dataesr/scientific-paragraphs-categorization huggingface dataset:
A consensus sequence was built using forward and reverse sequences with BioEdit software (Figure  S1 )  [43]. Strain identification was performed using nucleotide Blast on the NCBI nr/nt Nucleotide Collection (National Center for Biotechnology Information). Phylogeny was inferred from the alignment with reference 16S rDNA sequences retrieved from Genbank using MEGA7 software (version 7. 0)  [44]  (Table  S1 ). The Escherichia coli type strain sequence (ATCC 11775 T ; X80725) was used as the outgroup. The optimal tree was built using the neighbor-joining method (NJ)  [45]. The bootstrap support of nodes was assessed with 1000 replicates  [46]. The evolutionary distances were computed using the Jukes-Cantor method  [47].
Genotyping analysis 2. 2. 1. DNA extraction Genomic DNA (gDNA) was extracted from formalin-fixed paraffin-embedded (FFPE) samples (decalcification data not available). gDNA was isolated from tissue samples by using a Maxwell 16 Instrument (Promega) with the Maxwell 16 FFPE Plus LEV DNA purification kit (Promega) according to the manufacturer's instructions.
After 2 min at 80°C, the plate was kept at room temperature for 20 min before processing the pyrosequencing reaction. Pyrosequencing involved use of PyroMark Gold Q24 reagents (Qiagen) on a PyroMark Q24 (Qiagen). The sequences analyzed were GACGACCTA for IDH1 and GCAGGCACG for IDH2. The nucleotide dispensation order was CGTACTAGACTA for IDH1 and CGCTACTGTACAC for IDH2 (Table  1 ). Results were analyzed using PyroMark sw 2. 0. 6 software (Qiagen).
1 cycle sequencing kit (Applied Biosystems) according to the manufacturer's protocol, and 2 µM primers. The sequencing products were purified by using the Big Dye Xterminator purification kit (Applied Biosystems) before running on a 3130 Genetic Analyzer (Applied Biosystems). The sequencing data were analyzed by using Sequencing Analysis software (Applied Biosystems). The sequencing profile was not interpretable for 17/88 cases (19. 3%) due to unsuitable genetic material.
Statistical analysis Statistical analysis involved use of GraphPad Prism 5 for Windows (GraphPad Software. La Jolla, CA, USA). We compared patients with a skull-base chondrosarcoma to those with a chondrosarcoma involving the facial skeleton and those with a chondrosarcoma of the larynx and trachea. Continuous variables were compared using Mann-Whitney test, categorical variables using Fisher's exact test. P < 0. 05 was considered significant.
Objective: this review aims to analyze scientific articles about cell proliferation index using Ki-67 in odontogenic keratocyst and compare these papers to estimate the average index of this lesion. Material and Methods: two researchers performed a literature search independently in the MEDLINE/PubMed database and 28 articles containing relevant data were selected. Results: the immunohistochemical analysis methodology showed great variability among all the papers, with unclear and unified methodologies, making the comparison among different studies difficult. Conclusion: considering odontogenic keratocyst as a lesion with an uncommon clinical behavior, an adequate classification for it is necessary, so an appropriate treatment with a good prognosis for the patient can be established according to its nature.
Material and Methods Two researchers, independently, conducted a literature search on the MEDLINE/PubMed database in June 2018. The search had no year restriction, using the abstract format and sorted by most recent. The Medical Subject Headings (MeSH) terms used were: Ki-67 antigen, odontogenic cyst, keratocysts and mitotic index. Those words were combined with other terms: odontogenic keratocyst, keratocystic odontogenic tumor, Ki-67 and cell proliferation index. The search was performed in two stages: the first used the keywords chosen in different combinations, and the articles were selected based on their titles. Articles that were case reports and that were not in English language were excluded. On the second stage, the researchers accessed the full texts of articles that were considered eligible for inclusion for further evaluation and selection.
Unclear methodology 2 Impossible to reach a single PI value with the information on the paper 11 PI calculated counting the positive cells per millimeters of basal membrane 2 Article with median and divided into two scores: more than 10% and less than 10% of immunopositivity. 1 The authors did not provide the PI separately for syndromic OKC and sporadic OKC 1  Three papers divided the indexes into layers (basal and parabasal). 14, 17, 23 One provided only the number of positive cells and the total number of cells counted but did not provide a PI. 24 Two papers divided the OKC evaluation according to inflammatory density  25  and in follicular and extrafollicular OKC. 21 To include these papers in our study, an overall PI was calculated for each study. All data collected are shown in Tables  2  and 3.
Acknowledgment This work was funded by grants from the  National Science Center, Poland  ( UMO-2016/21/B/NZ1/00292  to JP,  UMO-2014/15/D/NZ6/02546  to DM,  2016/23/N/NZ1/01513  to ZN) and  NIH  ( DE022597  and  DE026280  to JP and  R01 EB026332  to MB).
Consequently, remote oral diagnosis is considered as a tele-expertise to fit with the actual legal framework of telemedicine. An oral tele-expertise can be defined in four steps (figure  1 ): 1. Recording: A healthcare giver from the nursing home who has been trained in dentistry and teledentistry has to record medical data, dental history and videos from the patient's mouth. In the e-DENT project the device used is an intra-oral camera using fluorescence and in the Tel-e-Dent project it is an endoscope. 2. Transmission: the files recorded by the caregiver are sent securely to a specific server to be stored in compliance with the law. 3. Analysis: the dentist, identified by the patient, connects to the server and can read the medical data and videos. He/she then analyses all of the information and proposes a diagnosis and a treatment plan and he/she will develop a proposal for a specific medical pathway adapted to the medical status, cognitive issue and oral treatment needed.
Oral informed consent was provided by all participants, and all data were kept confidential. Baseline characteristics of the patients including age, sex, location of the lesions, Fitzpatrick's skin type, and duration of the lesions were recorded. Participants were instructed not to use topical products on their skin as of the night prior to the biophysical assessments. Before measurements, participants were asked to rest and relax in the supine position for 20 minutes in a standard atmosphere (20°C-25°C; 25%-30% humidity). The measurements occurred in the same room (the room that was used for rest and relaxation) and chair, with the same ambience and room setting, and all measurements were performed between 9:00 a. m. and 12:00 p. m. Measurements were done on one of the prominent LP lesions and on perilesional and symmetrical uninvolved skin.
The SLEB was defined as a clearly visual low-echogenic band in the upper dermis, directly underneath the epidermal entrance echo. SLEB thickness was evaluated in micrometers by measuring the vertical distance between the lower margin of the epidermal entrance echo and the lower margin of the echopoor band. All statistical analyses were performed using SPSS software, version 18 (SPSS Inc. , Chicago, IL). Mean and standard deviation (SD) were used for the description of the quantitative data, and a comparison of the quantitative data between the two groups was performed with a paired sample t test. The statistical significance level was defined as p b. 05.
CASE REPORT In November 2018, a 26-year old female was referred for hemoptysis and cough of several weeks' duration. Her past medical history was significant for the diagnosis, at the age of 13 years, of an ameloblastoma of the right mandible (tooth 48) treated by surgery with clear margins and no evidence of locoregional relapse since then. A CT scan was performed which showed numerous bilateral nodules. The patient underwent a CT-guided percutaneous lung biopsy of one of these nodules. Histological examination was in favor of lung metastasis of ameloblastoma. The patient was enrolled in our molecular screening program after signature of informed consent (Bergonié Institut Profiling study, NCT02534649) and gave also written informed consent for the publication of this case report. Next-generation sequencing of the metastatic tumor sample was performed as previously described and revealed the presence of a BRAF gene V600E mutation [Figure  1 ;  (2) ].
Methods This literature review focused on large terrestrial mammalian carnivores (>15kg,  Ripple et al. , 2014)  and their ungulate prey. We further included the coyote (Canis latrans, 7-18 kg,  Way, 2007)  since it is the top predator of ungulates in several ecosystems. We conducted a literature search using the Web of Science Core Collection database (WoS hereafter). We searched all publications for the following key words: "antipredator strategy" (we also checked with the word "anti-predator" throughout), "antipredator behaviour (or behavior)", "antipredator responses", "risk of predation", 'predation risk", "risk effect", "non-lethal effect", "non-consumptive effect", "ecology of fear", and "landscape of fear". All of these keywords were combined (with the Boolean connector AND) with the three following keywords: "large mammal", "large herbivore" and "ungulate".
We then performed a search with the keyword "predation risk" combined with the 29 species of large terrestrial carnivores (Appendix A(a)). Based on titles and abstracts, we excluded publications that were not empirical studies of NCE in a large carnivore-ungulate system. 266 publications were selected for a full and comprehensive reading. Studies that were either reviews or modelling works (n = 55) were excluded at this stage. We then excluded studies that quantified predation risk by using habitat data only (n=53), studies that focused on the CE of predators on juvenile prey (n = 9), and studies that looked at the effect of prey on their predator (n = 14). At the end of this selection process, we retained 135 publications (see Appendix B for the summary of the selection process of the publications kept for this review, and see Appendix C for the list of publications).
Only a few studies existed between 1992 and 2004, and since then the number of publications has kept increasing (Fig. 1 ). This increase is due to the growing interest for NCE in the study of predator-prey interactions in large mammals but also to the improved technology (e. g. GPS technology, camera trapping) that makes these studies possible to conduct. During our literature search, we realized that most of the studies before the 1990s are not properly referenced electronically in WoS (missing keywords, abstract…). Thus, for this systematic review, we only considered studies from 1992 onwards. We are aware that a few pioneering studies took place before 1992, particularly with regard to vigilance behaviour, but we favoured maintaining a systematic approach based on the post-1992 WoS database over including pre-1992 studies that we were aware of, and therefore likely missing others.
Level of anthropization of the study systems Only 15% of terrestrial lands and inland water are protected areas (World Database on Protected Areas). However, 62% of the 135 studies reviewed here were in protected areas. Basing our understanding of NCE from studies conducted in protected areas only would lead to biased predictions about the strength of NCE in contexts where human and their activities may interact with predator-prey interactions  (Smith et al. , 2015; review in Kuijper et al. , 2016). Here, we used the human footprint index (from  Sanderson et al. , 2002, dataset from SEDAC, WCS and CIESIN)  to evaluate the level of anthropization of the systems studied. In our dataset, 96 studies (out of the 135 selected for this review) reported GPS coordinates and we were able to collect the geographic position of an additional 37 studies (we encourage future studies to consistently report the GPS coordinates of the study systems).
Methodology used in NCE studies A wide array of methodologies were used to assess predation risk on the one hand, and prey responses to predation risk on the other hand. The type of data used to assess predation risk can be classified into six categories: GPS or VHF telemetry, direct observation, camera trap, census (e. g. transect sampling), sign of predator presence (e. g. scat or carcass of prey), and simulated (olfactory or auditory) cue (Fig. 3 ). The many ways of assessing predation risk from these different types of data are presented in details in  Moll et al. (2017). The type of data used to assess prey responses to predation risk can be classified into seven categories: GPS or VHF telemetry, direct observation, camera trap, census (e. g. transect sampling), sign of prey presence, biological sample (e.
Zebra case study and comparison with alternatives to KDE I analyzed data collected from a plains zebra (Equus quagga) in and near Hwange national park, Zimbabwe (26. 861E, -18. 624 N). The study individual (individual local identifier: Ganda) was monitored between Jan 2011 and Sept 2012 with a collar-mounted GPS that recorded one location every hour  [34]. I rescaled the recent Hwange vegetation map  [35]  at a 150 m resolution and pooled vegetation classes into 4 categories to reduce computing time for this illustration case. Other landscape features known to influence zebra space use included water holes, a railway with adjacent road that marks the northern border of the park, and a town. These were all included in the step selection model (Eq. 3). I compared four variants of the KDE methodology: a naïve reference function-based bandwidth optimizer that did not account for temporal autocorrelation (KDE), the robustized approach where the data were subsampled before analysis (KDEr), the AKDE approach where the autocorrelation structure was incorporated in the reference function (AKDEc), and finally the new methods (E-AKDE and SE-AKDEc).
For KDE and KDEr I used the kde2d function in R-package MASS. The position autocorrelation time τ required to subsample the data for KDEr was estimated from the ctmm. fit routine in R-package ctmm  [33]. For AKDEc, I used the akde function in ctmm within the recommended analytical protocol  [33]. For E-AKDE, the algorithm and mathematical justification are described (with words) in Additional file 1. For SE-AKDEc, I used the AKDE bandwidth but then applied the environmental interaction weights when computing the distribution. I also implemented three non-KDE methodologies. First, I used the asymptotic distribution of the fitted Ornstein-Uhlenbeck model to draw the ellipses that most closely approximated the home range and core area. Second, I implemented the movement-based kernel density estimator (MKDE), in which the kernels are replaced by step selection functions, i.
In addition, the core area was markedly irregular in shape, reflecting the avoidance of the densest woodland cover type and the preference for pure grassland. The composition of the home range remained similar across all methodologies. In particular, while c. 30% of the raw data was recorded in grassland, only 1-6% of the estimated home range was estimated to be grassland. This result stems from the scarcity and patchiness of grassland in the area, meaning that Fig. 1  The 50% (dark grey) and 95% (light grey) isopleths of the utilization distribution of a plains zebra, as estimated by the 8 estimators in this study. The dashed diagonal line represents a railway with adjacent road that marks the border of the national park where the zebra was captured. KDE, KDEr and AKDEc represent three strategies to choose the bandwidth of the kernel density estimator.
Samples taken included water (from various sampling sites including bank-side, centre of the water body, water from within aquatic vegetation, shaded areas and sun exposed areas), soil (underwater sediment), detritus, dominant aquatic and semi-aquatic plant species, algae and biofilms. In addition, abiotic data such as dissolved oxygen, pH, conductivity and water temperature were measured. Only five samples out of 163 were found positive for both IS2404 and KR in three different water bodies from the Sinnamary and Tonate districts. All three positive sites were typologically similar, with areas that were highly stagnant, forming shallow water bodies. There was no statistical significance for differences in abiotic data between positive and negative sites. These three freshwater bodies with qPCR positive samples were on floodplains, suggesting that in French Guiana, this type of environment constitutes a source for environmentally-persistent mycobacteria or a "receptacle" concentrating these bacilli from further upstream (Fig.
Biodiversity Drivers of M. ulcerans Distribution Across Freshwater Habitats A study on a larger number of environmental samples was conducted to study the links between the drivers of freshwater foodwebs in French Guiana and the emergence of M. ulcerans in the environment. A collection of 3600 invertebrates and fish were sampled and catalogued from 17 freshwater sites across French Guiana  [7, 13]. Landscape data as a measure of land use along a gradient from urban through agricultural to pristine rain forest were extracted from two maps including the CORINE 2006 European land cover map using satellite-derived data and the Hansen deforestation map. Each site was surveyed twice in the rainy season, during February and June 2013. Each insect taxon was positioned in the foodweb using a measure of stable isotopes (nitrogen δ 15 N and carbon δ 13 C).
For each site, the M. ulcerans load was calculated for each taxon from the qPCR data for both IS2404 and KR molecular markers. Each taxon's regional mean trophic niche width (metric of total available trophic niche space within a site), vulnerability (number of potential predator species for each prey taxon), connectance (a standard measure of foodweb complexity) and generality (number of potential prey species each predator taxon can consume) were calculated and weighted by the abundance at each site. The relationships between land cover data and trophic niche width were identified at the local site level using general additive models (GAMs) of the level of deforestation within each buffer zone in addition to urban and agricultural cover. A total of 78 different freshwater taxa were identified, of which 383 specimens representing 44 taxa tested positive for both IS2404 and KR.
Library AMPure XP beads (Beckman Coulter, Inc. ) were constructed from 1. 4 g genomic DNA with an end-repair step and quantified using a Qubit assay (Life Technologies, Carlsbad, CA, USA). Then, 74. 28 ng was loaded onto the flow cell, and 1,359 pores were activated and analyzed online using the WIMP workflow. A total of 59,875 reads were generated after a 23-minute run; 53,206 reads analyzed by the software EPI2ME yielded 130. 6 Mb, an average 2. 18-kb length, and a maximum read length of 68. 2 kb (SRA number ERR3336325). Adding MinION reads to MiSeq reads yielded 367 contigs assembled using SPAdes software version 3. 5. 0 (9) with a 5,267,061-bp genome and a 65. 5% GC content (ERS3388536). Contigs of under 800 bp were removed after BLASTn analysis against the NCBI database (identified as possible contaminants).
Annotation using Prokka version 1. 12 (10) yielded 5,266 predicted genes, 5,215 protein-coding genes, and 51 RNA genes comprising 47 tRNAs, 3 rRNA operons, and 1 transfer-messenger RNA (tmRNA). In addition, MiSeq and MinION reads were mapped with the most closely related Mycobacterium liflandii 128FXT plasmid, pMUM002 (GenBank accession number EU271968), using CLC Genomics Workbench version 7 to yield one 190,582-bp plasmid (62. 6% GC content) encoding genes for mycolactone synthesis (mlsA1 [20 kb], mlsA2 [4 kb], and mlsB [16 kb]). Mapping detected IS2404 (184 bp, 8ϫ depth normalized with a monocopy rpoB gene) and IS2606 (1,404 bp, 2ϫ normalized depth). Genomic similarities estimated using the OrthoANI software tool version 0. 93. 1  (11)  and in silico DNA-DNA hybridization estimated using the Genome-to-Genome Distance Calculator (GGDC) version 2.
Moreover, the genetic similarities between these two isolates may orient further research on the reservoirs of M. ulcerans in French Guiana, focusing, for instance, on amphibians and, more generally, on a wide variety of freshwater species. Data availability. The Mycobacterium ulcerans CSURP7741 genome sequence has been deposited at NCBI under the BioSample accession number ERS3388536. MiSeq reads have been deposited under the SRA accession number ERR3335404, and MinION reads have been deposited under the SRA accession number ERR3336325.
ACKNOWLEDGMENTS J. S. and N. H. received a Ph. D. grant from the  Fondation Méditerranée Infection (Marseille, France). M. C. was funded by a postdoctoral fellowship from the  Agence FIG 1 Pangenome tree incorporating Mycobacterium ulcerans strain CSURP7741  and all available complete M. ulcerans genome sequences in the NCBI database (as of January 2019). Nationale de la Recherche  ( ANR-17-CE35-0006-01 PRIME ). This work was supported by the  Agence Nationale de la Recherche  ( ANR-17-CE35-0006-01 PRIME ) and by the  French government  under the  Investissements d'Avenir (Investments for the Future) program  managed by the  ANR  (reference, Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence Alpes Côte d'Azur and European funding FEDER   PA 0000319 IHUBIOTK.
Datasets Environmental impacts of pig production chains based on European local pig breeds were estimated for 48 farms: 25 farms raised the Gascon breed in the Noir de Bigorre production chain in France (FR; these farms representing 42% of all farms in the production chain), eight farms raised Mora Romagnola pigs in Italy (IT; 26% of farms raising this breed) and 15 farms raised the Krškopolje breed in Slovenia (SI; 12% of farms raising this breed). The farms were classified as farrow-to-feeder (4 in FR, 1 in IT and 7 in SI), feeder-to-finish (10 in FR, 0 in IT and 4 in SI) and farrow-to-finish (11 in FR, 7 in IT and 4 in SI) farms. Piglets enter in the feeder system with an average BW of 9. 3, 11. 2 and 12. 4 kg in FR, SL and IT, respectively (Table  1 ). The assessment was based on responses to surveys obtained through interviews with farmers and/or their employees.
Life cycle inventory Resource use and emissions associated with the production and delivery of inputs for crop production came from the ecoinvent database V3 (SimaPro LCA software 8. 0, PRé Consultants, Amersfoort, The Netherlands). Energy use for lighting and heat lamps in farrowing units was calculated, but not the emissions and resources used to construct buildings or outdoor sheds. Veterinary and cleaning products were not included. For the feed ingredients that are coproducts (e. g. soybean meal, rapeseed meal, wheat bran, whey powder), resource use and emissions were economically allocated.
Production of feeds and feed ingredients Feed composition was collected on farms from the labels on bags. The farmers provided information about the crude protein (CP) composition and total phosphorus (P) content of feed mixtures produced on-farm (Table  1 ). To further calculate nutrient contents of feeds produced on-farm, feed formulas and nutrient contents of feed ingredients provided in the INRA-AFZ feed tables  (Sauvant et al. , 2004)  were used. Life cycle inventories (LCIs) of feed ingredients in France came from the EcoAlim dataset  (Wilfart et al. , 2016) , while LCIs for feed ingredients in Slovenia and Italy were adapted from it based on yields and fertilization rates in each country (Table  A3 ; supplementary material). Additional processes were obtained from AgriFootPrint® database to include impacts of processing feed ingredients and to obtain a complete LCA of feed ingredients (Table  A4 ; supplementary material).