dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion In this section, we discuss the fitting procedure of the current given by Eq. (  27 ) obtained from the MCV approach with the PITT measurement in the stage transitions during graphite lithiation at rates x close to 0. 2, 0. 5 and 1. Indeed, it is known that in the plateau regions, the diffusion coefficient cannot be evaluated in contrast to the stage transitions related to single diffusional processes. The Matlab solver lsqnonlin is used to fit the PITT current response using Eq. (  27 ) by minimizing the following cost function: , = O* T)U ( ) -*( )P * T)U ( ) where * T)U is the experimentally-measured PITT current and * is the calculated current. Figure  2  presents the fitting for one PITT current response following the applied potential step at x = 0. 2, achieved with a good precision equivalent to 10 -4 of order of magnitude.
Data generation In the numerical study, we use the data from  [3, 7]  such as the fixed maximum shelf life, average demand, coefficient of variation (CV = σ/µ), number of period. By following  [3] , we normalize all cost parameters on the purchasing cost v, which is set to 1. The unit holding cost ratio h/v is varied from 0. 02 to 0. 04. The unit lost sales cost p is determined from the lost sales cost ratio p/(v + p + h), which is varied between 90% and 99%. The unit out-dating cost ratio, w/v is varied between 0 and 1. The fixed ordering cost for each order placed is varies from 250v to 500v. The maximum shelf life is M = 2 or 3. The number of periods is T = 7. The mean demand for each period is computed by µ Dt = f t. µ D where the demand pattern in each period is f t = {0. 12, 0. 13, 0.
The eight most intense ions (Top 8) underwent HCD (Higher energy collisional dissociation) fragmentation with collision energy of 27%, thus obtaining an MS/MS spectrum acquired at a resolution of 17500. A dynamic exclusion of 40 sec was used. Peptide identification was performed using X!Tandem software  (Craig & Beavis, 2004)   The B. cinerea database provided to the software was retrieved from Ensembl (http://fungi. ensembl. org/Botrytis_cinerea/) and previously functionally re-annotated on UseGalaxy from published data  (Amselem et al. , 2011)  and automatic annotations obtained with Pfam  (Finn et al. , 2010). A database of standard contaminants has also been added. The identified proteins were filtered and grouped with X!Tandem Pipeline v3. 4. 0  (Langella et al. , 2017). Data with a peptide E-value < 0.
01, at least two peptides per protein and a protein Evalue of 10 -4 were retained. The FDR (False Discovery Rate) was estimated at 0. 02% for peptides and 0% for proteins. Identified proteins, peptides and their corresponding spectra were deposited in PROTICdb database  (Langella et al. , 2013)  at the following URL http://moulon. inra. fr/protic/botrytis_signalling using the following login: "review" and password (required during the review process only): "review". Data will soon be accessible under doi 10. 15454/1. 5506737620833718E12.
Quantification, statistical analysis and annotation Quantification was based on the analysis of the MS1 XIC (eXtracted Ion Current) of each of the identified peptides, by using the MassChroQ software v2. 2  (Valot et al. , 2011). XIC data (peak intensity integration) was used to quantify proteins for which at least two specific peptides were quantified in at least 95% of the samples. For proteins that did not reach this criterion, detected peaks were counted (peak counting, PC) instead of being integrated. For XIC quantification, normalization was performed according to the median sample/reference ratio of peptide ion XIC values, the reference being a WT sample. Peptide ion missing XIC data were imputed according to the correlation between peptides of the same protein. The relative abundance of each protein was computed by summing the intensities of its specific peptides.
An analysis of variance (linear model) on log 10 -transformed abundances was performed to study the variations according to the strain. Proteins with adjusted p-value < 0. 01 and a fold-change (mutant/WT) above 1. 5 or below 0. 66 were considered significantly affected by the mutant. For PC, an analysis of variance was performed by using a general linear model and the same criteria as those employed for XIC quantification were used to consider a protein significantly affected by the mutation. The R software (version 3. 4. 4 (R Core Team, 2018)) was used for all statistical analyses. After grouping proteins into regulatory groups, functional enrichment according to biological processes was analysed with Blast2GO  (Gotz et al. , 2008)  for each group. This enrichment uses an exact Fisher test with a p-value < 0.
Quantitative proteomic analysis of signal transduction mutants ∆bos1and ∆sak1 mutants' protein content were compared to the parental wild-type strain B05. 10 exponentially grown under axenic in vitro conditions. After tryptic digestion and peptide analysis by LC-MS/MS, protein identification and quantification were performed. 67% of the detected spectra could be assigned to peptides. 2425 proteins out of the 11701 predicted B. cinerea proteins (20. 7% of the theoretical proteome) were detected in the total dataset. XIC analysis allowed to obtain quantitative profiles of 1263 proteins out of the previously mentioned 2425 (52%) and the others were analysed by peak counting (PC).
WD is associated with high tolerance to shade and drought, and slow growth rates  (Ameztegui et al. , 2017; Markesteijn, Poorter, Bongers, Paz & Sack, 2011). For all four traits, higher values are taken to mean greater competitive advantage in these forest ecosystems. We address our questions and hypotheses by combining a global vegetation-plot database (sPlot;  Bruelheide et al. , 2019) , a global plant trait database (TRY;  Kattge et al. , 2011) , and global information on naturalized alien plant species  [Global Naturalized Alien Flora (GloNAF); van Kleunen et al. , 2015].
| sPlot and GloNAF databases We used the sPlot database, which contains vegetation plots across the globe in which composition and abundance of plant communities were recorded (www. idiv. de/splot ;  Bruelheide et al. , 2019). To identify alien species in the plots, we matched the sPlot database with the GloNAF database  (van Kleunen et al. , 2019). We focused on neophytes, defined as species that were introduced after the year 1492 in the respective regions. The GloNAF database contains lists of alien species for non-overlapping regions (countries or regions within countries) across the world. We could thus match all plots from sPlot with a region of GloNAF, and label the alien species in the plots. Species that were not labelled as "alien" were labelled as "native".
| Functional traits We focused on specific leaf area (SLA; cm 2 /g), adult tree height (H; m), oven-dried seed mass (SM; mg) and wood density (WD; g/cm 3 ). We obtained trait information for all species in the study plots from the global trait database TRY  (Kattge et al. , 2011) , by constructing a taxonomic backbone as described in  Bruelheide et al. (2019). In sPlot, trait values for species without data in TRY were covered by gap-filling using a Bayesian hierarchical probabilistic factorization (for details see  Schrodt et al. , 2015) , which gave similar results to non-gap-filled data  (Bruelheide et al. , 2019). All traits are based on species-level data; hence, we could not include intraspecific trait variation.
| Human influence and climatic conditions We used the human influence index (HII;  Gallardo, Zieritz & Aldridge, 2015)  as a proxy for human influence. The data were obtained from the Socioeconomic Data and Applications Center (SEDAC) via http://sedac. ciesin. colum bia. edu/data/set/wilda reas-v2-human -influ ence-indexgeogr aphic /, and had a 1-km 2 resolution. The proxy is based on nine variables related to human population pressure (human population density), human land use and infrastructure (built-up areas, night-time lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). Human influence may especially favour alien species through transport, use and planting of these species in the surrounding anthropogenic areas, which could increase propagule pressure in forests (i.
| Statistical analyses To test for differences in abundance between the native and alien ranges across species, we used a linear mixed-effects model including species and plot as random intercepts, using the lmer function of the lme4 package  (Bates, Maechler, Bolker & Walker, 2015 ; Figure  2 ). We formulated a two-level hierarchical linear regression model (using Bayesian inference) to test for effects of multivariate trait dissimilarity, competitive trait differences [in specific leaf area (ΔSLA), plant height (ΔH), seed mass (ΔSM) and wood density (ΔWD)], human influence index (HII) and the standardized precipitation and evapotranspiration index (SPEI) on relative abundance, and to test how these effects differ between native and alien ranges and depend on species' traits. The first level of the model describes the species-specific responses of relative abundance to the abovementioned predictor variables, range (native versus alien range), and the two-way interactions of each predictor variable with range.
The variance inflation factors of the variables included in the model were all <4, indicating no problem of collinearity  (Zuur, Ieno & Elphick, 2010). To analyse the hierarchical linear regression model, we applied Bayesian inference because this can accommodate the complex hierarchical structure of our model and data. Parameter estimation was done using the jags software  (Plummer, 2013)  and the rjags package in R  (Plummer, Stukalov, et al. , 2016). We set uninformative priors for all parameters; for all slopes of the fixed effects and predictors of the random slopes we set priors with mean = 0 and variance = 10 3 , and for the standard deviation parameters (associated with the slopes of the fixed effects and predictors of the random slopes) we set priors with a uniform distribution between 0 and 100.
We checked for convergence of the model visually by plotting the posterior sample distribution and trace plot of each parameter (Supporting Information Appendix S2), and by evaluating Gelman and Rubin's univariate and multivariate potential scale reduction factors  (Gelman & Rubin, 1992) , using the gelman. diag function of the coda package in R  (Plummer, Best, Cowles, & Vines, 2006). The univariate and multivariate potential scale reduction factors were all ≤1. 01, indicating that the MCMC sampler converged. We found no significant phylogenetic signal in our model output (Supporting Information Appendix S3). A mathematical model description can be found in Supporting Information Appendix S4, and the R script in Supporting Information Appendix S5. All analyses were performed in R version 3.
ACK N OWLED G M ENTS This work was supported by funds designated to T. M. K. under the  Helmholtz Recruitment Initiative of the Helmholtz Association. M. T. v. d. S. was supported by the  research programme Rubicon  with project number  019. 171LW. 023 ,financed by the  Netherlands Organisation for Scientific Research (NWO). sPlot was initiated by sDiv, the  Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( FZT 118 ), and now is a platform of iDiv. H. B. , J. D. , O. P. , B. J. -A. , U. J. , J. K. , M. W. and T. M. K. received funding through iDiv; M. v. K. was supported by the  German Research Foundation DFG  (project number  264740629 ), A. G. G. by  FONDECYT   11150835 ,  CONICYT-PAI 82130046  and a  Marie Curie Intra European Fellowship  within the 7th  European Community Framework Programme  (Project  PIEF-GA-2010-274798 ), J.
Subjects and study design This was an open-label, multicenter, phase 2 study of LY2152799 (galunisertib) in patients with HCC (Clinical-Trials. gov: NCT01246986). The study had multiple parts; reported here is part C, which evaluated galunisertib in combination with sorafenib in patients with advanced HCC who had not received prior systemic treatment (see Figure  S1 , Supplementary Digital Content 1, http://links. lww. com/ CTG/A55). Galunisertib was administered orally at a dose of 80 or 150 mg b. i. d. in the safety lead-in cohort and 150 mg b. i. d. in the expansion cohort, on days 1-14 in 28 day cycles. All patients received sorafenib at the standard starting dose of 400 mg b. i. d. Sorafenib was adjusted based on previously published guidelines (4). Eligible patients were adults with a histologic diagnosis of HCC not amenable to surgery, measurable disease by Response Evaluation Criteria in Solid Tumor (RECIST) 1.
Median TTP and the secondary objective of OS were estimated using the Kaplan-Meier method, with the log-rank tests to compare distributions of TTP or OS between subgroups. TTP was measured from the date of first dose to the first date of objective progression of disease. For each patient who was not known to have had a progression of disease as of the data-inclusion cutoff date for this analysis, or who died without progression of disease, TTP was censored for the analysis at the date of the patient's last tumor assessment before that cutoff date. OS was measured from the date of first dose to the date of death from any cause. For each patient who was not known to have died as of the data inclusion cutoff date for this analysis, OS duration was censored for the analysis at the date of last prior contact.
Biomarker response was defined as. 20% decrease in the biomarker from baseline values at any time point, based upon thresholds employed in previous studies  (32). TTP was compared between biomarker subgroups at baseline (AFP $ 400 ng/mL vs AFP , 400 ng/mL; TGF-b1 less than or greater than/equal to median). TTP and OS were compared between biomarker responders and nonresponders postbaseline. All confidence intervals of treatment effects are provided at a 2-sided alpha level of 0. 05, unless otherwise stated. Statistical software used was SAS version 9. 2 or higher (SAS Institute, Cary, NC).
Subgroup analysis TTP and OS were analyzed by baseline biomarker values: AFP ,400 ng/mL and $400 ng/mL (Figure  2a ) and TGF-b1 less than or greater than/equal to the baseline median (1956 pg/mL) (Figure  2b ). Patients with baseline AFP $ 400 ng/mL or patients with baseline TGF-b1 $ baseline median (1956 pg/mL) demonstrated trends toward longer TTP. TGF-b1 responders (at least 20% decrease from baseline at any time point) had significantly longer OS (22. 8 vs 12 months, P 5 0. 038) (Figure  3 ). For AFP responders and nonresponders, OS was 17. 9 months and 20. 6 months, P 5 0. 49. Limited archival tumor samples (n 5 24) were available for analysis. Because of the limited amount of tissue, a comprehensive evaluation of all targets was not possible, including for gene expression studies. Immunohistochemistry data were obtained for expression of TGF-b RII and pSMAD2.
Because there was no control arm and no evaluation of peripheral regulatory T-cell counts in this study, it cannot be concluded whether the combination of agents had an additive effect on improving the immune response in HCC by reducing peripheral T regulatory cells. Unlike previous studies of other novel agents in combination with sorafenib confounded by additive toxicity  (35) (36) (37) , the combination of sorafenib plus galunisertib showed manageable toxicity and tolerability, without apparent increase of sorafenib toxicities, and without impact on the sorafenib PK profile. The favorable safety profile of galunisertib coupled with its mechanism of TGF-b RI signaling inhibition supports its potential benefit in other combinations, including an ongoing study in combination with nivolumab (NCT02423343) based upon preclinical synergistic activity of galunisertib and checkpoint inhibitors  (38).
We monitored plasma TGF-b1 levels at baseline and during treatment and analyzed its effect on outcomes. Patients with TGF-b1 levels greater than median at baseline had a longer TTP than those below the median (not statistically significant). Patients who were classified as TGF-b1 responders, with a decline in circulating TGF-b1 on treatment,  also demonstrated a trend toward prolonged TTP, although cut-points for defining response have not been validated. Furthermore, OS was significantly longer in TGF-b1 responders compared to nonresponders (22. 8 vs 12 months, P 5 0. 038). These data are consistent with previous findings suggesting TGF-b1 plasma levels as a marker for response to targeted therapies  (28, 39). We investigated factors that may have contributed to the OS and found that, as expected, patients receiving additional treatments after they stopped the experimental treatment (12/47; 25.
ACKNOWLEDGEMENTS This work was supported by  Eli Lilly and Company, Indianapolis, IN, USA. We thank the patients and their caregivers for participating in this trial. We also thank the investigators and their support staff who generously participated in this work and the trial personnel. Writing assistance was provided by  Caryl J. Antalis, PhD ,  Eli Lilly and Company. Parts of this data were previously presented at ASCO 53rd Annual Meeting in June 2017 in Chicago, IL,  USA.
e. , unprocessed aerial images taken by the UAV, which are used to produce the orthomosaic). The consequent loss of image quality is an important consideration for high-throughput phenotyping. Most MPE is still done manually, such as using shapefiles  [12] , the ArcGIS editor  [13] , the Fishnet function of Arcpy in ArcGIS  [14] , or the QGIS tool for plot extraction  [15]. Manual extraction is not only time-consuming, depending on the size of the whole field, but also potentially less accurate because it is prone to human bias. In this paper, we propose a semi-automated MPE solution based on image analysis. We tested two kinds of crops on the proposed method on six different field datasets. We chose sugar beet and soybean because of the importance of both crops are expected their unique growth patterns in each  [22, 23].
Experimental Fields and Image Acquisition. Six field datasets were used as experimental units (Table  1 , Table  S1 ). The plants were sown in precise plot configurations, and all trial fields were managed according to the ordinary local management practices. Plant Phenomics Page 5 of 21 Before image acquisition, the waypoint routing of the UAV was specified by using a selfdeveloped software tool that automatically creates data formatted for import into Litchi for DJI drones (VC Technology, UK). This readable waypoint routing data allowed highly accurate repetition of flight missions so that all images had the same spatial resolution. Orthomosaics of the georeferenced raw images were then produced in Pix4Dmapper Pro software (Pix4D, Lausanne, Switzerland; Table  S1 ). UAVs overflew each field several times during the growing period of each crop.
For each dataset, the inputs were either self-evident (type of image, number of columns, number of crop rows per column, global orientation of the columns) or determined from the field conditions and image resolution (image input, noise). The targeted area in the orthomosaic (i. e. , the field) had to be manually delimited so that the program would be applied to the desired area. Datasets 5 and 6 were binary images and datasets 1 to 4 were RGB images. One image, dataset 4, had to be resized because it was too large for our computer to handle. Because the targeted area was manually delimited by the user, the program outputs could change slightly between trials. Input details are available in Table  S2.
Comparison results Plant Phenomics Page 12 of 21  The program successfully identified microplots in all trial fields (Fig. 4 ). The mean IOU among the datasets was 91% (±%3), indicating a 91% overlap between program-determined and manually determined microplot areas. Moreover, among these fields, an IOU of < 80% was obtained only for dataset 2, which comprised 20 individual microplots Plant Phenomics Page 13 of 21 (the lowest IOU being 71%). Mean precision and recall were 95% (±2%) and 95% (±2%), respectively. These results indicate that neither manually determined nor program-determined microplots showed a tendency to be wholly included in the other. Therefore, the IOU can be understood to indicate a shifting of microplot boundaries between them. * Reverse calculation could not be perfomed on dataset 4 due to a lack of the required inputs.
Computation The computational times (Table  3 ) varied among the trial images, but some global trends can be observed by comparing the computational times in Table  3  with dataset information provided in Table  S1  and S2. The program was slower overall when dealing with larger images, which is to be expected because the program involves image manipulation. The computational time required for binary segmentation depended mostly on the type of input (binary or RGB), whereas the time required for microplot extraction and reverse calculation depended mainly on the number of microplots in the trial field, because this number determines the number of images that must be manipulated in each of these steps.
Also please note that application of the program is limited by the available computer, as shown by the example of dataset 4. The MPE method used by Easy MPE is different from previously published methods. Easy MPE uses image parameters and information on the field geometry to adapt itself to the field, whereas in the method proposed by Khan and Miklavcic  [12] , a cellular grid is laid over the image so that each individual rectangular cell is optimally aligned. Online software (e. g. ,  [11] ) also uses a grid and requires the user to indicate the positions of several microplots. Easy MPE asks for a working zone delimitation and no other image manipulation, increasing its userfriendliness. This method has been built as a piece of a suite of open-source programs for highthroughput phenotyping, along with Easy PCC  [18]  and future additions.
Background and objective: In this paper, we introduce a new R package goftte for goodnessof-fit assessment based on cumulative sums of model residuals useful for checking key assumptions in the Cox regression and Fine and Gray regression models. Methods: Monte-Carlo methods are used to approximate the null distribution of cumulative sums of model residuals. To limit the computational burden, the main routines used to approximate the null distributions are implemented in a parallel C++ programming environment. Numerical studies are carried out to evaluate the empirical type I error rates of the different testing procedures. The package and the documentation are available to users from CRAN R repositories. Results: Results from simulation studies suggested that all statistical tests implemented in goftte yielded excellent control of the type I error rate even with modest sample sizes with high censoring rates.
Conclusions: As compared to other R packages goftte provides new useful method for testing functionals, such as Anderson-Darling type test statistics for checking assumptions about proportional (sub-) distribution hazards. Approximations for the null distributions of test statistics have been validated through simulation experiments. Future releases will provide similar tools for checking model assumptions in multiplicative intensity models for recurrent data. The package may help to spread the use of recent advocated goodness-of-fit techniques in semiparametric regression for time-to-event data.