dataesr/scientific-paragraphs-categorization huggingface dataset:
). LR, BN, DT and lazy classifiers have lower SPEC differing to NN* from -13% to -5% and lower ACC ranging from -6% to -3%. DIR enesmble classifier has -2% SPEC difference. On the other hand, NN has 2% higher SENS compared to NN*. All other base and ensemble classifiers have almost same SPEC and ACC performance (±1%). BN and LWL have lower SPEC. The ensemble classifiers reach the highest SENS and SPEC while SIR and NIR reach the highest ACC (see Figure3fourth column). External Balanced Dataset BN and SMO have lower SENS, -9% and -5% respectively, with LR reaching the highest, +7%. All other base and ensemble classifiers have almost same SENS performance (±1%). LR have lower SPEC than NN*. BN has the lowest ACC (-3%). RF, NN and all the ensemble classifiers reach +5% higher SPEC, +2% higher ACC, higher F1 (≈ + 0.
08) and DP scores (≈ U + 0. 38). LR has the lowest F1 score (-0. 1) and the highest DP (>3). Summing up, NN and RF and ensemble classifiers have the best performance. BN has the lowest performance (see S C R I P T Figure 3 third column). N A Internal Imbalanced Dataset M BN, DT, RF, IBk and function classifiers have lower SENS comparing to NN*. DT reached the lowest score (-24%) and the rest circa -8%. NN and LWL have ≈ +3% SENS. DIR has -4% SENS while all the other ensembles have ≈ +1% SENS except BIR (-2%). BN and lazy classifiers have lower SPEC (≈ -4%). All other base and ensemble classifiers have ≈ +1% SPEC performance with LR reaching +3%. BN and DT have the lowest ACC and F1. LR and IBk have -6% ID ACC. NN classifier has +2% ACC. The rest of base classifiers and DIR have -2% ACC. The ensemble classifiers have good ACC performance (±1%) and higher F1 score.
LR and ensemble classifiers (except DIR) have higher DP. Summing up, all base Reliabillity Dataset C C E P T E D BN reaches the highest SENS (+36%) and IBk the lowest (-50%). LR and LWL have +29% A difference while RF, DT, BIR, LIR and SIR reach +22% from NN* (64%). LR scores the classifiers, except NN and LWL, have lower SENS. BN and lazy classifiers have also lower lowest (-38%) and SMO the highest (+4%) SPEC. All classifiers have lower SPEC than NN*. SPEC and ACC. Ensemble classifiers, DIR excluded, have +1% higher performance (see Figure 3 second column). LR has lower F1 than the other classifiers (-0. 06). NN has the highest DP improvement (+0. 39) in contrast with LWL (-0. 85). Summing up, LR, DT, BN and lazy classifiers do not perform as well for almost all metrics. All ensemble classifiers have similar performance to the best base classifiers, NN and RF (See Figure3first column).
External Imbalanced DatasetAll classifiers outperformed NN* SENS except LR (-7%). The highest score is reached by LWL, IBk, RF, NN and ensemble classifiers (+13%). DT, BN and LWL have lower SPEC than NN* (≈ -5%). In contrast, all other base and ensemble classifiers have almost the same SPEC performance (±1%). LR is the only classifier having lower ACC (-3%). RF, NN and ensemble classifiers have +7% higher ACC with SIR and NIR reaching the highest. NN, SIR and NIR have the highest F1 score (+0. 06) and DP (+0. 45). BN and DT have the lowest (-0. 08 and -0. 24 for F1 and DP respectively). Summing up, LR has the lowest performane. DT, BN and LWL classifiers have the highest ACC (+13%) while IBk reaches the lowest (-30%). LIR has among the highest ACC (+11%), F1 (+0. 06) and DP (+0. 33). DT has the lowest F1.
The goal of this study is to test classifiers on reaching an overall good performance referring to all datasets and metrics combined, avoiding overfitting and underfitting. In this respect, ensemble methods produce the best results compared to base classifiers. Classifier U S C R I P T U performance differs by class balance (BD, ID), validation process (internal, external, reliability) and the performance metric considered (e. g. sensitivity, precision). We integrate S C R I P T all cases in a Copeland Index to compare classifiers in a singular ranking. N N unwieldy. A A Regarding base classifiers, RF and NN showed the best performance in contrast to LWL and IBk which showed the lowest performance. Linear regression classifiers (LR, GLM*) performance is poor for most metrics and the Copeland Index.
Simple linearity cannot predict toxicity when independent variables vary. Support vector machines capture these variations much better, applying linear regression after combining attributes to form a more separable dataset. Trees (RF, RF*) and NN are the best base classifiers; RF has a better performance than C C E P T E D M Bayes Network has excellent results in the reliability validation but moderate in the external validation. The dataset used in this study is designed for a QSAR application, using numerical attributes. Bayes Networks operate on nominal attributes; in this study, we discretize the numeric attributes into nominal ranges. Discretization was performed on the final training dataset so attribute values in the reliability or validation data may correctly be Model performances corresponding to datasets with missing values are not included in M the Copeland Index.
The generation of missing values is random and does not account for joint probabilities of missing data (e. g. all quantum-mechanical values missing as not measured in an experiment) due to the experimental design. Missing values in toxicological datasets is a common issue that modellers have to deal with. We tested the model performance with different replacement probabilities of missing values. BN, LWL, RF and SMO base classifiers can perform well maintaining their accuracy levels to those using the C C E P T E D dataset with no missing values. As expected, Bayes networks and the Naïve Bayes classifiers NN in some cases, such as in EXT ID validation or REL. RF decreases the variance through A predicted according to the training ranges. In the ensemble (BIR) classifier, BN improves all A in LWL handle missing values well [91].
RF through randomising and bagging and SMO bootstrap aggregation (ensemble-algorithm that improves accuracy, bagging) resulting in its metrics and ranks third in the Copeland Index. through combining attributes, manage to rebuild the toxicological outcome through attribute good prediction capabilities and ranking RF as the best base classifier. [1] RF and NN classifiers applied in this study perform better than RF* and NN* from Choi, Ha[1]. This may be attributed to the 60-40 split of the original dataset for training and validation and mostly to the classifier parameters set-up. Choi, Ha[1]do not provide details on the classifier parameters therefore an accurate reproduction using their classifiers is not possible. For instance, the number of hidden layers in NN and tree depth in RF used in this study may vary.
The best triad (LIR) shows a high performance across most datasets, particularly the overall performance for reliability dataset. Other triads, that included the best base classifiers, The Copeland Index clearly reveals ensemble classifiers have higher scores than base classifiers, confirming the results in Figure3, voting's potential to increase the predictive ability. The best classifier in this ranking (LIR) did not contain the two best base classifiers combined (e. g. NIR). interrelations. BIR, SIR, LIR and DIR also demonstrate robustness despite missing values. NN, although accurate in whole dataset predictions, show poor performance when values are.
Statistical analysis Experiments were performed at least three times with at least three different healthy donors. The data were presented as mean ± standard deviation (S. D. ). Statistical analysis was performed using GraphPad Prism 7 for Windows (GraphPad Software, San Diego, CA). The 2tailed Student's t-test was used for comparison of two groups, one-way and two-way ANOVA and Tukey's multiple comparison tests were used, when appropriate, for comparison of more than two groups. P < 0. 05 was considered as significant.
A small inhibitory effect was observed with aggregation induced by a low concentration of thrombin (Additional file 2: Fig. S2K, L ), which possibly reflects the thrombin binding site of GPIbα  [20, 44]. For the possible Syk activation by EB, we analyzed the phosphorylation kinetics of the Syk activation marker, tyrosine 525/526, which is located in the activation loop of the kinase domain  [45]. Washed human platelets stimulated with EB showed increased, but transient Syk phosphorylation at Y525/526 in a time dependent manner. EB-induced Syk Y526 phosphorylation was confirmed by LC-MS/MS (data not shown). Additionally, we investigated the phosphorylation of Syk at Y352, which is located in the SH2 kinase linker domain and shows an important role in Syk activation  [23, 46]. Furthermore, we also measured the tyrosine phosphorylation of direct Syk substrates, inker for activation of T-cells (LAT) and phospholipase Cγ2 (PLCγ2), as indicator of Syk kinase activity.
Platelet aggregation was stopped after 60 s using Laemmli buffer. Phosphorylation of c Syk Y525/526, e Syk Y352 and g PLCγ2 Y759 were analyzed by western blot. Quantification of p-Syk d Y525/526 and f Y352 compared to total Syk and h PLCγ2 Y759 compared to total PLCγ2. Data are from 3 different experiments with platelets from 3 healthy volunteers and presented as means ± S. D. ns: not significant, *p < 0. 05 classical mechanism of Syk activation in human platelets, the convulxin/GPVI pathway  [10] [11] [12]. For the analysis of PKA/PKG effects on GPVI-mediated signaling pathways, we used 50 ng/ml convulxin to stimulate washed human platelets. Convulxin-stimulated platelet aggregation was completely inhibited by iloprost and riociguat (Additional file 8: Figure  S8A, B ). Then, convulxin-induced Syk phosphorylation in the presence of a vehicle control, iloprost or riociguat was analyzed.
Acknowledgments: The authors thank  François Jomard  from the  GEMaC laboratory  for the SIMS measurements and discussion. This work was carried out in the framework of a project of  IPVF (Institut Photovoltaïque d'Ile-de-France ) and has been supported by the  French Government  in the frame of the program of investment for the future ( Programme d'Investissement d'Avenir- ANR-IEED-002-01 ).
These results also show that the free water influence on concrete behavior depends on both the amount and nature of this porosity: the effect of modifying the entrained air porosity is much weaker than that of capillary porosity. To better quantify the effect of concrete saturation ratio, results are then completed by triaxial testing performed on OC with intermediate saturation ratios. Lastly, an empirical triaxial failure criterion of concrete, which takes into account the uniaxial strength, porosity and saturation ratio, is proposed; this new criterion accurately reproduces the results presented, as well as, a large database obtained from previous studies.
Acknowledgments The GIGA press was installed in the  3SR Laboratory  within the framework of a cooperative agreement signed with the  French Ministry of Defense. This research program could be performed thanks to financial support received from the  French Alternative Energies and Atomic Energy Commission  ( CEA Gramat ). The authors would also like to thank  Dr. Benjamin Erzar ,  Jean-Luc Descone  and  Jean-Benoît Toni  for their scientific and technical advices.
Adherence to inhaler corticosteroid treatment Adherence is determined by the researcher at all visits. The definition of adequate adherence is either > 70% adherence as measured by the Smartinhaler® electronic logging device or by participant/parent report of being adherent most or all of the time. This definition allows for missing Smartinhaler® data, e. g. at baseline assessment, failure of the device or nonavailability of the device, and also the real-world scenario where there is a discrepancy between the Smar-tinhaler® data and participant/parent report.
Outcomes {12} Primary outcome The primary outcome is prescription for (and/or use of) ≥ 1 course of OCS for asthma attacks in the 12 months after randomisation (yes/no). The decision to prescribe OCS is made by clinicians independent of the research team and working in accordance with the national guidelines  [23]. The primary outcome is captured from parental report at the 3, 6, 9 and 12 month assessments. Where data are not available at 12 months, the general practice (GP) at which the participant is registered is contacted to capture primary outcome data.
The benefit of this approach is inclusion of all individuals where there is ≥ 1 clinical assessment. Unscheduled healthcare attendance (yes or no) will be compared between treatment groups using generalised estimating equations and, if deemed appropriate, the number of unscheduled healthcare attendances will be compared using Poisson regression. Full details will be described in the statistical analysis plan for the study. Comparison of quality of life (using the PAQLQ) at the final assessment (12 months) between treatment groups will be assessed using analysis of covariance, adjusting for minimisation variables, baseline values and other appropriate baseline predictors. The influence of any missing data on the robustness of the findings will be examined using sensitivity analyses incorporating multiple imputation or other relevant strategies under alternative assumptions.
Acknowledgements The authors are indebted to the following persons who have helped deliver the RAACENO trial:  Mrs J Wood  and  Mrs V Bell  for implementation of protocol;  Miss A Fraser  for data coordination; colleagues in the  Clinical Trials Unit in Aberdeen (Centre for Healthcare Randomised Trials, CHaRT ); the  Clinical Research Networks in East of England ; the  Scottish Primary Care Research Network ; the local recruiting teams, participants and participant parents and care givers. The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the  Health Technology Assessment Programme, National Institute for Health Research (NIHR ),  NHS  or the  Department of Health.
Data Collection We presented this curriculum in the following contexts: r Three clerkship orientation sessions (students). r Two departmental grand rounds (faculty, nurses, staff, and students). r Two faculty development sessions (faculty). r One nursing development session (nurses). We collected audience response data from the three clerkship sessions and the two departmental grand rounds on the impact of this curriculum (Appendix D: Session Evaluation). To facilitate cross-generational communication, these sessions were facilitated by a group of core faculty involved with film development, residents, and medical students. Those who attended had the option to answer evaluation questions directly after the film screening using audience response technology. Questions in these sessions focused on respondent characteristics, perceptions, and experience in witnessing events.
Evaluation Data We collected evaluation data from 176 students after they participated in our film/discussion curriculum in two grand rounds sessions (psychiatry and anesthesiology), two clerkship orientation sessions, and one clerkship bridge session. Most of the students (47%) were 20-30 years old, 45% were 31-40 years old, and 8% were 41 years or older. Fifty percent were female, 46. 3% were male, and 3. 8% chose "other" as their gender. Evaluations from our faculty development workshop in September 2017 rated the session as excellent (5 of 5) and commented, "We had a great discussion about the problem. " We also collected evaluation data from 42 educators (residents, fellows, and community or academic faculty) participating in the curriculum as part of grand rounds in psychiatry and anesthesiology.
SAXS images were normalized by the exposure time and were integrated by using the Fit2D software. The scattering angles 2θ were calibrated using the position of known diffraction peaks from a standard silver behenate powder, and the SAXS intensity was finally reported as a function of the modulus of the scattering vector q = (4π/λ) sin θ. Film Preparation. Films were prepared at T = 23 °C by slot-die coating onto sodalime glass slides (75 mm × 25 mm dimensions, Menzel). An amount of 160 μL from aqueous HS, S, and H suspensions was coated on the glass by using aluminum slot dies with different gap clearances (10 and 120 μm), and different starting polymer concentrations in water were used to achieve different uniform film thicknesses (Figure  S4 ). Water evaporation was been studied by recording the weight loss of the different HS, S, and H samples over time (Figure  S5 ).
J). Lymphatic exudate is enriched in melanoma-associated miRNAs, which are mostly carried in EVs Several miRNAs have been shown to be potential mediators of metastasis and involved in the formation of the premetastatic niche, and, after literature research, we focused on 66 miRNAs that had been shown to be associated with metastatic melanoma (Table  S1 ). We next assessed the lymphatic exudate and plasma for expression of these 66 miRNAs using a custom miRNA array and found that most were at significantly higher concentrations in the lymphatic exudate than in the plasma (Fig. 2  A and Fig. S2  A). Interestingly, these roughly correlated with miRNA expression levels reported in The Cancer Genome Atlas (TCGA) datasets for cutaneous metastatic melanoma, both in primary and metastatic tumors (Fig.
Proteomic analysis of lymphatic exudate reveals distinct signatures related to nodal metastatic spread As the samples were collected from stage IIIA-C melanoma patients undergoing radical LAN following a positive sentinel LN, we could divide patients into two groups based on the nodal metastatic spread at the time of lymphatic exudate collection (Fig. S2 B ). Patients with no evidence of further nodal spread beyond the sentinel LN were defined throughout the study as LAN-negative (LAN neg ), correlating with a less advanced disease, whereas patients with nodal involvement were defined as LAN-positive (LAN pos ), correlating with a more advanced disease. (A) Heat map of melanoma-associated miRNAs contained in plasma and lymphatic exudate of metastatic melanoma patients (n = 12, data sorted by average expression levels in lymph).
(B) TCGA data showing average expression levels of the same miRNAs, in the same order, in primary melanoma (n = 104) and at metastatic sites (regional LN metastasis, n = 226; distal metastasis, n = 67). Gray bars reflect miRNAs that were not reported. (C) Volcano plot of miRNA expression in lymphatic exudate (L) relative to that in plasma (P; each miRNA averaged across all patients, with red indicating the miRNAs with P < 0. 05). (D) Within the lymphatic exudate (Lymph. exud. ), content of lymph-enriched melanoma-associated miRNAs in EV and non-EV fractions of metastatic melanoma patients. (E) Comparative average miRNA expression in the EV fraction from lymphatic exudate (L-EV) or plasma (P-EV; n = 11, data sorted by average expression of miRNAs in EV fraction, raw data in Table  S1 ). We next performed an in-depth comparative proteomic analysis of the lymphatic exudate and plasma from LAN neg and LAN pos metastatic melanoma patients, as well as lymph from HCs for comparison (noting the important differences in collection methods).
3 C ). This was in line with previous observations that increased metastatic potential correlates with down-regulation of gene and protein expression overall  (Duffy, 1992; Olson and Joyce, 2015; Wojtukiewicz et al. , 2015) , as well as data from the TCGA showing more genes are down-regulated in metastatic lesions compared with primary tumor in the cutaneous metastatic melanoma dataset (Fig. 3 D ). Using the Ingenuity pathway analysis (IPA), we found that the overall proteomic signature predicted to be specific for melanoma was uniquely associated with the lymphatic exudate, but not the plasma (Fig. 3 E ). Interestingly, the LAN pos patients had a higher activation z-score than LAN neg patients (Fig. 3  E and Table  S1 ). Other pathways, including cancer, cellular movement, cell-cell signaling and interaction, and cell death and survival, were all quantitatively more predicted in the lymphatic exudate than in the plasma (Table  S1 ).
However, since the analyzed lymphatic exudate was collected after primary tumor excision, the protein signatures could partially reflect the wounding response to the LAN; therefore, we narrowed our analysis to proteins that have been reported in the publicly available Human Protein Atlas database to be up-or down-regulated specifically in melanoma as compared with healthy tissue. As before, we found that in most cases, expression of these proteins was unique to the lymphatic exudate (Fig. 3  F and Table  S1 ), with decreased levels overall in the lymphatic exudate of patients with bulky nodal disease (LAN pos ) compared with LAN neg (Fig. S2 B ). We next performed IPA analysis on the enriched melanomaassociated proteins found in the lymphatic exudate of patients and compared them with proteins in the HC lymph.
, 2010). Nonetheless, the analyses presented here can be performed with much smaller volumes, and thus collecting lymph from patients undergoing primary tumor and sentinel node dissection at the same time (i. e. , in breast cancer patients) would allow a more comprehensive analysis of specific tumorpredictive biomarker signatures. Furthermore, the relevance of collecting lymph during primary tumor or sentinel node dissection is strengthened by the recent decision to remove radical LAN from standard clinical protocols on metastatic melanoma patients following a recent multicenter selective lymphadenectomy trial II study showing no survival benefit  (Faries et al. , 2017). Taken together, our data suggest that lymph from cancer patients is a rich source of tumor-derived factors and may provide a highly valuable source for identifying protein, miRNA, EV, and cellular fingerprints of each patient's cancer.
Protein identification Raw files from each technical and biological replicate were filtered, de novo sequenced, and assigned with protein ID using Peaks 8. 0 software (Bioinformatics Solutions) by searching against the human Swiss-Prot database (January 2017; 158,154 entries). The following search parameters were applied for labelfree quantitative (LFQ) analysis: trypsin restriction for enzyme and one allowed missed cleaved at one peptide end. The parent mass tolerance was set to 15 ppm using monoisotopic mass, and fragment ion mass tolerance was set to 0. 1 daltons. Carbamidomethyl cysteine (+57. 0215 on C) was specified in PEAKS 8. 0 as a fixed modification. Methionine, lysine, proline, arginine, cysteine, and asparagine oxidations (+15. 99 on CKMNPR), deamidation of asparagine and glutamine (NQ-0.
98), and pyro-Glu from glutamine (Q-18. 01 N-term) were set as variable modifications. Data were validated using the false discovery rate (FDR) method built in PEAKS 8. 0, and protein identifications were accepted with a confidence score (-10lgP) >15 for peptides and (-10lgP) >15 for proteins; a minimum of one peptide per protein was allowed after data were filtered for <0. 8% FDR for peptides and <1% FDR for proteins identifications (P < 0. 05). An independent validation of the MS/MS-based peptides and protein identification was performed using Scaffold (version Scaffold_4. 7. 3, Proteome Software). The Scaffold built-in option "MuDPIT" was used to combine multiple files from technical replicates of each sample. Peptide identifications were set at >95. 0% probability by the Peptide Prophet algorithm with Scaffold delta-mass correction.