dataesr/scientific-paragraphs-categorization huggingface dataset:
Recently Li et al. [16]  extended these methods to propose KS goodness-of-fit tests for checking the FG model assumptions. Empirical studies of the finitesample properties suggested that the KS goodness-of-fit test for the PH or PSH assumptions based on cumulative sums of residuals are more sensitive to general non-PH or non-PSH alternatives than other tests assuming specific time-varying covariates effects (Kvaloy and Neef  [17] , Li  [16] ). Moreover, Kvaloy and Neef illustrated the good frequency properties of Cramer-von Mises (CvM) and Anderson-Darling (AD) type statistics against general non-PH alternatives. Two packages in R propose KS-type GOF tests for checking the model assumptions in the Cox model and the FG model, respectively the gof  [18]  and crskdiag  [19]  packages. Each package has its own advantages and limitations.
The package gof allows to test for PH using the maximal deviation and a CvM type statistic, but provides no test for checking the functional form of a covariate. The package crskdiag implements GOF tests based on cumulative sums of model residuals that extend the class of GOF tests first pioneered by Lin for the Cox model to the FG model  [16]. In both packages, the p values are estimated using a similar Monte Carlo method, but slightly different from the method originally proposed by Lin in the Cox model. Neither gof nor crskdiag implements AD test statistics to test for PH and PSH against general alternatives. The main purpose of this manuscript is to give an overview of the functionalities of a new R package goftte  [21]   The remainder of this manuscript is organized as follows. In Section 2 we present the different test statistics implemented by the package and the different methods available to approximate their asymptotic null distributions.
Package description As Monte Carlo method is characterized by long lead times, the main computations were carried out using primitive functions using C++ code, and possibly parallelized in the C++ environment. Results are made available to the user by two main generic S3 function, fcov() and prop(), including methods for R objects of class coxph{survival}  [24] , cph{rms}  [25] , crr{cmprsk}  [26]. Compiled binaries are available for Linux, macOS, Solaris and Windows platforms. The package with detailed documentation is distributed freely by CRAN (Comprehensive R Archive Network). Table  1  summarizes the different testing functionalities made available in the R packages goftte, gof and crskdiag.
Examples As an illustrative example, we consider the dataset "pbc" available in the R package survival  [24]. The data consists of times to occurrence of a first event (death, transplantation) or censoring in a set of 418 patients with primary biliary cirrhosis of the liver followed at the Mayo Clinic, of whom 161 died and 25 had liver transplant first. Following Dickson et al. [27]  and Li et al. [9] , we used multivariate PH and PSH regressions models with terms for edema, age at diagnosis, and log-transformed serum albumin, serum bilirubin and prothrombin time, to model the overall survival and the cumulative incidence of death without transplant. To assess the PH or PSH model assumptions, we first estimated the regression coefficients using the functions coxph{survival} and crr{cmprsk} and test for PH/PSH using the functions prop.
crr<-crr(ftime=time,fstatus=status,cov1=cbind(age,edema,log(bili),log(albumin),log(protime)),failcode=2) # Test for PH/PSH assumptions coxph. prop<-prop(model=fit. coxph,variable=c("age","edema","log(bilirubin)","log(albumin)","log(protime)"), R=20000, plots=50, seed=10) crr. prop<prop(model=fit. crr,ftime=time,fstatus=status,cov1=cbind(age,edema,log(bili),log(albumin),log(protime)),failcode=2 ,variable=c("age","edema","log(bilirubin)","log(albumin)","log(protime)"),R=20000, plots=50,seed=10) The same conclusions hold using te package gof. The default KS p-values for PH were almost identical, only the differences for CvM statistics were more pronounced because of the lack of homogeneity of the definitions between the packages. For example, using gof and goftte, the p value of KS statistics for Edema were respectively p=0.
0210 and p=0. 0218, while the p value of CvM statistics for Edema were respectively p=0. 0244 and p=0. 0474. # Regarding PSH assumption, the KS test rejected the null hypothesis at the 5% significance level for Edema and log prothrombin time whatever the package used. The CvM and AD statistics as implemented in gofttte led to the same conclusions. There were some noticeable differences in p values for PSH between crskdiag and goftte. As an example, the p value of the KS test for PSH for log(Bilirubin) was p=0. 0757 using crskdiag and p=0. 2868 using goftte. As more than 50% of the patients were censored, this large difference can be attributed to the lack of Type I error control observed with crskdiag (Supplementary table  2 ). Several authors have emphasized the inadequacy of the linear functional form for untransformed bilirubin in Cox regression (Lin & Wei  [10] ; Leon & Tsai  [28] ; Kvaloy & Neef  [17] ) and suggested a model with logarithm transformation of bilirubin as an alternative  [15] ,  [16]  For illustrative purposes, we estimated the regression coefficients assuming untransformed bilirubin with other covariates left unchanged, and then checked the adequacy of untransformed bilirubin in both regression settings.
Discussion The Cox model  [1]  and the Fine and Gray model  [12]  are the most widely used statistical regression models for analyzing right-censored survival and competing risks. Two different R packages, respectively the package gof  [18]  and the package crskdiag  [19] , implemented omnibus KS tests checking the Cox and the Fine and Gray models with cumulative sums of residuals. The package goftte  [21]  can be viewed as add-on to the package gof allowing assessing misspecification in the functional form of a covariate as proposed in Lin et al. [15]  and detecting general non PH alternatives using AD type statistic. It also provides diagnostic tools and goodness-of-fit methods based on cumulative sums of residuals for checking the Fine and Gray model assumptions, including CvM and AD statistics against general non PSH alternatives not available from crskdiag  [19] ,.
Two resampling methods have been implemented in goftte to derive the p values, a first method pioneered in Lin &al. for checking the model assumptions in the Cox model  [15] , and a similar method implemented in gof and crskdiag  [20]. Numerical results showed that all different test statistics have empirical rejection rates close to the expected 5% error rate. In few cases where the empirical rejection rates were outside of the expected range, the Lin method exhibited some conservative properties as opposed to the Liu method. In Cox regression settings, unlike gof, the package goftte allows to test the functional form of a covariate and to test against general non PH alternatives using AD test statistics  [17]. In FG regression settings, the package goftte allows to test against general non PSH alternatives using different test statistics, including CvM and AD type statistics.
All constructs were validated through Sanger sequencing of the entire coding region. The UAS promoter was combined with the SV40 polyA sequence (ttaattgtttattgcagcttataatggttacaaataaagcaatagcatcacaaatttcacaaataaagcattttttt cactgcattctagttgtggtttgtccaaactcatcaatgtatcttatcagcggccgc), which allows for high expression and stabilization of the Plk4 sequences. To generate all Plk4 transgenic stocks, the PhiC31 integrase-mediated transgenesis system was used. The different Plk4 constructs, flanked by NotI and AscI, were cloned into a P[acman] plasmid containing the attB site (DGRC, GeNBank EF106980) and injections were performed by BestGene (BestGene Inc, CA, USA). The Bloomington stock BI9738 (y 1 w 1118 ;PBac {y + -attp-9A}VK00020) was used as attP-containing docking site strain. All transgenes were inserted in the third chromosome in the insertion site 99F8.
The secondary antibody solution was then removed and membranes washed 5 times for 10min in PBST. Finally, membranes were incubated with SuperSignalTM West Pico Chemiluminescent Substrate (34080, Termo ScientificTM) and revealed using the BioRad Chemidoc MP system. Images were analyzed using ImageLab software. In Vitro Kinase Assays and Mass Spectrometry Bacterially-expressed constructs of Drosophila Plk4 (amino acids 1-317) C-terminally tagged with FLAG-His 6 and Drosophila Spd2 N-terminally tagged with either Glutathione S-Transferase (GST) or Maltose-binding protein (MBP) were purified on HisPur resin (ThermoFisher), glutathione resin (NEB) and amylose resin (NEB), respectively, according to manufacturer's instructions. Prior to assay, purified proteins were resolved by SDS-PAGE and scans of the Coomassie-stained gels analyzed by densitometry (ImageJ, NIH) to determine protein purity.
Image Acquisition Images of squashed preparations were collected with a x100 objective on a Leica DM6B epifluorescence microscope, this microscope was equipped with an ORCA-Flash4. 0 V2 Digital CMOS camera (Hamamatsu) and controlled by the Metamorph software 7. 7 (Molecular devices). Images of whole mount brain lobes were acquired on a Nikon A1R inverted TiE confocal microscope with a 40X 1. 3NA or a X60 1. 4 NA objective in NIS Element software. For the characterization of Spd2 and Fzr localization in interphase centrioles, images were acquired with a N-SIM Nikon microscope in 3D SIM mode before image reconstruction using the NIS Elements software  (Gustafsson et al. , 2008). The system is equipped with an APO TIRF SR 100x 1. 49NA oil immersion, a laser illumination (488nm 200mW, 561nm 100mW, 640nm 100mW) and an EMCCD DU-897 Andor camera.
Characterization of Centriole Behavior The characterization of centriole movement was performed on raw data taking into account the Z-stacks required to follow centriole behavior in x, y and z. Z-stacks were used to generate projections to follow the centriole behavior over time. In Ctrl cells, after mitosis, the two centrioles display stereotypic movements as described previously  (Rebollo et al. , 2007; Rusan and Peifer, 2007)  and this behavior was taken for the basis of characterization of centriole behavior in the different genotypes. Centrioles were considered ''Apical'' when behaving as Ctrl apical centrioles-maintaining a stable and fixed position closer to the apical cell cortex throughout interphase. The apical cell cortex was established by the position of the large aster, at the end of telophase, typical of the asymmetric spindle in NBs.
ACKNOWLEDGMENTS We dedicate this work to the memory of  Giuseppe Boccia. We thank  J. Raff ,  Y. Bellaiche ,  T. Lee ,  C. Doe , the  Bloomington Stock Center, and BestGene  for stocks and reagents;  L. Sengmanivong ,  V. Fraisier , and  L. Leconte  of the  NIC  at the IC for valuable help with microscopy;  P. Maiuri  and  M. Rujano  for help with data analysis;  V. Marthiens ,  D. Vargas ,  S. Gemble ,  O. Goudiam ,  A. Echard ,  M. Piel ,  A. Audibert ,  J. Azimzadeh ,  M. H. Verlhac ,  M. Bornens ,  J. Raff , and  C. Gonzalez  for discussions and/or critical comments on the manuscript. Financial support from an  ERC  starting grant CentroStemCancer  242598 , a FRM installation and ATIP grants, the  Institut Curie  and  CNRS. G. C. R. is grateful for support from  NCI P30CA23074  and  NIGMS   R01 GM110166  and  GM126035.
Gluconobacter oxydans FtsH is included as outgroup. Branch lengths correspond to substitutions per site, bootstrap values are indicated in %. The sequence accession numbers are shown in parenthesis. (C) Neighboring genes flanking ftsH1 on the core genome and ftsH2 on the genomic island. Genes upstream of ftsH1 are ftsJ (EWH24233. 1) and a divergently transcribed PA4752 encoding a conserved hypothetical protein (EWH24234. 1), while genes downstream are folP (dihydropteroate synthase-EWH24231. 1), glmM (phosphoglucosamine mutase-EWH24230. 1), and tpiA (triosephosphate isomerase-EWH24229. 1). Genes upstream of ftsH2 are cls (cardiolipin synthase), disaggregase encoding clpG GI , small heat shock protein encoding shsp20 GI and transcription factor encoding dna GI , whereas the downstream gene is trxA GI (thioredoxin).
Bioinformatic Analysis Protein sequences FtsH1 (acc. no. : EWH24232. 1) and FtsH2 (acc. no. : EWH27927. 1) of P. aeruginosa SG17M were used as queries to search for FtsH1 and FtsH2 homologs in the NCBI databases by BLASTP using standard parameters. Proteins homologs over the entire length of the sequence, >93%, were considered. These database searches retrieved FtsH1/FtsH2 homologs with an pUC18T-mini-Tn7T-Gm Tn7L/Tn7R for insertion into the Tn7 site; ori, ColE1-derived ori; oriT, origin of conjugative transfer; T0T1, transcriptional terminators; Ap r ; FRT-Gm r -FRT  Choi and Schweizer, 2006  pBK-miniTn7-gfp2 miniTn7-gfp-Gm delivery vector  Koch et al. , 2001  identity/homology limit of 46/64%. Proteins were aligned using ClustalX2  (Thompson et al. , 1997)  using standard parameters. As an outgroup, the FtsH homolog of Gluconobacter oxydans was selected which showed 92% query cover for FtsH1 of P.
aeruginosa. The aligned sequences were subjected to phylogenetic analysis using neighbor-joining (NJ) and maximum likelihood (ML) in MEGA7. 0  (Kumar et al. , 2016). The Poisson model was used as an amino acid replacement model. The robustness of the phylogenetic tree topologies was evaluated by bootstrap analysis with 1,000 replications. The 16S rRNA gene sequences of the type strains of species included in the FtsH tree were retrieved from the NCBI database and phylogenetic analysis using NJ and ML (shown) was performed in MEGA7. 0 with essentially the same results.
Sequences were aligned with MUSCLE using default settings (Edgar, 2004)  and displayed in JalView (Waterhouse et al. , 2009). (B) Phylogenetic analysis of closest homologs of FtsH1 and FtsH2 proteins from different taxonomic groups of bacteria. Examples of strains with multiple FtsH1/FtsH2 homologs are included in the tree: Ralstonia pickettii DTP0602 (4 homologs), Alicycliphilus denitrificans K601 (3 homologs), Pseudomonas ludensis (2 homologs), and Streptococcus pneumoniae SMRU2535 (3 homologs). Gluconobacter oxydans FtsH is included as outgroup. Branch lengths correspond to substitutions per site, bootstrap values are indicated in %. The sequence accession numbers are shown in parenthesis. (C) Neighboring genes flanking ftsH1 on the core genome and ftsH2 on the genomic island. Genes upstream of ftsH1 are ftsJ (EWH24233.
coli FtsH, respectively. Sequences were aligned with MUSCLE using default settings (Edgar, 2004)  and displayed in JalView (Waterhouse et al. , 2009). (B) Phylogenetic analysis of closest homologs of FtsH1 and FtsH2 proteins from different taxonomic groups of bacteria. Examples of strains with multiple FtsH1/FtsH2 homologs are included in the tree: Ralstonia pickettii DTP0602 (4 homologs), Alicycliphilus denitrificans K601 (3 homologs), Pseudomonas ludensis (2 homologs), and Streptococcus pneumoniae SMRU2535 (3 homologs). Gluconobacter oxydans FtsH is included as outgroup. Branch lengths correspond to substitutions per site, bootstrap values are indicated in %. The sequence accession numbers are shown in parenthesis. (C) Neighboring genes flanking ftsH1 on the core genome and ftsH2 on the genomic island.
This aims of this study was to estimate the bodyweight of FH dairy cows from birth to first child by knowing growth patterns and creating a standard curve for dairy cattle growth at BBPTU-HPT Baturraden dairy cow in Baturraden district, Banyumas Regency, Purwokerto, Central Java. Data obtained in the form of secondary data from births in 2005-2016 were 1437 tails and primary data were 353 tails. Data were analyzed using SAS 9. 4 program with NLIN procedures (Non-Linear) using Gompertz models. The results show that the Gompertz models is easy in the calculation process with the data deviations that are close to the field of data and the models have a high degree of accuracy with the coefficient of determination (R 2 ) 99. 83%. The conclusion of this study is that the resulting curve of the Gompertz model can be used as a standard growth curve of Friesian Holstein dairy cattle in Indonesia from birth to first calf.
Materials and Methods This research was conducted in August 2018 at the Center for Superior Livestock Breeding and Animal Feed Forage (BBPTU-HPT) Baturraden dairy cattle, Kemutug Lor Baturraden District, Banyumas Regency, Purwokerto, Central Java. Baturraden has a height of ±675 m above sea level (asl)  (Soribasya, 1980). Average temperatures ran ge from 18-28 o C, humidity of 70-80% with rainfall of 6,000-9,000mm/year. The primary data used in this study were 353 female FH cows, newborns to 25 months of age and secondary data including: date of birth, birth weight, weaning age of a female child, weaning weight, age of first marriage, first birth weight, age of first child and bodyweight of first child. The tools used in the form of measuring sticks and tape measure Rondo. Measurement parameters are: 1) Bodyweight, i.
Chemical analysis After the cultivation run, the biomass was separated by filtration and discarded, whereas the filtrate was stored at -20 °C. All analyses were performed with the use of an ultraperformance liquid chromatography system Acquity (Waters, USA) coupled with high resolution mass spectrometer Synapt G2 (Waters, USA). The concentrations of lovastatin (in its β-hydroxy acid form), (+)-geodin, asterric acid, and butyrolactone I in the liquid samples were determined according to the procedure described previously by  Boruta and Bizukojć (2016). The semi-quantitative analysis of a molecule putatively identified as monacolin J acid was performed with the use of TargetLynx™ software (Waters, USA) on the basis of peak area values corresponding to the fragment ion of m/z = 303. 1969, which exhibited the greatest intensity on the mass spectrum.
rubens or M. racemosus were confronted with A. terreus after 24 h of growth in the preculture medium, the final titers of lovastatin exceeded the concentration observed in Bcontrol B,^but not in Bcontrol A^(Fig. 2a ). Generally, the mean lovastatin titers reached in the Bcontrol A^flasks were noticeably closer to the values recorded for P. rubens co-cultures than for C. globosum or M. racemosus variants (Fig. 2a ). The concentration values corresponding to (+)-geodin (Fig. 2b ) and asterric acid (Fig. 2c ) were relatively more varied than those of lovastatin and this behavior was reflected by the error bars representing the standard deviation values based on experimental triplicates (Fig. 2b, c ). If a complete dataset including the results for all initiation strategies is considered, the highest levels of (+)-geodin (Fig.
INTRODUCTION The interaction of high sea level and heavy precipitation may cause compound flooding (CF), a coastal hazard that can result in substantial damages and fatalities  (1) (2) (3) (4). Prominent examples of CF in Europe are the flash flood in Lisbon, 1967  (5) ; the Avon flood in Bristol, 2014; and the Ravenna flood in 2015  (3). In 2012, The Netherlands almost experienced a flooding of the water board Noorderzijlvest, which led to precautionary evacuation  (6). The recently released pan-European HANZE (Historical Analysis of Natural Hazards in Europe) database  (7)  lists 24 co-occurrences of storm surges and river floods along the Irish, U. K. , Belgian, and Polish coasts, the French Atlantic and Mediterranean coast, and the Italian Adriatic coast. Estimating the coastal flooding risk is essential for policy-making, disaster risk reduction, and engineering practices.
RESULTS To characterize extreme sea level, we consider daily maximum values of the superposition of storm surges (including waves) and astronomical tides. In the following, we will refer to these maxima simply as sea level. Storm surges and waves are simulated with the hydrodynamic D-FLOW Flexible Mesh (FM)  (17, 20, 21)  and Wavewatch III  (17, 20, 22)  models, respectively, forced with ERA-Interim reanalysis data  (23)  for present climate (1979-2014), and with data from six selected Coupled Model Intercomparison Project Phase 5 (CMIP5) models  (24)  for present  and future climate (2070-2099). Astronomical tides are simulated separately considering the effects of SLR during the century (see Materials and Methods for more details). Precipitation is directly taken from the reanalysis and the six CMIP5 models.
Data Storm surges were simulated with the DFLOW FM model using a flexible mesh setup (forced with 6-hourly wind and atmospheric pressure fields)  (17, 20, 21, 29). Waves were simulated with the model Wavewatch III  (17, 20, 22)  (forced with a 6-hourly wind field). Astronomical tides were simulated every 6 hours using the FES2012 model  (29, 31) , which makes use of satellite altimetry data. The resulting sea level data are available every ~25 km along the coastline. Comprehensive validation and detailed information of the models can be found in  (17, (20) (21) (22) 29). Our analysis was based on quantile values; therefore, we did not bias correct simulated data. Sea level and precipitation data were based on ERA-Interim and six selected models from the CMIP5 multimodel ensemble (i. e. , ACCESS1-0, ACCESS1-3, GFDL-ESM2M, GFDL-ESM2G, CSIRO-Mk3-6-0, and EC-EARTH).
7th percentiles s 99. 7 and p 99. 7 ). To allow for a robust estimation, we applied a parametric copula-based bivariate probability distribution. Applying a parametric model for the full range of values, one would run the risk of biasing the representation of the extreme tail by the bulk of the bivariate distribution where most data occur. Therefore, we applied the model only to pairs of high values. We selected pairs where, simultaneously, sea level and precipitation values exceed the individual 95th percentiles (s sel and p sel , respectively). In a very few locations, one might end up with selecting few pairs only. Here, we reduced the selection threshold 0. 95 to ensure that at least 20 pairs of values were selected (never below 0. 9). Clusters of selected event pairs separated by less than 3 days were replaced by a unique event, which assumed the maximum sea level S and precipitation P observed in the cluster (fig.
Acknowledgments: We acknowledge the  World Climate Research Programs Working Group on Coupled Modelling , which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP, the  U. S. Department of Energy 's  Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. We acknowledge the E-OBS dataset from the  EU-FP6  project  ENSEMBLES  (http://ensembles-eu. metoffice. com) and the data providers in the  ECA&D  project (www. ecad. eu). Funding: E. B. received funding from the  Volkswagen Foundations CE : LLO  project (Az. :  88468 ) and M. V. from the " EUPHEME "  ERA4CS  project.
To compensate these adverse impacts of the recycled rubber, AF were added to the composite system. The elasticity modulus of the composites is enhanced under favor of the rigid nature of the AF. However, AF could not effectively compensate for possible adverse effects of recycled rubber in strain at break and maximum strength  [10, 11]. As an intermediate result, for the applications which require high mechanical resistance, the use of 30 wt. % EPDM rubber containing groups (LR3AL compositions) can be avoided. Moreover, to verify the significance of the AF and recycled EPDM rubber incorporation on the mechanical properties, two-way ANOVA statistical analyses were implemented on the results reported in Table  3  by using Minitab 17 software. During two-way ANOVA, effect of recycled EPDM rubbers (R wt.