dataesr/scientific-paragraphs-categorization huggingface dataset:
Marginal model The marginal distribution of extreme values is usually modeled using a GEV distribution in an annual maxima framework or a generalized Pareto distribution (GPD) in a peakover-threshold framework  (Coles, 2001). Strictly speaking, we were in neither of the two frameworks since we were working with a synchronous dataset that has been composed of peak-over-threshold events at individual stations and complemented with the corresponding events at the remaining stations. The event sets at the individual stations were well fit by the GEV distribution. The generalized extreme value distribution (GEV)  (Coles, 2001)  was not rejected for both types of events in most catchments according to the Anderson-Darling test statistic computed using the procedure proposed by  Chen and Balakrishnan (1995)  (level α = 0.
, 2009). There is an enormous number of possible R-vine tree sequences to choose from. Dißmann et al. (2013)  therefore proposed an automated model selection and estimation technique which is implemented in R-package VineCopula  (Schepsmeier et al. , 2017). 5. Fisher copula. The Fisher copula arises when components of a Student random vector are squared. This construction procedure follows the construction of the chisquare copula family which is obtained when squaring the components of a normal random vector  (Bárdossy, 2007). The Fisher copula is characterized by the two parameters ν and , similar to the Student t copula. The Fisher copula generalizes the class of centered chisquare dependence models since it tends to the centered chi-square copula as ν → ∞. For = ( 1 ,. , d ) T , the d-dimensional Fisher copula can be expressed by C F ,ν (u 1 ,.
The new matrix still consisted of the former 10 × 10 matrix in the upper left corner completed with s vectors of correlation values for the new stations with respect to the other stations. The vectors of correlations could be derived via an empirical correlogram. The empirical correlogram consisted of pairwise correlations plotted against the river distance between stations. To derive the values for an ungauged location, its distance to all gauged stations was determined. The correlogram could then be used to derive the correlations corresponding to these distances. These were used to extend the matrix. The new extended matrix could finally be used as a parameter of the Fisher copula to generate a flood event set for the gauged and ungauged locations in the dataset. The individual steps of this procedure are outlined in more detail below.
5. The extended matrix was then used to simulate n = 1000 random flood event sets for a set of stations comprising gauged and ungauged locations using the new as a parameter in the Fisher copula. The second parameter of the Fisher copula ν, which corresponds to the number of degrees of freedom of the Student distribution and is by the Fisher copula required to be an integer, was found to be only weakly sensitive to an extension of. This was assessed by reestimating ν using the new simulated data. The estimated ν remained at ν = 7 when adding only one station and slightly increased for s > 10 to ν = 8. The adaptation of the Fisher copula to the spatial context was validated by using leave-one-out cross-validation. To do so, 1 of the 10 gauged stations was considered ungauged, and a Fisher copula fit to this reduced dataset.
, 2018a) , the use of the region of influence approach  (Hosking and Wallis, 1997) , or the copula-based clustering algorithm proposed by  Pappadà et al. (2018)  could be applied if the marginal distributions were of particular interest and if a larger dataset was available. Alternatively, new regionalization techniques based on catchment similarity in terms of empirical copulas  (Grimaldi et al. , 2016 ) could be developed. A larger dataset would also be desirable for reliable estimation of the dependence structure. One possibility of increasing the dataset would be to exploit the whole content of a continuous runoff series instead of only using a flood event sample as suggested by  Serinaldi and Kilsby (2017). So far, the spatial Fisher copula model has only been applied to the Thur study region.
Classification of evidence This observational study provides Class III evidence that the proportion of patients with at least one relapse is similar after 1 year of treatment with TRF or DMF in patients with RRMS. The OFSEP cohort OFSEP is a network of French neurologists, mainly from 34 MS centers. Data are collected prospectively during each visit or hospitalization on a standardized clinical form and collected in the European Database for Multiple Sclerosis (EDMUS) software. 13 Individual case reports include identification and demographic data, medical history, biological, electrophysiologic, and MRI data, treatments, as well as key episodes in MS course (date of relapses, date of secondary progression, dates and level of disability progression). Data are checked for consistency using automatic controls in the software.
Standard protocol approvals, registrations, and patient consents All patients included in the OFSEP cohort sign an informed consent to have their medical data collected in routine practice used after anonymization and aggregation for research purposes. The reasons behind treatment withdrawal were prospectively collected in the OFSEP cohort, as the neurologist following the patient had to fill in a short questionnaire selecting different reasons for treatment discontinuation (local, general, or biological intolerance, lack of effectiveness, pregnancy, desire to become pregnant, other). Local intolerance referred for example to cutaneous adverse event with injectable treatments, general intolerance referred for example to fever or myalgia in case of flu-like syndrome, and biological intolerance referred to a biological abnormality observed under treatment like for example elevated liver enzymes.
Serious adverse events are specifically enclosed in the form. The initial folder has received the agreement of the Comité Consultatif sur le Traitement de l'Information en Matière de Recherche (CCTIRS) (December 18, 2013, reference 13. 591bis, and June 27, 2014) and the Commission Nationale de l'Informatique et des Libertés (CNIL) (May 6, 2014, request 914. 066, authorization decisions DR-2014-110 and DR-2014-327). The OFSEP cohort has been registered in clinicaltrials. gov (NCT02889965). For the current study, data were extracted on December 15, 2017. The study was registered in clinicaltrials. gov (NCT03302442).
Outcomes Study schedule The baseline was the DMF or TRF initiation date. Since our study was based on data from real-life practice, MRI and EDSS evaluations were not scheduled. Therefore, we defined 2 periods. First, we considered data collected between 6 months preinitiation and 3 months postinitiation as baseline MRI or EDSS. In case of multiple visits within this period, we retained the closest visit from the beginning of the treatment. Second, we considered data collected between 9 and 18 months postinitiation as 1-year MRI or EDSS. In case of multiple visits within this period, we selected the closest visit from the first postinitiation anniversary.
Outcomes The main outcome was the proportion of patients with at least 1 relapse at 1 year postinitiation. Relapses were defined by any new or recurrent exacerbation of neurologic symptoms without fever that lasted for at least 24 hours. Five secondary endpoints were also analyzed after 1 year: the proportion of patients with an increased EDSS score compared to baseline, the proportion of patients with at least one new T2 lesion, the proportion of patients with no evidence of disease activity (NEDA3) (i. e. , no new T2 lesion, no increase in the EDSS, and no relapse),  15  the proportion of treatment discontinuation due to inefficacy, and the proportion of treatment discontinuation due to side effects. We also reported the 6 previous outcomes at 2 years postinitiation. We considered data collected within 21 and 30 months postinitiation as 2-year MRI or EDSS.
Statistical analyses Baseline comparisons were performed using Student t tests or χ 2 tests for quantitative or categorical variables, respectively. In order to deal with confounders, we chose a propensity score-based approach-the inverse probability weighting (IPW) estimator with stabilized weights  16  -to estimate average treatment effect. Multivariable logistic regressions were used to obtain the weights. Calibration was evaluated by plotting observed vs predicted probability of receiving DMF instead of TRF (figures e-1 to e-12, doi. org/10. 5061/dryad. 60tv071). Regarding goodness-of-fit for each model, we did not further explore the violation of the log-linearity assumption for continuous explanatory variables. Variables associated with the outcomes were considered as possible confounding factors (p < 0.
20), in accordance with recent recommendations. 17, 18 One propensity score model was therefore constructed for each outcome (table  1 ). Compared to the consideration of the true confounders subvector, that is, the variables associated with both treatment allocation and outcome, our strategy for variable selection in the propensity score models may improve estimation precision without adding bias. 19 We did not include variables only associated with treatment allocation, since it has been demonstrated that such instrumental variables may increase the bias of the effect estimation. 20 We checked that these possible confounding factors were associated with standardized differences lower than 10% in the corresponding pseudosamples (tables e-1 to e-12, doi. org/10. 5061/dryad. 60tv071). For each comparison, a new set of propensity scores was assessed based on the subsets of eligible patients.
Positivity assumption was graphically evaluated (figures e-1 to e-12, doi. org/10. 5061/dryad. 60tv071). The outcomes were then modeled according to logistic regressions by using weighted likelihood maximization and robust variance estimator. For time-to-event analysis, cause-specific Cox models were estimated by maximizing the partial weighted likelihood with a robust estimator for the variance. 21 The adjusted survival curves were obtained using the weighted Kaplan-Meier estimator and compared using the adjusted log-rank test. 22, 23 l analyses were performed using the 3. 3. 2 version of R software.
Results Baseline characteristics: DMF is used in more active disease A total of 3,686 patients fulfilled the inclusion criteria, 713 treated with TRF and 1,057 with DMF (figure  1 ). The characteristics of the 1,916 nonincluded patients due to missing EDSS or MRI at baseline are reported in table e-13 (doi. org/ 10. 5061/dryad. 60tv071). When compared to the included patients, the main clinically relevant differences were a lower relapse rate 1 and 2 years prior to treatment initiation, an older age at treatment initiation, a longer disease duration, and a higher previous exposure to DMTs. These results indicate that the patients retained for analysis experienced more active disease than the others. However, it is worth noting that there was no difference in terms of percentage of nonincluded patients for both treatments (51.
7224, adjusted log-rank test). The corresponding cause-specific hazard ratio of DMF vs TRF was 0. 97 (95% CI 0. 81-1. 16). This lack of difference between the 2 treatments at 1 and 2 years postinitiation was also observed for the proportion of EDSS increase. We selected the patients for whom an EDSS was available at 1 (n = 1,305) or 2 (n = 1,109) years postinitiation. When comparing included and excluded patients due to nonavailable EDSS, no significant difference was observed, except for the percentage of previously treated patients for the analysis after 2 years (tables e-14 and e-15, doi. org/10. 5061/dryad. 60tv071). Among the 1,305 patients (cohort B1, figure  1 ), the observed crude percentage of patients with an increase in EDSS level was 27. 3% in the TRF group vs 26. 4% in the DMF group (p = 0.
org/10. 5061/dryad. 60tv071). This suggests, as expected, that patients with more active disease are more likely to have control MRIs after 1 or 2 years. For this subgroup of 835 patients (cohort C1, figure  1 ), after weighting on the propensity scores, the confounder-adjusted percentage of patients with new T2 lesions was 46. 9% (95% CI 41. 3%-52. 3%) in the TRF group vs 42. 9% (95% CI 38. 6%-47. 1%) in the DMF group, showing a lack of significant difference (OR 0. 85, 95% CI 0. 64%-1. 13%, figure  2 ). In the 2 years postinitiation, 1,015 patients were excluded because no MRI was performed and compared to the included patients (table e-17, doi. org/10. 5061/dryad. 60tv071), confirming more active disease in patients with a control MRI. The confounder-adjusted percentage of patients with new T2 lesions after 2 years was 72.
Clinical outcomes were not significantly different in DMF-vs TRF-treated patients. By contrast, we found fewer new T2 lesions for DMF than TRF at 2 years postinitiation, correlating with the greater percentage of patients reaching NEDA3 in DMF-treated patients. In agreement, we also reported more frequent treatment withdrawals due to lack of effectiveness after 2 years for patients under TRF. However, a consequence of this higher effectiveness of DMF was a higher rate of treatment withdrawal because of side effects. Five other studies compared TRF and DMF. The first one was a network meta-analysis of RCTs of the different molecules used for MS, 8 while 2 others were based on a US claims database. 9, 10 The last ones were based on observational databases from neurologists in Germany  11  and Italy.
To our knowledge, no validated IPW method has been proposed and the data collected in our cohort do not allow to precisely compute the person-time. Furthermore, the choice of treatment may depend on measures other than the ones collected at baseline. Therefore, one cannot exclude possible confounders that were not taken into account. For example, smoking could be relevant information for treatment response that is not collected in the database. 27 Finally, the follow-up was limited to 2 years postinitiation. A longer and more standardized follow-up would have been ideal to correctly perform time-toevent analyses and will be considered in future studies. Considering these weaknesses, interpretations of the results should be made with caution, particularly the lack of clinical difference observed between the 2 treatments after 1 and 2 years of use, which is probably restricted to the more active patients of the cohort and could not be extended to the whole cohort.
Study funding Supported by  Fondation ARSEP ,  CHU Nantes (Appel d'Offre Interne), and Association ANTARES. The French Observatoire of Multiple Sclerosis  is supported by a grant provided by the  French State  and handled by the  Agence Nationale de la Recherche , within the framework of the  Investments for the Future program , under the reference  ANR-10-COHO-002. The study was supported by  West Neurosciences Network and Société Francophone de Sclérose en Plaques, Fondation ARSEP , and by  Appel d'Offre Interne du CHU de Nantes. Disclosure D. Laplaud  has received consulting and lecturing fees, travel grants, and unconditional research support from  Biogen ,  Genzyme ,  Novartis ,  Merck Serono, Roche ,  Sanofi Aventis, MedDay , and  Teva Pharma. L. Barbin ,  R. Casey , and  M. Debouverie  report no disclosures relevant to the manuscript.
Yeast 2-hybrid assays and co-immunoprecipitation/mass-spectrometry showed V-Crk avian sarcoma virus CT10 oncogene homolog-like (CRKL) as a novel interaction partner of SASH1, further confirmed by domain mapping, site-directed mutagenesis, co-immunoprecipitation, and dynamic mass redistribution assays. CRKL-deficient cells were generated in parental or SASH1-deficient cells. Metastatic capacity was analyzed with an orthotopic mouse model. Expression and significance of SASH1 and CRKL for survival and response to chemotherapy was assessed in patient samples from our department and The Cancer Genome Atlas data set. RESULTS: SASH1 expression is down-regulated during cytokine-induced EMT in cell lines from colorectal, pancreatic, or hepatocellular cancer, mediated by the putative SASH1 promoter.
The N-Terminal SH3 Domain of CRKL Interacts With a PXXPXK Motif of SASH1 Next, the binding mode between SASH1 and CRKL was mapped using full-length and truncated SASH1 constructs with N-terminal GFP or C-terminal V5 epitopes (Figure  4A ) expressed in HEK293 cells, as described before. 20 Immunoprecipitation showed that only constructs containing the C-terminal part of SASH1 bind to endogenous CRKL (Figure  4B  and C ). However, the NLS showed no impact on CRKL binding (Figure  4C ). CRKL is known to bind specific PXXPXK/R motifs of its downstream effector proteins (eg, C3G), mediated by its N-terminal SH3 domain (SH3N). 21, 22 nalysis of the SASH1 primary structure (Research Collaboratory for Structural Bioinformatics Protein Data Bankentry: O94885) showed 3 PXXPXK motifs within the C-terminal part as putative CRKL binding sites (aa865-870, 984-989, and 1016-1021).
(C) To screen for potential interaction partners of SASH1, a yeast 2-hybrid screen was performed with SASH1 as bait protein and a human placental cDNA bank. Shown are protein coding hits of good and high confidence (significance A-C), and the corresponding number of articles from "<gene name> and EMT" searches in the PubMed database. In addition, SASH1 with an N-terminal GFP tag was transiently expressed in HEK293 cells and precipitated to analyze co-precipitating proteins via mass spectrometry. Yeast 2-hybrid hits that also were found to interact with SASH1 via co-precipitation/mass spectrometry are indicated. (D) Endogenous CRKL co-precipitates endogenous SASH1 in HCT116 and HEK293 cell lysates, while the isotype control antibody shows no signal. (E) HEK293 cells transiently expressing SASH1 with a C-terminal V5 epitope were stained for immunofluorescence microscopy (scale bar: 10 mm) with an anti-V5 and anti-CRKL antibody.
To address the question of whether SASH1 and CRKL play a clinical role and are involved in survival of patients with colorectal cancer, genetic alterations and aberrant expression of both genes were assessed in the publicly available TCGA data set consisting of 233 colorectal cancer patients, with full sequence information and available follow-up data in the cBioPortal (Memorial Sloan Kettering Cancer Center, New York, NY) platform. 29, 30 SASH1 was down-regulated or homodeleted, and CRKL was increased or genomically amplified in 10. 8% of cases (23 of 213). There was a trend toward mutual exclusivity of both alterations in individual patients. Kaplan-Meier analysis showed a significantly reduced overall survival in the subgroup with up-regulated CRKL and/or down-regulated SASH1 (Figure  7D ).
CRK has been shown to activate SRC by association with its negative regulatory kinase C-terminal SRC kinase,  47, 48  which also may apply to CRKL. In the absence of SASH1, SRC signaling also could be activated directly by binding of effector proteins (eg, C3G in this study) to the CRKL SH3N domain, leading to activation of small GTPases. According to our working model, loss of SASH1 induces EMT and formation of metastases owing to a lack of CRKL inhibition. Our in vitro and in vivo findings are supported by data obtained from clinical samples that show a striking decrease of SASH1 and an increase of CRKL expression in colorectal cancer compared with normal colon mucosa. Moreover, analysis of the TCGA data set on an independent patient cohort showed that genetic loss of SASH1, and/or amplification of the CRKL gene locus, occur in approximately 11% of patients, and are associated significantly with worse overall survival.
20 The luciferase reporter construct was generated by cloning the proximal 1 kb of the SASH1 gene regulatory region into the pGL3 vector. For induction of epithelial-mesenchymal transition, cells were stimulated with 10 ng/mL TGF-b1 (Stemcell Technologies, Cologne, Germany) or 20 ng/mL TNF (Gibco, Waltham, MA) for 48 hours in normal growth medium. Proliferation and response to 5-fluorouracil (24hour treatment) was probed using the Cell Proliferation Kit II (XTT) from Roche (Mannheim, Germany). The following antibodies were used: E-cadherin (3195; CST), b-actin (3700; CST), SASH1 (A302-265A; Bethyl, Montgomery, TX), GAB1 (A303-288A; Bethyl), C3G (Bethyl A301-965A), RFP (5F8; Chromotek, Martinsried, Germany), V5 epitope tag (MA5-15253; Thermo Scientific, Waltham, MA), Poly(ADP-Ribose)-Polymerase 1 (ab137653; Abcam, Cambridge, UK), GFP (3H9; Chromotek), CRK-II (sc-289; SCBT, Heidelberg, Germany), CRKL (05-414; EMD Millipore, Darmstadt, Germany), CRKL (sc-319; SCBT), ZEB1 (HPA027524; Sigma, Steinheim, Germany), vimentin (ab92547; Abcam), Y118 phospho-paxillin (2541; CST, Danvers, MA), paxillin (12065; CST), Y416 phospho-Src family (6943; CST), Src (2123; CST), human mitochondria (ab92824; Abcam), human E-cadherin (ab40772; Abcam), RFP-trap (rta-10; Chromotek), and GFP trap (gta-10; Chromotek).
0381). (C) Organ sections were analyzed for the presence of tumor cells (brown staining) by immunohistochemistry with human-specific antibodies against mitochondria or E-cadherin. In parental and compound SASH1/CRKL-deficient cells, peritoneal carcinomatosis frequently occurred (arrowheads), with metastatic lesions showing central necrosis (asterisk). SASH1-deficient cells spawned metastases enclosed by normal tissue (arrows), which showed low E-cadherin staining compared with the parental line (lower panels). (D) Genomic deletion of SASH1 and/or amplification of CRKL expression was observed in 10. 8% of colorectal cancers (23 of 213 patients) and was associated significantly with poor overall survival in Kaplan-Meier analysis (log-rank test; n ¼ 213 patients; P ¼. 0266), based on the TCGA data set.
Mass spectrometer settings were as follows: full MS (automatic gain control target, 2E5; resolution, 60K; m/z range, 350-1500; maximum ion injection time, 60 ms); MS/MS (normalized collision energy, 30% ± 5%; detector, ion trap; isolation window, 1. 6 m/z; dynamic exclusion time setting, 30 s; automatic gain control target, 2E4; maximum injection time, 100 ms). Fragmentation was permitted for a precursor with a charge state of 2 to 7. Protein identification was performed using Mascot 2. 2 (Matrix Science, Boston, MA) with a concatenated forward and reverse SwissProt (Swiss Institute of Bioinformatics, Lausanne, Switzerland) Human database (April 13, 2016, 40,422 entries) generated with DBToolKit. ProteinScape 2. 1 (Bruker, Billerica, MA) was used to run Mascot with the following parameters: 1 missed cleavage, carbamidomethylation of Cys as a fixed modification, acetylation of protein N-ter and oxidation of Met as variable modifications, MS tolerance at 5 ppm and MS/MS tolerance at 0.
Analysis of Human Tissue Samples Seventy-six patients with stage III primary colon cancer were analyzed, who underwent curative surgery (R0) and provided informed consent for analysis of their tissue and data before surgery at the Department of Surgery (Technical University of Munich, Munich, Germany). The tissue was shock-frozen immediately after resection. Clinicopathologic characteristics of the patients are summarized in Figure  7E. Patients with documented local recurrence were not included in this study to circumvent putative bias by poor surgical technique. Ethical and data protection standards of this study were ensured by supervision of the Ethics Committee of the Faculty of Medicine (Technical University of Munich), which approved the study (#1926/07, #5428/12). Tumor-specific overall survival and recurrence-free survival (ie, distant metastasis-free survival) was considered as the primary end point for risk prediction.