dataesr/scientific-paragraphs-categorization huggingface dataset:
Key Points • We provide a comprehensive analysis of PTPN2-associated genomic alterations and clinical implications in a large cohort of T-ALL. • PTPN2 loss associates with mutations in the IL7R/JAK-STAT signaling pathway, PHF6 and WT1, but is exclusive from PTEN deletions. Protein tyrosine phosphatase nonreceptor type 2 (PTPN2) is a phosphatase known to be a tumor suppressor gene in T-cell acute lymphoblastic leukemia (T-ALL). Because the full clinicobiologic characteristics of PTPN2 loss remain poorly reported, we aimed to provide a comprehensive analysis of PTPN2 deletions within a cohort of 430 patients, including 216 adults and 214 children treated according to the GRAALL03/05 (#NCT00222027 and #NCT00327678) and the FRALLE2000 protocols, respectively. We used multiplex ligationdependent probe amplification to identify an 8% incidence of PTPN2 deletion, which was comparable in adult (9%) and pediatric (6%) populations.
Clinical trials Adult patients (16 to 59 years old) were included in the GRAALL03/ 05 trials, which were registered at clinicaltrials. gov (GRAALL-2003, #NCT00222027; GRAALL-2005, #NCT00327678). Pediatric patients (1 to 20 years old) were treated in 10 French pediatric hematology departments, members of the FRALLE study group, according to the FRALLE 2000 T guidelines, a German BFMinspired protocol. 20 Patients aged from 16 to 20 years were either treated in the GRAALL (22/216 patients, 10%) or in the FRALLE protocol (12/214 patients, 5. 6%) according to the initial health care center (either adult or pediatric departments). Studies were conducted in accordance with the Declaration of Helsinki and approved by local and multicenter research ethical committees.
Immunophenotypic and molecular characterization of T-ALL samples [23]  Multiplex ligation-dependent probe amplification (MLPA) analysis MLPA analysis was performed using the MRC Holland (Amsterdam, The Netherlands) SALSA MLPA probe mix P383-A1 TALL according to the manufacturer's recommendations. Polymerase chain reaction products were separated by capillary electrophoresis on an ABI-3130 device. Coffalyser software, available at http:// www. mlpa. com, was used for the analysis.
Next-generation sequencing A custom capture Nextera XT gene panel (Illumina) targeting complete coding exons and their adjacent splice junctions of 78 genes was designed based on targets known to be mutated in T-ALL and/or important in T lymphopoiesis. DNA Libraries were prepared using Nextera Rapid Capture Enrichment and underwent 2 3 150-bp paired-end sequencing on an Illumina MiSeq sequencing system with MiSeq Reagent Kit v2 (Illumina). Briefly, sequence reads were filtered and mapped to the human genome (GRCh37/hg19) using in-house software (Polyweb; Institut Imagine, Paris, France). Annotated variants were filtered according to the following criteria: (1) coverage ,303, ,10 alternative reads, and variant allelic fraction ,7% were filtered out; (2) polymorphisms described in dbSNP, 1000Genomes, EVS, Gnomad, and EXAC with a calculated mean population frequency.
Statistical analysis Group comparison for categorical and continuous variables was performed with Fisher's exact and Mann-Whitney U tests, respectively. The cumulative incidence of relapse (CIR) was calculated from complete remission to relapse date, censoring patients alive without relapse at last follow-up. Overall survival (OS) was calculated from the date of diagnosis to the last followup date by censoring living patients. Survival analysis was performed using the Kaplan-Meier method, and the curves were compared using the log-rank test. Statistical analysis was performed with Stata software, version 12 (StataCorp, College Station, TX). All P values were 2-sided, with P ,. 05 considered statistically significant.
PTPN2 deletion incidence in T-ALL A total of 430 T-ALL was screened for PTPN2 deletions by MLPA analysis. Two hundred sixteen adult patients and 214 pediatric patients were prospectively treated in the GRAALL-2003-2005 and the FRALLE-2000 protocols, respectively. The incidence of PTPN2 deletions in the whole cohort was 8% (33 patients, 48% monoallelic, 52% biallelic), in line with published data  7, 8, 24  (Table  1 ). No significant difference was observed between adult and pediatric T-ALLs, as we identified PTPN2 deletions in 9% of adult patients (50% monoallelic and 50% biallelic; supplemental Tables  3  and 4 ) and 6% of the pediatric population (46% monoallelic and 54% biallelic; supplemental Tables  5  and 6 ). High-resolution array CGH was performed for a subset of 88 of the 430 patients analyzed by MLPA (supplemental Figure  2 ) and identified 7 PTPN2 deletions all also identified by MLPA analysis.
01; Table  1 ; Figure  2 ). Moreover, 85% of the PTPN2-deleted population presented mutations of NOTCH1/FBXW7 vs 63% of wild-type patients (P 5. 01; Table  1 ). We then took advantage of our published next-generation sequencing data from 194 of the 216 adult patients included in this study. 25 mong those 194 patients, 19 harbored PTPN2 deletions. Overall, at least 1 mutation was detected in 19/19 PTPN2-deleted T-ALL and 173/175 wild-type patients. The comparison of the mutation and large deletion frequencies between PTPN2-deleted and wild-type patients is shown in Figure  2 , with a focus on alterations presented by at least 5% of the whole cohort. Data regarding all the abnormalities are reported in supplemental Table  7. A significant association between mutations in IL7R/JAK-STAT signaling pathway (including DNM2, SH2B3, STAT5B, IL7R, JAK3, JAK1) and PTPN2 deletions was observed.
Acknowledgments The GRAALL was supported by grants  P0200701  and  P030425/ AOM03081  from  Le Programme Hospitalier de Recherche Clinique, Minist ère de l'Emploi et de la Solidarit é, France , and the  Swiss Federal Government in Switzerland. This work was supported in the Necker Hematology Department by grants from  INCA  PLBIO PLAN CANCER 2014-19-ASNAFI. The authors express their gratitude to " Association Laurette Fugain " and " Enfants et Sant é"  for financial support.
An example from Dupont Pioneer  (Jeschke and Ahlers, 2018)  studied the effect of foliar fungicides (alone or combined with an insecticide) on soybean across 279 on-farm trials and shared the trials' average yield differences through a histogram and ranking of trials by decreasing yield response values. Despite the number of trials involved, only the average yield response per trial was reported and without explanations of variability in yield response. In another example, the South Dakota On-Farm Research program allows for sorting experiments into different categories. The Nebraska On-Farm Research Network and the Iowa Soybean Association On-Farm Network have online searchable databases which allow users to query individual summary trial data by year, crop and, management practice, but this is not sufficient to understand general patterns in treatment effects and gain novel insights from the data.
We defined priors for the three parameters of the model (i. e. , μ, 2 a s and 2 e s ). The priors for the intercept μ and for the regression parameter β represent the distribution of the mean of the log ratio (or the yield difference) and the distribution of the effect of the explanatory variable X, respectively. These priors are independent Gaussian distributions with a mean of zero and a variance of 2. With a variance of 2, the log ratio and the regression parameter can take either a high positive or a low negative value depending on the dataset. The priors of the variances of the random effect, 2 a s , and of the residual error, 2 e s , are independent inverse Gamma distributions with parameters ν/2 and ν/2, where the degree of belief ν is equal to 0. 002. The parametrization of the priors is specific to the R package MCMCglmm  (Hadfield, 2010).
Visualization The graphical features are implemented through an interactive and dynamic graphical web-tool. We used Shiny, an R package from RStudio  (RStudio Team, 2015; Chang et al. , 2016)  that combines numerous extension packages. The webapplication has a user interface divided in two parts: the sidebar menu on the left and the main panel on the right (Fig. 2 ). On the sidebar menu, the user can select the crop and then a specific management practice. Then, the user has access to different components organized into a list on the sidebar menu. The main panel, located on the right side of the interface, returns visuals described in Fig. 1. The main panel has interactive features such as zoom-in, zoom-out, filter, select and pointer-hovering to interact with data and graphical information.
More detail about the structure of our web-application and how to use it are available in the supplemental material. Our data-analytics framework was implemented for a total of 34 different management practices tested by the Iowa Soybean Association. The data related to the different management practices are stored in different datasets (one dataset per management practice) and differed by number of trials, yield value and years of experiments (Table  2 ). The following section provides examples of the implementation of the data-analytics framework for three case studies: foliar fungicide on soybean, row spacing on soybean and soilapplied insecticide on corn.
, 2013b). The objective of these trials was to study the effect of a foliar fungicide (Headline) on soybean compared to a control by quantifying the yield response across a wide range of environmental and management practices. Materials and Methods. The foliar fungicide Headline was tested in 206 trials (Fig. 3 , left) over 9 yr  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) 2015)  and compared to a control (untreated). These experiments used yield data collected on combines equipped with GPS. The active ingredient of Headline is pyraclostrobin. Most of the applications were done by farmers using ground sprayers, but in ~20% trials the foliar fungicide was applied by airplanes. The time of application varied between trials but most of them were done at the crop stage R3 (beginning pod development).
The experimental design was the replicated strip, where the two treatments are applied in strips without randomization  (Fig. 3, right). A pair of strips (foliar fungicide and the control) constitutes a replicate. Each trial which was part of the network had a minimum of three replicates. Some trials required more than three replicates to capture the entire field for spatial analysis of yield responses. The width of the individual strip depends on the size of application equipment and can range from 4. 6 to 27. 4 m. The length of the strips depends on the  Trials were well distributed throughout Iowa, with a majority located in the Des Moines Lobe (Fig. 3 , left). Therefore, our data cover a broad set of environmental conditions and field management across Iowa. Disease development in soybean fields can be affected by different environmental conditions or management practices.
AcKnoWLedGMents The authors thank  Xiaodan Lyu ,  Suzanne Fey ,  Samantha Tyner ,  Eric Hare  and  Halley Jeppson  for their technical assistance programing ISOFAST. We are thankful to many farmer participants and agronomists along with the staff of the  Iowa Soybean Association On-Farm Network  for executing on-farm trials and collecting data. This research was funded by the  Iowa Soybean Association checkoff. The work of DM was partly funded by the STIMUL (Scenarios towards integrating multiscale land use tools) flagship project as part as part of the " Investments d'Avenir" Programme (LabEx BASC ;  ANR-11-LABX-0034 ) and by  Institut Carnot Plant2Pro  (project  L-i-cite ). This work was partly based on research supported by the  Chateaubriand Fellowship of the Office for Science & Technology of the Embassy of France in the United States.
Alternatively, other rounds of reviews may be needed before the recommender reaches a favorable conclusion. He/she also might decide not to recommend your article. In this latter case, the reviews and decision will be sent to you, but they will not be published or publicly released by PCI Ecology. They will be safely stored in our database, to which only the Managing Board has access. You will be notified by e-mail at each stage in the procedure. Thanks in advance for submitting your revised version. Yours sincerely, The Managing Board of PCI Ecology. Preprint DOI: doi: https://doi. org/10. 1101/629931 Reviewed by Nicolas Deguines, 2019-10-02 16:01.
" L247-248: authors mentioned in their response that there had been an error in the first version of the MS and have corrected it. However, I still don't see how 'the average values of fruit and seed weight [which would almost inevitably produce decimal values, or am I missing something?] fitted to Poisson distributions' could work. Looking at R scripts provided by the authors (e. g. L324-330 in 'removing out obs. R'), I see the use of package glmmTMB when modeling the response variable 'mean. seedw' but Poisson distribution should only work with so-called count data (i. e. positive integer), as stated for example in a glmmTMB related paper: https://journal. r-project. org/archive/2017/RJ-2017-066/RJ-2017-066. pdf. This is an important concern here as I don't understand how this could work and this requires a clarification.
2018) to improve statistical transparency. So I still suggest to add 95% confidence intervals. Figures Figure  2:  Relative to my previous comment on panel A: points don't go over 1 but the prediction line does. I see two issues with this panel: 1) what is the grey band surrounding the black line? I understand it describes uncertainty around the predicted effect, but is it a confidence interval and at which level (95% or 99%)? 2) given these are results from a glmm based on a binomial distribution (L245-247), neither the estimated effect (the black line) nor its uncertainty (grey band) should be predicted at values >1. For example, when using function predict() or add_ci() (package ciTools) in R on a binomial gl(m)m model, predicted values stay within the boundaries of the its associated distribution.
• Line 98. I suggest to remove 'us'. • Line 127 and many other similar places. Please specify what is the error around the mean. I assume it is standard deviation, but it does not hurt to be precise. Minor points: • Line 176-184. Recently 10. 1111/1365-2656. 12963 have come up with a new algorithm to compute the normalized NODF. Although the difference between the two algorithms appears to be small (10. 1111/1365- 2656. 12964), I suggest the authors to re-compute the normalized NODF for the sake of accuracy (the code can be found in github. com/CHoeppke/maxnodf). • Line 199. Is it "generalized" or "general"? • Line 249. Perhaps 'small' is better than 'low'? • Line 349. I am not sure if James et al. 2012 have claimed that nestedness destabilizes the dynamics. Instead, I think they have claimed nestedness has an almost null effect on the dynamics.
To address these questions, we generate controlled synthetic data that only involve phylogeny, in the absence of functional constraints. Our DCA-based method correctly identifies pairs of synthetic "sequences" that share evolutionary history, even without any training set, thanks to an Iterative Pairing Algorithm (IPA). (Strikingly, this high predictive power is obtained in the absence of real couplings from interactions, from purely phylogenetic correlations. ) On this synthetic dataset, we find that the DCA-based IPA and a phylogeny-based IPA reach similar performances, with DCA slightly outperforming the phylogenetic method for large training sets. We then show examples of natural proteins without known direct physical interactions but with shared evolutionary history that can be accurately paired by our various methods, thus illustrating the importance of phylogenetic correlations in real data.
The data generation process is illustrated in Fig. 1. Specifically, we consider perfect binary trees for simplicity. The ancestral chain, composed of uniformly randomly distributed bits, is duplicated, giving rise to two chains, and mutations are performed independently in the two duplicate chains: each mutation changes the state of one uniformly randomly chosen bit of the chain. Then, the new chains are duplicated again, and so on. We employ two different models for the occurrence of mutations. In the simplest model, a fixed number of mutations per total chain length is performed along each branch of the tree, i. e. between two duplication steps. In a more realistic model, the number of mutations per branch is drawn in a Poisson distribution with fixed mean. After a given number n of duplication steps ("generations", representing ancestry in terms of speciation or gene duplication events), a final dataset of 2 n chains is obtained (see Fig.
1 ). In practice, we take n ≤ 12 to have tractable datasets of a few thousands of final chains. Throughout, we perform inference using these final chains, which correspond to contemporary sequences in a natural dataset. In each of these chains, the state of each bit is uniformly distributed. However, correlations exist between chains due to their shared evolutionary history. The strength of these correlations depends on the number of mutations per branch and on how close the chains are along the phylogenetic tree. In order to introduce the notion of species in a minimal way, we randomly group chains into sets of equal size m, each representing a species. The m different chains within a species can then be thought of as paralogs, i. e. homologs sharing common ancestry and present in the same genome.
Note that the present minimal model with random species is realistic in the case where exchange between species (i. e. horizontal gene transfer) is sufficiently frequent. We finally cut each chain of the final dataset in two halves of equal length. These halves, denoted by chain A and chain B, thus represent a pair of proteins that possess the same evolutionary history. Next, we blind the pairings for the chains A and B from some species (testing set) and ask whether DCA-, MI-and similarity-based methods are able to pair each A chain with its "evolutionary partner", namely with the B chain that possesses the same evolutionary history, starting from the known pairs (training set).
Inference methods We test several inference methods to predict pairings between chains A and B in our synthetic datasets. For each of them, performance is assessed both with a training set and without a training set. In the first case, the parameters defining scores are computed using the training set and employed to pair data in the testing set  [4, 15]. In the second case, we employ the Iterative Pairing Algorithm (IPA) developed in Refs. [22, 41]  to bootstrap the predictions starting from initial random within-species pairings. Below, we present the various inference methods assuming that there is a training set. The extension to the training set-free case is then performed exactly as described in Refs. [22, 41]. Matlab implementations of the MI-IPA, the DCA-IPA and the Mirrortree-IPA on our standard HK-RR dataset are freely available at https://doi.
org/10. 5281/zenodo. 1421781, https://doi. org/10. 5281/zenodo. 1421861 and https://doi. org/10. 5281/zenodo. 3377592 respectively. Training set statistics. To describe the statistics of a training set of synthetic paired chains AB, of total length 2L (where L is the length of a chain A or B), we employ the empirical one-site frequencies of each state σ i ∈ {0, 1} at each site i ∈ {1,. , 2L}, denoted by f i (σ i ), and the two-site frequencies of occurrence of each ordered pair of states (σ i , σ j ) at each ordered pair of sites (i, j), denoted by f ij (σ i , σ j ). Correlations are then computed as C ij (σ i , σ j ) = f ij (σ i , σ j ) -f i (σ i )f j (σ j ). Pseudocount. When dealing with real protein sequences, pseudocounts are often introduced to avoid mathematical issues such as divergences due to amino-acid pairs that never appear, both with DCA  [5] [6] [7] 15]  and with MI  [41].
Given the relatively small number of mutations per branch, many of the resulting chains AB possess substantial similarities arising from their common evolutionary history. Specifically, Fig. S2A  shows the histogram of Hamming distances between all B chains in the dataset, featuring a typical fraction 0. 3 of sites with different states. The degree of similarity between two given chains arises from their relatedness along the phylogenetic tree used to generate the data. We ordered the chains employing this phylogenetic tree, so that sister chains are closest to one another etc. (see Fig. 2A ). Next, we randomly picked 75% of the chains to form a substantial training set, and inferred a DCA model from this training set (see Methods). We employed the inferred couplings e ij (σ i , σ j ) to compute effective interaction energies E AB (Eq.
1) between all chains A and all chains B of the remaining 25% of the dataset, which constitutes our testing set. The effective interaction energies obtained are shown in Fig. 2B. Importantly, the diagonal of the matrix, corresponding to actual evolutionary partners, features small energies. Furthermore, a nested block structure is apparent in the matrix, reflecting the phylogenetic tree (recall that chains A and B are both ordered according to the tree as shown in Fig. 2A ). Specifically, for 22% of chains A in the testing set, the smallest DCA effective interaction energy E AB is obtained with their evolutionary partner (corresponding to the diagonal in Fig. 2B ). In Fig. S2 , we further demonstrate that those chains B that have smaller E AB with a chain A than its evolutionary partner B are very similar to that chain B and strongly related to it.
Furthermore, if the dataset is divided into random species with 4 chains AB each (see Methods), the smallest E AB for a chain A within its species accurately identifies its evolutionary partner B for 93% of chains A of the testing set. Hence, with a large training set, DCA is able to learn phylogenetic correlations, and to identify evolutionary partners. Recall that the usual goal of DCA is to infer couplings e ij (σ i , σ j ) stemming from actual interactions, which do not exist in our synthetic data.
4) that only relies on sequence similarity. All these methods predict pairings much better than the chance expectation (yellow) and reach very high fractions of true positives for training sets larger than ∼100 pairs AB (see Fig. 3A ). Better performance is obtained when pairings are predicted using the Hungarian algorithm, which finds a global optimal one-to-one matching within each species (see Methods) than when simply picking for each chain A the optimal partner B within its species  [41]. Fig. 3A  further shows that with the first approach, the Mirrortree method performs better for small training sets, while DCA and MI outperform it for larger datasets. These differences become almost negligible when using the Hungarian algorithm. Since the pairing task becomes harder when the number of pairs per species increases, we next studied how performance is affected by this important parameter.
B: Fraction of pairs correctly identified (TP fraction) versus number of pairs per species, employing the same methods (and same colors) as in panel A, and a training set of 50% of the total dataset. In both panels, data was generated using a tree of 10 generations, with exactly 5 mutations per branch, out of 200 bits in each chain AB, thus yielding 1024 chains AB. Species were built randomly, and some of them were chosen randomly to build the training set, the remaining ones making up the testing set. Yellow curves show the chance expectation, i. e. the average TP fraction obtained for random within-species pairings. Results are averaged over 100 replicates in panel A and 20 replicates in panel B, each corresponding to a different realization of the branching process used for data generation.