dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This research was partly conducted under the framework of  UNESCO   IGCP  project  692 -" Geoheritage for Geohazard Resilience ". B. v. W. d. V. received support from the  Agence Nationale de la Recherche of the French Government , through the  programme 'investissements d'Avenir'  ( 16-IDEX-0001 CAP 20-25 ) and F. V. was supported by the  ÚNKP-20-3 New National Excellence Program of the Ministry for Innovation and Technology  from the source of the  National Research, Development and Innovation fund.
Geosciences 2021 ,Figure 4. 20214 Figure 4. Shaded Light Detection and Ranging (LiDAR) digital terrain models (DTM) of the stu are shown with the 500 m × 500 m data grid (described in Section 2. 6). Thematic data were dete mined for every cell. Source of the DTM: [34]. Red outlines are indicating the extents of the stud scoria cones.
Reptiles were captured in the area of the shelter, on the same walls where the sticky traps were placed (Fig. 1c ), by lassoing or by hand. Species of reptiles were identified using reference keys  [41] , and then physically examined to assess their health status. Anamnestic data (e. g. , species, biological stage, sex, physical abnormalities such as tail loss or predator-induced wounds) were recorded in each animal's file. A small amount of blood was obtained via lizard tail fracture or by cardiocentesis when animals were adults and non-gravid females. Blood samples were stored at -20 °C and tail tissue in 70% ethanol. For each animal, blood smears were performed and then assessed for the presence of Leishmania parasites  [42]  using Diff-Quik stain  [43]. Smears were rinsed in tap water to remove excess stain, and later evaluated using an optical microscope (LEICA DM LB2, Germany).
All sequences were aligned using the ClustalW program  [50]  and compared with those available in GenBank using the BLASTn tool (http:// blast. ncbi. nlm. nih. gov/ Blast. cgi). To determine genetic clustering of L. tarentolae, the representative ITS1 sequences obtained from lizard, sand fly and dog samples and from reference strains of L. tarentolae and L. infantum were phylogenetically analyzed along with those of other Leishmania spp. available in the GenBank database. Phylogenetic relationships were inferred using the maximum likelihood (ML) method based on the Kimura 2-parameter model  [51] , and discrete gamma distribution was used to model evolutionary rate differences among sites, selected by bestfit model analysis and based on the lowest score obtained by Bayesian information criterion (BCI) using MEGA6 software  [51].
e. , 1252 S. minuta and 29 P. perniciosus). Of female sand flies, 161 scored positive for Leishmania spp. (12. 6%) by dqPCR (Table  2 ). Among them, 155 S. minuta (95. 7%) and two P. perniciosus (1. 2%) were positive for L. tarentolae, whilst four S. minuta scored positive for L. infantum (2. 5%), and only one was co-infected (0. 6%) for both Leishmania species. In addition, of 22 engorged females tested (14 S. minuta and eight P. perniciosus), the host mitochondrial cytb was amplified from 10 specimens (45. 4%, nine S. minuta and one P. perniciosus). Cytb sequences detected in S. minuta displayed 99. 67% of nucleotide identity with that of Homo sapiens (GenBank: JN315800), whilst that from P. perniciosus showed 84. 4% of identity with lagomorph species Ochotona cansus (GenBank: MN547415).
tarentolae species identification showing a nucleotide identity of 98. 7% with the reference sequence (GenBank: KU680858) available in the GenBank database and with L. tarentolae strain RTAR/IT/81/ISS21-G. 6c. The phylogram of ITS1 showed a close phylogenetic relationship by clustering all L. tarentolae sequences herein obtained in a species-specific clade (Sauroleishmania), with the exclusion of the other Leishmania species (bootstrap value of 95%) (Fig. 4 ). Sequences obtained for L. tarentolae from lizards, dogs and sand flies were deposited in GenBank (MW832546, MW832547, MW832548).
Acknowledgements The authors thank the veterinarian ( Oana Gusatoaia ) and the staff of the dog shelter who collaborated during field activities. The authors also thank  Riccardo Paolo Lia  ( Department of Veterinary Medicine, University of Bari, Italy ) for optical microscopy images used in figures,  Viviana Domenica Tarallo  ( Department of Veterinary Medicine, University of Bari, Italy ) for assistance with sand fly morphological identification and serological testing, and  Margherita Ceccoli  ( Department of Veterinary Medicine, University of Bari, Italy ) for assisting with laboratory activities.
However, studying evolution in TNBC has been challenging due to the difficulty of obtaining samples at all stages of the disease. In this context, we performed whole-exome sequencing (WES) on 16 samples from four patients with clinically aggressive disease who did not respond to several lines of treatment in order to analyze the evolutionary patterns of the cancerous cells, and to describe their genomic allelic composition and mutational patterns. Our analysis included a set of follow-up samples, such as tumor samples (before and after treatment), as well as samples from lymph nodes, skin metastases, and normal tissue. Our data show an extensive intra-and interpatient genetic heterogeneity. Additionally, these data indicate an early divergence to lymph nodes during TNBC carcinogenesis. Finally, we identified potential candidate genes that could be suitable for targeting with therapeutic precision agents which may contribute to preventing treatment failure in the future.
SNV e Indels Identification Sequencing reads were aligned to the human genome reference hg19 with BWA-MEM  [15]. GATK tools were used to sort reads, mark duplicates, and for base recalibration  [16]. Single nucleotide variations (SNV) were called with Mutect2  [17]  and annotated with ANNOVAR  [18]. Variants were filtered as follows: (i) variants with an allelic frequency of less than 0. 001 in 1000 genomes, ExAC and ESP6500 databases, were included; (ii) common SNP were excluded, unless they were reported as pathogenic in ClinVar; (iii) variants with at least two reads in both DNA chains in the tumor samples were kept; and (iv) variants with an allelic fraction of ≥0. 05 were also kept. We used ExAC to exclude non-pathogenic natural variation, considering that this repository includes the sequencing data of 5789 exomes of Latin and admixed Americans.
Driver genes were identified using two well-curated gene lists: Intogen v. 2019 (https://www. intogen. org/) and the list from Yates  [5]. Then, driver mutations were defined as pathogenic if they were previously described in ClinVar as pathogenic, if at least two out of three algorithms-SIFT, PolyPhen2 or MutationTaster-predicted them as deleterious, and if they were known hotspot mutations in COSMIC  [19]  and cBioportal  [20]  (Table  S2 ). All driver variants were manually curated by inspection of the BAM files using the IGV software  [21].
Evolutionary Tree Construction Phylogenetic trees were constructed with SNV, indels, and CNV, using maximum parsimony principles  [26]. Variants were coded with 0 and 1 to indicate the absence and presence of mutations, respectively, and processed in mesquite (http://www. mesquiteproject. org/) to generate nexus files. The phylogenetic trees were analyzed in PAUP (http: //paup. phylosolutions. com/) using a heuristic method and rooted with normal samples. The reliability of trees was evaluated with the Bootstrap Method  [27]. The trees generated were saved in Newick format and visualized with the FigTree software (http://tree. bio. ed. ac. uk/software/figtree/).
Patients with TNBC Exhibit a Variety of Clinical Scenarios For this study, we reviewed the database of patients diagnosed with TNBC and treated at the INCAN during April 2007 and April 2010. We chose 16 FFPE samples from four patients with aggressive disease and high-quality DNA to perform WES. Four clinical settings were represented by the samples: (i) primary treatment-naïve tumor, (ii) treated tumor, (iii) lymph nodes, and (iv) skin metastasis. All patients were diagnosed at advanced stage and received NAC treatment. None of them achieved a pCR and all died within three years of their diagnosis (Figure  1a ,b; Table  S1 ). Patient TNBC11 was a woman diagnosed at age 53 with a locally advanced TNBC. A first biopsy of the primary tumor was obtained and had pathogenic variants in TP53 and EP300, as well as amplifications in MCL1 and IGF1R (Figures  1  and 2 ).
Mutational Processes Act on Different Stages of TNBC To identify the mutational processes acting on TNBC, a mutational signature analysis using deconstrictSigs in the R language was performed. Eleven of the 30 mutational signatures previously described by Alexandrov  [28]  were detected with a median of 3. 5 per sample (range 1-5). Five of these signatures have been previously identified in BC, while the remaining six are identified for the first time in BC patients (Figure  3 ). Signature S01 (age-related) was prevalent in all samples and contributed the highest mutational rate. Signature S03 (homologous recombination deficiency) was detected in 81% of the samples, S06 (DNA mismatch repair deficiency) was detected in 44%, and S12 (unknown etiology) in 31%. Other signatures were found in fewer than 20% of the samples.
Acknowledgments: Héctor  Martínez Gregorio  is a doctoral student from the  Posgrado en Ciencias Biológicas ,  Universidad Nacional Autónoma de México  ( UNAM ) and has received a scholarship from  CONACyT. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the  International Agency for Research on Cancer/World Health Organization.
3 4Figure 3. 33 Figure 3. Mutational signature profile in TNBC patients. The samples are ordered according to the hierarchical grouping by mutational signature composition described above. The band of COSMIC breast describes the signatures found in the database of COSMIC Sanger, and the color palette shows the frequency of signatures in each sample. Rate is expressed as the relative frequency of the mutations associated with the corresponding signature. Mutational signatures previously identified in breast cancer samples in the COSMIC database are shown in red boxes. Patients are color-coded as in Figure 2. The tissue origin of the sample is indicated.
3 4 5 Figure 5. 355 Figure 5. Actionable alterations in TNBC according to the oncoKB database. The figure depicts the actionable alterations found in our study and the corresponding FDA-approved drug. The drugs were segmented according to tumor type. Signaling pathways are indicated on the right and the patient identification on the left. BC: Bladder Cancer; BRCA: Breast Cancer; CRC: Colorectal Cancer; NSCLC: Non-Small Cell Lung Cancer; OV: Ovarian Cancer; and ST: Solid Tumors.
Statistical Analysis Association studies were carried out with contingency tables using the Chi2 test and Fisher's exact test when Chi2 was not applicable. The Baptista-Pike method was used to obtain the 95% confidence interval for Odds Ratio (OR). Spearman's rank correlation coefficient rho was used to measure the correlations between anti-CCP2 antibodies, AhFibA, and antibodies to the 5 peptides and tested against the null hypothesis of rho =0. Statistical analysis was carried out using PRISM software for Windows (GraphPad Software, 169 San Diego, California, USA) and IBM ® SPSS ® Statistics version 20. The significance threshold was set at p < 0. 05.
PimpleFoam was used as a fluid solver, and it is described in the OpenFoam documentation  [26]  as a "Transient solver for incompressible, turbulent flow of Newtonian fluids on a moving mesh". It is based on an algorithm coupling velocity to the pressure equations. Equation (  8 ) was used to construct a first estimate of the velocity U i *. The pressure, P, was deduced from Equation (  9 ) using U i *. The velocity matrix could then be corrected. This operation was repeated until the solution reached the convergence criteria, which were set to 10 -6 for the error, before moving to the next time step (Figure  3 ). The motion of the structure was computed using the module named sixDoFRigidBodyMotion , which was added to PimpleFoam to model the fluid-structure interaction. This module computes the forces acting on the blades and converts them into point displacement for the close-rotor mesh.
3D Geometries and Mesh All the 3D blade geometries of this work were generated using the open source software QBlade  [27]. The hub geometry was completed with Blender (open CAD software) to model the entire rotor and hub. All the meshes were made using the meshing tool SnappyHexMesh of OpenFoam. They were unstructured except on walls where structured layers were added to control the boundary layer. A numerical flume was built to avoid side effects and to capture the turbulent wake. The AMI (arbitrary mesh interface) method used in  [12]  was activated to avoid mesh deformation during the rotor rotation.
Numerical Setup The turbulence intensity was equal to 5% in all the simulations according to the experiment. In all the simulations, the time step was calculated to respect the CFL condition, ensuring the numerical stability of the code for CFL < 0. 5, such that: CFL = u max • ∆T ∆x i < 0. 5, (19) where u max is the maximum velocity magnitude in the domain, ∆T is the computing time step in OpenFoam and ∆x is the length of the local cell at the u max position. The physical parameters of each simulation are described in Table  4  below:.
Flow-Induced Model Validation The lack of experimental data is critical when validating flow-induced models. As explained before, it is widespread to force the rotation with a motor in experimental approaches. The free rotation speed value cannot be determined with certainty, but owing to the mesh convergence study (see Section 3. 1), we know that the model is adequate for estimating forces in forced rotation cases. There are two ways to check the model validity. Firstly, the time evolution of the rotor speed. As long as the motion is computed only using the fluid forces, the direction of rotation and the rotor acceleration are parameters to be monitored. The total mass of each solid geometry was calculated based on its density, fixed to the fluid density to avoid the Archimedean thrust effects.
Measures Each physical therapist that participated in the study received a protocol for administering the questionnaires and informational packages intended for participants consisting of: written information regarding the research protocol, an informed consent sheet, a social demographic questionnaire and the questionnaires of interest in a random order. BCS were asked to fill out the following questionnaires in their Dutch version: the CSI, VAS, WPI, HADS, EORTC and a social demographic questionnaire. The CSI-9 was extracted from the full CSI. The same questionnaires were administered to female healthy volunteers except for the VAS and WPI. Measures were taken to limit missing data. That is, during data collection, each participant's questionnaires were checked for completeness, and, in case of incomplete responses, the participant was encouraged to complete the missing questions.
Statistical Analyses All statistical analyses were performed using the SPSS version 25 for Mac (IBM Corp. , Armonk, NY, USA). Data distribution was visually inspected by Q-Q plots and the Kolmogorov-Smirnov test. Associations were calculated with Pearson correlation coefficient for parametric variables and Spearman's correlation coefficients for nonparametric variables. If the correlation coefficient was >0. 9, this was considered as a very strong association, between 0. 7 and 0. 9 was a strong association, 0. 5 and 0. 7 was a moderate association, between 0. 3 and 0. 5 was a weak association and <0. 3 was a negligible association. The Cronbach α was used to measure internal consistency. An α between 0. 50 and 0. 60 was considered poor, between 0. 60 and 0. 70 was considered acceptable, between 0.
Results Data collection took place from November 2015 until March 2016. The present exploratory cross-sectional multi-center study included 69 BCS and 37 female healthy volunteers. Data of 65 BCS were used for analysis, because four were excluded before analysis: three because of metastasis and one because of pain unrelated to cancer. Data were missing for the subscale EORTC insomnia for only one patient, who was excluded for the corresponding analysis. For the other subscales, no missing data were present. The participants characteristics of the study sample are presented in Table  1. Cronbach's α coefficient for internal consistency was 0. 921 for the full CSI and 0. 860 for the CSI-9. All associations for the CSI and CSI-9 are presented in Table  2. Statistical analysis showed a very strong association between the CSI and CSI-9 (r = 0.
Data Collection The information was collected through a survey of 100 grass-fed dairy-cattle farmers in the mountain uplands of the Cantal and Puy-de-Dôme regions, France. Two survey campaigns were led in 2005 (n = 72 surveys) and 2009 (n = 28 surveys), focused on traditional Salers breed-system farmers and specialised dairy farmers. All surveys were conducted in the farms and included two interviewers, in a single pass, and a questionnaire. The breeders were recruited in 2005 from among the 90 breeders listed by the Tradition Salers Association, with the aim of analysing the relationships between the uses of fields and their geographical characteristics. The breeders were selected in 2009 by the Cantal and Puy-de-Dôme milk control authorities, with the aim of analysing the relationships between calving periods and forage management, using a questionnaire designed to collect the same information on fields and farms as in 2005.
Data Analysis The data were analysed at the grass-field scale, defined as an area of land used for the same purpose throughout the grazing season  [2]. The use made of each field was described from turn-out to pasture in spring until return to stall in the autumn, via eight variables. The starting use-date and ending use-date served to capture the time-in-use (in days) and its position in the season. The date of first use (cut or graze) was characterised in relation to a theoretical date of the beginning of ear emergence (TDBE), which was calculated on the premise that the Massif central permanent grassland starts heading (ear emergence) at 120 calendar days at an altitude of 400 m. a. s. l. , with a 6-day lag every further 100 m. a. s. l. interval  [16]. Details of the date of first cut and number of cuts were collected to complete the descriptive data on grass-resource harvesting.
Each field class was then related to the corresponding geographical characteristics. ANOVA was performed using XLSTAT software to determine the significant differences between the field groups and between the field clusters. When the result of ANOVA was significant, pairwise means were compared using the Tukey test, with a significance threshold set at 0. 05. The proportional slope variable was angular (arcsine square root) and was transformed to check the hypothesis of normal distribution and homogeneity of variances  [18]. For the same purpose, distance from the farmstead and surface area were natural log-transformed.
Results The total survey dataset counted 2341 fields: 1148 (49%) were grazed-only, 962 (41%) were grazed-and-cut, and 231 (10%) were cut-only (Table  2 ). The grazed-only fields were used earlier in the season and for longer periods, whereas the cut-only fields were used later in the season and for shorter periods. Grazed-and-cut fields were intermediate between grazed-only and cut-only on all use-type criteria. Grazed-only fields were the steepest and highest fields, grazed-and-cut fields were the lowest fields, and cut-only fields were the smallest and furthest from the farmstead (Table  2  and Figure  1 ).
Acknowledgments: The authors warmly thank the farmers surveyed, the ex-ENITA (now VetAgro Sup since 2010) students majoring in 'Livestock farming and production systems' in 2005 and 2009 who administered the surveys, the non-profit Association Tradition Salers,  Hervé Molénat  and  Sylvain Bouscayrol  for their valuable assistance on data processing,  Fabienne Blanc  and  Claire Agabriel  for their expert input and engagement as student supervisors, and METAFORM LANGUES (2 avenue  Michel Ange 63000 Clermont-Ferrand ) for the translation of the manuscript.
Cardiomyocyte Isolation and Size Determination Mouse cardiomyocytes were isolated using a standard protocol based on enzymatic procedures  (Mitra and Morad, 1985; Wolska and Solaro, 1996)  with minor modifications. After isolation, the cardiomyocytes were placed in a Petri dish and the images were captured with an inverted brightfield microscope (Axiovert). The images were used to analyze cardiomyocyte size by ImageJ software.
Then, the proteins were transferred onto a nitrocellulose membrane by a semi-dry transfer system (Trans-Blot Turbo, Bio-Rad) at 25 V, 1. 3 A for 10 min. After transfer, its quality and equal loading were checked by the Ponceau staining. After destaining, the membrane was saturated with 4% skim milk in TBS-Tween 0. 1% for 2 h at room temperature (RT) and incubated with a primary antibody (2 h at RT or overnight at 4 • C). Then the membrane was washed with TBS-Tween 0. 1% and incubated with an HRP-conjugated secondary antibody for 1 h at RT. The blots were developed with chemiluminescence reagent (ECL Prime, GE Healthcare) using CCD camera (ImageQuant LAS 4000, GE Healthcare). The quantification of signals was done using ImageQuant TL software (GE Healthcare). Tubulin, actin or total protein for the phosphorylated proteins (total and phosphorylated protein were probed on different membranes) signals were used for normalization.
After washing with PBS for 3 × 5 min, sections were incubated with secondary antibodies diluted in BSA/PBS (1 mg/mL) for 1 h at RT in obscurity. The sections were further washed in PBS and mounted with Vectashield medium containing DAPI for nuclei labeling (Eurobio, France). Acquisition of the images was carried out with a Leica TCS SP2 AOBS confocal microscope (LEICA Microsystems Heidelberg, Germany) equipped with a HCX PL APO CS 63×/1. 40 oil objective and driven by LasX software. Laser excitations were 350-364 nm, 488 nm, 543 nm and 638 nm and fluorescence emissions were precisely collected between 420 and 470 nm, 500 and 540 nm, 560 and 660 nm, and 650 and 690 nm for DAPI, FITC or Dylight488, rhodamine and Cy5, respectively. Optical sections were acquired by the mean of 9-12 µm z-stacks with a z-step of 1 µm and confocal pinhole size was at 1 (Airy units) for all channels.
Calorimetry Energy expenditure was determined using indirect calorimetry. Oxygen consumption (VO 2 ), carbon dioxide production (VCO 2 ) and respiratory exchange ratio (RER) were monitored using an indirect open circuit calorimeter in individual cages over 36 h  (LE 405,. Airflow rate was obtained from a gas pump and a flow meter. Data were recorded using a computerassisted data acquisition program (Metabolism Calculation Software, Version 2. 0. 2). Energy expenditure (EE) was calculated according to the following formula provided by the supplier: EE = (3. 815 + 1. 232 × VO 2 /VCO 2 ) × VO 2 × 1. 44 and was expressed in kcal/kg/h of body weight.
e. , describe comparisons within the levels each factor, when ignoring the levels of another factor), interaction of the two factors (P Factor1 × Factor2 ; indicating if the effect of one factor depends on the level of the second factor), and Holm-Sidak or Student-Newman-Keuls test for pairwise comparisons (p). Statistics was performed using Sigma Plot (Systat Software, San Jose, CA, United States). In figures, P-and p-values are given in gray with two digits after comma when not significant, and in black with three digits after comma when significant. A value of P or p < 0. 05 (for interaction P < 0. 1) was considered statistically significant.