dataesr/scientific-paragraphs-categorization huggingface dataset:
Data from WAIS Divide were collected  (Tyler J. Fudge, 2014)  and are divided in two different datasets : raw data from 6. 4 to 11. 4 ka B1950, and adjusted data after 11. 4 ka B1950. Up to 11. 4 ka B1950 the data were well-resolved and there was no reason to make adjustments. From 11. 4 ka B1950, Fudge (2014) started making multiple tracks along the WD ice core which needed further treatments. Normalization was performed by subtracting the mean conductivity of the section, then dividing by three times the standard deviation. The mean and standard deviations were calculated from a subset of the data to prevent volcanic signals from obscuring the annual signal after the data have been normalised  (Tyler J. Fudge, 2014). EDC data were published by  Udisti et al. (2004). We first determined a set of obvious major tie points and adjusted the time scale by taking them into account.
2. 3 The CO2 record from WD onto the WD2014 air age scale The atmospheric CO2 data from the WD ice core have been published by  Marcott et al. (2014). They consist in 1,030 measurements between 23,000 and 9,000 years B1950 with a median resolution of ~25 years. There was at least one duplicate sample per depth and the mean standard deviation was ~1 ppm. There is an unexplained ~4 ppm systematic positive offset of the WD CO2 record compared to other ice core CO2 records such as EDC  (Parrenin et al. , 2013) , Siple Dome and Law Dome  (Pedro et al. , 2012a). CO2 is measured in the air bubbles entrapped into the ice core while temperature is recorded through the isotopic content of the ice. Because of the late closure of the air bubbles inside the firn, the air is younger than the surrounding ice at a given depth.
In Figure  3 , the optimal fit is shown, together with the uncertainties in the timings of the break points (the initial fit values are given in the table S1 in Suppl. Mat. ). We also computed the break points with the Mudelsee Breakfit algorithm  (Mudelsee, 2009)  and obtained results in agreement within the uncertainty range (see Table  1 ). However, our method is equally robust, mathematically simpler and less subjective. Indeed, the LinearFit method allows the dataset to be used as a 4 whole, where the BreakFit algorithm requires the user to delimit several time intervals introducing subjectivity in the determination of the break points. The bootstrap resampling used in the BreakFit algorithm to estimate modelling uncertainty is robust and statistically correct but both more complicated and more parameter dependant than our method.
2. 5 The CH4 records from WD and EDC onto the WD2014 air agescale The atmospheric CH4 data measured on the WD ice core were measured using a laser spectrometer and a continuous flow analysis (CFA) method  (Rhodes et al. , 2015) , which allows for a ~5 cm resolution  (Rhodes et al. , 2013). At EDC, the discrete measurements have been obtained by gas chromatography  (Loulergue et al. , 2008) , with a resolution of several meters. As previously explained, Δage at WD is calculated using a firn densification model, which is constrained with nitrogen-15 data  (Buizert et al. , 2015). At EDC, Δdepth, the depth shift between synchronous air and ice levels, is calculated using 1) an estimate of the LID based on nitrogen-15 data  (Dreyfus et al. , 2010)  and assuming a zero convective zone, 2) an average firn relative density (density divided by the density of pure ice) of 0.
found a 260 ± 130 year lag of CO2 (taking into account the uncertainty in the determination of Δdepth), and at the ACR end where no significant time delay between ATS and atmospheric CO2 was found. The non-significant differences between ATS and ATS2 (Figure  1 ) indicate that our updated atmospheric CO2 dataset is responsible for the different time delays computed. This testifies of the importance of the data resolution measurements in the determination of leads and lags. Until now, only links on millenial time scale between ATS2 and CO2 were determined, but the growing resolution of these data also revealed a centennial time-scale relationship. Indeed, both of the 16. 3 and 14. 8 abrupt CO2 rises  (Marcott et al. , 2014)  are clearly visible on the ATS2 signal (Figure  3 ). We performed 2 other LinearFit tests of the data with different numbers of break points.
(2013)  found a lag of CO2, and a lead of CO2 at the end of the ACR period where  Parrenin et al. (2013)  found no lead nor lag. We also confirm the ice-air depth shift computed at EDC by  Parrenin et al. (2013) , assuming the nitrogen-15 data represent the Lock-In Depth. In particular, we confirm that no convective zone existed at EDC during this time period. Our study provides new constraints for climate and carbon cycle models.
Statistical analysis The ITT population, defined as all patients who were randomised and received at least one dose of a study drug, was used for all analyses. Using data from a Caucasian-only subpopulation (comprising approximately 90% of the non-Asian patients in the GOAL study), the primary endpoint was analysed and found to have little difference compared with the overall non-Asian population, so data from the non-Asian population are presented here. The statistical analysis of the proportion of patients who achieved Well-Controlled asthma and Total Control of asthma was performed using logistic regression with covariates for baseline FEV 1 , sex, age and treatment group. The analysis of the ICS dose at which Well-Controlled asthma and Total Control of asthma was first achieved was performed using proportional odds logistic regression on dose with the same covariates.
The effects of treatment on exacerbation rates were determined using Poisson regression with generalised estimating equations with the same covariates (treatment differences were expressed as the ratio of FP/SAL over FP, with treatment ratios greater than 1 in favour of FP/ SAL). Time to achieve Well-Controlled asthma and Total Control of asthma was assessed using inverse Kaplan-Meier plots with Log-Rank tests. The statistical analysis of mean change in clinic FEV 1 (L) was performed using analysis of covariance with covariates for baseline FEV 1 , sex, age and treatment group (treatment differences were expressed as a difference of FP/SAL from placebo in FEV 1  [L] , with positive treatment differences in favour of FP/SAL). Analyses were completed using SAS 9. 3 (Cary, NC, USA).
As was observed in the total GOAL study population  [3] , maintaining treatment also had a clear benefit on health status in both the Asian and non-Asian populations studied in this analysis. Insufficient patients in each stratum of the Asian population completed the AQLQ to allow a by-stratum analysis, therefore, the AQLQ data were analysed across in the entire dataset (244 Asian patients in total). The analysis showed that Asian patients experienced a substantial improvement in health status such that the AQLQ score approached or exceeded the value of 6, suggesting that asthma no longer had a significant impact on QoL  [18]. The incidence of exacerbations in the Asian patients was low and in keeping with the total study population as previously reported  [3]. There was a significant improvement in FEV 1 for stratum 1 and stratum 3 (0.
Acknowledgements This work received funding from the  European Union 's  Seventh Framework Programme  [ FP7/2007-2013, project EarlyNutrition , grant agreement n°289346]; the  MRC Metabolic Diseases Unit award  [ MC_UU_12012/4 ]; the  Biotechnology and Biological Sciences Research Council  [ BB/M001636/1 ]; the  British Heart Foundation  ( PG/14/20/30769 ) and the  São Paulo Research Foundation  (Process number:  2014/20380-5 ).
Ethics approval and consent to participate The protocol has been approved by Ethics Committee of Dongzhimen Hospital affiliated to Beijing University of Chinese Medicine. Trial will be conducted under supervision of the clinical trials center in Dongzhimen Hospital. General management will follow Good Clinical Practice and Standard Operating Procedures. All data will be anonymized while participants must give written informed consent. Patients may leave this study at any time point without any constraint. There is a 2-week run-in period before the randomization, and the follow-up will be 24 weeks, including 12-week treatment period with visit once every 4 weeks, and 12-week washout follow-up. Estimated 240 patients will be randomly assigned to either SQJZ or placebo in a 2:1 ratio.
Data collection Before initiation of the project, investigators from different branch center will participate in training to ensure the standard administration of data collection. The assessments of primary and secondary endpoints will be performed by 2 blinded investigators who are not involved in the randomization at the same time, and final scores will come from the average of the 2 investigators. If participant is unable to continue their face-toface interview for any reason, we will collect as much information as possible and record it through making phone calls. To promote data quality, double data entry will be used.
Statistical analysis The analysis of the primary variable will be based on the full analysis set: all randomized, treated patients who had a baseline and at least 1 postbaseline measurement for the primary variable. Only when the participant is not taking any drugs after randomization can he be ruled out. For missing value caused by dropout, the remaining date will use the last observation to carry forward. For missing value in the middle of complete cases, the most recent value of not missing date will be carried forward. Analyses of the safety data will be based on the safety set: patients who were receiving at least 1 dose of trial medication. Baseline demographic and clinical characteristics will be tabulated as means and standard deviations for continuous variables, and proportions and percentages for categorical variables.
Standard baseline characteristics of sex, age, and medical history will be summarized for all patients. Group comparisons will be made using Fisher exact test for categorical data and analysis of variance, with independent factors for treatment of continuous data. The primary objective of this study is to test the hypothesis that Chinese herbal formula SQJZ will benefit PD patients in alleviating NMS. Mixed-model repeated-measures analyses will be used to assess between-group differences in the modeled change in scores from baseline to 24 weeks. The change score from baseline at each visit will be the dependent variable. Independent variables in the model for fixed effects will be treatment (2 levels: SQJZ and placebo), visit (5 levels: baseline, week 4, week 8, week 12, and week 24), treatment-by-visit interaction, concomitant treatment (yes or no), baseline measures (UPDRS, NMSS), sex, age, and so on.
An unstructured covariance matrix will be used to model the within-patient variance-covariance errors. If the unstructured covariance structure matrix results in a lack of convergence, the heterogeneous Toeplitz covariance structure followed by the heterogeneous autoregressive covariance structure will be used. The Kenward-Roger approximation will be used to estimate the denominator degrees of freedom. Similar methods will be used for assessment of secondary endpoints. For safety analysis, the incidence of adverse events (symptoms, signs, diseases, laboratory tests, or abnormal electrocardiogram manifestations) will be tabulated. Chi-square test or Fisher exact test will be conducted for assessing the differences between SQJZ formula and placebo. All analyses will be performed using the software of Statistics Analysis System by a statistician, blinded to treatment allocation.
Protocol amendments and dissemination If there is any amendment to the protocol, approval must again be sought from the ethics committee before implementation. Participants who are randomized to placebo group will get a 12-week SQJZ and follow-up as post-trial compensation. Participants' personal information will be confidentiality. Only relevant code will be sent to statistic analysis. The final dataset will be available to the principal investigator and the independent statistician. Data will be stored in a locked cabinet at the Dongzhimen Hospital Beijing University of Chinese Medicine, China, for 5 years. Results will be published on the website of ClinicalTrials. gov in accordance with the CONSORT2010 Statement. We envisage the results will be published in high-impact generalist and specialist journals.
Zooplankton were collected by sieving water (10 L) through a mesh (100 μm) and counted on a stereo-microscope (Bresser microscope, Germany) at 40× magnification. Phytoplankton (<100 μm) were fixed with Lugol and variable volumes (5-50 ml) were settled following the Utermöhl method  (Utermöhl, 1958). Samples were counted on an inverted microscope (Leica DMI 3000 B), and at least 400 cells were identified to the genus level. Phytoplankton biovolumes were assigned using reference data  (Kremer, Gillette, Rudstam, Brettum, & Ptacnik, 2014). Heterotrophic bacteria and autotrophic picoplankton were fixed with formaldehyde, stained with Sytox Green, counted in triplicates using a Gallios flow cytometer (Beckman Coulter), and subsequently analyzed using the Kaluza software. Samples were counted under blue laser excitation (488 nm), and based on the autofluorescence of the pigments, it was possible to differentiate autotrophic picoplankton as picochlorophyta (chlorophyll a emission at 695 nm) and picocyanobacteria (phycoerythrin emission at 520-620 nm).
We fitted linear mixed models using temperature and temperature fluctuations as the difference of temperature in F relative to ambient water temperatures (∆_Temp) and TP as fixed-effects model; as a random effect, we used sampling time (as number of days of the experi-  We used a logistic regression to investigate the effect of the treatments and other environmental parameters (SRP, TP, NO 2 , NO 3 , and NH 4 ) on the dominance of one phytoplankton taxon on the entire community. As dichotomous function, we set the Cyanobacteria relative volume with the threshold as relative biovolume >70% (CyanoDom >70%). The entire dataset was analyzed (192 numbers of observations) and data were log transformed prior to statistical analysis. The statistically significant difference value was set at p <. 05.
| Physicochemical parameters Water temperatures were continuously kept +4°C higher in the heated treatment (T) relative to the control treatment (C; Figure  1 ). Temperature in the temperature fluctuations treatment (F) was fluctuating relative to T (Figure  1 ) from -3. 5°C reached on October 2014 to a maximum of +7. 73°C reached on February 2015 and relative to C from +0. 27°C to +8. 7°C (Figure  S1 ). The temperatures were significantly different among the treatments (ANOVA; df = 2, F = 14. 4, p <. 001), and the whole dataset, including the seasonal variations, ranged from 0. 01°C to 23. 5°C (Data S1), whereby the same amount of energy was supplied to T and F. Concentrations of SRP ranged from 0 to 14. 2 μg/L and TP from 2. 4 to 83 μg/L, thus covering a wide seasonal range from oligo-to eutrophic status in all treatments.
ACKNOWLEDGMENTS We thank  Hermann Hofreiter ,  Michael Mayr , and  Hannes Hager  for the technical support, Johan and  Andreas Bäckman  for designing and setting up the temperature regulations in the mesocosms as well as  René Weiss  and the  Danube University Krems  for assistance with the flow cytometer analysis. This study was supported by the  Austrian Science Fund  (project I  1057-B25 ) as part of the BioDivERsA 2011-2012 joint call ( Biodiversity Dynamics  and  Tipping Points In Our Future Freshwater Ecosystems ).
Simulating the di↵usion process In  [2] , the authors provide explicit formulas for the probabilities of fixation as well as approximations for the times to loss or fixation of an allele. Due to the bi-dimensionality of our model which largely increases the di culty of mathematical calculations, fixation probabilities as well as laws of times to fixation, loss and absorption (either loss or fixation) of allele a are determined using simulations of equations (2a) and (2b). These simulations are run using a script written in C++ (and available on Dryad). The stochastic elements of the equations, B 1 t and B 2 t are obtained by successive samplings from a normal distribution with mean 0 and variance dt. dt is the size of the time step and is a parameter fixed at the beginning of the simulation, which we have set to 10 4 for a carrying capacity K = 100 and and 10 5 for K = 10 and 1.
Acknowledgements: This work was partially funded by the Chair " Modélisation Mathématique et Biodiversité " of VEOLIA-Ecole Polytechnique-MNHN-F. X. , and was also supported by the  Mission for Interdisciplinarity at CNRS  and by a public grant as part of the  Investissement d'avenir project , reference  ANR-11-LABX-0056-LMH ,  LabEx LMH. Diala Abu Awad  was funded by the  Agence National de la Recherche  ( ANR SEAD -ANR-13-ADAP-0011 ).
Statistical Analysis The data refer to the comparison between the subjects attending the sailing course (Group A + Group B: experimental treatment) and the subject in the wait list (Group B: usual treatments). As shown in the Fig. (2) , those subjects that skipped more than more than four lessons were excluded from the analyses (n = 18, because lost to post-intervention or discontinued intervention), in order to better assess the effectiveness of the experimental intervention. Data were analyzed with the Statistical Package for Social Science (SPSS) for Windows (Chicago, Illinois60606, USA), version 21. The descriptive statistics (mean ± sd; %) were used for nominal and continuous variables to point out the characteristics of the samples (age; gender; marital status; education; diagnosis; psychosocial variables).
ACKNOWLEDGEMENTS We are warmly grateful to all the users for participating in the research; to  Francesca Colla  and the administrative workers of the  Department of Medical Science and Public Health, University of Cagliari  for their precious organizational support; to  Franco Ricci ,  Simone Marceddu ,  Marco Porcedda  and the other sailors of the  Scuola Italia in Vela, Quartu S. Elena (CA), Italy  for their support; to the mental health operators and volunteers of  Casa Emmaus, Iglesias (CI), Italy and Il Gabbiano ONLUS, Monserrato (CA), Italy  for their collaboration.
Results and Discussion The lactonase SsoPox was engineered for higher phosphotriesterase activity using structure-based combinatorial libraries. By comparing structures of enzymes with similar topology, it has been possible to redesign, using modelling tools, the active site cavity of SsoPox to mimic as closely as possible that of BdPTE (Fig. 1 ; Table  S1 ). This method consisted of two main steps: (i) the identification of mutations corresponding to equivalent positions at the enzyme active sites using structural alignment and (ii) the rational selection of mutations, in a position with no equivalent residue, which mimicked the shape and chemical nature of the target enzyme cavity. A mutation dataset was thus obtained and used to develop a combinatorial library consisting of random combinations of our pre-selected mutations.
Methods Structure-based identification of mutations. Sharing the same (α/β) 8 barrel topology, SsoPox and BdPTE structures (Pdb ID 2vc5 and 1dpm, respectively) were superimposed (Fig. 1 ) using PyMol  59  , making it possible to identify structurally equivalent residues at their respective active sites. In an effort to reshape the SsoPox active site, side chains that were well superimposed between the two enzymes were mutated using Coot into the residue present in BdPTE  60  , with the exception of V27 which was mutated in Ala (and not Gly) to avoid increased entropy (Fig. 1 ; Table  S1 ). However, due to the major differences in loop 8 and 7 between PLLs and PTEs, numerous residues were not superimposable. These active site residues (Y99, L228, F229 and W263) were therefore mutated in an effort to mimic the BdPTE active site cavity in terms of shape and chemical nature, as illustrated by the chimeric reconstruction of SsoPox carrying all these mutations (Figure  S1 ).
The remaining ammonium sulfate was eliminated by injection on a desalting column (HiPrep 26/10 desalting, GE Healthcare; ÄKTA Avant) and concentrated to 2 mL for separation on exclusion size chromatography (HiLoad 16/600 Superdex TM 75 pg, GE Healthcare; ÄKTA Avant). Final purity was monitored by SDS-PAGE and protein concentration was measured with a NanoDrop 2000 spectrophotometer (Thermo Scientific). Kinetic assays. Generalities. Catalytic parameters were evaluated in triplicate at 25 °C, and recorded using a microplate reader (Synergy HT, BioTek, USA) and the Gen5. 1 software, in a 6. 2 mm path length cell for 200 µL reaction in 96-well plate, as previously explained  30. Catalytic parameters were obtained by fitting the data to the Michaelis-Menten (MM) equation using Graph-Pad Prism 6 software.
When V max could not be reached in the experiments, catalytic efficiency was obtained by fitting the linear part of MM plot to a linear regression using Graph-Pad Prism 6 software. For some SsoPox variants, the MM plot was fitted to the substrate inhibition equation using Graph-Pad Prism 6 software enabling us to determine a K I for several substrates. Consequently, the calculated catalytic efficiencies in these conditions are true only at low substrate concentrations. In some other cases, saturation could not be reached, therefore k K / cat M values were determined using linear regression. For Coumaphos, data were fitted to one-phase decay non-linear regression. Phosphotriesterase activity characterization. The kinetic assays were carried out in activity buffer. The conditions used for pesticides were as follows: ethyl-paraoxon (I), ethyl-parathion (II), methyl-paraoxon (III) and methyl-parathion (IV) hydrolysis was recorded at 405 nm (ε = 17000 M -1.
ml -1 and 9 µg. ml -1 respectively. The reactions were monitored until the plateau was reached. Experimental measures were obtained using Gen5. 1 software, then analyzed with GraphPad Prism 6 software. Curves were then fitted using One-Phase Decay non-linear regression with the equation (1): = - × + - Y Y Plateau e Plateau ( 0 ) (1) kt ( ) where Y0 = 0% and Plateau = 100%. The rate constant k was determined and the time required to observe a 95% decontamination was calculated accordingly. The curves and results of the fits are shown in Figure  S4. Degradation at 95% was confirmed using a final point by GC/MS for all pesticides with a 10 -3 enzyme-to-substrate ratio. 100 µL of activity buffer solution was first extracted with 100 µL chloroform. Organic extracts were analyzed by using a Clarus 500 gas chromatograph equipped with a SQ8S MS detector (Perkin Elmer, Courtaboeuf, France).
1 µL of organic extract was volatilized at 220 °C (split 15 mL. min -1 ) in a deactivated FocusLiner with quartz wool (SGE, Ringwood, Australia) and compounds separated on an Elite-5MS column (30 m, 0. 25 mm i. d. , 0. 25 mm film thickness) for 12 minutes using a temperature gradient (80-280 °C at 30 °C. min -1 , five minutes' hold). Helium flowing at 2 mL. min -1 was used as the carrier gas. The MS inlet line was set at 280 °C and the electron ionization source at 280 °C and 70 eV. Full scan monitoring was performed from 40 to 400 m/z in order to identify chemicals by spectral database search using MS Search 2. 0 operated with the Standard Reference Database 1 A (National Institute of Standards and Technology, Gaithersburg, MD, USA). m/z is the mass-to-charge ratio of the base peak fragment detected for each molecule.