dataesr/scientific-paragraphs-categorization huggingface dataset:
The variables "date" and "structure" were included as fixed factors; whereas, "location" (three sampling locations within the field plot) was included as random factor nested in "field". Akaike Information Criterion (AIC) was used to compare models; models were simplified by progressively removing non-significant variables to obtain the minimal adequate model. Residual plots of the models were inspected visually for outliers. Differences between means were inspected using Tukey's HSD test at p < 0. 05. Variations in carbon stable isotope ratios of gnats and midges, plant-and leafhoppers and spiders by sampling date were analysed using linear mixed-effects models. The dataset was non-orthogonal because not all species were equally represented at each sampling date. Prior to the analyses, data were inspected for homoscedasticity (Fligner-Killeen test) and normality (Shapiro-Wilk test).
41 ± 0. 41‰ for carbon and nitrogen, respectively, after Vander Zanden and Rasmussen  (2001). Values on the contribution of prey taxa to the diet of spider species were analysed using linear mixed-effects models after arcsine transformation. The dataset was non-orthogonal because not all spider species were equally represented at each sampling date. The independent variables "date", "spider species" and "structure" were included as fixed factors; whereas, "field" was included as random factor. Data inspection prior to the analysis and model selection was conducted as described above. Statistical analyses were performed in R version 3. 3. 1 (R Core Team 2016) and the packages nlme  (Pinheiro et al. 2019) , afex  (Singmann et al. 2018 ) and multcomp  (Hothorn et al. 2008). Figures were plotted using the R package ggplot2  (Wickham 2009).
Acknowledgements Open Access funding provided by Projekt DEAL. The authors would like to thank the local farmers for allowing us to conduct research on their fields,  Antonio "Tony" Salamantin  for driving and his help in the field,  Marianne Teichmann ,  Jörn Panteleit  and  Christel Fischer  for their help with sample determination, and the reviewers for their helpful comments. This study was funded by the  German Ministry of Education and Research (BMBF)  as part of the project " Land-use intensity and Ecological Engineering-Assessment Tools for risks and Opportunities in irrigated rice-based production systems " (LEGATO-http://www. legato-proje ct. net).
Circulating tumor cells Levels of circulating tumor cells (CTCs) and circulating endothelial cells (CECs) in the peripheral blood prior to treatment (baseline) and during treatment at weeks 6, 18, and 24 were assessed. The number of CTCs in 7. 5 mL of blood was measured using the CellSearch system (Veridex LLC, Raritan, NJ, USA)  [18]. CTCs were defined as EPCAM-positive nucleated cells expressing cytokeratins but not the leukocyte common antigen CD45  [19]. In a few cases, e. g. , when the available blood volume was below 7. 5 mL, we performed CTC enumeration using an EPCAMbased immumomagnetic enrichment and flow cytometry procedure  [20]. In this assay, CTCs were defined as nucleated EPCAM-positive and CD45-negative. In order to combine the data from the two assays, we reported the CTC values as CTC per mL of blood.
Genomic data analyses Raw sequencing reads were analyzed as described previously  [26]. Briefly, reads were aligned to the human whole genome (hg19) requiring perfect matches. Features were created by merging overlapping alignments and total read counts reported for each. To generate a small RNA-based classifier of treatment response, patients were categorized as responders if they had achieved a pathologic treatment response > 30% and non-responders if they had stable or progressive disease. Pathologic response was assessed by comparing the maximum cumulative diameter of the target lesion(s) at the time of diagnosis as assessed by imaging studies with the size of the tumor in the final surgical pathology. Due to the small number of patients who achieved pCR or microscopic residual disease, a genomic classifier on the basis of achievement of pCR or microscopic residual disease could not be generated.
We employed LASSO regression on this subset as implemented in the R package glmnet  [28] , in order to find small RNA features with optimal ability to classify responder vs. non-responder status. A binomial regression model to predict responder status was created from these optimal classifiers, operating on z-score values from both sequencing and qPCR measurements. Raw C T s from qPCR measurements were normalized using hsa-miR-22-3p. We chose this miRNA based on its robust expression and very low variance across samples in the small RNA sequencing data. Accuracies of model predictions were calculated by summing the true positives and true negatives divided by the total predictions. ROC curve areas and p values were calculated using the R package "verification. ".
Generation of a small RNA classifier that predicts response to letrozole plus bevacizumab Macrodissected tumor tissue from research biopsies obtained at the time of diagnosis was successfully sequenced in 26 and 15 patients assigned to Let/Bev and Let arms respectively. Differential gene expression analyses yielded a large number of small RNAs to be significantly associated with response to letrozole/bevacizumab. hsa-mIR-187-3p was the highest ranking differentially expressed small RNA between responders and non-responders to Let/Bev in terms of significance (Supplementary Appendix). Increased hsa-mIR-187-3p expression in breast cancer has been associated with an aggressive, invasive phenotype and has been shown to be an independent predictor of outcome  [30]. To overcome the biases associated with small RNA library preparations and the complexities of genomic data analyses, we filtered for small RNAs likely to reproduce the sequencing data when measured by qPCR (see the "Methods" section).
We identified a set of five small RNAs as an optimal letrozole/bevacizumab classifier by LASSO regression (Fig. 3a ). These include two miRNAs (hsa-miR-141-5p, hsa-miR-449), two small nucleolar RNAs (SNORD51, SNORA21), and fragments of 7SK RNA. hsa-mIR-187-3p was not predicted to generate qPCR data concordant with sequencing measurements. To confirm the sequencing data for these five candidate classifier small RNAs, we measured them in the same set of samples by qPCR. We found a significant agreement (p < 0. 01) between sequencing and qPCR data for all but one (SNORA21, Supplementary Figure  2 ). To evaluate their performance as classifiers of response to letrozole/bevacizumab in both types of measurement (sequencing and qPCR), we z-scored the sequencing data and created a binomial regression model comprising the five LASSO-selected small RNAs.
When we applied the classifier to the sequencing data that it was trained on, the classifier had perfect performance (ROC AUC = 1, p = 7. 1 × 10 -6 ). We then applied the classifier to z-scored qPCR data from the same training samples and observed that the classifier still performed well (ROC AUC = 0. 934, p = 6. 2 10 -5 , accuracy = 0. 885; Fig. 3b ). We chose a threshold for calling responders in this data set that maximized model accuracy (Fig. 3b ). As a robust verification of the classifier, we applied it to z-scored qPCR data from breast tumor samples collected from patients that received the same treatment (letrozole/bevacizumab) in a separate independent clinical trial  [15]. We successfully sequenced 16 macrodissected tumor samples from the validation cohort. We observed good performance of the classifier in these samples as well (Fig.
It targets the p40 subunit of the cytokines IL-12 and IL-23 inhibiting the activity of these cytokines. IL-12 and IL-23 are important drivers of chronic inflammation that are a hallmark of diseases such as Crohn's Disease and UC. The efficacy and safety of UST in patients with moderately to severely active UC has been evaluated in a phase III, randomized, double-blinded, placebo (PBO)-controlled study (UNIFI; NCT02407236)  8. UNIFI was conducted both in patients for whom conventional therapy had failed but who had not experienced failure of biologic therapy, and in patients who had experienced failure of prior biologic(s). The objective was to evaluate the efficacy and safety of IV UST in the induction phase (8 weeks), and, in patients who had a clinical response to induction with UST, to evaluate SC UST in the maintenance phase (44 weeks).
Literature search and study selection To contextualize the data from the UNIFI trial against other approved therapies, a SLR was performed to identify comparable trials for the treatment of moderately to severely active UC. The review was conducted in accordance with guidelines from the National Institute for Health and Care Excellence (NICE)  11  and the European Network for Health Technology Assessment (EUnetHTA)  12. The full protocol for the SLR has been registered with PROSPERO (CRD42019131015)  13. A literature search was conducted on electronic databases using a predefined search strategy. The MEDLINE, MEDLINE-IN-PROCESS, EMBASE and Cochrane Library databases were searched on 14 August 2018 and the searches were updated on 22 January and 28 March 2019. Conference abstracts were captured through EMBASE.
In addition, hand searches of the websites of health technology assessment bodies, clinical trial registries and drug regulatory websites were conducted to capture unpublished data. Search strategies and the databases hand-searched are listed in Supplementary Appendix 1. Searches were restricted to English language publications. Eligibility criteria were established using the Population, Intervention, Comparator, Outcome and Study (PICOS) framework described in Table  1. The comparators were restricted to advanced therapies (biologic and tofacitinib [TOF]) and did not include conventional therapies, as biologic treatments and TOF were relevant to the population of interest. Titles and abstracts of retrieved studies were reviewed for eligibility by two independent reviewers, and potentially relevant publications were reviewed in full against the inclusion/exclusion criteria.
Study selection Studies were included in the NMAs if they met the following criteria: reported data on clinical remission and clinical response at end of induction (6-8 weeks) and end of maintenance (approximately 1 year); included one or more relevant comparator (ADA, IFX, GOL, TOF and VDZ), at doses and regimens corresponding to the European Medicines Agency (EMA) licensed indication; reported efficacy outcomes stratified by prior biologic failure (BF) status (or exposure status). Studies assessing exclusively Asian patient populations were excluded from the base case because of concerns over comparability, but were included in sensitivity analyses.
To enable a NMA of 1 year trial results, response-based trials were recalculated to correspond to a treat-through design, maintaining the initial randomization at start of induction. The same approach has been previously used in UC to convert data from the PURSUIT trial (GOL) to correspond to a treat-through trial design using mathematical conversions  16, 17. Moreover, this approach takes into account both initial and delayed responses to induction treatment (or placebo), in accordance with labels and clinical practice. Complete treat-through data are available for the trials ACT-1, ULTRA-2 and VARSITY. For PURSUIT and UNIFI, all data are available to allow re-calculation of the trial results to a corresponding treat-through design, with the exception of maintenance data for placebo induction non-responders.
For OCTAVE and GEMINI-1, maintenance data for placebo induction responders and placebo induction non-responders were not available. Maintenance data for the PBO arm were imputed where these data were missing for either induction responders or non-responders. Imputations were based on individual patient data from PURSUIT, ACT-1 and UNIFI, and published data from GEMINI-1. Maintenance rates for placebo induction responders show a high degree of consistency across trials (see Supplementary Appendix 4). Maintenance rates for placebo induction non-responders are very low, as could be expected. For GEMINI-1, maintenance data for vedolizumab (VDZ) induction non-responders were available for the entire population  18  , but this had to be re-estimated to obtain the efficacy rates for the NBF and BF populations separately.
For OCTAVE, data for induction non-responders continuing on TOF 10 mg were only available from an open-label experiment phase of the OCTAVE trial and these were used. Based on the observed and imputed data, the endpoint data corresponding to the end of 1-year of full treatment were calculated as described in Supplementary Appendix 5. The treat-through arms included both the induction and maintenance treatment phases and were denoted in the presentation of 1-year results as "inductionmaintenance", with the common PBO arm denoted as "PBO -PBO". Individual study results from different doses for a treatment were pooled if no dose-response relationship was apparent for the 1-year endpoints. For the studies with re-randomized response-based designs, the total numbers of patients in the re-randomized arms were re-calculated to correspond to the ITT total.
Endpoints were assessed from week 6 to 8 in induction trials, and from weeks 52 to 60 after the start of induction in the maintenance trials included in the NMA. Only studies reporting end of maintenance results between 52 and 60 weeks were included in the 1-year NMAs. The final data inputs for each NMA are shown in Supplementary Appendix 7. Comparing the re-calculated clinical remission rates for ADA and VDZ for ULTRA II and GEMINI, these were similar to those from the head-to-head VARSITY trial, which did not require imputation.
Efficacy in non-biologic failure population As no dose-response relationship was apparent in the trial data, doses were pooled for the analysis. Unpooled doses were used in a sensitivity analysis, and pooling was found to have no major impact on the point estimates for the ORs but reduced uncertainty (Supplementary Appendix 10). The data input graphs are presented in Figure  4. For clinical remission, the re-calculated data inputs at 1 for GEMINI-1, VARSITY and ULTRA-2 show a high degree of consistency. The NMA results (fixed-effects model) are shown in Table  2. Additionally, the assessment of inconsistency conducted for the loop in the clinical remission network identified no significant inconsistency (see Supplementary Appendix 11). Clinical response. Clinical response was analyzed from six studies.
There was a possible source of heterogeneity in that endoscopic measurement in the OCTAVE trial was centrally assessed, whereas for all other studies the local assessment was used. As described in Singh et al. 32  , this can influence the absolute and endoscopic-mucosal healing rates in the active arms, which were solely based on data from the OCTAVE trial. Another source of heterogeneity was that the inclusion criteria for BF in UNIFI included anti-TNFs and VDZ, whereas in the other trials, BF refers to anti-TNFs only, and in the case of the ULTRA-II trial, excluded patients with primary non-response to an anti-TNF. Lastly, the UNIFI trial grouped patients according to prior BF or non-failure, whereas other trials grouped patients either according to anti-TNF naïve/experienced or biologic naïve/ experienced.
While no imputation of active-arm efficacy was needed for the anti-TNF, TOF and UST treatments, imputations were required for the PBO rates for the PBO non-responders from studies with GOL and UST, and additionally for PBO responders for studies with TOF and VDZ. Where imputations were required for the maintenance PBO data, the availability of individual patient data from multiple studies allowed these to be reliably informed. After imputation (where needed), the results of the individual study arms for VDZ 300 mg -VDZ 300 mg Q8W and ADA 160/80/40 mg -ADA 40 mg EOW corroborated with the results of the head-to-head VARSITY trial (VDZ vs. ADA for the same doses), which further provides strength and credibility to the imputation approach. An additional sensitivity analysis using a multiple imputation approach was undertaken to account for uncertainty related to using fixed values for the imputation.
other treatments. A number of limitations are associated with the analysis of maintenance data for the 1-year NMA specifically, including data limitations for GEMINI I (efficacy for VDZ delayed responders only available for the overall population, and the need to impute long-term placebo rates), and the requirement to re-calculate the total number of ITT patients in each arm based on the proportions included in the re-randomized responder arms. Nevertheless, as previously mentioned, the results seem to corroborate with VARSITY. Some of the limitations were considered to bias results against UST, and therefore the results may be viewed as somewhat conservative. The OCTAVE trial re-randomized both PBO and TOF induction responders, resulting in patients who responded to induction with placebo being able to receive TOF for the first time in the maintenance phase (if randomized to active maintenance treatment).
IniƟal search conducted on 14 August 2018. Updated searches conducted on 22 January 2019 (publicaƟon date from 01/01/2018-22/01/2019) and 28 March 2019 (publicaƟon date from 01/01/2019-28/03/2019). Identification Records identified through database searching Embase (N=3,077) + Pubmed (N=1,913) + Cochrane (N=1,719), N=6,709 Additional records identified through other sources N=15 Duplication: N=1,732 Records screened for titles and abstracts Screening N=4,992 Exclusion due to irrelevance: N=4,762 Full text articles to be assessed for eligibility N=230 Not population of interest, N=79 Not intervention of interest, N=63 Not outcome of interest, N=21 Inclusion Studies to be included in the narrative review N= 39 Not study type of interest, N=10 Not language of interest, N=8 Figure 2. PRISMA diagram.
1 database  [100]  used to estimate the speciation of transition metals in PB, Fe 3+ precipitates in water or PB to form hematite. Fe (III) could thus be unavailable to react with DTT, inducing inhibition of DTT oxidation and preventing the trigger of a Fenton-like oxidation  [101, 102]. With regard to Section 2 of Figure  1 , thermodynamics do not allow the oxidation of DTT by Zn (II) or Mn (II). This can explain the OP DTT close to the MDL found for Zn (II). On the contrary, the highest of all OP DTT is found for Mn (II), suggesting that this metal is at least partly in a higher valence state. In fact, Mn 2+ may be complexed or precipitated with PB constituents. It has been shown that these associations may enhance the formation and the stabilization of Mn (III)  [103] , which can thermodynamically oxidize DTT.
Moreover, the complexation of transition metals with DTT is theoretically possible (as suggested for Cu (II) and Ni (II) previously) although the DTT concentration is about 1000 times lower than the phosphates one. Unfortunately, DTT is not available in the MINTEQ database. The mechanisms to explain the differences in OP DTT for each metal are not yet fully understood, but some hypotheses can be made. Transition metals in PB may undergo different reactions influencing their speciation. They can form ions, or charged or neutral complexes, or they can precipitate. Thus, their speciation in various matrices may significantly influence the rates of DTT depletion.
Funding: This research was funded by the  Regional Council "Hauts-de-France", the Ministère de l'Enseignement Supérieur et de la Recherche , and the  European Fund for Regional Economic Development (FEDER)  through the  CPER CLIMIBIO  project, and by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 " for the  CaPPA project (Chemical and Physical Properties of the Atmosphere ).
The selection of these five themes was based on literature and previous work  [22]. The objective was to cover a fairly wide range of themes associated with rural health care. In each ED, we selected a local "champion" to help identify participants and act as a knowledge broker. We recruited participants in each ED according to their position or engagement, diversity of profile (sex, age, profession, etc. ) and interest in participating. Suggestions from the champion and snowballing were used to recruit additional participants until the members of the research team involved in preliminary analysis felt that further data collection was no longer adding to the analysis (data saturation)  [23]. Participants were approached face-to-face by champions, and by phone and email by the research team.
Participants were informed about the main researcher's reasons for conducting the project and about the interviewer role. Group interviews were preferred with homogenous groups (citizens, nurses, prehospital staff, diagnostic resources). Individual interviews were conducted with stakeholders with a unique perspective (decision-makers, unique health professionals such as pharmacists, and elected representatives) or who were not available to participate in group interviews (all physicians and some nurses). An experienced research associate academically trained in qualitative research conducted (JPM, M. A. Anthropology, male) the interviews by telephone, videoconferences that took place at both hospitals or in person at the Baie-St-Paul hospital, from March to May, 2016. Socio-demographic data was collected on each participant.
Analysis Quantitative data was analysed using descriptive statistics (means and percentages), with Excel 16. Content analysis of qualitative data was conducted thematically and inductively  [24]  using NVivo11. Thematic coding was performed by MPR and CTP, research associates academically trained in qualitative research (parallel analysis of 40% of the interviews) and discussed to achieve consensus. Analysis and interpretation of qualitative data were achieved by discussions with the lead investigator (RF), co-investigators (JPF, HA, GD) and an expert in qualitative research/rural health (HS). Preliminary analysis were discussed during two focus groups (one in each ED) with diverse stakeholders (nurses, doctor, manager, champion). Quotes presented below were translated from French to English by the research team and validated by a professional medical translator (LB).
The individual published projected NMO sensitivities of JUNO  [20]  and PINGU  [27, 28]  exceed those of the current-generation oscillation experiments. Even more crucially, as has been shown in  [33] , the joint fit of the oscillation data from JUNO and PINGU will profit from strong synergy effects. These are brought about by tension between the fit values of ∆m 2  31 within the wrong NMO-similar to but stronger in magnitude than the tension encountered by the current global fits referred to above. Since the underlying neutrino oscillation physics is the same, synergy will also occur between JUNO and the imminent IceCube Upgrade. We will demonstrate explicitly that even in this combination, the synergy is strong enough to decisively exclude the wrong ordering. JUNO  [20]  is a 20 kt liquid scintillator (linear alkylbenzene) detector currently under design and construction near Jiangmen in South China.
Deployed in an underground laboratory with 700 m overburden, it is designed to measure the disappearance of MeV-energy νe produced by the Yangjiang and Taishan nuclear power plants at a distance of approximately 53 km. At the start of data taking, anticipated in 2021, the number of operational reactor cores is expected to be eight instead of the baseline number of ten. Therefore, we conservatively perform the JUNO analysis with both configurations. In the detector, the reactor νe convert to positrons via the inverse beta decay (IBD) process  [34, 35]  on protons. This results in a characteristic pair of prompt and delayed photon showers, which are detected by about 17 000 20-inch photomultiplier tubes (PMTs). The high number of photoelectrons per event (1200 p. e. /MeV)  [20]  allows for a percent-level neutrino energy resolution.