dataesr/scientific-paragraphs-categorization huggingface dataset:
Data access Primary Data: cDNA and gDNA raw data for YRI LCLs can be accessed through the GEO accession number GSE52442. RNA-seq data for primary monocytes and YRI LCLs can be accessed through the GEO accession number GSE53837. ChIP-seq data for primary monocytes can be accessed through the GEO accession number GSE53837. cDNA and gDNA raw data after NFjB perturbation (treatment and controls) for the two LCLs trio can be accessed through the GEO accession number GSE61254. Referenced Data: cDNA and gDNA raw data for monocytes and fibroblasts can be accessed through accession number EGAS00000000119 at EGA and the GEO accession number GSE52442, respectively. RNA-seq data for fibroblasts can be accessed through the GEO accession number GSE53837. ChIP-seq for fibroblasts and LCLs can be accessed through the GEO accession number GSE53837.
Acknowledgements The authors are grateful to all of the study participants, including the volunteers from the  Cambridge BioResource  who donated blood for this study (http://www. cambridgebioresource. org. uk/). SNP genotyping for AE determination in the monocyte samples was performed by the  SNP&SEQ Technology Platform in Uppsala, Sweden  (www. genotyping. se),and  National Genomics Infrastructure, Science for Life Laboratory, Sweden. We thank  Anders Lundmark  for his assistance. This study was supported by the  Cardiogenics EU   FP6  project (Grant Number  LSHM-CT-2006-037593 ), the  European Sequencing and Genotyping Infrastructure (ESGI)  ( EU FP7 2007-2013  under Grant Number  262055 ), the  Swedish Research Council  (Grants No  A028001  and  C0524801 ), the  Knut and Alice Wallenberg Foundation  ( KAW 2011.
After delivery, cord blood sample was collected. All blood samples were tested after collection of all study data was complete. All sera were frozen at 280uC until use. Serum FT3, FT4, and TSH were assayed on an ACS-180 SE automate using chemiluminescence immunoassay. Variability was 3. 3%, for FT3, 6. 6%, for FT4, and 8. 4% for TSH. The limits of detection were 0. 3 pm/L, 1. 3 pml/L, and 0. 02 IU/L, respectively. Serum levels of anti-TPO in maternal and cord blood were detected by direct chemiluminescence immunoassay on an ACS-180SE automate. The limit of detection was 15 UI/L. Results are expressed as numbers and/or percentages for categorical variables and means 6 standard deviation (SD), or median [min; max], for continuous variables, unless stated otherwise. We compared hormonal levels between mother blood samples at the third trimester of gestation and fetal cord blood samples of their fetus.
Each pregnancy was considered as an independent event. All quantitative variables were compared with non parametric Wilcoxon's test. Pearson's correlation coefficient was computed to assess the link between maternal and fetal thyroid parameters (FT3, FT4, TSH, and anti-TPO). All tests were twotailed, p-values less than 0. 05 were considered significant. Statistical analyses were performed using Statview Software V5 (SAS Institute Inc, Chicago, IL, USA; Copyright 1992-1998 Version 5. 0. ).
NRAS mutation confers resistance to ROS1 inhibitors To further validate our observations on the role of RAS in mediating resistance to ROS1 TKIs, we generated HCC78 cells resistant to crizotinib, a ROS1 inhibitor that has demonstrated clinical activity in ROS1+ NSCLC and is currently undergoing clinical trials in ROS1+ patients (NCT00585195). We obtained cells resistant to 0. 72 µM and 1. 4 µM crizotinib (Fig. 3A ). As was the case with JNJ-ROS1i-A (Suppl. Fig. 4A ), crizotinib resistant cells displayed a reduction in ROS1 phosphorylation, decreased ROS1 expression (both at the protein and mRNA level) and sustained AKT and MAPK activation (Suppl. Fig. 4A, 4B ). Sequencing did not reveal mutations in KRAS, BRAF or PIK3CA in these cells (Suppl. Fig. 4C  and Suppl. Table  1 ). Interestingly, an NRAS activating mutation, Q61K, was detected in crizotinib resistant, but not parental HCC78 cells (Fig.
Elevated KRAS expression confers resistance to ROS1 inhibitors As the role of RAS proteins in conferring resistance to tyrosine kinase inhibitors can be due not only to the presence of activating mutations, but also to amplification and elevated expression of RAS genes  [23, 24] , we wondered whether these mechanisms could also confer resistance to ROS1 inhibitors. HCC78 cells (bearing a normal KRAS copy number) were transduced with a wt KRAS expression construct (Suppl. Fig. 5A, 5B ). These cells displayed a significantly reduced sensitivity to crizotinib, JNJ-ROS1i-A and foretinib (Fig. 4C ). Altogether these data show that genetic alterations (such as activating mutations and gene amplification) leading to constitutive activation of RAS family proteins can confer resistance to ROS1 inhibitors.
Financial support The research leading to these results has received funding from the  AIRC   IG 11819  and  IG 5984  to SG;  NIH/ NCI   5K12CA086913 Paul Calabresi Award in Clinical Oncology Research  and  P50 CA058187   University of Colorado Lung Cancer SPORE  grant to RCD;  P30 CA046934   Cancer   Center Support Grant  to MVG; this work was partially supported by a research grant funded by  Janssen Pharmaceutica NV  to PMC.
A search for neutral Higgs bosons in the minimal supersymmetric extension of the standard model (MSSM) decaying to tau-lepton pairs in pp collisions is performed, using events recorded by the CMS experiment at the LHC. The dataset corresponds to an integrated luminosity of 24. 6 fb -1 , with 4. 9 fb -1 at 7 TeV and 19. 7 fb -1 at 8 TeV. To enhance the sensitivity to neutral MSSM Higgs bosons, the search includes the case where the Higgs boson is produced in association with a b-quark jet. No excess is observed in the tau-lepton-pair invariant mass spectrum. Exclusion limits are presented in the MSSM parameter space for different benchmark scenarios, m max h , m mod+ h , m mod- h , light-stop, light-stau, τ-phobic, and low-m H. Upper limits on the cross section times branching fraction for gluon fusion and b-quark associated Higgs boson production are also given.
Event selection The events in this analysis have been selected with dedicated triggers that use a combination of electron, muon and tau lepton trigger objects  [77] [78] [79]. The identification criteria and transverse momentum thresholds of these objects were progressively tightened as the LHC instantaneous luminosity increased over the data-taking period. In the eτ h and µτ h final states, events are selected in the 2011 (2012) dataset with an electron of p T > 20  (24)  GeV or a muon of p T > 17  (20)  GeV and |η| < 2. 1, and an oppositely charged τ h of p T > 20 GeV and |η| < 2. 3. The tau lepton is required to have I τ h of less than 1. 5 GeV. To reduce the Z → ee, µµ contamination, events with two electrons or muons of p T > 15 GeV, of opposite charge, and passing loose isolation criteria are rejected.
The invariant-mass spectra show no clear evidence for the presence of a MSSM Higgs boson signal so exclusion limits are obtained. For the calculation of exclusion limits a modified frequentist criterion CL s  [87, 88]  is used. The chosen test statistic q, used to determine how signalor background-like the data are, is based on a profile likelihood ratio. In this study, two searches are performed: • a model independent search for a single narrow resonance φ for different mass hypotheses in the gluon fusion and b-quark associated Higgs boson production modes; • a search for the MSSM neutral Higgs bosons, h, A, and H in the ττ mass spectrum. In the case of the model independent search for a single resonance φ, the profile likelihood ratio is defined as q µ = -2 ln L N obs |µ • s + b, θµ L N obs | μ • s + b, θ , with 0 ≤ μ ≤ µ, ( 5 ) where N obs is the number of observed events, b and s are the number of expected background and signal events, µ is the signal strength modifier, and θ are the nuisance parameters describing the systematic uncertainties.
The value θµ maximizes the likelihood in the numerator for a given µ, while μ and θ define the point at which the likelihood reaches its global maximum. The ratio of probabilities to observe a value of the test statistic at least as large as the one observed in data, q obs µ , under the signal-plus-background (µ • s + b) and background-only hypotheses, CL s (µ) = P(q µ ≥ q obs µ |µ • s + b) P(q µ ≥ q obs µ |b) ≤ α, ( 6 ) is used as the criterion for excluding the presence of a signal at the 1α confidence level (CL). Upper limits on σ • B(φ → ττ) at 95% CL for gluon fusion and b-quark associated neutral Higgs boson production for a single narrow resonance are obtained using Eqs. 5 and 6. The expected limit is obtained by replacing the observed data by a representative dataset which not only contains the contribution from background processes but also a SM Higgs boson with a mass of 125 GeV.
To extract the limit on the gluon fusion (b-quark associated) Higgs boson production, the rate of the b-quark associated (gluon fusion) Higgs boson production is treated as a nuisance parameter in the fit. A search for MSSM Higgs bosons in the ττ final state is also performed, where the three neutral MSSM Higgs bosons are present in the signal. In light of the recent Higgs boson discovery at 125 GeV, a search for a MSSM signal versus a background-only hypothesis has lost validity. A modified CL s approach has been also adopted in this case, which tests the compatibility of the data to a signal of the three neutral Higgs bosons h, H, and A compared to a SM Higgs boson hypothesis, with inclusion of the backgrounds in both cases. To achieve this a physics model is built according to M(µ) = [µ • s(MSSM) + (1 -µ) • s(SM)] + b.
Results The invariant-mass spectra show no clear evidence for the presence of a MSSM Higgs boson signal. Therefore 95% CL upper bounds on tan β as a function of the pseudoscalar Higgs boson mass m A are set for the traditional MSSM benchmark scenario m max h , and the recently proposed benchmark scenarios, m mod+ h , m mod- h , light-stop, light-stau, and τ-phobic. In the case of the low-m H scenario, limits on tan β as a function of the higgsino mass parameter µ are performed, where m A is set to a value of 110 GeV. A test of the compatibility of the data to a signal of the three neutral Higgs bosons h, H, and A compared to a SM Higgs boson hypothesis is performed as described in Section 7, using the test statistics given by Eq. 8. The simulation of the SM Higgs boson signal at 125 GeV used in the statistical analysis, is the same as in the dedicated SM Higgs boson search in the ττ decay mode  [29] , which includes the contributions from gluon fusion, vector boson fusion, and Z or W boson and top-quark associated Higgs boson production.
7. The expected and observed limits are computed using the test statistics given by Eq. 5. To extract the limit on the gluon fusion (b-quark associated) Higgs boson production, the rate of the b-quark associated (gluon fusion) Higgs boson production is treated as a nuisance parameter in the fit. For the expected limits, the observed data have been replaced by a representative dataset which not only contains the contribution from background processes but also a SM Higgs boson with a mass of 125 GeV. The observed limits are in agreement with the expectation. The results are also summarized in Tables  13  and 14   The expected and observed limits are computed using the test statistics given by Eq. 5. For the expected limits, the observed data have been replaced by a representative dataset which not only contains the contribution from background processes but also a SM Higgs boson with a mass of 125 GeV.
Summary A search for neutral Higgs bosons in the minimal supersymmetric extension of the standard model (MSSM) decaying to tau-lepton pairs has been performed using events recorded by the CMS experiment at the LHC. The dataset corresponds to an integrated luminosity of 24. 6 fb -1 , with 4. 9 fb -1 at 7 TeV and 19. 7 fb -1 at 8 TeV. Five different ττ signatures are studied, eτ h , µτ h , eµ, µµ, and τ h τ h , where τ h denotes a hadronically decaying tau lepton. To enhance the sensitivity to neutral MSSM Higgs bosons, the search includes the case where the Higgs boson is produced in association with a b-quark jet. No excess is observed in the tau-lepton-pair invariant mass spectrum. Exclusion limits are presented in the MSSM parameter space for different MSSM benchmark scenarios, m max h , m mod+ h , m mod- h , light-stop, light-stau, τ-phobic and low-m H.
Expected and observed 95% CL upper limits for σ(ggφ) • B(ττ) (pb) at 8 TeV centerof-mass energy as a function of the Higgs mass m φ , where φ denotes a generic Higgs-like state. The expected and observed limits are computed using the test statistics given by Eq. 5. For the expected limits, the observed data have been replaced by a representative dataset which not only contains the contribution from background processes but also a SM Higgs boson with a mass of 125 GeV. × 10 -1 6. 15 × 10 -1 8. 55 × 10 -1 1. 14 1. 50 8. 45 × 10 -1 180 3. 12 × 10 -1 4. 05 × 10 -1 5. 52 × 10 -1 7. 41 × 10 -1 9. 74 × 10 -1 5. 49 × 10 -1 200 2. 37 × 10 -1 3. 08 × 10 -1 4. 19 × 10 -1 5. 56 × 10 -1 7. 48 × 10 -1 5. 17 × 10 -1 250 1. 30 × 10 -1 1. 69 × 10 -1 2. 28 × 10 -1 3. 11 × 10 -1 3. 95 × 10 -1 3. 15 × 10 -1 300 7.
Expected and observed 95% CL upper limits for σ(bbφ) • B(ττ) (pb) at 8 TeV centerof-mass energy as a function of the Higgs mass m φ , where φ denotes a generic Higgs-like state. The expected and observed limits are computed using the test statistics given by Eq. 5. For the expected limits, the observed data have been replaced by a representative dataset which not only contains the contribution from background processes but also a SM Higgs boson with a mass of 125 GeV. × 10 -1 4. 99 × 10 -1 6. 68 × 10 -1 8. 94 × 10 -1 1. 18 8. 14 × 10 -1 180 2. 64 × 10 -1 3. 45 × 10 -1 4. 59 × 10 -1 6. 27 × 10 -1 8. 40 × 10 -1 6. 59 × 10 -1 200 1. 92 × 10 -1 2. 50 × 10 -1 3. 39 × 10 -1 4. 60 × 10 -1 6. 10 × 10 -1 5. 53 × 10 -1 250 1. 09 × 10 -1 1. 41 × 10 -1 1. 90 × 10 -1 2. 62 × 10 -1 3. 42 × 10 -1 2. 16 × 10 -1 300 7.
However, it should be noted that the ANN reconstructor was trained with the same simulation, albeit with different data. This is not possible for the on-sky implementation. The ultimate goal is to develop a reconstructor which is insensitive to changes in the atmospheric optical turbulence profile. Therefore, we do not wish to train the ANN with on-sky data as this would result in the reconstructor learning the concurrent profile. Instead we propose to train the ANN off-line on an AO calibration bench. Canary is an ideal AO demonstrator on which to develop this reconstructor. The Canary calibration unit is used to generate the training data sets for CARMEN. We need to generate the training datasets on the same bench as we intend to use on-sky. This is because the neural networks, as with any reconstructor, will be sensitive to the relative alignment errors of the wavefront sensors.
We have used the Canary calibration bench to generate a training dataset. This bench consists of deployable four natural and three laser guide stars and two phase screens. The turbulence strength is distributed between these two phase screens with a ratio of 0. 7:0. 3. Initially we attempted to only use the stronger phase screen for the training (with a measured r0 = 0. 25 m). However, it became apparent that this phase screen alone did not have enough phase variability (i. e. was not big enough) for the statistics to converge for a suitable training set. If we do not train with sufficient variability in the input phase then the performance of the reconstructor is severely compromised  (Osborn et al. 2012). Therefore, for the training, we use the two available phase screens and place them as close together as possible.
We then counter rotate them at different angular velocities to increase the variability, or independent realisations, of the phase that we measure. We place the phase screens at the ground and take 10000 iterations of WFS slopes, the angular velocities are defined so that the system is exposed to all possible combinations of the two phase screens. We then move the two layers up through the simulated atmosphere space together in small increments. The dataset then includes the influence of turbulent layers at all possible altitudes. We train CARMEN to reconstruct the on-axis target slopes (i. e. the slopes that an on-axis WFS would measure if one were available) regardless of atmospheric turbulence profile. The input to the reconstructor will be the measured off-axis slopes from the guide star WFSs.
The reason for this is that by using both phase screens we can increase the variability and the strength of the turbulence for the bench training. Using two phase screens induces further errors as there is a finite separation between them. It follows that the phase screens will therefore be conjugate to different altitudes on the calibration bench. We estimate that the two phase screens are separated by approximately 500 m (5 mm on the bench). The trained dataset will therefore contain information on double layers of separation 500 m. This will inevitably lead to errors in the reconstruction of single layers. Errors in the conjugation altitude and lateral positions of the guide stars will mean that the geometry of the light cones on the bench will be different to that on-sky, inducing errors in the reconstructed on-axis slopes.
We believe that the lower performance was caused by insufficient data upon which the neural network was trained. This includes both the lack of variability in the phase screens that are used for generating the training dataset for the ANN reconstructor and the fact that there was turbulence above the altitude that the training data was acquired for. We maximised the phase variability in the training dataset by using two counter-rotating phase screens placed close to each other. This introduced a further issue that the two phase screens are actually displaced in altitude relative to each other. However, despite these limitations on the training of CARMEN we still achieve a performance within 5% in terms of Strehl ratio and 15 nm in rms error of the optimised L&A tomographic reconstructor. We also show that the reconstructor is performing a tomographic reconstructor as the performance is significantly better than that of GLAO in similar atmospheric conditions.
ACKNOWLEDGMENTS DG appreciates support from  CONICYT , through  FONDE-CYT grant   11110149. AG appreciates support from  CON-ICYT , through  FONDECYT  grant  1120626. FJdC appreciates support from the  Spanish Economics and Competitiveness Ministry , through grant  AYA2010-18513. This work was supported by  Agence Nationale de la Recherche  ( ANR ) program  06-BLAN-0191 ,  CNRS / INSU, Observatoire de Paris , and  Universit Paris Diderot Paris 7 in France ,  Science and Technology Facilities Council  ( ST/K003569/1  and  ST/I002781/1 ), University of Durham in the UK and  European Commission   Framework Programme 7 (EELT Preparation Infrastructure  Grant  211257  and  OPTICON Research Infrastructures Grant 226604 and312430 ). The Jacobus Kapteyn Telescope is operated on the island of  La Palma  by the  Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofsica de Canarias.
Additional pathways involved in T-cell activation, endothelial signalling, hypoxia response and plasminogen-activating cascade were also recognized. Similar data were obtained showing differential signatures between low and high inflammatory subsets of RA patients. 46 ene expression profiling in established RA further revealed three main types of synovial tissue, the first with mainly T and B cells, antigen-presenting cells and MHC gene signature, a second signature included mainly stromal genes and a third showed mixed information. 40 In relation with the difference in genetic predisposition involving T-cell genes and the autoantibody status, the first group may be speculated to reflect pathology associated with the presence of ACPA although this was not investigated in this paper. Studies combining gene expression and modern imaging could also reveal association between molecular signatures (for example, involving neo-vascularization genes) with signals in power Doppler ultrasound examination, which is predictive of progression towards early RA.
83  analyzed gene expression profiles in whole blood using a microarray of 747 genes selected from public database of SAGE (serial analysis of gene expression) for activated T cells, dendritic cells, monocytes and macrophages. Eighteen genes differentially expressed between responders and nonresponders were identified over the course of treatment, but these genes did not predict response before treatment. They, however, reported a clear difference in the kinetics of IFN-related genes during infliximab treatment between responders and nonresponders. A further study measuring 15 gene of a predetermined set of IFN-response genes using real-time PCR in an independent group of patients before and post treatment and showed an increase in type-I IFN response gene expression 1 month after treatment in patients who had a poor clinical response to treatment.
6] [97]  So far, one publication reports gene expression for tocilizumab. PBMC gene expression was assessed before and 4 weeks after treatment in a small group of patients. 98 Four genes (CCDC32, DHFR, EPHA4 and TRAV8-3) were associated with response, and another 59 showed changes with treatment. The four predictive genes for response were not included in any of the published response signatures for antibody-mediated TNF blockade. Conclusion and future need for validation. Not surprisingly, maybe gene expression signatures for a given clinical end point published to date do not always overlap and have not always been re-validated. The only exceptions were the signatures for progression from preclinical phase of RA  69, 70  and the prediction of response to rituximab. 91, 92 The often observed lack of reproducibility can be explained by the heterogeneity in technical protocols and microarray platforms used (notably when comparing in-house array to commercial ones), data analysis methods and, most importantly, the choice of clinical assessment of response to treatment (Table  3 ).
Prednisone and MTX treatment affected gene signatures Leflunomide PBMC 10 DualChip 282 Hierarchical cluster, Before vs 12 Treatment of early RA Soldana et al. patients genes statistical weeks after Downregulation of many genes with early environment ''R'' treatment RA IFX Synovium 10 RA In-house array Hierarchical cluster, Before vs Genes specifically changed in Lindberg et al. 30 000 cDNA statistical after 9 week patients who have a good response spots environment 'R' of to IFX treatment treatment Synovium 18 RA Human cDNA microarrays Supervized hierarchical Responders vs non- Patients with high expression of genes involved in tissue van der Pouw Kraan et al. 79 18 000 clustering. Tree view responders inflammation before treatment are gene ontology to IFX more likely to benefit from IFX analysis (PANTHER therapy Whole 18 RA Customized database) Cluster analysis Responders Unique set of genes with Sekiguchi et al.
EuroSIDA Cohort The EuroSIDA study is a prospective, observational cohort study including HIV-1 infected patients from 31 European countries as well as Israel and Argentina  [12]. To date, nine cohorts have been enrolled starting in May 1994. At enrollment, demographic characteristics and clinical information are obtained. Every 6 months CD4 count and viral load data as well as data on antiretroviral drugs used are collected in a standardized report form and forwarded to the coordinating center. The pre-specified non-AIDS events included are those used in previous EuroSIDA publications on this topic  [11]  and the expert opinion of members of the steering committee as events which are likely to have a major impact of HIV associated morbidity and mortality. These data have been routinely collected since January 2001 and include non-AIDS malignancies, pancreatitis, severe liver disease with hepatic encephalopathy (grade 3 or 4), cardio-and cerebrovascular events (i.