threshold is currently used in ongoing trials (NCT02361528 and NCT04990232). Notably, recent observational studies  (21, 22 ) also reported on this threshold to define groups of patients at increased risk of deleterious outcomes and similarly used 8,000 AB/C as a threshold for identifying immunosuppression. That said, next prospective investigations may focus on a more continuous adjustment of the IPP algorithm according to various levels of immunosuppression. A second limitation is that the present study is monocentric and retrospective. Results remain to be confirmed in an independent dataset, encompassing data from different centers.
ACKNOWLEDGMENTS We thank  Katia Imhoff  for her support for statistical analysis, as well as  Maxime Bodinier ,  Claire Tardiveau , and  Marie-Angélique Cazalis  for sharing their expertise during data analysis and interpretation. REAnimation Low Immune Status Marker (REALISM) study group members are as follows:  Hospices Civils de Lyon : 7 Medical Affairs, bioMérieux S. A. ,  Marcy l'Etoile ,  France. Supplemental  digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals. lww. com/ccmjournal). Drs. Brengel-Pesce  and Monneret contributed equally. Drs. Peronnet ,  Blein, Venet ,  Brengel-Pesce , and Monneret contributed to conceptualization. Dr. Blein  contributed to data curation.
Drs. Blein  and  Terraz  contributed to formal analysis. Drs. Peronnet ,  Blein, Cerrato, Fleurie, Llitjos, Kreitmann, Terraz, Conti, Gossez, Rimmelé, Textoris ,  Lukaszewicz ,  Brengel-Pesce , and Monneret contributed to investigation. Drs. Rimmelé ,  Textoris , and  Lukaszewicz  contributed to resources. Drs. Fleurie ,  Brengel-Pesce , and Monneret contributed to supervision. Drs. Peronnet  and  Terraz  contributed to visualization. Drs. Peronnet ,  Brengel-Pesce , and Monneret contributed to writing-original draft. Drs. Blein ,  Venet, Cerrato, Fleurie, Llitjos, Kreitmann, Terraz, Conti, Gossez, Rimmelé, Textoris ,  Lukaszewicz ,  Brengel-Pesce , and Monneret contributed to writing-review and editing. The REAnimation Low Immune Status Marker study received funding from the  Agence Nationale de la Recherche  through a grant awarded to BIOASTER (grant number  ANR-10-AIRT-03 ) and from bioMérieux,  Sanofi , and  GlaxoSmithKline.
Peaks were detected and integrated using the Minora algorithm embedded in Proteome Discoverer. Quantitative data were considered for proteins quantified by a minimum of two peptides and a statistical p-value lower than 0. 05. Protein abundances were calculated as the sum of all unique peptide intensities. Protein ratios were calculated as the median of all possible pairwise peptide ratios for a given protein. Only unique peptides were considered for this calculation. Normalization was performed based on total peptide amount. Data were searched by SEQUEST through Proteome Discoverer 2. 2 (Thermo Fisher Scientific Inc. ) against a collection of peptide sequences originating from the conceptual translation of RNAseq data from Euscelidius variegatus. The dataset analyzed during the current study are available in the Transcriptome Shotgun Assembly database on NCBI: Euscelidius variegatus, transcriptome shotgun assembly repository, GenBank accession: GFTU00000000.
Acknowledgements We acknowledge  Delphine Desqué ,  Denis Lacaze ,  Marielle Levillain  and  Thierry Lusseau  for plant and insect production. We thank  Atika Zouine  and  Vincent Pitard  for technical assistance at the  Flow cytometry facility ,  CNRS UMS 3427 ,  INSERM US 005 ,  Univ. Bordeaux, F-33000 Bordeaux, France. Francesca Canuto  was supported by a Ph. D. fellowship from  INRAE-SPE  and the  Regional Council of Nouvelle-Aquitaine  (Grant  AAPR2020A-2019-8404510/810 ). This research was funded by the  Plan National contre le Dépérissement du Vignoble  (Project  RISCA  coordinated by  Audrey Petit of French Wine and Vine Institute).