dataesr/scientific-paragraphs-categorization huggingface dataset:
The RNA concentration was determined with the NanoDrop 1000 spectrophotometer (Thermo Scientific) and quality was tested using the Agilent 2100 Bioanalyzer (Agilent Technologies). Only samples with RIN >8 were used. One µg of RNA was reverse transcribed using Superscript II reverse transcriptase (Invitrogen) in a reaction of 25 µL. Samples at a 1:100 dilution were amplified in an Applied Biosystems 7500 Fast system using the Power Sybr Green PCR master mix (Life technologies). Data analysis was performed with the comparative Cq method (Pfaffl 2001) using the average value of PCR efficiencies obtained with LinRegPCR software and normalized to Gapdh. DNA microarray. DNA arrays were carried out by Bioarray ® (Alicante, Spain) using SurePrint G3 Rat Gene Expression 8 × 60 K (Agilent, Santa Clara, California, USA).
Bioinformatics. Raw data extraction and statistical analyses were performed using Bioconductor packages in the R programming environment. Background correction was performed using the 'normexp' method (offset = 10) implemented in the LIMMA package to adjust local median (M) background estimates. Background-corrected intensity data were normalized using the Loess method to remove the bias within each array, and A-value quantile normalization (Aquantile) was used to remove the bias between arrays. The principal component analysis was carried out to ensure the correct separation of the samples. Normalized data were adjusted to a linear model in LIMMA to determine the target genes differentially expressed between groups. P-values were computed with empirical Bayes moderated t-statistics at the level of 0.
05 to calculate differential gene expression in the paired comparisons FSK-CT, NE-CT, and VP16-Null. The lists can be found in the Gene Expression Omnibus database (GSE80967). Differentially expressed genes were ranked according to Gene Symbol and t-statistic in absolute value. Mouse orthologous were extracted using the Gene Analyzer Tool from the RGD (Rat Genome Database) website (http://rgd. mcw. edu) to allow for comparisons with the Q1-TRAP and neu-CREB lists, which are from mice. TRAP lists were retrieved from GSE13379 files. Gene expression was normalized using LIMMA. In the case of the neuronal TRAP lists (below), we averaged the profiles of the several cortical subtypes (Cck+, Pnoc+, cholinergic and neurons from layers 5a, 5b and 6) to minimize their functional and molecular diversity.
Differentially expressed gene lists filtered by the first quartile of the TRAP list (5220 genes) were divided into UP and DOWN depending on the logFC and associated to GO terms (Biological Process and Cellular Compartment) with the software ClueGO v1. 4  41. The analysis was independently conducted in the UP and DOWN lists to gain insight into the predominant direction of change, although it is worth stressing that functional changes normally encompass both up-regulation and down-regulation of genes due to complex homeostatic adjustment of positive and negative feedback loops. The parameters used were enrichment/depletion: two-sided hypergeometric statistical test; correction method: Bonferroni; GO term range levels: 5-10; minimal number of genes for term selection: 3; minimal percentage of genes for term selection: 2%; κ score threshold: 0.
5; general term selection method: smallest p-value; group method: κ; minimal number of subgroups included in a group: 2; minimal percentage of shared genes between subgroups: 50%. The resulting GO terms were analysed for semantic similarity (cut-off value of 0. 4) with ReviGO software  42  to reduce redundancy, and then manually grouped into categories based on GO ancestors and gene overlapping between terms. To identify CREB target genes we used PSCAN  18  to predict CRE-binding sites of the TRANSFAC database in the promoter region (-450/+50) of selected genes, and the database of Salk Institute  19. Additional software included 'gplots' (http://CRAN. R-project. org/package=gplots) and 'RColorBrewer' packages (http:// CRAN. R-project. org/package=RColorBrewer) to compute heat maps and hierarchical clustering.
GSEA (www. broadinstitute. org/gsea/index. jsp)  20. Lists of DEG filtered by Q1-TRAP were pre-ranked by fold-change and analyzed for enrichment in gene sets from up-regulated or down-regulated genes in the pairwise comparisons TC-WTC (VP16-CREB lesion vs WT lesion) and WTC-WT (lesion vs control) (GSE68187)  9. Only enriched sets with a normalized p-value (NOM p-val) < 0. 05 were considered significant. Of note, the same platform (Agilent) and procedure was used to identify DEG in the in vitro and in vivo microarrays.
Acknowledgements This work was supported by  Ministerio de Economía y Competitividad, Spain  (grant numbers  BFU2012-38844  and  BFU2016-79735-P  to E. G. ;  SAF2011-22506  grant to L. M. V. ;  SAF2014-56197-R  to A. B. an L. M. V. ;  PCIN-2015-192-C02-01  and  SEV-2013-0317  to A. B. );  Autonomous Government of Catalonia AGAUR  (grant number  2014SGR984  to E. G. and R. M. );  Generalitat Valenciana  (grant number  Prometeo/2012/005  to A. B. ); and the  Brain & Behavior Research Foundation  ( NARSAD Independent Investigator Grant ) and the  Alicia Koplovitz Foundation  to A. B. ). L. M. V. was additionally supported by a  Ramón y Cajal contract (Ministerio de Economía y Competitividad ), followed by a  Miguel Servet contract (Instituto de Salud Carlos III, Ministerio de Economía y Competitividad , and  Fondo Social Europeo   2014-2020 ).
Discovery announcement It only took a few minutes for the online data analysis software to identify GW150914 as an interesting event and less than a day for the LIGO and Virgo collaborations to realize that this was a strong BBH candidate recorded at a time when no known problem was affecting the aLIGO detectors. Yet, the discovery was only announced five months later, on Thursday February 11 2016. In the meantime, extensive data analysis was carried out to make the detection case rocksolid. Parallel to this, communication offices and LIGO-Virgo outreach groups were preparing the announcement  [4]  together with all the players involved: observatories, scientific collaborations, funding agencies from the various partner countries (USA, UK, Germany, France, Italy to name a few), laboratories and universities.
It is just a coincidence that the size of black holes in Newtonian gravity and General Relativity are the same; at the point of forming a black hole, Newtonian gravitational theory is no longer valid, and General Relativity must be used. created to go along with them. New projects are in preparation as well. Only a sample of these efforts can be presented in these proceedings; the reader interested in knowing more is welcome to visit our websites and/or to contact the various E&O groups  [12]. In addition, a forum has been setup to answer questions from the general audience: question@ligo. org. Among the variety of E&O resources, the LIGO Open Science Center (LOSC) [13] is playing a key role. Similar to, for instance, the CERN open data portal [14], the LOSC is providing free access to some of the LIGO datasets.
Outlook A wide range of E&O activities related to GW and GW detection by giant interferometric detectors have been presented in these proceedings. While many of these projects started a while ago, the first two detections did change everything. There are real data and excitement to share with the general public, teachers and students! As these proceedings are being written the second LIGO Observation Run ("O2") has started  [28]. The two aLIGO detectors may be joined by the Advanced Virgo detector (AdV) next spring. More generally, the worldwide network of interferometric detectors will grow in the coming years while the sensitivity of the individual instruments will improve. More events should be detected (not only BBHs, but possibly other sources, for instance involving a neutron star) with the main challenge being to locate a GW source in the sky, possibly by doing a multi-messenger analysis (GW + electromagnetic radiation + possibly neutrinos).
Dynamical dark energy has been recently suggested as a promising and physical way to solve the 3 sigma tension on the value of the Hubble constant H0 between the direct measurement of Riess et al. (  2016 ) (R16, hereafter) and the indirect constraint from Cosmic Microwave Anisotropies obtained by the Planck satellite under the assumption of a ΛCDM model. In this paper, by parameterizing dark energy evolution using the w0-wa approach, and considering a 12 parameter extended scenario, we find that: a) the tension on the Hubble constant can indeed be solved with dynamical dark energy, b) a cosmological constant is ruled out at more than 95% c. l. by the Planck+R16 dataset, and c) all of the standard quintessence and half of the "downward going" dark energy model space (characterized by an equation of state that decreases with time) is also excluded at more than 95% c.
l. These results are further confirmed when cosmic shear, CMB lensing, or SN Ia luminosity distance data are also included. However, tension remains with the BAO dataset. A cosmological constant and small portion of the freezing quintessence models are still in agreement with the Planck+R16+BAO dataset at between 68% and 95% c. l. Conversely, for Planck plus a phenomenological H0 prior, both thawing and freezing quintessence models prefer a Hubble constant of less than 70 km/s/Mpc. The general conclusions hold also when considering models with non-zero spatial curvature.
g. [5]  for a definition). Indeed, from the most recent analysis of Planck data  [2] , one obtains the constraint A lens = 1. 15 +0. 13  -0. 12 at 95% c. l. , i. e. higher than the value A lens = 1 expected in ΛCDM at more than two standard deviations. While the A lens and τ anomalies are internal inconsistencies present in current Planck data, the parameters derived from the Planck data, assuming ΛCDM, are also in tension with other external, i. e. non-CMB, datasets. The current most statistically relevant tension is probably between the value of the Hubble constant derived from Planck and the one directly obtained from local luminosity distance measurements. Indeed, the recent value reported by Riess et al. (R16, hereafter) of H 0 = 73. 24 ± 1. 74 km/s/Mpc at 68% c. l. (R16 hereafter,  [6] ) is in tension at more than 3 standard deviations with the Planck result of H 0 = 66.
Indeed, we will find that there is no preference for extra neutrino species ("dark radiation") when allowing for dynamical dark energy. Recently, in  [11] , it has been shown that if one considers a 12 parameter extended scenario, the tension on the Hubble parameter can be solved by a dark energy equation of state w < -1, while the neutrino effective number is fully compatible with the standard expected value of N eff = 3. 046. The tension between the Planck result and the R16 value can therefore be better explained by introducing a dynamical form of dark energy, with a cosmological constant excluded at more than 95% c. l. A similar conclusion has been recently obtained in  [24]  in a complementary approach. Since a dark energy component with a constant-withredshift equation of state suffers from the usual "why now?" problem of the cosmological constant and is generally not expected in physically-motivated scenarios, inferring w = -1 from the data immediately triggers interest in a possible evolution of the dark energy equationof-state with redshift.
Finally, we also consider variation in the gravitational lensing amplitude of the CMB angular spectra A lens. This scales the CMB lensing strength on all scales, relative to the prediction of the model being considered. We also consider two more scenarios in addition to our baseline model. In one case, we fix A lens = 1 since, at the moment, the origin of this anomaly is still unclear. In practice, A lens is an effective parameter that could just be compensating for a statistical fluke in the data. It is therefore important to investigate if the inclusion of A lens has an impact on the constraints on the dark energy equation of state. As a second additional scenario, we also fix A lens = 1 but we now vary the curvature density Ω k since, again, the Planck angular spectrum data within the ΛCDM model suggest a closed universe at about two standard deviations.
We analyze these cosmological parameters by using, firstly, the temperature and polarization CMB angular power spectra released by Planck 2015  [4]. This dataset includes the large angular-scale temperature and polarization anisotropy measured by the Planck LFI experiment and the small-scale anisotropies measured by Planck HFI, and we refer to it as "Planck". We then consider the following additional data sets: • R16: As "R16", we consider an external gaussian prior on the Hubble constant H 0 = 73. 24 ± 1. 74 km/s/Mpc at 68% c. l. , as measured by  [6]. • BAO: We make use of the baryon acoustic oscillation data from 6dFGS  [37] , SDSS-MGS  [38] , BOSS-LOWZ  [39]  and CMASS-DR11  [39]  surveys as in  [1]. We refer to this dataset as "BAO". • JLA: We include luminosity distances of supernovae Type Ia from the "Joint Light-curve Analysis" derived from the SNLS and SDSS catalogs  [40].
We refer to this dataset as "JLA". • WL: We consider the weak lensing galaxy data from the CFHTlens  [8]  survey with the priors and conservative cuts to the data as described in  [1]. Parameter Prior Ω b h 2 [0. 005, 0. 1] Ωch 2 [0. 001, 0. 99] τ [0. 01, 0. 8] ns [0. 8, 1. 2] log[10 10 As] [2, 4] Θs [0. 5, 10] mν (eV) [0, 5] w0 [-3,0. 3] wa [-2,2] N eff [0. 05,10] dns d ln k [-1,1] A lens [0,10] Ω k [-0. 3,0. 3] Table  I. External flat priors on the cosmological parameters assumed in this paper. Note that Θs is used for the likelihood evaluation but H0 is quoted for parameter constraints. • Lensing: We indicate with "lensing" the information we can derive from CMB lensing from the Planck trispectrum detection  [41]. We also study the effect on the preferred type of dark energy behavior from variations in the Hubble constant prior, from values of 66 to 74 km/s/Mpc.
This can be viewed as a phenomenological study, or values from different data sets or analyses (e. g. [42] ). In order to derive constraints on the parameters, we use the July 2015 version of the publicly available Monte Carlo Markov Chain package cosmomc  [43] , that has a convergence diagnostic based on the Gelman and Rubin statistic and includes the support for the Planck data release 2015 Likelihood Code  [4]  (see http: //cosmologist. info/cosmomc/), implementing an efficient sampling by using the fast/slow parameter decorrelations  [44]. While we focus our attention on cosmological parameters, we also vary the foreground parameters as described in  [4]  and  [1].
A. 12 Parameter analysis The main results of our analysis are reported in Table  II  where we report the constraints at 68% c. l. on the 12 parameters of our theoretical framework, using different combinations of datasets. Since the goal of this paper is to constrain dynamical dark energy in this extended parameter space, we show the confidence levels contour plots in the w 0 -w a plane in Figure  1  for the Planck+R16 case as well as in combination with several other datasets. The R16 Hubble constant prior has the main effect of significantly ruling out at more than 95% c. l. the region w 0 ≥ -1 and w a ≥ 0. In other words, the R16 -3. 0 -2. 4 -1. 8 -1. 2 -0. 6 w -2 -1 0 1 2 w a Planck + R16 Planck + JLA + R16 -3. 0 -2. 4 -1. 8 -1. 2 -0. 6 w 0 -2 -1 0 1 2 w a Planck + R16 Planck + BAO + R16 -3.
0 -2. 4 -1. 8 -1. 2 -0. 6 w 0 -2 -1 0 1 2 w a Planck + R16 Planck + WL + R16 -3. 0 -2. 4 -1. 8 -1. 2 -0. 6 w 0 -2 -1 0 1 2 w a Planck + R16 Planck + lensing + R16 Figure  1. 68. 3% and 95. 4% confidence level constraints on the w0-wa plane in a 12 parameter extended space for the Planck+R16 data, and combined with several other datasets. Only in the case of Planck+R16+BAO (top right panel), is a cosmological constant within the 95. 4% c. l. In all other cases a cosmological constant and the region (w0 > -1, wa > 0) is excluded at more than 95. 4% c. l. prior not only rules out a cosmological constant as already discussed in  [11] , but all the freezing quintessence models and the half of the downward going dark energy models with w a > 0 and w 0 > -1. As one can clearly see from Figure  1 , once the R16 prior is included, Planck data combined with the JLA, lensing, or WL dataset all disfavor the (w a ≥ 0, w 0 ≥ -1) region (though the last two do not significantly add to the constraints currently).
BAO data are however still in tension with the Planck+R16 dataset. The tension between BAO and Planck+R16 (already noticed in  [11]  and  [20] ) is present also in our 12 parameters analysis and can be clearly seen in the two different constraints for the Hubble constant: H 0 = 73. 9 ± 2. 0 km/s/Mpc for Planck+R16 and H 0 = 65. 6 +3. 1 -2. 1 km/s/Mpc at 68% c. l. for Planck+BAO, therefore inconsistent at about 2. 9 standard deviations. When the BAO dataset is included, a small region with w a ≥ 0, w 0 ≥ -1 (including the cosmological constant) is back in agreement with data in between 68% and 95% c. l. Moreover, it is interesting to examine the case of the other constraining external data set: the supernovae. The combined Planck+R16+JLA dataset is shown in the top left panel of Figure  1. From this dataset almost the entire w a > 0 region seems to be disfavored, i.
e. the whole class of downward going models is excluded at more than 95% c. l. from this combination of data. We also note that of the allowed region for upward going models, almost none of it corresponds to standard thawing quintessence as we discuss later, i. e. both of the standard quintessence classes are disfavored. Looking at the other parameter constraints in Table  II , we also notice that the A lens parameter is always larger than the expected value of A lens = 1 for any combination of data, with the exception of the case when the CMB lensing dataset is included. However, the conclusions on dark energy seem unaffected by this. Considering the contour plots in Figure  1 , bottom right panel, we indeed see that the exclusion of the region (w a ≥ 0, w 0 ≥ -1) is stable also for the Planck+R16+lensing dataset.
This is somewhat reassuring. However, the Planck+R16+lensing dataset also suggests the presence of a neutrino mass at Σm ν ≈ 0. 46 eV at slightly below two standard deviations. From Planck+R16 we found S 8 = 0. 754 ± 0. 041 that is in complete agreement with the value S 8 = 0. 745 ± 0. 039 from the KIDS-450 lensing survey  [17]. To highlight the importance of the Hubble constant prior, we show its effect in Figure  2. Here we plot the Planck+R16 contour as in Figure  1 , but also indicate how the best fit w 0 -w a values shift if we replace the R16 prior on H 0 with different central values (and the same absolute uncertainty). In addition, we overlay two model lines: one is the "mirage" line, where a time-varying dark energy gives the same distance to the CMB last scattering surface as for a ΛCDM model, and the other line is centered on the typical behavior of thawing quintessence.
B. 11 Parameter analysis (fixing A lens = 1) Since A lens > 1 for most of the data set combination, it is useful to further test the impact of a variation in the effective parameter A lens on our results. We have performed an analysis in a restricted parameter space, fixing A lens = 1, and report the results in Table  III  and in Figure  3. The main conclusions about w(a) obtained when varying A lens remain robust for the case of A lens fixed to the standard value of 1. As seen in Figure  3 , there is no significant shift in the best fit with respect to the 12parameter case reported in Figure  1 , while some of the allowed regions shrink moderately around it. For example, the region (w 0 < -1, w a > 0) is now even more excluded by the Planck+R16+JLA data set. We however found that the BAO dataset is in this case even more in tension with Planck+R16 and that convergence in the MCMC chains is difficult to reach for the Planck+R16+BAO combined data.
This is also clearly shown in Table  III  where the constraints for the Hubble constant are now in tension by more than 3 standard deviations. Further tension is present in the not complete overlap of the constraints in the w 0 -w a plane for Planck+BAO and Planck+R16 (see Figure  3 , top right panel). In this case we therefore decide not to include the Planck+BAO+R16 constraint. It is however interesting to note that also the Planck+BAO dataset does not prefer the (w 0 > -1, w a > 0) region. We conclude that the effect of including A lens as a twelfth fit parameter in the analysis is just to make the constraints in the (w 0 , w a ) plane slightly narrower, with no significant shift in the best fit values. As seen in Table  III , fixing A lens = 1 results also in stronger bounds on neutrino masses, higher values for the r.
IV. CONCLUSIONS We have investigated the constraints on dynamical dark energy in an extended parameter space, considering the simultaneous variation of 12 parameters. This is particularly of interest because in this extended parameter space the Planck and R16 datasets are consistent. Moreover, they point to a value for the dark energy equation-of-state w < -1. Note that in this extended. 68. 3% and 95. 4% constraints on the w0-wa plane in an 11 parameter extended space, fixing A lens = 1. Only in the case of Planck+BAO (top right panel) is a cosmological constant still within 95. 4% c. l. The allowed parameter space in the (w0 > -1, wa > 0) region is also reduced relative to the 12-parameter case. For Planck+R16+JLA, the entire region with wa > 0 is now even more strongly excluded. parameter space, there is no preference for extra dark radiation, i.
e. N eff greater than the standard concordance value. Studying the dark energy equation-of-state phase space, we have indeed found that the Planck+R16 dataset not only rules out a cosmological constant at 95% c. l. but also all standard quintessence models, both freezing and the conventional thawing classes. This result is robust to different combinations of data, including the WL, JLA, or lensing datasets. Moreover, when the JLA dataset is included, also the remaining region of "downwards-going" models (w a > 0) is disfavored at about 95% c. l. A tension remains however with the BAO data set. The Planck+R16+BAO dataset still allows a cosmological constant and a small portion of the freezing (w 0 > -1, w a > 0) region. We have also tested the stability of these results through two further variations.
Restricting to a smaller 11-parameter space by fixing the lensing amplitude A lens = 1, the results hold with just a reduction of the available model volume. In this case also, the freezing (w 0 > -1, w a > 0) region starts to be incompatible with The R16 prior is highly incompatible with the cosmological models preferred by the Planck+BAO and Planck+JLA datasets in this case. In the left panel, we see that from these data sets that the (w0 > -1, wa > 0) region is barely excluded. In the right panel we show constraints from the Planck+R16+lensing and Planck+R16+WL data sets. These constraints are significantly weaker with respect to the previous cases, but the (w0 > -1, wa > 0) region is still excluded at nearly 95% c. l. the Planck+R16+BAO data set. Secondly, we then allowed spatial curvature as a free parameter.
When Ω k is varied, the Planck+BAO and Planck+JLA datasets provide values for the Hubble constant that are no longer compatible with the R16 prior. We found that also in this case when considering the constraints on w(a), the freezing region is not favored by the data, though the thawing region is only mildly disfavored. The same conclusion in the 12-parameter space that includes curvature is obtained from the Planck+R16+lensing and Planck+R16+WL datasets. In summary, taking all data sets at face value, we find that both the freezing class of quintessence and the region of parameter space typical of the thawing class of quintessence are generally disfavored. One needs either w 0 > -1 but highly negative w a (as preferred, say, by Planck+JLA+R16) or w 0 < -1. Even the first option will also have w < -1 at some redshift, so phantom models of dark energy seem preferred.