dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA was purified with TRIzol using RNeasy Fibrous Tissue Mini Kit (QIAgen). RNA was assessed for quality by Bioanalyzer (Agilent, Santa Clara, CA, USA) at Mount Desert Island Biological Laboratory (Salisbury Cove, ME, USA). Illumina TruSeq RNA sequencing stranded library construction and transcriptome sequencing were conducted at the Delaware Biotechnology Institute at the University of Delaware using an Illumina HiSeq2000 according to manufacturer's specifications. 2. 6. RNAseq Analysis. FastQC was utilized to assess quality scores of RNAseq reads. Quality scores indicated no trimming to be necessary. De novo partial assembly was done using CLCBio. CLCBio NGS Genomics Workbench (v. 6. 0) (QIAgen) was used to compare transcript abundance between F1 and F3 transcriptomes. Expression value normalization is based on the reads per kilobase per million mapped reads (RPKM) to compensate for read length  [22, 23].
The variance in transcripts between the FG (F3) selected transcriptome and the F1 transcriptome was analyzed using normalized Baggerley's test. A Bonferroni correction for multiple comparisons was applied subsequent to Baggerley's test for a more stringent screen. The cutoff point was Baggerley's/Bonferroni 𝑝 less than or equal to 10 -7. Differentially expressed transcripts were categorized as matching annotated genes, as genes that are uncharacterized, or as having no matches. Uncharacterized genes were contigs that matched published genes in GenBank whose identity and function are not known. Placements were based on open tBLASTx searches of the GenBank database with an 𝐸-value threshold of 𝐸 = 10 -4. After identifying a pathway that was differentially expressed, the expression level of other members of the pathway was examined by creating a local BLAST database of all Mya contigs and searching for orthologues (mollusk sequences when possible, but more often other invertebrate or mammalian sequences were used when no molluscan orthologues were found in GenBank).
Agarose gels and melting curves were run with the products of each gene of interest to ensure that the primers were amplifying a single product. RNA purity and integrity was assessed via Thermo Scientific NanoDrop 2000c spectrophotometer and/or QiAxcel Advanced, using manufacturer specifications. Standard concentrations of primers were kept through all of the experiments, and template concentration was assessed Via NanoDrop and then standardized. qPCR was conducted using SYBR green master mix (Promega). C 𝑇 values, mean, standard deviation, and melting curves were generated by the instrument software CFX Manager (Bio-Rad). The C 𝑇 values between technical replicates were found to be consistent by coefficient of determination (𝑅 2 ) and efficiency. Reference genes, whose expression levels were constant between F3 (FG) and F1 individuals, were used to normalize the data.
Annotation and Gene Ontology. Gene annotation was carried out using the BLAST2GO program  [27] , FASTAformatted sequences representing the unique upregulated transcripts were uploaded to the program, and BLASTX or BLASTn searches were carried out. Some data mining was performed using BLASTX through a CLCBio workflow (QIAgen). Gene Ontology for candidate genes was assessed using AmiGo at Gene Ontology (http://www. geneontology. org/), GoTermFinder  [28] , UniProt (http://www. uniprot. org/), and GO Slim (http://go. princeton. edu/cgi-bin/GOTerm-Mapper)  [29]. The NOD2-pathway figure was drawn using Pathvisio (http://www. pathvisio. org/).
STRING. The network analysis was conducted with the software STRING v. 10  [30]. We chose the top 100 most BLAST hits with the greatest expression difference. We searched for network interactions using the closest annotated genome in STRING, the purple sea urchin, Strongylocentrotus purpuratus (BioProjects # PRJNA13728, PRJNA56067, and PRJNA10736). We filtered the STRING v. 8 human interactome to include only interactions which had a confidence score ≥0. 4 (medium stringency).
05). A volcano plot showed that the most highly differentially expressed genes were downregulated (Figure  S3 ). The genes were sorted by Bonferroni 𝑝 value and then sorted by fold-expression difference between F1 and FG inbred lines. More than 50% of the contigs had no useable BLAST hits (BLAST cutoff 𝐸 = 10 -10 ) due to noncoding transcripts, transcriptome contigs that only represented untranslated regions, and poor sequence database representation of mollusks. Of the 415 unique, significantly differentially expressed transcripts in FG clams, putative annotation could be determined for 162 based on sequence similarity by BLASTX searches while the majority had no significant similarity to protein sequences in the nr database (cutoff 𝐸-value = 10 -10 ). Genes with the largest positive and negative expression differences were often structural genes (Table  1 ).
Gene Ontology and Network Analysis. In order to categorize the function of the genes in our differential expression of RNAseq screen, we took 484 genes with the highest differences in expression between F3 FG and F1 clams. Of those genes with useable BLAST hits, 19% had Gene Ontology related to cell structure, 17% related to signaling or growth, 10% related to nutrient metabolism, 10% related to synthesis of critical macromolecules, and 2% related to energy metabolism (Figure  2 ). Metabolic genes represented 21% of the characterized genes. For example, fatty acid synthase transcripts were 22fold higher in the FG individuals (𝑝 < 10 -274 ). By manual inspection, we also determined that five of the transcripts for the NOD-like receptor signaling pathway were differentially expressed in the screen (Figure  3 ).
A post hoc search of our RNAseq results for other pathway members yielded three that were consistently and significantly upregulated (NF𝜅B, JNK, and ERK), one that had multiple isoforms upregulated but none of which reached significance singly (RIPK2), and eight other related proteins that were not significantly differentially expressed (ERBIN, iKKB, CASP8, TAK1, TRAF6, TRIP6, SGT1, and CARD6). One intermediate member of the pathway was not represented: no BLAST hits corresponded to TRAF. The cluster analysis in STRING draws association data from several databases, including the Kyoto Encyclopedia of Genes and Genomes (KEGG)  [30] , and was used to establish genes sharing a common biological pathway (Figure  4 ). The most complete network of differentially expressed genes was involved in cytoskeletal processes and protein translation.
The high number of unannotated or uncharacterized genes in the screen limits the scope of our interpretations, primarily due to noncoding genes and having some fragmented transcripts where the contigs only contain the UTR portion of the transcripts. The representation of mollusks in the NCBI database is low, particularly some of the bivalves of interest  [49]. There may be overrepresented genes or pathways that are not well annotated. For this reason, network analysis must be interpreted conservatively. To analyze organismal gene expression, we turned to high-throughput transcriptome analysis (RNAseq). Lack of representation in the database of annotated mollusks prevented us from identifying over half of the transcripts. We determined that RPS3A served as a stable gene in the qPCR analysis and chose BIRC2 as a differentially expressed gene to analyze.
Conclusions Our sequence database contributions and annotation will serve to improve the bivalve representation in GenBank (Bio-Project accession # PRJNA221373, SRA accession numbers # SAMN02361211-16). Our results show that suites of genes involved in structural remodeling, signaling, and apoptosis correlate with a fast-growth phenotype. Functional analysis of some of these genes, such as BIRC-2, will inform analysis of growth regulation in these ecologically and economically important species. Since these genes lie at the crossroads of immunity, growth, and cancer, there are a range of biomedical implications. In addition, elucidation of growth in bivalves can have implications for conservation and policy for M. arenaria.
Acknowledgments The authors thank  Dr. Gerard Zegers ,  Benjamin King , and  Dr. Charles Walker  for critical reading of the manuscript. Access to the partially assembled M. arenaria genome was kindly provided by  Dr. Charles Walker ,  U. New Hampshire. The  Downeast Institute for Applied Marine Research and Education  and  Kyle Pepperman  provided F1 and FG clams. Benjamin King , Staff Scientist,  MDI Biological Laboratory , conducted the initial transcriptome assembly. Bruce Kingham , Director,  U. Delaware Sequencing  & Genotyping at the  Delaware Biotechnology Institute , was responsible for library construction and transcriptome sequencing. Research reported in this publication was supported by an  Institutional Development Award (IDeA ) from the  National Institute of General Medical Sciences of the National Institutes of Health  under Grant no.
The differences were analyzed at 2 min post-stimulation for all phosphosites shown except for phospho-ERK (pERK), which was analyzed at 5 min post-stimulation. experiments confirmed that a major species among them corresponded to Syk, whereas the amount of other Tyr(P) proteins binding to the substrate-trapping reagent was very low (Fig. 3B ). Similar results were obtained using murine platelets (data not shown). Together with the data on TULA-2 substrate specificity, this result argues that Syk is the major substrate of TULA-2 in this system and supports the idea that TULA-2 down-regulates the entire GPVI-triggered signaling pathway by dephosphorylating Syk, a key upstream PTK of this pathway.
The enzyme reaction rate was measured within the linear region of the reaction progress curves to determine the initial velocity. Pilot experiments conducted previously confirmed that this time point is situated in the linear region. The initial velocity was plotted against the substrate concentration to determine the kinetic parameters of TULA-2 (k cat , K m , and k cat /K m ) using Prism software. Statistics-All densitometric analyses were performed using Odyssey Image Studio version 2 software. The Prism software package (GraphPad Software, La Jolla, CA) was used for statistical analysis and graphic representation of data.
Acknowledgments-We thank  Dr. R. L. Geahlen  for a generous gift of Syk-encoding DNA constructs. We also thank  Monica Wright  for excellent animal work and  Dr. Ivan Tasovsky  for technical advice. TULA-2 Inhibits Syk by  Dephosphorylating Tyr (P)  346 OCTOBER 21 ,  2016 • VOLUME 291 • NUMBER 43 JOURNAL OF BIOLOGICAL CHEMISTRY 22439.
PATIENTS AND METHOD Study Design and Patients The patients were selected through an e-mail sent to physicians belonging to the French Internal Medicine Society (SNFMI) and the French Vasculitis Study Group (FVSG). A search was also carried out in the FVSG database to obtain additional patients. Patients had to satisfy the following 2 criteria to take part in the study. First, diagnosis of AAV between 1990 and 2015, and positive ANCA result. We enrolled patients with GPA, MPA, and EGPA, according to the 1990 American College of Rheumatology (ACR) modified criteria. 6 Second, diagnosis of histologically proven ND was also a prerequisite. We did not include patients with other types of vasculitis or patients with dubious AAV. Diagnosis of ND was established on the basis of medical expertise and histological evidence.
Context. Stellar activity is an important source of systematic errors and uncertainties in the characterization of exoplanets. Most of the techniques used to correct for this activity focus on an ad hoc data reduction. Aims. We have developed a software for the combined fit of transits and stellar activity features in high-precision long-duration photometry. Our aim is to take advantage of the modelling to derive correct stellar and planetary parameters, even in the case of strong stellar activity. Methods. We use an analytic approach to model the light curve. The code KSint, modified by adding the evolution of active regions, is implemented into our Bayesian modelling package PASTIS. The code is then applied to the light curve of CoRoT-2. The light curve is divided in segments to reduce the number of free parameters needed by the fit.
We update an existing code for activity features modelling, and implement it into our Bayesian modelling package PASTIS. Then, we use it to model the light curve of CoRoT-2. The clearly visible activity pattern of the star of this system, both outside and inside the transits, has made it a widely studied case in the literature. Our goal is to take advantage of the modelling in order to more consistently determine the transit parameters. The paper is structured as follows: in Section 2 we present our method; in Section 3 we describe the test case and the application of our method; in Section 4 and 5 the results are presented and discussed; and we conclude in Section 6.
2014). Their rapidity of execution allows them to be implemented in MCMC algorithms. MCMC methods have already been proven effective in order to find best-fit values, uncertainties, correlations, and degeneracies for the photometric spot modelling problem  (Croll 2006). Hence, we decided to exploit the additional transit modelling capabilities of an analytic starspot modelling code and the efficiency of MCMC simulations. We implemented the code KSint into the MCMC algorithm used by PASTIS  (Díaz et al. 2014; Santerne et al. 2015). Hereafter, the combination of the codes will be referred to as KSint + PASTIS. KSint models a light curve containing both planetary transits and activity features. The transits, modelled with the formalism of  Pál (2012) , are characterized by the planet-to-star radius ratio k r , orbital period P orb , orbital inclination i p 1  , eccentricity e, planet argument of pericentre ω, and mean anomaly M.
The flux was then divided by the resulting polynomial. Data set CZ. This data set was prepared for comparison with a standard transit fit, where non-occulted features are taken into account in the data reduction phase, as done by  Czesla et al. (2009). The normalization was performed following the prescription of these authors. For every bin i, we calculated the normalized flux z i as z i = f i -n i p + 1, (1) where f i is the observed flux in bin i, n i is the value of the secondorder polynomial fitted to the out-of-transit local continuum in the same bin, and p is the largest flux value in the light curve. This last parameter is assumed to be the least affected by dark spots. Data set SP. This data set was used for the fit of activity features (the label stands for "spots") while fixing the transit parameters.
To reduce the computation time, we followed Huber et al. (2010a) and sampled the out-of-transit flux every 2016 s (33. 6 min). This corresponds to about one-third of the orbital period of the CoRoT satellite  (Auvergne et al. 2009). Given the stellar rotation period, this corresponds to a resolution of ∼ 1. 9 • /bin, which is sufficient not to significantly affect the out-of-transit flux measurements. The transits were sampled every 160 s, i. e. with a resolution of ∼ 0. 1 • /bin. The transits are ∼ 0. 09 days long, which means about 49 points were left for each transit. This sampling keeps enough information to force some features to be modelled inside the transits. The uncertainty of the binned data was calculated as the standard deviation of the points in each bin, divided by the square root of the number of points in the binning window.
The average relative uncertainty on this data set is 3. 9 • 10 -4. After resampling, the flux was divided by its maximum value and no transit normalization was performed. Data set SPT. This data set was prepared for active regions and transit fitting. It was prepared as data set SP, but full resolution was kept inside the transits. The average relative standard deviation on this data set is of 1. 12 • 10 - 3. Data set MN. The out-of-transit model fitting data set SP was used to normalize the transits. The preparation of this data set is described in more detail in Section 3. 3.
Reference fits Fit S: activity neglected. Fit S was performed on data set S as a standard transit fit. We used a modified version of the EBOP code 3  (Nelson & Davis 1972; Etzel 1981; Popper & Etzel 1981)  included in the MCMC functionality of PASTIS. The priors for the free parameters are listed in table 2. Because of the biases in our current knowledge of the radius distribution of planets, the incompleteness of the transit surveys, and other systematic effects, we followed  Díaz et al. (2014)  and used a Jeffreys prior for the planet-to-star radius ratio k r. We used a Jeffreys prior for the orbital separation-to-stellar radius ratio a/R , which is used by EBOP in place of the stellar density. A normal prior was also used for the orbital period P orb. To impose an isotropic distribution on orbit orientations, we set a sine prior for the orbit inclination i p.
056)/(1 -0. 081) = 2. 7%. The same MCMC simulation was also performed with KSint instead of EBOP in order to verify its correct behaviour for a standard transit-only fit. Ten chains were run for every MCMC set. The chains were then thinned according to their correlation length and merged into a single chain, giving the credible intervals for the parameters of that segment. To be considered robust, a merged chain was required to consist of at least a thousand uncorrelated points. Fit CZ: activity taken into account during normalization. For this fit we used the same method as for fit S, but on data set CZ. (a) Referred to the initial time of a segment. ( b) Starting from the best solution of fit SP.
The light curve modelling was carried out on the data set SP. The transit parameters (P orb , ρ , i p , k r , e, ω, u a , u b ) were fixed to the values of  Alonso et al. (2008). A uniform prior was imposed for the mean anomaly M. The priors indicated in table 2 were used. The quasi-perpendicularity between the sky-projected stellar spin axis and the planetary orbit found by  Bouchy et al. (2008)  allowed us to model the system as if these two were perpendicular. Alonso et al. (2008)  found the planet inclination to be of 87. 84 • with respect to the plane of the sky. We therefore fixed the stellar inclination to the same value with respect to the line of sight. A uniform prior centred on the value found by  Lanza et al. (2009)  was set for the stellar period P. Uniform priors were used for all the parameters of the activity features.
For each feature, a uniform prior was set for t max. Values of t max external to the time limits of the light curve were allowed in order to induce the fit to exclude possible features in excess. The MCMC could do this by adjusting t max , I, and E, to which uniform priors were assigned, too. The contamination factor was fixed to the value found by  Gardes et al. (2012) (8. 81%). This parameter was fixed because active regions can be considered a contaminant factor  (Csizmadia et al. 2013). A uniform prior was set for the flux relative offset and the jitter. The optimal number of active regions was determined by trials. We increased it until we obtained residuals with a normally distributed dispersion centred on zero and with a width comparable to the photometric data dispersion. Our model needs several tens of active regions to model the entire light curve, in agreement with the results of  Silva-Valio et al.
We then extracted the maximum-likelihood solution among the chains satisfying this criterion. The probability of finding the global maximum of the likelihood function increases with the number of chains run. However, we note that our method does not allow us to identify the global solution of the starspot inversion problem. With this approach we obtained a fit of the activity features parameters suitable for the fit of the transit parameters. Also, the maximum-likelihood solution allowed us to explore the active regions configuration on the stellar surface. This is discussed in greater detail in the following sections. Spot-transit fit (fit SPT ). We carried out a simultaneous fit of the transit parameters and of the activity features both in-transit and out-of-transit. This fit was performed on data set SPT , divided into segments as for fit SP.
Model-based normalization (fit MN). The fit consists of a standard transit fit where transit normalization is performed via the modelling of the out-of-transit features. The maximumlikelihood solutions obtained from fit SP were re-computed without the planet. They were merged into a single light curve and used to normalize the transits, obtaining data set MN (Section 3. 2). Equation 1 was used, with n i indicating the model and p the flux offset value. For each segment, this parameter was fixed to the maximum-likelihood value obtained with fit SP. Its uncertainty, obtained with fit SPT , was quadratically added to the standard deviation of the flux. In this way, the uncertainty due to the normalization technique was evaluated. Once the transits were normalized, a standard transit fit was performed with EBOP.
Such features would therefore increase the apparent transit depth. We adopted a similar approach to  Czesla et al. (2009) , but worked on the deepest transit only in order not to average any activity feature. Figure  5  shows the series of transits with the best solution of fit SPT overplotted. The sixth transit in the first segment, in yellow in Figure  5 , is the deepest. This transit was isolated from data set SPT. According to fit SPT , this transit is affected by a bright spot, whose position during the transit is shown on the left side of Figure  6. By fitting a single transit, KSint + PASTIS does not disentangle the bright spot from the transit profile. For this reason, we fixed the configuration of the active regions obtained with fit SPT on the first segment of the light curve. Instead, the transit parameters k r , i p , ρ , and the jitter were set as free parameters.
We repeated the transit fit and obtained k r = 0. 1689 ± 0. 0008, i = 87. 63 ± 0. 53 • , and ρ = 1. 336 ± 0. 062 ρ , in agreement with the results obtained by fit MN. Figure 5 also shows that for many transits, the model is not able to correctly reproduce the complex structure of the transit profile. This does not invalidate our previous results, as we used transits which were correctly modelled. However, the plots indicate the presence of short-lived or irregularly shaped features inside transits which are not reproduced by the model. The low level of detail introduced by the re-sampling on data set SP and the consequent approximated SP modelling hid the need of additional features for transit modelling. Silva-Valio & Lanza (2011)  needed seven to nine features to model each transit. In our approach, instead, this number of features is used to globally model tens of days of data.
In order to get closer to the correct number of features needed for both the in-transit and the out-of-transit data, one possibility is to perform fit SP on data set SPT from the beginning, i. e. to use full resolution on the data. However, because of the large number of needed features and the large computational weight, we are not yet able to do this. We plan an optimization of the code in order to reduce its execution time and to be able to address the problem in a more efficient way.
Summary and conclusions We presented a method for the fit of transit photometry which takes the impact of activity features on the transit parameters into account. This approach is based on an improved version of the analytic code KSint, which models both non-occulted and occulted features and their evolution. The method was applied to the light curve of CoRoT-2 in two ways. The first is based on the simultaneous modelling of activity features and transits in a non-normalized light curve. The second consists in normalizing the transits by using the modelling of the out-of-transit light curve, and then in a standard fit of the so-normalized transits. In particular, we allowed for the presence of bright spots in our model, which was not usually done in previous studies. The results of our method were compared with other approaches presented in literature.