dataesr/scientific-paragraphs-categorization huggingface dataset:
2014). Goshawk dietary data collected in Kielder Forest suggests that as the breeding population of goshawks increased, the mean number of owls killed each year by goshawks has also increased. An average of 5 [3-8; 95% CI] owls were killed each year when less than 15 goshawk home-ranges were occupied, compared to an average of 159 [141-176; 95% CI] owls killed each year when more than 24 goshawk home-ranges were occupied (see Appendix S1). Consequently, as predation on owls has increased with the abundance of goshawks in the forest, we used the total number of occupied goshawk home-ranges in a 964 km² area of Kielder Forest as a proxy of temporal variation in predation risk. However, as goshawks were monitored over a larger area than tawny owls, we also used an additional proxy of temporal variation in predation risk.
These spatial covariates of predation risk were calculated for each owl territory, every year. Although common buzzards Buteo buteo are abundant in our study site and are known to kill tawny owls  (Mikkola 1976 ), we did not include buzzards in any of our analyses of owl predation risk. This was because dietary data showed us that buzzard predation on owls in our study site was negligible (unpublished data). None of the temporal proxies of food availability were significantly correlated with the temporal covariates of predation risk. However, no two proxies of predation risk or two proxies of food availability were included in the same model as they were collinear (see Appendix S3 for all cross correlation coefficients). All temporal and spatial covariates were standardised (had a mean of 0 and a standard error of 1) to enable their effect sizes to be compared.
Juvenile survival As recapture data were not available for male owls in all years, our analysis of juvenile owl survival was based on female owls only, ringed as chicks between 1985 and 2012 (N=1,082), with the last recapture of individuals in 2013. The sex of individuals never recaptured as adults or sexed as chicks using DNA was unknown, as juvenile owls cannot be accurately sexed without molecular analyses. However, the sex ratio of chicks born in our study site was even 1:1 (N =312, over 4 years;  Appleby et al. 1997). Consequently, we randomly assigned half the number of chicks born each year minus the number known to be female as females, as done in previous analyses  (Nichols et al. 2004; Millon et al. 2010). The rest of these chicks were assumed to be males and excluded from the analysis.
Acknowledgments We thank  B. Sheldon  and two anonymous reviewers for all their helpful comments on a previous version of the manuscript. Our thanks also go to  M. Davison ,  B. Little ,  P. Hotchin ,  D. Anderson  and all other field assistants for their help with data collection and Forest Enterprise, particularly  Tom Dearnley  and  Neville Geddes  for facilitating work in Kielder Forest. This work was partly funded by  Natural Research Limited  and a  Natural Environment Research Council  studentship  NE/J500148/1  to SH and grant  NE/F021402/1  to XL. Forest Research funded all the fieldwork on goshawks, tawny owls and field voles during 1973-1996. In addition, we are grateful to  English Nature  and the  BTO  for issuing licences to visit goshawk nest sites.
Approximately 1 in 300 persons (0. 3 %) has low or undetectable TPMT enzyme activity, whereas about 11 % are heterozygous for TPMT gene mutations resulting in intermediate thiopurine methyltransferase activity  [3]. Currently, 37 alleles responsible for TPMT deficiency (TPMT*2-38) are known  [4]. However, three alleles: TPMT*2 (c. 238G[C, p. Ala80Pro, rs1800462 in the exon 4), TPMT*3A (a combination of c. G460A, p. Ala154Thr, rs1800460 in the exon 6 with c. 719A[G, p. Tyr240Cys, rs1142345 in the exon 9) and TPMT*3C (c. 719A[G) are responsible for 80-95 % of inherited TPMT deficiency in different populations all over the world  [5]. According to the Single Nucleotide Polymorphism Database (dbSNP, 1000 Genomes) the variants c. 460G[A, c. 719A[G and c. 283G[C of the TPMT gene are reported with the global minor allele frequency of 1.
High Resolution Melting Analysis We designed primers to cover c. 1. The sequence of primers for exon 6 and 9 amplification of the TMPT gene had been published previously  [14]. Products were amplified using the Type-it HRM PCR Kit [Qiagen] on a Rotor-Gene Q instrument [Qiagen]. The PCR was performed according to the manufacturer's instructions in a volume of 16 lL containing 8 lL of 29 HRM PCR Master Mix, 0. 55-2. 2 lL of each primer (5 pmol/lL) and 50 ng of DNA. PCR amplification conditions in the four loci were as follows: 95 °C of pre-incubation for 5 min, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing for 30 s at 55 °C and extension at 72 °C for 10 s. The HRM analysis was carried out from 70 to 90 °C, raising the temperature by 0. 1 °C degree at each step. Data collection and analysis was performed using Rotor-Gene Software, version 2.
2c ), and sequencing revealed three corresponding genotypes T/T, T/C, and C/C in position c. 94. In terms of the validation all three repeats of the HRM analyses for each sample showed consistent results. Moreover, the genotypes identified with multiplex HRMA for TPMT gene loci c. 238G[C, c. 460G[A  and c. 719A[G as well as ITPA gene locus c. 94C[A indicated 100 % accuracy for the whole group compared to the data from RFLP analysis and DNA sequencing. The dropout rate from the established multiplex HRMA was 4. 17 % (3 samples per 72), thus staying above our general research mutation screening success rate target of 95 %. The optimization was designed to obtain equal peak height of both products in melt profile. Therefore, the PCR containing primer ratios 1:1, 2:1, 3:1 and 4:1 were prepared and analyzed by HRMA.
Statistical analysis Variance analyses were carried out using PROC GLM of SAS (SAS Version 9. 1. SAS institute Inc. , Cary, NC, USA) separately for each animal type to test the effect of cooking  1 ). Least square means separation was carried out using the Tukey test and differences were considered significant at P < 0. 05. A C C E P T E D M A N U S C R I P T temperature (Table To study relationships between meat sensory scores, Z-scores were calculated for each temperature to remove animal type (gender and breed) and country effects. Z-scores represent the deviation of each observation relative to the mean of the corresponding animal type in each country and were calculated using PROC STANDARD of SAS, which standardizes data to a mean of 0 and standard deviation of 1. More precisely, the standard score of a raw score x was calculated using the following formula:  = µ  where: μ is the mean animal type and σ is the standard deviation of each corresponding animal type  (Gagaoua, Terlouw, Boudjellal, & Picard, 2015a).
The PROC CORR of SAS was used to determine the Pearson's correlation coefficients between the attributes. Correlation coefficients were considered significant at P < 0. 05. Principal component analyses (PCA) were carried out to visually illustrate the effects of temperature on the distribution of the sensory attributes for all animal types (assessed by the UK sensory protocol) and for young bulls (assessed by the French protocol). The PCA's were based on partial datasets of the Z-scores calculated per animal type, sensory protocol and endpoint cooking temperatures using PROC PRINCOMP of SAS. The Kaiser-Meyer-Olkin (KMO) measure, known also as Kaiser's Measure of Sampling Adequacy (MSA) was applied to test the validity of the sampling  (Gagaoua et al. , 2015b). The overall MSAs were computed using PROC FACTOR of SAS and are given for each PCA.
Relationships between sensory attributes Consistent correlations in partial datasets of the Z-scores (all animal types) were found between the different sensory attributes at the two cooking end-point temperatures for the samples assessed in the UK (Table  5 ). Tenderness and juiciness were positively correlated (P < 0. 001; r = 0. 33 vs. 0. 42; at 55 and 74°C, respectively). Tenderness was positively correlated with beef flavour at both temperatures (P < 0. 05; r = 0. 15 vs. 0. 29; at 55°C and 74°C, respectively) and negatively correlated (P < 0. 05; r = -0. 15) with abnormal beef flavour at 55°C only. Beef flavour was negatively correlated with abnormal beef flavour (P < 0. 001; r= -0. 62 and -0. 56, at 55°C and 74°C, respectively) and was positively correlated (P < 0. 05) with juiciness at 74°C.
Similarly, when the dataset of the young bulls assessed by the French protocol were considered, consistent correlations were found between the different sensory attributes (Table  6 ). Some correlations were similar to those found for the complete UK data set. At both 55 and 74°C, tenderness was positively correlated with juiciness (P < 0. 001; r = 0. 40 and r = 0. 65, respectively) and beef flavour (P < 0. 001; r = 0. 40 and r = 0. 50, respectively). In addition, at both temperatures, juiciness was correlated with beef flavour (P < 0. 01; r = 0. 23 vs. 0. 49; at 55 and 74°C, respectively). Beef flavour was negatively correlated (P < 0. 05) with abnormal beef flavour at 74°C only. Relationships between sensory scores were visualized using PCA. When the PCA was computed using the UK sensory ratings for samples of all animal types (Figure  1.
Acknowledgements This work was part of the  EU   FP6  Integrated Project ProSafeBeef, contract no. FOODCT-2006-36241. The INRA reference of the French young bulls is AQ284. Funding by the  European Union  with respect to animal production and laboratory analyses is greatly acknowledged. We also thank all colleagues involved in the management of the animals in the different countries and those who assisted with data collection, slaughtering of the animals and muscle sampling. A grant from  PROFAS B+ program  to  Dr. Mohammed Gagaoua  (PhD) is gratefully acknowledged.
Wavefront reconstruction and discussion The calculation of the wavefront was performed using Igor Pro and ImageJ softwares. For the three images presented in Fig. 4 , taking into account the 7 by 7 central spots, the program determines the coordinates of the centroids of each spot which are directly related to the local slopes of the THz wavefront. It means that the wavefront is reconstructed for a beam diameter of 12 mm. The final calculation of the wavefront is performed by using a least-squares method in which the wavefront W(x,y) is represented by a linear combination of the Zernike polynomials  [12] : ( , ) ( , ) i i i W x y a Z x y =  (2) where a i represents the amplitudes of the polynomial Z i , each polynomial corresponding to one specific aberration. The calculated wavefront of the planar (resp.
The SNP occurrence was 63. 90% in patients with sporadic PC (n = 36), and decreased to 27. 30% in cancer-free control subjects (n = 44) (Figure  1 ). The frequency of the T allele was 37. 50% (n = 72) in the PC cohort versus 17. 05% (n = 88) in the control group (P-value = 0. 0017). Comparing the T allele frequency in the PC group with the rs488087 SNP data bank (UCSC genome browser, n = 1275) showed that data remained significant (37. 50 % versus 23. 966%, P-value = 0. 0041). In addition, the T allele frequency of the control group showed no significant difference with that of the rs488087 data bank (P-value = 0. 0669). Therefore, the limited size of cohorts analyzed by Martinez et al. [53]  did not appear to impair the statistical significance of their results. Sequence analysis of patients with non-malignant disease of the pancreas (non-MPD) revealed that 36.
[53]  found no significant difference between the OC group (23. 70%) and control group (17. 05%) (P-value = 0. 1108), or between the OC group and the rs488087 data bank (23. 70% versus 23. 966%, P-value = 0. 4725). In view of the clinical interest of these results, Martinez et al. [53]  constructed two probes to discriminate between the C and T SNP alleles of BSDL by droplet digital PCR (ddPCR). Analyses obtained on 143 patients by ddPCR matched 100% of those genotyped by Sanger sequencing. This absolute concordance demonstrates the high specificity of these probes to rs488087 SNP and their capacity to allow its simple, rapid and specific detection. Martinez et al. also used specific properties of this ddPCR technique  [77]  to count the number of copies of DNA target sequence in each of the DNA samples tested by Sanger sequencing.
In the case of cancer, some associations between the presence of a SNP and splicing modification have been identified. This is the case for the gene encoding cyclin D1 (CCND1) which displays an A870G silent polymorphism that alters splicing and therefore induces a susceptibility to the development of lung cancer [79]. Many SNPs can generate new splicing sites in exons of the p53 gene, thereby producing a truncated protein  [80]. Since the C-terminal domain of BSDL is involved in intracellular processing of BSDL as well as its enzyme activity,  [81]  the authors determined whether the SNP rs488087 could affect BSDL mRNA splicing by an in silico study (unpublished data). Results obtained using the "Human Splicing Finder" software (http://www. umd. be/HSF/) revealed that the presence of the minor T allele induces the formation of a regulatory sequence of ESE type splicing.
Acknowledgments The authors are particularly grateful to  Drs. Marc Bartoli  ( Aix-Marseille Université, INSERM, UMR 910, Marseille, France ),  Martin Krahn  ( Assistance Publique Hôpitaux de Marseille, Hôpital de la Timone-Enfants, Département de Génétique Médicale, Marseille, France ),  Fréderic Fina  ( LBM, Assistance Publique Hôpitaux de Marseille, Hôpital Nord, Service de transfert d'Oncologie Biologique, Marseille, France ),  Mehdi Ouaissi (Aix-Marseille Université ,.
Event reconstruction and selection The event selection has been optimized to identify tt events in the µ+jets final state. A singlemuon trigger with a muon p T threshold of 24 GeV and a restriction on the pseudorapidity |η| < 2. 1 is used to collect the data samples. Isolation and identification criteria are applied at the trigger level to achieve manageable rates with minimal loss of efficiency. The physics objects used in this analysis are reconstructed with the CMS particle-flow (PF) algorithm  [28, 29]. The PF algorithm reconstructs and identifies each individual particle in an event using combined information from all CMS subdetectors. The energy of photons is directly obtained from the ECAL measurement. The energy of electrons is determined from a combination of the electron momentum measured at the primary interaction vertex by the tracker, the energy of the matched ECAL cluster, and the total energy of the associated bremsstrahlung photons.
Matrix element method The matrix element method  [35] [36] [37] [38]  is a technique that directly relates theory with experimental events. The compatibility of the data recorded with the leading-order (LO) matrix element (ME) of a certain process is evaluated. The probability that an event is produced by this process is calculated using the full kinematic information in the event. The probability P(x i |H) to observe an event i with kinematic properties x for a certain hypothesis H is given by: P(x i |H) = 1 σ obs (H) f PDF (q 1 ) f PDF (q 2 )dq 1 dq 2 (2π) 4 |M(y, H)| 2 q 1 q 2 s W(x i , y)dΦ 6. (3) The given probability is equivalent to an event likelihood. In this equation, q 1 and q 2 represent the parton energy fractions in the collision, f PDF (q 1 ) and f PDF (q 2 ) are the PDFs, s is the centreof-mass energy squared of the colliding protons, and dΦ 6 represents the phase space volume element.
Acknowledgments We thank  Werner Bernreuther  for kindly providing the LO matrix elements describing tt production and subsequent decay in the µ+jets channel, valid for both on-and off-shell top quarks. We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;.
Development of a PFN1-relevant BC xenograft model Investigation of PFN1 expression in three BC cell lines showed that its protein levels did not differ signifficantly between the T24 (invasive), T24M (metastatic) and RT4 (non-invasive) cells (data not shown). As such, to monitor PFN1 expression throughout cancer invasion and progression, we generated NOD/SCID xenografts, utilizing the T24M metastatic cell line. PFN1 expression was monitored for 60 days. As shown (Figure  1A  and 1B), PFN1 levels decreased with tumor progression (quantified optical absorbance of 155. 5±25. 0 au at 30 days compared to 102. 5±7. 4 au at 60 days, p<0. 0001 Student's t-test). The average tumor size concomitantly increased from 6. 9±0. 2 mm 3 (Day 30) to 8. 0±2. 2 mm 3 (Days 60). This PFN1 expression pattern is in agreement with our previous observations in human tissue specimens, when comparing non-invasive (T1G3) and invasive T2 + cancer  [18].
Decrease of Wnt5b, PLC4b and CREB1 in MIBC human tissue Considering the following criteria: a) involvement of a protein in altered pathways following PFN1 down-regulation based on the functional annotation/pathway analysis results; b) the differential expression/ excretion based on the secretome/proteome analysis and the gene expression data and c) availability of the antibodies; immunohistochemical analysis of bladder cancer tissue sections was further performed for Wnt5b, PLCb4, cAMP response element-binding protein1 (CREB1) and ANGPT1. The selection of these factors was made to increase reliability of the aforementioned observed in vitro and in vivo changes and pathway analysis predictions. Sections from NMIBC (pT1) and MIBC (pT2+) cases were used. All factors were found to be expressed in the BC tissue specimens (Figure  8A  and 8B ).
Human GAPDH (Forward: 5 '-G C A A A T T C C A T G G C A C C G T-3′, Reverse: 5'-T C G C C C C A C T T G A T T T T G G-3′ ) was used as internal positive control. The reaction was performed in an ABI Prism 7000 apparatus (Applied Biosystems) and the analysis was conducted using the ABI Prism 7700 SDS software (Applied Biosystems). Data were analyzed by the relative quantification (ΔΔCt) method as described previously  [49]. Τhe 2 -ΔΔCt formula was used to determine fold expression differences. Each assay was performed in duplicates and data represent the mean ± SD of at least two independent experiments.
000 (Santa Cruz Biotechnology, Texas, USA) was used as a control to ensure equal loading. The membranes were subsequently incubated with an anti-rabbit HRP-conjugated secondary antibody (GE Healthcare, United Kingdom) at a dilution 1:10. 000 or an anti-mouse HRP-conjugated secondary antibody (Santa Cruz Biotechnology Inc. ) at a dilution 1:2. 000 and developed using the ECL (Perkin-Elmer, MA, USA) detection system. Films were scanned (GS-800, BioRad scanner) and analyzed using Quantity One software (BioRad Laboratories Inc. , CA, USA).
Proteome profiler array of conditioned media (CM) For the preparation of the CM, 1×10 6 T24MshPFN1 or T24MshSCR cells were cultured until 80% confluent, and the media were replaced with DMEM containing 0. 5% (v/v) FBS to prevent protein aggregation. The cells were cultured for a further 24 hours and the CM was collected and concentrated approximately 10-fold using ultra filtration units with a 3-kDa cut-off (Millipore Ltd. Ltd. ), and analyzed for specific proteins using proteome profiler arrays for angiogenesis growth factors (Catalog #ARY007, R&D Systems Inc. , Minneapolis, USA) according to the manufacturer's instructions. Quantitation of the detected spots was performed using the Quantity One Software 4. 4. 1 (BioRad Laboratories Inc. , Amersham, England). Two replicates per sample were analyzed.
Finally, the sections were counterstained in Gill's hematoxylin (Sigma-Aldrich). To evaluate the staining for PFN1 expression in murine tissue, an image analysis approach was implemented by Image J software 1. 38× software (http://rsb. info. nih. gov/ij/). For CD31 stainings a rabbit anti-CD31 polyclonal antibody (Abcam, ab28364) was used in dilution of 1:250. Quantitation of signal was assessed by Image J software 1. 38× software (http://rsb. info. nih. gov/ij/).
Subsequently, sections were incubated overnight at 4°C with rabbit anti-Profilin1 monoclonal antibody (Enzo, Life Sciences, NY, USA), in 1:500 dilution or appropriate isotype controls. A three-step procedure (Avidin-Biotin-Peroxidase, Vector Laboratories, Burlingame, CA, USA) was used for visualization, with diaminobenzidine as a chromogen according to the kit instructions. Finally, sections were counterstained with hematoxylin and mounted. A quantitative approach was then followed to confirm pathologists' observations using the Image J software (http://rsb. info. nih. gov/ij/) [52]. In brief, tissue sections were scanned under the microscope using X20 magnification. Depending on tissue size, different number of images (5-10) were acquired per section. Ten-50 fields of identical area dimensions per image were selected for mesurement analysis.
Mean values of intensity were calculated per image and subsequently for each section. All raw data-measurements per sample are provided in Supplementary Table  S1. The statistical analysis was based on two-tailed Student's T-test of unequal variance. Values of p<0. 05 were considered as statistically significant. Correlation analysis of the tissue staining measurements at the human BC patients level was performed using Spearman rank correlation coefficient. Correlation was marked as significant for p<0. 05. The correlation analysis was performed with SPSS software v. 22 (SPSS Inc, USA).
Total mRNA sequencing Total RNA was isolated from T24MshPFN1, T24MshSCR or T24M cells using the RNeasy kit (Qiagen, Valencia, CA, USA), as described previously and according to the manufacturer's protocol. Concentration and purity of RNA were assessed using the NanoDrop ND 1000 spectrophotometer (Nanodrop products, Wilmington, DE, USA) at 260 nm. In total 6 samples were analyzed including T24MshPFN1, T24MshSCR or T24M cells (in two biological replicates each). The preparation of libraries and the sequencing of the mRNA along with the analysis of the raw data were performed by GenomeScan B. V Company. The RNA concentration was assessed using the Life Technologies Qubit (Thermo Fischer Scientific, Waltham, MA USA). Further evaluation of the quality and integrity of isolated RNA was conducted using Agilent Bioanalyzer (Agilent Technologies Inc.