dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing data were processed using the Illumina Pipeline Software version 1. 82. Heat maps were produced from normalised expression data using Cluster 3. 0 for computation and JTreeview for visualisation. Cell viability assay. Cells were plated in 96-wells and treated with DMSO or CMA overnight. Quantification of viable cells was performed using a CellTiter-Blue Cell Viability Assay (Promega, #G8090) according to the manufacturer's instructions. Measurement of caspases-3/7 activity. Cells were plated in 96-wells and treated with DMSO or CMA. After incubation an equal amount of living cells were lysed and quantification of caspases-3/7 activity was performed using the Caspase-Glo 3/ 7 Assay (Promega) according to the manufacturer's instructions. Type I IFN bioassay. Mouse type I IFN levels in the supernatants of stimulated T cells or BMDCs were determined by incubating LL171 cells stably expressing an ISRE-luciferase construct with the respective supernatants for 5 h.
LL171 cells were lysed in passive lysis buffer (Promega) and luciferase activity was measured using luciferin as substrate. Immunoblotting. Cells were lysed in 1 × Laemmli buffer and denatured at 95 °C for 5 min. Cell lysates were separated by 10% SDS-PAGE and transferred onto nitrocellulose membranes. Blots were incubated with anti-STING (D2P2F), Phospho-TBK1 (D52C2), Phospho-NF-κB p65 (93H1), cleaved caspases-3 (Asp175) (5A1E), p53 (1C12) (all Cell Signaling) and anti-TBK1 (108A429) (Novus Biologicals) (all 1:1000 dilution). As a secondary antibody anti-rabbit-IgG-HRP (1:2000 dilution) (Santa Cruz Biotechnology) was used. Anti-β-actin-HRP or GAPDH (both 1:5000 dilution) was used as control. ECL signal was recorded on the ChemiDoc XRS Biorad Imager and data were analysed with Image Lab (Biorad).
Quantitative RT-qPCR. Total RNA was isolated using the RNAeasy Mini Kit (Qiagen) and cDNA was synthesised using the RevertAid First Strand cDNA Synthesis kit (Fermentas). Quantitative RT-qPCR was performed in duplicate using Maxima SYBR Green Master Mix (Thermo Scientific) on an Applied Biosystems 7900HT machine. For all gene expression data beta-Actin was used as an endogenous normalisation control. Primer sequences are as follows: mmACTB forward, 5′-AGCCATGTACGTAGCCATCC-3′; mmACTB reverse, 5′-CTCTCAGCTGT GGTGGTGAA-3′; mmIFNB1 forward, 5′-CTCCAGCTCCAAGAAAGGAC-3′; mmIFNB1 reverse, 5′-TGGCAAAGGCAGTGTAACTC-3′; mmIFIT2 forward, 5′-GCTCTGGAAAAGGACCCGAA-3′; mmIFIT2 reverse, 5′-GCTTCAGTGC-CAAGAGGACT-3′; mmPUMA forward, 5′-CAAGAAGAGCAGCATCGACA-3ʹ; mmPUMA reverse, 5ʹ-TAGTTGGGCTCCATTTCTGG-3ʹ; mmNOXA forward, 5ʹ-GGAGTGCACCGGACATAACT-3ʹ; mmNOXA reverse, 5ʹ-TTGAGCACT CGTCCTTCA-3ʹ; mmBIM forward, 5ʹ-GCCAAGCAACCTTCTGATGT-3ʹ; mmBIM reverse, 5ʹ-CTGTCTTGCGGTTCTGTCTG-3ʹ; mmBAD forward, 5ʹ-CGAAGGATGAGCGATGAGTT-3ʹ; and mmBAD reverse, 5ʹ-CCCACCAG-GACTGGATAATG-3ʹ.
Acknowledgements We thank  Nathalie Jordan  for the preparation of VLPs,  Fabio Martinon  for providing immortalised macrophages,  Jerry Adams  for Vav-BCL-2 and  Christine Möser  for providing Tnf -/-and Tnf -/-Tbk1 -/-mice. This research was supported by  SNSF  grants ( BSSGI0-155984 ,  31003A_159836 ), the  Novartis Foundation  for medical-biological Research ( 15C177 ) and by the  Else Kröner Fresenius-Stiftung  (grant  2014_A250 ) to A. A. , the  Austrian Science Fund (FWF) , grant ( I1298 ;  FOR2036 ) to A. V. and  ERC grant  ( 677251 ) to L. A. F. S. is recipient of a  DOC-fellowship  of the  Austrian Academy of Science (ÖAW).
For most cases where α is high, the aberrations in the solution arising from the slip velocity and temperature jump are relatively small, and can be tolerated. Kogan  [56]  presents a cogent explanation for the necessity of slip boundary conditions for treating flows at small Knudsen numbers. A primer on the compressible Navier-Stokes equations, accommodation coefficients, and the slip velocity and temperature jump boundary conditions can be found in Karniadakis et al. [57]. The volume conditions refer to the material properties of the fluid (N 2 , Ar, and Xe gas) and the solid (Al 2 O 3 ) used in the simulations. For the former, CFD-ACE+ contains a database of elemental and molecular species with their respective atomic or molecular mass available by default. The Lennard-Jones length or collision diameter for each species is entered into the database using values from Poling et al.
[58]. It is also necessary to input values for the isobaric specific heat, thermal conductivity, and dynamic viscosity of each gas species. These parameters are obtained from the NIST Thermophysical Properties of Fluid Systems database  [59]  as a piecewise linear function of temperature (in increments of 1 K from the boiling point) at a constant pressure of 1 Torr (as PR and MiniPR operate in the 0-10 Torr range). The mass diffusivity data of the each gas species are collected from Amdur and Mason  [60] , Amdur and Schatzki  [61] , Dymond  [62] , Hutchinson  [63] , Winn  [64] , and Winter  [65] , wherein the values are quoted for atmospheric pressure (760 Torr). For self-diffusion in gases, the empirical relation D ∝ p -1  [66]  is used to calculate the respective values for 1 Torr. A fifth-order Maclaurin series in temperature is fitted to the 1 -Torr mass diffusivity data, and the polynomial coefficients are entered into the CFD-ACE+ database.
Each participant will value their present health at the date of questionnaire completion. Participants will also complete a Visual Analogue Scale which will provide us with a value for the participant's self-rated health at the time of survey completion. Permission has been granted for the use of the MHQ and EQ-5D-5L from the relevant agencies. Resource use questionnaire (early DD RCT only): Participant self-reported information on service use will be collected at 3, 6, 9, 12 and 18 months post randomisation to capture the intensity of use of different healthcare services, including primary, community and social care. Unit cost data will be obtained from national databases such as the BNF and PSSRU Costs of Health and Social Care  44. Where these are not available the unit cost will be estimated in consultation with the lead hospital finance department.
Statistics A separate statistical analysis plan (SAP) with full details of all statistical analyses planned for the data of this study will be drafted early in the trial and finalised prior to any primary outcome analysis. The SAP will be reviewed and will receive input from the Trial Steering Committee (TSC) and the Data and Safety Monitoring Committee (DSMC). The analysis will be undertaken using R, STATA (StataCorp LP), or other well-validated statistical packages. Descriptive statistics will be used to describe the demographics between the intervention groups. Continuous outcome measures will be presented as a difference in means, together with a 95% confidence interval (CI) for each dose cohort (where applicable) and overall. Binary and categorical outcomes will be presented as numbers and percentages in each category, as well as the difference in proportions with the corresponding 95% CI.
Trial organization and administration The study has received approval from the South Central -Oxford B Research Ethics Committee and the MHRA, and will be carried out following local legal and regulatory requirements. The trial is registered with the European Clinical Trials Database (EudraCT: 2015-001780-40) and has the Ethics Reference: 15/SC/0259. All substantial amendments to the protocol will be submitted to the ethics committee for approval. The study is sponsored by the University of Oxford. All procedures relating to the trial and personnel involved will be carried out in accordance with Medical Research Council Clinical Practice, applicable UK legislation and the ethical principles of the Declaration of Helsinki. This study protocol follows SPIRIT guidelines  48. The trial will be reported in line with the CONSORT statement  49.
Our laboratory data showed that DD is a localised inflammatory disorder  30. The nodular tissue comprises mainly myofibroblasts, which both secrete and contract the matrix, leading to digital contractures  55. Pro-inflammatory cytokines are secreted by the infiltrating immune cells and we have found that only TNF leads to the differentiation of precursor cells into myofibroblasts, and that TNF inhibition downregulates the myofibroblast phenotype  30. These findings form the basis of our current clinical trial, where we will recruit participants with early stage DD to receive either adalimumab or saline at 3 monthly intervals injected directly into the nodules over a 9 month period and followed up for a further 9 months. The majority of patients with flexion deformities of the digits are treated by surgery.
This provides the opportunity to obtain tissue for analysis following injection of the IMP. Therefore, we will recruit individuals scheduled for surgical excision of DD, administer adalimumab or placebo two weeks before surgery and determine the effect on the myofibroblast phenotype in the surgically excised tissues. This part of the trial will assess the efficacy of different doses of adalimumab at the mRNA and protein level to inform the molecular mechanism of action of anti-TNF in DD. The myofibroblast is responsible for all forms of fibrosis  56  and the data from this trial may inform future studies on other fibrotic diseases such as frozen shoulder. Study publication policy: The Investigators will be involved in reviewing drafts of the manuscripts, abstracts, press releases and any other publications arising from the study.
We also referenced a publication by Thurston (1987) who used tonometry and reported softening of the palm following excision of Dupuytren's tissue. We will also use ultrasound scan to assess the size of Dupuytren's nodules but as there are no previous data using this modality for Dupuytren's disease we have chosen this parameter as a secondary objective. It would be helpful if a brief description of measuring nodule hardness is provided considering it is the primary outcome. Response: Page 11, Column 1: This section has been amended to read: Tissue hardness will be measured with a tonometer, ensuring that the gauge is balanced perpendicular to the skin without application of external pressure by the observer. Five readings will be obtained on each occasion. A photograph obtained at base line marking the spot to be measured will be used for all subsequent measurement to ensure consistency of placement of the tonometer.
This now reads: Progression to treatment will be determined by clinicians who are not part of the trial team. Is the rationale for, and objectives of, the study clearly described? Yes Is the study design appropriate for the research question? Yes Are sufficient details of the methods provided to allow replication by others? Yes Are the datasets clearly presented in a useable and accessible format? Yes Competing Interests: No competing interests were disclosed. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. References to be added by authors.
al 2008) showed that there was a strong correlation between in MHQ score and change in DD digital fixed flexion deformity. Reference 38 (Herweijer between in MHQ score and change in DD digital fixed flexion deformity. Reference 38 (Herweijer et al 2007) demonstrated that the MHQ can detect change in patients before and after surgery for DD. The MHQ has been shown to have high internal consistency which should be useful when exploring the natural course of DD, where changes occur slowly over time (Shauver and Chung 2013). Is the rationale for, and objectives of, the study clearly described? Yes Is the study design appropriate for the research question? Yes Are sufficient details of the methods provided to allow replication by others? Yes Are the datasets clearly presented in a useable and accessible format? Yes doi:10.
Microarray and statistical analyses For microarray analyses, samples were sent to the Yale Center for Genome Analysis and hybridized to a NimbleGen Eukaryotic Gene Expression 12 Â 135 K microarray (Roche NimbleGen, Madison, WI, USA). The microarray data are available at the Gene Expression Omnibus database (https://www. ncbi. nlm. nih. gov/geo/) under submission number GSE100237. Log transformed, normalized intensity for each sample was used for downstream analysis by MeV software suite (http://mev. tm4. org/). Statistical significance between treatment groups was determined using the linear models for microarray (limma) function within the MeV suite. Expression changes were considered significant with a nominal p value < 0. 05 (see Supplementary Table  1e3  for significantly altered genes).
Cell survival is transcriptionally regulated by macrophage alternative activation ATMs exhibit a unique phenotypic transition from M2 to M1 activation in the lean versus obese state. The Th2 cytokine-Stat6-Ppard/g axis has previous been shown to maintain the M2 phenotype of ATMs  [3, 15]. To better understand the physiological function of M2 ATMs, we performed gene expression profiling using samples from RAW 264. 7 cells stably overexpressing Ppard (RAW-Ppard) treated with vehicle, GW501516 (a synthetic Ppard ligand), or Th2 cytokine Il-4 [see Supplementary Table  1e3  for significantly altered genes; original data in Gene Expression Omnibus database #GSE100237 (https://www. ncbi. nlm. nih. gov/geo/)]. DAVID functional annotation clustering analyses were employed to identify pathways significantly altered by either GW501516 or Il-4 treatment compared to vehicle control  [27, 28].
ACKNOWLEDGMENTS We thank  Dr. Tiffany Horng  ( Harvard T. H. Chan School of Public Health ) for help with the inflammasome studies. This work was supported by the  National Natural Science Foundation of China  (No. 81603205 ),  Hunan Provincial Natural Science Foundation of China  (No. 2016JJ3179 ) and Research Funds from  Xiangya Hospital of Central South University  (No. 2014Q03 ) (L. D. ); Research Funds from  Ministry of Science and Technology of Taiwan  (Y-H. L. );  NIH F31GM117854 (R. K. A. )  and  F31DK107256  (N. H. K. ); and  NIH   R01DK113791  and  American Heart Association   16GRNT31460005  (C. H. L. ).
3. 0  [25]  (Fig. 1 ). The 3D data (specimen TMM M-8671 from the Texas Memorial Museum) is available from the DigiMorph digital library (http://www. digimorph. org/specimens/ Mus_musculus/heterozygous/adult/whole/). The homologous points capture the vertebral shape in three dimensions characterizing the morphology of the vertebral centrum and the neural arch (Table  1 ). The atlas (first cervical vertebra) is not included in the geometric morphometric analysis due to its highly unique morphology. It lacks specific serial homologies with postatlantal cervicals, and thus, several landmarks cannot be applied to it. Analysis and visualization of the geometric morphometric data is performed using the software MORPHOLO-GIKA  [26]. First, the 3D coordinates of all landmarks are superimposed using a generalized Procrustes analysis (GPA).
The superimposition removes all information unrelated to shape  [27]. Next, a relative warps (RW) analysis is performed to reduce the dimensionality of the dataset. With the applied settings, this method is equivalent to a principal components analysis, and reveals similarity relationships among vertebrae within the cervical vertebral column. The RW analysis constructs a morphospace in which shape variation can be quantified. The shape differences are visualized with three-dimensional thinplate splines. Furthermore, a cluster analysis using the single linkage algorithm in combination with the Euclidean similarity index is performed on the superimposed landmark coordinates. This joins the vertebrae based on the smallest distance between them. Eventually, the quantitative morphological analysis results in the establishment of the morphological subunit pattern in the cervical vertebral column.
Acknowledgements I thank  Prof. Oliver W. M. Rauhut  ( SNSB-Bayerische Staatssammlung für Geologie und Paläontologie, Munich, Germany ) and  Prof. Gert Wörheide  ( Ludwig-Maximilians-Universität, Munich, Germany ) for valuable discussions. Dr. Timothy Rowe  ( University of Texas at Austin, USA ) and  Ms. Megan Demarest  kindly provided high-resolution CT data of the studied mouse skeleton. Dr. Jessica A. Maisano  (High-Resolution X-ray CT Facility & DigiMorph. org, Department of Geological Sciences, The University of Texas, USA ) is thanked for support. I am indebted to  Ann C. Burke  for many useful suggestions and corrections to the text, and four anonymous colleagues for useful reviews.
92 and 0. 88, respectively. Data preparation involved interpolation of hourly precipitation station data based on Thiessen polygons. The station data are collected and made available for this study by the Service Public de Wallonie 1. Daily minimum and maximum temperatures from the freely available gridded E-OBS dataset (0. 25 • × 0. 25 • resolution;  Haylock et al. , 2008)  were disaggregated to hourly values using radiation data at Maastricht (Royal Netherlands Meteorological Institute 2 ) and a sine function. Daily potential evaporation was derived with the Hargreaves formula  (Hargreaves and Samani, 1985)  and disaggregated to hourly values using a sine function during the day and no evaporation at night. Precipitation and temperature data were available for the period from 1 January 2000 to 31 December 2010.
Modelling protocol Eight international research groups participated in this model comparison study using one or several rainfall-runoff models. A total of 11 models were used, consisting of 7 independent models and 4 models from the SUPERFLEX framework  (Fenicia et al. , 2011). A modelling protocol was prescribed to enable a comparison of the results. The protocol for the modelling involved a split-sample calibration and validation for predefined periods using a common dataset  (Klemeš, 1986)  for the Ourthe catchment. The validation consisted of a blind validation in time (same catchment, but a different period) for the Ourthe catchment and a blind validation in space (same period, but different catchments) for the nested Ourthe Orientale and Ourthe Occidentale catchments and for the neighbouring Lesse and Semois catchments.
Blind validation implies that only time series of forcing data (and no discharge observations) were given to the participants. Calibration was carried out for the Ourthe at Tabreux for the period 1 January 2004 to 31 December 2007, using 2003 as a spin-up year. Nash-Sutcliffe efficiency (E NSE ;  Nash and Sutcliffe, 1970 ) and E NSE of the log of the flows (E NSElog  ; Collischonn et al. , 2008)  were used as objective functions for calibration. E NSE was chosen as an objective function for calibration because it is a common metric in hydrology to assess model performance with regard to high flows. E NSElog was chosen as a second objective function to take low flow performance into account as well. Participants were free to use a calibration method of their choice to estimate parameter values as long as they used the prescribed objective functions.
Acknowledgements. The authors would like to thank the  Service Public de Wallonie ,  Direction générale opérationnelle de la Mobilité et des Voies hydrauliques, Département des Etudes et de l'Appui à la Gestion, Direction de la Gestion hydrologique intégrée (Blvd. du Nord 8-5000 Namur, Belgium ) for providing the precipitation and discharge data. We would like to thank  Bernhard Becker  for organising the Meuse symposia that have led to this fruitful cooperation and  Frederiek Sperna Weiland  for the valuable discussions and her contribution in data preparation. Further, we would like to thank  Michel Pirotton  for his review and  Pierre Archambeau ,  Sébastien Erpicum  and  Michel Pirotton  for the analysis of the impact of the Nisramont Dam presented in the Supplement. We would like to thank  Rohini Kumar  and two anonymous reviewers for their valuable comments which helped us to improve the manuscript.
■ ACKNOWLEDGMENTS We are thankful for the financial support of the  European Commission  who supported this work by  FP7-NMP-2013-SMALL-7, SiNERGY (Silicon Friendly Materials  and  Device Solutions  for Microenergy Applications), Contract  604169 , and  CERCA Programme/Generalitat de Catalunya. A. S. would like to acknowledge the  EU   Horizon 2020 research and innovation programme  under the  MSCA  grant agreement No. 658057.
Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc. ). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we focus on SA of the Tunisian dialect. We use Machine Learning techniques to determine the polarity of comments written in Tunisian dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian dialect corpus of 17. 000 comments from Facebook. This corpus shows a significant improvement compared to the best model trained on other Arabic dialects or MSA data.
To overcome this problem, we start by using and evaluating the performance using available resources from MSA and dialects, then we created and annotated our own data set. We have performed different experiments using several machine learning algorithms such as Multi-Layer Perceptron (MLP), Naive Bayes classifier, and SVM. The main contributions of this article are as follows: (1) we present a survey of the available resources for Arabic language SA (MSA and dialectal). (2) We create a freely available training corpus for Tunisian dialect SA. (3) We evaluate the performance of Tunisian dialect SA system under several configurations. The remainder of this paper is organized as follows: Section 2 discusses some related works. Section 3 presents the Tunisian dialect features and its challenges.
Related work The Sentiment Analysis task is becoming increasingly important due to the explosion in the number of social media users. The largest amount of SA research is carried for the English language, resulting in a high quality SA tools. For many other languages, especially the low resourced ones, an enormous amount of research is required to reach the same level of current applications dedicated to English. Recently, there has been a considerable amount of work and effort to collect resources and develop SA systems for the Arabic language. However, the number of freely available Arabic datasets and Arabic lexicons for SA are still limited in number, size, availability and dialects coverage. It is worth mentioning that the highest proportion of available resources and research publications in Arabic SA are devoted to MSA  (Assiri et al.
The sentiment classifier is built by extracting discriminative features from annotated data and applying a Machine learning algorithm such as Support Vector Machines (SVM), Naïve Bayes (NB) and Logistic regression etc. Generally, the best performance is achieved by using n-grams feature, but also Part of speech (POS), term frequency (TF) and syntactic information can be used. (Shoukry and Rafea, 2012)  examined two machine learning algorithms: SVM and NB. The dataset is collected from the Twitter social network using its API. Classifiers are trained using unigram and bigram features and the results show that SVM outperforms NB. Another machine learning approach was used in  (Rushdi-Saleh et al. , 2011b)  where they build the opinion corpus for Arabic (OCA) consisting of movie reviews written in Arabic.