dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods We conducted a literature search through PubMed and ClinicalTrials. gov for randomized, placebo-controlled clinical trials lasting 12 months or more using ''Charcot-Marie-Tooth type 1A disease'' and its synonyms' ''hereditary motor and sensory neuropathy'', ''peroneal muscular atrophy'' and ''distal spinal muscular atrophy'' as the search terms. MEDLINE search terms are given in Appendix. We also checked the bibliography of identified trials. The outcomes of interest were the change from baseline in CMTNS  [16]  and ONLS  [17]  after one year of treatment or Placebo, hence only trials measuring CMTNS or ONLS were selected. CMTNS and ONLS are considered as the main clinical scales for impairment and disability, respectively, in CMT1A disease  [15]. Studies measuring at least one of these two outcomes were selected.
8, and an ANCOVA analysis at a two-sided 5 % level). In this context, designing a confirmatory Phase 3 study for a treatment showing such stabilization in CMT1A would require a much larger sample size and longer study duration, making it clearly unrealizable. It confirms the idea that an effective treatment for this disease should bring an improvement, rather than the mere ability to slow or stabilize the disease progression  [12, 14]. Even if this effect seems quite marginal, a standardized re-analysis of all AA patient-level data would be of great interest. Lastly, this meta-analysis supports an improvement in both CMTNS and ONLS with PXT3003 treatment, statistically significant when compared to Placebo. This improvement could herald an early, meaningful change in the disease course. Conducting a meta-analysis of clinical trials in CMT1A is challenging because of the small number of studies and of the heterogeneity of study protocols in terms of recruitment criteria, study duration, balance of groups, and statistical analysis.
A metal hammer tip was used to ensure the widest range of excitation. The data was acquired through Data Translation DT9837B module into the PC USB Port, as seen in Fig. 2(b). Data acquisition was triggered by the force transducer in the impulse hammer. The test set-up enabled measuring time domain voltage data of the force from the hammer tip and the signals from three accelerometers, with 10 kHz sampling frequency. The input channels saved 8000 data points each, i. e. the lengths of the saved signals were 0. 8 s. The discrete Fourier transform of temporal signals is found by taking the fast Fourier transform (FFT). The natural frequency is obtained from the phase angle of the complex frequency response function as it passes through ±90. Since three accelerometers were recording during four separate impulse events (at each corner), one full measurement of a plate produced twelve sets of time-acceleration series from the accelerometers.
Simplex method Simplex algorithm based inversion from Lamb wave data has been conducted before by Karim et al. [33]. They showed a continuous convergence zone in the neighborhood of the solution point in the parameter space. They also conducted a sensitivity study and cautioned that convergence to a false solution may result from a poor initial guess. The Simplex method based approach used here is modified as it employs a two-step procedure, where the  initial guess is improved by the results from the previous sensitivity study and numerous pre-inversions. The details for the implementation of this approach can be found in  [9]. At its core, function fminsearch of Matlab software is used.
SGA Simple genetic algorithm (SGA) is a search and optimization method mimicking the process of natural selection. SGAs are stochastic, global search methods, fundamentally different from traditional gradient based techniques as they avoid the local character of conventional exploration, escaping the local minima. The toolbox described in Fig. 4  was self-developed in Matlab, based on the classic textbook by Goldberg  [34]. The aim was to produce a robust and reliable algorithm where the user has good control over details. Before application, the toolbox was successfully tested on various test functions where the solutions were beforehand known.
Numerical model and error estimates A finite element model was employed for the forward calculation of natural frequencies. The finite element code, written for Matlab by Ferreira  [36] , calculates the natural frequencies for free vibrations of laminated composite plates. All plates are hereby treated as a single-layer orthotropic material and, therefore, the inversion results  in effective flexural stiffnesses. The software was slightly modified to enable the calculation for all edges free boundary conditions, as this did not exist in the original code. As a compromise between precision and available computational power of a desktop PC, it was decided to use a 10Â10 in-plane finite element mesh to model the plates for forward calculations in optimizations. The details of error estimation for natural frequencies would require a lengthy description, adding little scientific value to current work.
Noah Noah Land-Surface model  Chen and Dudhia (2001)  with 4 layers. get. In the present study, the radiation flux data from CERES labeled as EBAF (Energy Balanced and Filled) have been used in its version 2. 7. These data are provided as monthly averages with a resolution of 1 •. In the CERES website 1 a complete description of the data elaboration process and its issues can be found. The raw data used by this dataset come from AQUA, TERRA and geostationary satellites. To produce the EBAF data, the energy balance is adjusted to that inferred by  Loeb et al (2012)  from the measured warming of the oceans. Cloud cover observations are not available in EBAF, so we used those from SYN1deg (the processing step previous to EBAF, before adjusting the energy balance). To check the robustness of the results, all the maps shown in the paper using CERES data have been reproduced with the independent GEWEX-SRB dataset, which does not use data from MODIS (Gupta et al 1 http://ceres.
25 • resolution. This dataset is a global soil reanalysis produced by running a LSM forced with data as realistic as possible. Actually, GLDAS data are available for 4 different LSMs, and one of them is the Noah LSM. Forcing Noah with observations allows soil variables to be comparable with those produced by WRF, avoiding the problems that arise when using direct observations. On the other hand, the use of a LSM reanalysis prevents the assessment of errors arising from the LSM. Thus, in the analysis of soil moisture (section 3. 3) we assume that the atmospheric forcing is the main source of error. Moreover, we did not change the LSM in our MPE, therefore, the uncertainty arising from the LSM was not addressed at all in this study (or in the EURO-CORDEX WRF simulations).
In the case of MPE-F, RSDS is close to the observation in most of the domain. Interestingly, the REFOR simulation is fairly different from MPE-G. Thus, again we see that the bias pattern for RSDS and temperature that appears in MPE-G needs more than 12-24 hours to build. A simple comparison with SYNdef1 total cloud cover (CLT) data has been carried out to check if the results are consistent to those found for the radiation fluxes. To compute CLT in WRF we followed  (Sundqvist et al 1989) , which assumes maximum overlapping in each cloud layer and random overlapping between layers. CLT biases in summer (figure  6 ) correlate well with those found for RSDS. The spatial patterns of the summer temperature biases in MPE-G and MPE-M are, thus, related to the cloud cover. Clouds can either warm or cool the surface depending on their altitude and the phase of the diurnal cycle.
Also, locally, advection can be as important for temperature as the point energy balance and, in fact, both processes can feed back. This complicates the rigorous attribution of temperature biases to cloud cover. Here, we try to get the most complete possible picture of what is happening into the model by looking at many variables, but analyses to rigorously address causal relationships would require a more systematic approach. Causality in non linear systems is not straightforward to define  (Sugihara et al 2012). A negative CLT bias appears over the Mediterranean Sea and its surrounding countries in all simulations except MPE-H and MPE-M. This bias is also found when comparing with data from an independent dataset (GEWEX-SRB, see supplementary material). On the other hand, ERA-INTERIM cloud cover is very similar to WRF over this region (not shown).
Most of the overestimation is bound to high latitudes, where the observations are uncertain and show an unrealistic discontinuity. However, comparison with GEWEX-SRB data (supplementary material) yields similar results, with an even larger overestimation of the albedo, so this feature is robust. A similar result was found by  Xu and Yang (2012)  over North America. However, according to  Mass (2013) , albedo does not seem to be the main cause of the winter cold bias, which is unknown, but likely related to the treatment of the snow pack by the land surface and/or surface layer schemes. Thus, the albedo   bias would be a feedback reinforcing too cold temperatures. The presence of the cold bias in REFOR is a measure of the part of this bias which appears immediately after starting from ERA-INTERIM.
Soil moisture Total soil moisture content (MRSO) plays a major role in the surface flux partition  (Jaeger and Seneviratne 2011). In this section, WRF MRSO is compared with data from the GLDAS reanalysis (see section 2. 2). For the sake of brevity, only results for summer will be shown. WRF overestimates MRSO (figure  11 ) in most places, except in the southern part of the domain. The bias is larger in the coldest (driest) simulations (MPE-G and MPE-M), and smaller in the warmest (wettest) (MPE-D). A comparison of the sensible and latent heat fluxes of WRF with GLDAS (not shown) reveals that the latter is too large in the simulations that overestimate RSDS the most (MPE-D, MPE-C, and MPE-A), while the former is correct. Thus, in these simulations the excessive soil moisture is influencing the energy partitioning (Bowen ratio), shifting it to a too large evaporation, and making difficult the occurrence of the "dry regime" where evapotranspiration is limited by soil moisture and not by incoming energy.
Annual cycles Previous sections have mainly focused in summer and winter. The maps shown show that the spatial autocorrelation is generally large. Thus, spatial averages make sense in most regions. In this Section we use spatiallyaveraged biases to gain better perspective of the temporal structure of the model error. The regions chosen are the so-called PRUDENCE regions, shown in Figure  1. We considered monthly time series (available as supplementary material), which show that the relative differences among the MPE members are preserved every year with very few exceptions. Thus, spatially-averaged monthly annual cycles have been computed, following these steps: First, the data from WRF, CERES and GLDAS have been bilinearly interpolated to the E-OBS grid and its land-sea mask has been applied.
The comparison with GLDAS data revealed also that WRF overestimates soil moisture content in most regions. The second bias is the pronounced cold bias appearing in the NE quarter of the domain during winter and spring. We found that, during those seasons, the differences in albedo correlated well with the temperature bias. Thus, the cold bias is partially related to this albedo overestimation. This was also found by  Xu and Yang (2012)  in other region (U. S. -Canada). Other authors  (Mass 2013)  found that the cold bias appears systematically over snow-covered regions, regardless of the albedo. Evidence suggests that the problem is a too crude representation of the snow pack or either some problem in the computation of the skin temperature or ground heat flux. The albedo would be acting as a feedback and not as the main cause of the bias.
Acknowledgements This work was partially supported by Projects  EXTREMBLES  ( CGL2010-21869 ) and  CORWES  ( CGL2010-22158-C02 ), funded by the  Spanish R&D Programme. WRF4G  ( CGL2011-28864 ) provided the framework to run the model; this Spanish  R&D  project is co-funded by the  European Regional Development Fund (ERDF). Partial support from the  7th European Framework Programme  ( FP7 ) through Grant  308291  (EUPORIAS) is also acknowledged. The E-OBS dataset was produced within the  EU-FP6  project ENSEMBLES http://www. ensembles-eu. org and the data was provided through the  ECA&D  Project http://eca. knmi. nl. Radiation data from CERES were obtained from the  NASA Langley Research Center Atmospheric Science Data Center. GLDAS data were acquired as part of the mission of  NASAs Earth Science Division  and archived and distributed by the  Goddard Earth Sciences (GES) Data and Information Services Center (DISC).
2. 2 Observational data E-OBS dataset E-OBS (Haylock et al 2008) is an observation-based gridded product that covers Europe with a daily fre- quency. In the present work precipitation and temper- ature data from the E-OBS v8. 0 in the 0. 5 • grid have been used. The dataset was produced by interpolating station data from the European Climate Asssessment and Data (ECA&D http://eca. knmi. nl). Recently, some studies have found problems and inaccuracies in E-OBS (Herrera et al 2010; Kysely and Plavcova 2010; Hof- stra et al 2009). These affect especially precipitation extremes, in areas with complex orography and scarce stations. Over some of these areas the station coverage improved in latest versions, while it remained poor in a few areas (e. g. North Africa). In general, the mean climatologies derived from E-OBS can be considered of reasonable quality (Herrera et al 2010), and our work will only use these.
Acknowledgments The work of A. D. was conducted within the framework of the  ILP LABEX  ( ANR-10-LABX-63 ) supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02 , and by the project  QHNS in the program -31 -ANR Blanc SIMI5  of the  Agence National de la Recherche. S. M. would like to thank the  SFTC  for support from Consolidated grant number  ST/J002798/1. The research of J. G. has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement no. [247252].
Then they were washed thoroughly with 1ϫ PBS, and fresh serumfree growth medium was added. The CM was collected after 24 h of incubation, filtered using a 0. 2-m membrane filter, and stored at Ϫ20 °C until use. Peripheral blood mononuclear cells were isolated from buffy coat obtained from normal blood donors at Kidwai Memorial Institute of Oncology (Bangalore, India) using the Ficoll gradient method. Later, monocytes were separated from other cells by the plastic adherence method for 2 h and cultured in DMEM under different conditions for 7 days as indicated. The following reagents were used in this study: recombinant MCSF (Biolegend), MCSF, SYK-and IGFBP1-specific siRNA (Dharmacon), MCSFR inhibitor GW2580 (LC Laboratories), Bay 11-7082 (Sigma-Aldrich), LY294002, U0126 and Bay 61-3606 (Calbiochem), anti-AKT and anti-phospho-AKT (Cell Signaling, 4691 and 4060, respectively), anti-IGFBP1 (R&D Systems, MAB675), anti-MCSFR (Abcam, ab89907), anti-MCSF (Novus Biologicals, NB110-57176), anti-CD68 (Biogenex, MU416-UC), anti-CD86 (Epitomics, 1858-1), anti-CD204 (Sigma-Aldrich, HPA000272), MCSF and IGFBP1 ELISA kit (R&D Systems; DY216 and DY871, respectively), and luciferase assay reagent (Promega).
A cycle of one full-scan mass spectrum (350 -1500 m/z) at a resolution of 70,000 followed by 10 data-dependent MS/MS spectra was repeated continuously throughout the nano-LC separation. All MS/MS spectra were recorded using normalized collision energy at a resolution of 17,500 (automatic gain control target, 1 ϫ 10 5 ; 80-ms maximum injection time). Data were acquired using the Xcalibur software (version 2. 2). Raw data analysis was performed using the MaxQuant software (version 1. 5. 0. 0)  (21). Retention time-dependent mass recalibration was applied with the aid of a first search implemented in the Andromeda software  (22). The peak lists were searched against the UniProt human database (release 2014_06, CPS entries), 255 frequently observed contaminants, and reversed sequences of all entries.
01. Supervised hierarchical clustering of significantly differentially expressed cytokines was carried out using the MultiExperiment Viewer. The volcano graph for 48 cytokines was drawn using R software (version 3. 1. 0). The log 2 -fold change expression value of 48 cytokines (x axis) and p value (y axis) were given as input. The prognostic significance was tested by univariate and multivariate Cox proportional hazard analysis using SPSS software (version 19). IHC results were analyzed by using a non-parametric test, the Kruskal-Wallis one-way ANOVA based on ranks, followed by a post hoc test. The results were expressed as mean Ϯ S. D. A p value of Ͻ0. 05 was considered significant for all analyses.
1B ), was selected for further investigation given 1) its well suggested role in the development and progression of tumors, including glioma  (24, 25) ; 2) its influence upon survival, proliferation, and differentiation of macrophages/microglial cells, which are present in abundance in the glioma microenvironment  (26, 27) ; and 3) the expression of MCSF receptor (MCSFR) only in microglial cells and not in glioma cells  (28) , suggesting that glioma cell-secreted MCSF may function through stromal cells and be one key determinant of glioma-stromal cell interactions underlying glioma progression. MCSF transcript levels were up-regulated in GBM, compared with normal brain tissue samples, in our cohort and three more cohorts, namely TCGA, REMBRANDT, and GSE22866 (Fig. 1C ). Whereas MCSF transcript levels were significantly higher in all grades of glioma compared with normal brain samples, serum MCSF was only significantly different in GBM when compared with healthy controls (Fig.
Among proteins exhibiting significant correlation with MCSF, SYK had the highest significance and positive correlation (p Ͻ 0. 0001; r ϭ 0. 43) (Fig. 2F ). Because SYK is shown to activate the PI3K pathway  (30 -34) , we investigated the possibility that SYK could be the activator of PI3K contributing to MCSF up-regulation in glioma. SYK transcript levels were found to be up-regulated in GBM when compared with normal brain samples in TCGA, REMBRANT, and GSE22866 cohorts (Fig. 2G ). Because a reverse phase protein array was not done in normal brain samples, SYK protein up-regulation in GBM samples could not be ascertained. However, we found a significant positive correlation between SYK transcript and protein levels (Fig. 2F ), suggesting that the SYK protein level may also be up-regulated in GBM.
Microarray data for MCSF gene expression from TCGA consisting of normal (n ϭ 10) and GBM (n ϭ 572) samples; a REMBRANDT data set consisting of normal (n ϭ 28), DA (n ϭ 65), AA (n ϭ 58), and GBM (n ϭ 227) samples; and a GSE22866 data set consisting of normal (n ϭ 6) and GBM (n ϭ 40) samples are shown as a scatter plot. A non-parametric t test was performed, and p values are indicated. The horizontal line in each plot represents the mean value. D, representative images (original magnification, ϫ160) of MCSF immunostaining in normal brain tissue (n ϭ 5), DA (n ϭ 10), AA (n ϭ 10), and GBM tumor tissue (n ϭ 41). Normal brain is negatively stained, whereas DA, AA, and GBM show variable staining, with maximum staining in GBM. E, MCSF transcript levels in various glioma cell lines estimated by the qRT-PCR method and normalized to SVG (an immortalized glioma cell line).
D, glioma cells were transfected with wild type MCSF promoter-dependent luciferase construct. MCSF promoter activity was measured by luciferase assay and normalized to ␤-galactosidase activity in presence of different pathway inhibitors, as indicated. E, MCSF secreted protein levels measured by the ELISA method in the presence of different pathway inhibitors, as indicated, in U251 and U87 cell lines. F, Spearman correlation analysis between MCSF transcript and SYK protein levels and between SYK transcript and SYK protein levels. MCSF and SYK transcript levels were analyzed using the data set derived from TCGA. r and p values are indicated in the graph. The straight line inside the graph indicates the mean. G, scatter plot representation of SYK transcript levels using the TCGA data set consisting of normal (n ϭ 10) and GBM (n ϭ 572) samples; the REMBRANDT data set consisting of normal (n ϭ 28), DA (n ϭ 65), AA (n ϭ 58), and GBM (n ϭ 227) samples; and the GSE22866 data set consisting of normal (n ϭ 6) and GBM (n ϭ 40) samples.
Acknowledgments-The results published here are in whole or in part based upon data generated by the  Cancer Genome Atlas (TCGA)  pilot project established by  NCI  and  NHGRI, National Institutes of Health. The use of data sets from  Institute NC  and  GSE22866  is acknowledged. The  Central Animal Facility, Indian Institute of Science , is acknowledged for animal experiments. LC-MS/MS analyses were performed using the facilities of the  Functional Proteomics Platform of Montpellier Languedoc-Roussillon.
Data on species occurrence and climate Georeferenced occurrences describing this species' distribution were gathered from field observations and the available literature. Records were only considered from 1990 onwards, and when locations were described in detail down to shore level (132 compiled records, see Appendix S1 in Supporting Information). These were gridded to 0. 1°resolution and duplicate entries were only considered once (resulting in 128 gridded records). Seasonal environmental predictors were produced to reflect the limiting factors and essential resources acting at each stage of this species' complex life cycle (see  Norton, 1977; Pereira et al. , 2011 Pereira et al. , , 2015a,b),b). Three-dimensional profiles of ocean temperature (OTemp), ocean salinity (OSal), nutrients (Phosphates and Nitrates) and light availability at the bottom of the ocean (Light) were averaged as summer (months = JAS), winter (months = ONDJF) and spring (months = MAMJ) environmental predictors (Table  1 ) and gridded to match the resolution of the distributional data (for technical details on environmental data refer to Appendix S2).
Population genetic structure and diversity Twenty-seven sites were sampled covering the whole distribution of S. polyschides by collecting a piece of the blade above the meristem from c. 25 haphazard individuals (Fig. 1a ; Table  2 ). Samples were preserved in silica drying crystals. DNA extraction, polymerase chain reaction and allele scoring were performed as in  Assis et al. (2013)  for seven microsatellite loci developed for this species  (Engel et al. , 2008). Loci were tested for stuttering and null alleles using the software Micro-checker 2. 2. 3  (Van Oosterhout et al. , 2004). F IS calculations and tests of Hardy-Weinberg equilibrium used Fstat 2. 9. 3  (Goudet, 1995) , randomizing alleles (10 4 runs) among individuals within sites. Genetic structure was inferred using Structure 2.
ACKNOWLEDGEMENTS This study was supported by  FCT -Portuguese Science Foundation (postdoctoral fellowship  to  JA SFRH/BPD/ 111003/2015  and projects EDGES and  EXTANT-EXCL/AAG-GLO/0661/2012 ) and  by  project  ECOKELP ANR-06-BDIV-012. We acknowledge sampling help by  M. Skage ,  C. Maggs ,  A. Peters ,  C. Daguin ,  L. Dupont ,  F. Rigal ,  B. Simon-Bouet , the marine operations department of the  Station biologique de Roscoff ,  L. Dupont ,  S. Mauger , genotyping help by  M. Valente  and  S. Mauger ,  G. A. Pearson  for revising our manuscript, the WCRPWG on coupled modelling and CCSM and MIROC for their outputs. We are grateful for the comments of the Editor and two anonymous referees.