dataesr/scientific-paragraphs-categorization huggingface dataset:
In subsequent individual patient's meta-analysis addressing the dose-dependent effects of CPAP  [80] , weight gain was greater with high-use therapeutic CPAP compared with high-use placebo CPAP (mean difference: 1. 45 kg). However, this limited weight gain was not accompanied by any adverse metabolic effects. In the European Sleep Apnea Database cohort, CPAP response was heterogeneous and CPAP usage was not found to systematically change BMI. Weight gain and increases in waist circumference were restricted to an OSA phenotype without established obesity  [81]. As recently supported by American Thoracic Society consensus guidelines, these data together suggest that additional therapies for body weight reduction must be recommended for overweight or obese patients with OSA initiated on CPAP  [82].
The methods were carried out in accordance with guidelines and regulations. The participants gave their informed consent and were compensated with 50HKD for their participation. Stimuli. The lexical statistics to describe our stimuli and later our participants' word-level networks come from the tonal fully segmented schematic representation (C_G_V_X_T) found in the updated version of the Database of Mandarin Neighborhood Statistics  102. The current instantiation of the database is freely available here: https://github. com/karlneergaard/Database_of_word-level_statistics. The stimuli consisted of 6 monosyllabic Mandarin words that were high in single edit neighbors (M: 21. 67; SD: 4. 13), and differed in syllable structure: ye1 (/iɛ 55 /, GV, edit 1 = 18); an3 (/an 214 /, VX, edit 1 = 23); wai4 (/uaɪ 51 /, GVX, edit 1 = 29); du2 (/tu  35  /, CV, edit 1 = 20); guo4 (/kuo 51 /, CGV, edit 1 = 22); ceng1 (/ tsʰəŋ 55 /, CVX, edit 1 = 18).
Network. Correct lexical items per each trial were constructed into undirected graphs wherein two given items shared an edge if they had an edit distance of one. Mean clustering coefficient (CC), mixing by degree (M), and the number of components per network (NC) were calculated through the use of the igraph package in R  105. At the word level, clustering coefficient (CC), is the proportion of neighbors who are also neighbors of each other and represented as falling between 0 and 1. In studies that contrasted the effects of high versus low CC, words high in CC have been tied to greater speech errors  89  , lower accuracy in auditory perception  90  and lexical judgment  90, 106  , slower reaction times in picture naming  89  , yet greater consolidation of novel words  107. At the network level, CC is a global measure of interconnectivity between nodes calculated by averaging local CC across all nodes of the giant component.
Results Responses were transcribed to pinyin by two native-Mandarin speaking volunteers. Using the above-mentioned database  102  , items were categorized as either real lexical items, or nonwords. Real lexical items consisted of syllables that could be ascribed to at least one Chinese character whether or not they qualified as monosyllabic words. Items were classified as correct responses if they did not correspond to a nonword in the database or were not repetitions. Exclusions of outliers were made according to trials that lied 2. 5 standard deviations above (WE: 11 trials; SN: 1 trial) and below (RE: 12 trials) the mean, and through the use of a boxplot: M (4 trials). Total number of exclusions (28) accounted for 4. 9% of all trials. According to the same criteria, no exclusions were made based on the distributional properties of CC.
Statistical analysis. The statistical analysis utilized the mcglm package in R  108  , which allowed for the fitting of regression models with multiple dependent variables, and distributional family types. Of the fluency (WE, RE, SN), and network variables (CC, NC), only M was best fit with a normal distribution. The tweedie variance function  109  was used to fit WE, RE, SN and CC, while the poisson-tweedie variance function  110  was used to fit the count variables, NC and Error. As can be seen in Table  2 , higher self-rated spoken-English proficiency showed a facilitative effect on greater CC. The proportion of syllable neighbors and errors produced by participants also significantly predicted English proficiency, such that higher proficiency participants produced less syllable neighbors (lower SN), and fewer errors than lower proficiency speakers.
Greater Num_Chinese equated both the production of less successively similar items (RE), and less successive syllable neighbors (SN). Networks of less coherence (high NC) were also produced by participants with greater Num_Chinese. Finally, multilingual speakers on average produced less errors (Multilingual, M: 1. 66; SD: 1. 85; Monolingual, M: 1. 93; SD: 2. 33). No effects were found for either WE, or M. To disentangle the effects displayed in Table  2 , in Fig. 4  we graphed interactions with tensor product smooths through the use of generalized additive models within the mgcv package in R  111. Figure  4a  illustrates that higher Num_Chinese participants produced low successive similarity (low RE), and a greater proportion of network components (high NC), while simultaneously illustrating the strong negative correlation between NC and RE previously shown in the correlation analysis.
Acknowledgements The funding for this study came from the  University of Macau Postdoctoral Fellowship , the  Excellence Initiative of Aix-Marseille University -A*MIDEX, a  French "Investissements d' Avenir" programme" , the  Hong Kong General Research Fund  project titled " Mandarin Alphabetic Words: A Study on the Role of Orthography in Morphological and Phonological Adaptation " (GRF no. 15608618 ), and the  Marie Sklodowska-Curie Innovative Training Network project titled  " Conversational Brains: A Multidisciplinary Approach " (ID:  P0001221 ).
Experimental design We first induced myocardial infarction (MI) by ligation of the left anterior descending artery (LAD) on day 0 (Fig. 1 ). Two days after surgery, oral treatment of the animals was initiated, with vehicle (peanut butter), firibastat (150 mg/kg) or enalapril (1 mg/kg) once daily. The used of peanut butter as vehicle reduces animal repetitive-gavage-induced stress  [9]  without altering the effectiveness of the molecules administered. Indeed, we have shown that enalapril given by oral route in peanut butter at the dose of 1 mg/kg inhibited heart ACE activity by 55% after 4 weeks treatment post MI (data not shown). The enzymatic activity of brain APA was measured two days, and one, two, three and four weeks post MI. Cardiac function was monitored by echocardiography two weeks after MI and animals were selected on the basis of their ejection fraction (EF): only animals with an EF below 55% post MI were retained for the study.
Acknowledgments We thank  Dr. Adrien Flahault ,  Center for Interdisciplinary Research in Biology, Collège de France , for helping us with statistical analysis and providing scientific advice. We also thank  Dr. Pierre Joanne ,  Institute of Biology Paris-Seine, Paris Sorbonne University , for scientific support and expertise in qPCR. Special thanks go to the animal facility of  Collège de France  and its staff, particularly  Matthieu Boukaissi , for his help and guidance. This work was supported by  Institut national de la santé et de la recherche médicale, Collège de France, l'Agence nationale de la recherche ANR LabCom CARDIOBAPAI and Quantum Genomics. Disclosures SB, YM, MK and FB are full-time employees and shareholders of  Quantum Genomics SA. The remaining co-authors have no conflicts of interest, financial or otherwise, to declare.
Differentiating males from hermaphrodites A6140 and all CA populations are androdioecious, with hermaphrodites and males segregating at intermediate frequencies  (Teotónio et al. , 2012; Theologidis et al. , 2014). We were able to reliably ( 97% accuracy) differentiate between the sexes based on behavioural and morphological traits extracted from MWT data. In brief, we fit an initial three-trait Gaussian mixture model to control data, scored all samples by the difference in fit between one-and two-component models, then used a larger set of samples from the tails of this distribution to train an extreme gradient boosting classifier with R package xgboost (R Core Team, 2018; Chen and Guestrin, 2016) using a set of 30 derived traits. In detail, we first evaluated a set of simple descriptions of individual size, shape and movement to find a subset of metrics that maximized the difference in preference for a two-component model between negative and positive controls: respectively, inbred wild isolate founders and two monoecious (M) populations which contained no, or very few, males; and three dioecious (D) populations with approximately 50% males (M and D populations were derived from A6140, see  Theologidis et al.
(2014)  and  Guzella et al. (2018) ). Starting with worm area, length, width, curvature, velocity, acceleration, and movement run length as parent traits from Choreography output, child traits were defined by first splitting parents by individual movement state (forward, backward, still) and calculating the median and variance of the distribution for each track. Traits with more than 1% missing data were excluded, and values were log-transformed where strongly non-normal (a difference in Shapiro-Wilk -log10(p)>10). Fixed effects of block and log plate density were removed by linear regression before model fitting. Two-component Gaussian mixture models were fit to tracks for each line/population (R package mclust  Scrucca et al. (2016) , V II spherical model with varying volume), orienting labels by area (assuming males are always smaller than hermaphrodites).
We sampled over sets of three traits (requiring three different parent trait classes, at least one related to size), and took the set maximizing the difference in median Integrated Complete-data Likelihood (ICL) between control groups (log area, log width, and velocity, all in the forward state). By this ranking, the 16 inbred founders and two monoecious populations fell within the lower 19 samples (of 77), while the three dioecious populations fell within the top 15 samples (although estimates appear to saturate at around 45% due presumably to density-dependent aggregation of males attempting to copulate). To build a more sensitive classifier robust to male variation beyond the range seen in control data, we then trained an extreme gradient boosting model using the full set of 30 traits on the top/bottom 20 samples ranked by ICL in the three-trait mixture model [R package xgboost,  Chen and Guestrin (2016) ].
3. 2, RStan version 2. 15. 1), which performs Bayesian inference using a Hamiltonian Monte Carlo sampling to calculate the posterior probability of the parameters given the observed data. We used multi-lognormal prior distibutions with mean transition rate of 2 s -1 and a coefficient of variation of 60%: ln(q i,j ) ∼ N (ln(2), 0. 6) (17) Throughout the rest of the methods section, we will denote q k the six off-diagonal elements of the Q matrix estimated by this model. Three separate iterations of the model were run containing the data from (1) inbred lines (2) populations and founders and (3) MA lines. This does not affect the final results as the transition rates for each plate are independent estimates in the model. Final plate-level estimates and all subsequent R code are available from our github repository (Section 9).
Controlling for experimenter and time of data acquisition With per-plate estimates for transition rates, we fitted preliminary models to account for the effects of assay design and homogenize data sets. We first corrected for experimenter effects using a set of 26 inbred lines that were measured by all three experimenters. For each transition rate we fitted the linear mixed-effects model (lmer function in R package lme4 Bates et al. (2015)): ln(q i,j ) = α + g l + π e + ζ b + (18) with α being the mean trait value of all the lines, g fixed effects of each line l, π fixed effects of experimenter e, ζ ∼ N (0, σ 2 ) a random effects of block b, and ∼ N (0,σ 2 ) the residual error. The same procedure was used to correct for a significant location effect, using a shared set of 64 inbred lines: ln(q k ) = α + g l + τ p + ζ b + (19) with τ the fixed effect of location p.
Repeatability Transition rate repeatabilities were computed from linear mixed models using the R package rptR  (Stoffel et al. (2017) ), with confidence intervals estimated from 1000 bootstrap samples. We used the univariate versions of the models presented in equation 21 and 23 to estimate the repeatability of A6140, CA[1-3]50 and CA[1-3]100 RILs and populations, and the MA lines from each ancestor (N2 and PB306), presented in Figure  S15.
G-matrix estimation Statistical analysis used the R package MCMCglmm (version 2. 26, MCM-Cglmm function with default parameters;  Hadfield (2010) ). We first removed the effects of environmental covariates recorded during data acquisition (Section 5. 2. 1) for each of three groups of populations (A6140, CA[1-3]50, CA[1-3]100), using the generalized linear mixed-effects model for each population and trait: ln(q k ) = α + g l + ζ b + η d + κ t + µ h + (20) with g and ζ as defined above (Section 5. 2. 6), and η, κ and µ the fixed effects of ln(density) d, temperature t and relative humidity h respectively (Sections 5. 2. 1 and 5. 2. 2). G-matrices were then estimated separately for each of the seven experimental populations (A6140, CA[1-3]50, CA[1-3]100). With Y the q × l matrix of q(= 6) transition rates and l inbred lines: Y = I l α + Z 1 G + Z 2 B + E, Y ∼ MVN q×l (1 l α, P = G + B + E).
Selection surface In  Noble et al. (2017)  we reported hermaphrodite fertility data on 230 inbred lines derived from several populations. Fertility includes fecundity and offspring survival to the L1 larval stage, and was measured in environmental conditions that closely followed those of experimental evolution (except for interaction with males and final larval density). We have no prior indication that hermaphrodite fertility trades-off with viability  (Chelo et al. , 2019) , and it therefore represents a major fitness component that we use here as a fitness surrogate. The log-transformed, covariate-adjusted fertility values for each line were downloaded from  Noble et al. (2017) , exponentiated, and divided by the sum of all values to obtain a relative fitness measure (w). For the six transition rates, we first obtained the mean value for each line by fitting a linear mixedeffects model using the lmer function in R: ln(q k ) = α k + z k,l + ζ b + η d + κ t + µ h + (29) with the factors being defined as in equation 20, and estimates of the interaction between the covariates density, temperature and humidity being retrieved.
This work was supported by the European Research Council (ERC-St-243285) and the Agence Nationale pour la Recherche (ANR-14-ACHN-0032-01, ANR-17-CE02-0017-01) to HT, and the National Institutes of Health (R01GM107227) to CB. BA was from 2010-2015 a Fundação para a Ciência e a Tecnologia PhD scholarship recipient (SFRH/BD/51177/2010). LN is a Marie Curie fellow (H2020-MSCA-IF-2017-798083).
Acknowledgements We thank  A. Crist ,  J. Garcia ,  H. Gendrot ,  C. Goy ,  V. Pereira ,  F. Melo , and  A. Silva  for help with worm handling and data acquisition;  P. McGrath  for worm strains;  R. Costa ,  R. Kerr ,  S. Proulx ,  P. Phillips ,  N. Scwierczek  for help with data analysis, hardware and software implementation;  L. -M. Chevin ,  C. Dillmann ,  M. -A. Félix ,  P. Phillips ,  A. Le Rouzic , and  A. Veber  for discussion.
Data and code for analysis are listed as separate files, archived in our github page (https://lukemn. github. io/cemee/), and will be archived in XXXX upon publication: • Input Data: -Experimental design: Nomenclature_conventions, Experimental_design_sample_sizes -Locomotion bias data: Cemee_Pop_WI/Final_merged_data_NGM. txt, MA_lines/Final_merged_data_MA_Lines. txt -Mean state frequencies: Cemee_Pop_WI/State_freq_NGM. txt • Ordered list Rcode for analysis and figure: 01_Analysis_MA_Lines. R, 02_Analysis_Cemee_Pop_WI. R, 02a_State_frequency. R, 02b_Beta_distributions_with_freq. R, 02c_Beta_distributions_with_freq_males. R, 02d_Analysis_Cemee_Pop_WI_CX12311. R, 02e_Repeatability. R, 02f_G_matrix_ellipses. R, 02g_Figures_S4_S5. R, 02h_Figure6. R, 02i_Figure2B_S6. R, 02j_Figure1A. R, 03a_Gamma_estimation_centered_on_A6.
Chemostratigraphic trends in Hole U1440B are consistent with the uppermost lavas forming off axis, whereas the lower lavas formed beneath a spreading center axis. Axial magma of U1440B becomes more fractionated upsection; overlying off-axis magmas return to more primitive compositions. Melt models require a two-stage process, with early garnet field melts extracted prior to later spinel field melts, with up to 23% melting to form the most depleted compositions. Mantle equilibration temperatures are higher than normal MORB (1,400 °C-1,480 °C) at relatively low pressures (1-2 GPa), which may reflect an influence of the Manus plume during subduction initiation. Our data support previous models of FAB origin by decompression melting but imply a source more depleted than normal MORB source mantle.
10. 1029/2018GC007731 Geochemistry, Geophysics, Geosystems Geological Survey, Siberian Institute of Geochemistry, and Japan Geological Survey). Count rates were corrected for absorption and secondary fluorescence using fundamental parameters in the Panalytical® SuperQ® software. A split of ~50 mg was dissolved using trace element grade hydrofluoric and distilled trace element grade nitric acid and analyzed with a Perkin-Elmer 9000 Elan ICP-MS at Centenary College for the rare earth elements (REEs) and 10 other trace elements. Each sample was spiked with an internal standard of In and Bi to correct for machine drift during analyses. Calibration was done using multielement trace element standards. USGS (United States Geological Survey) and international standards were used to check our calibration curves throughout the analyses.
In addition, we have access to data for 40 POOL samples, which were analyzed by ICP-OES (inductively coupled plasma-optical emission spetrometry) during the expedition and reanalyzed by XRF afterward  (Reagan et al. , 2015f). POOL samples were chosen by the Expedition 352 Science Party to encompass the breadth of chemical variability in the recovered igneous material, with the goal of eventually yielding a selfconsistent database of major, trace, and isotopic data on the same homogenized powders. Whole rock major and trace element chemistry is presented in supporting information Table  S01  (file 2018GC007731-Supporting-Information) and is archived at PANGAEA Data Archiving and Publication  (Shervais et al. , 2018). Expedition 352 scientists used a hand-held portable XRF (pXRF) shipboard to monitor chemical variations in the basalts as part of the core logging process and to define a preliminary chemostratigraphy used to guide Geochemistry, Geophysics, Geosystems subsequent sampling  (Ryan et al.
, 2017). The data from the pXRF were regressed against shore-based XRF data to determine how well they correlate, and the pXRF concentrations adjusted if needed to remove analytical bias (Cr = Cr p *1. 28 + 9. 8; Zr = Zr p *1. 38-5. 8). No adjustment was made for regressions within 5% of shore-based XRF values (K 2 O, TiO 2 , and Sr, V). These data are used to supplement shore-based data for the chemostratigraphy. During shipboard sampling, the pXRF data were used to help select both the POOL and personal samples. This ensured that all chemical groups were sampled for shore-based studies and that significant chemostratigraphic boundaries were identified.
Fractionation Modeling Fractionation modeling of the Lower N-FAB compositions was carried out using Comagmat petrologic modeling software  (Ariskin, 1999). Potential parental magma compositions were chosen based on several factors: high MgO content, low REE concentrations that parallel other FAB, and low incompatible element concentrations. The samples that best meet these criteria are high-MgO Unit 15e dolerites, which have primitive compositions that could be parental to N-FAB. D-FABs have Ce/Yb too low to be parental to N-FAB, whereas E-FABs have Ce/Yb, which are too high (Figure  8 ). P-FABs of U1440 Unit 3 have appropriate Ce/Yb ratios but very low REE concentrations (Figure  8 ). As a result, we use Unit 15e high-MgO dolerite for fractionation modeling here (352-U1440B-36R-1-W 42/45).
Subduction Initiation and Suprasubduction Zone (SSZ) Ophiolites It now seems well established that many SSZ ophiolites form during subduction initiation, in which slab rollback drives early decompression melting to form FAB, and later fluid-flux melting forms boninites and related rocks, before the establishment of stable steady state, downdip subduction (e. g. ,  Metcalf & Shervais, 2008; Pearce et al. , 1984; Reagan et al. , 2010; Shervais, 2001). While a full comparison with ophiolites is beyond the scope of this paper, we can make some observations on the IBM system as a paradigm for ophiolite formation. Our data show that the extent of melting in Izu-Bonin fore-arc FAB is higher than normal MORB and requires an unusually depleted source (e. g. ,  Yogodzinski et al. , 2018)  that was remelted during FAB formation.
Acknowledgments This research used samples and/or data provided by the  International Ocean Discovery Program (IODP). The authors are grateful for support from IODP, the  JOIDES Resolution Facility , and the scientific staff and crew aboard the  JOIDES Resolution during Expedition 352. Funding support to U. S. participants (J. W. S. , M. K. R. , J. G. R. , and W. N. ) from the  Consortium for Ocean Leadership  and from the  National Science Foundation  is gratefully acknowledged ( OCE-1558689  to  Shervais ,  OCE-1558647  to Reagan,  OCE-1558855  to Ryan, and  OCE-1558608  to Nelson). Prytulak was supported by  NERC  directed grant  NE/M010643/1. Chapman was supported by the  Australia-New Zealand IODP consortium  and the  ARC LIEF scheme  ( LE140100047 ). Almeev was supported by  R. A. by the  German Science Foundation  (DFG, Project  AL1189/8-1 ).
Pearce was supported by  NERC (UK)  grant ( NE/M012034/1 ). Kurz appreciates support by the  Austrian Academy of Sciences  and funding by the  Austrian Science Fund  ( FWF  Project  P27982-N29 ). Kirchenbaur appreciates funding by the  German IODP consortium  and the  BGR Germany. Reviews by  Marcel Regelous ,  Gene M. Yogodzinski , and two anonymous reviewers are gratefully acknowledged. Supporting information table is archived at PANGAEA Data Archiving and Publication (PDI-18813) (https://doi. pangaea. de/10. 1594/PANGAEA. 895122 ).
Computational experiments In this section, we validate the proposed model through a case study based on the Qom Monorail Project (QMP) data. The conflict between two objective functions is shown based upon the results of the case study. To solve the bi-objective model for this case study, the TH aggregation function (see Sect. 4. 4) is used for 15 nodes of the case study by GAMS software (24. 0. 1 version) using CPLEX as solver for the mixed-integer linear programs (MILP). Afterwards, various sensitivity analyses are carried out to analyze the sensitivity of the objective functions to the input parameters. In order to examine the model with large-sized problems, a performance comparison is carried out between the proposed meta-heuristics using the Turkish Data Set (TDS).