dataesr/scientific-paragraphs-categorization huggingface dataset:
Results: The newspaper dataset had 432 articles. The cluster analysis resulted in 5 clusters, which were named as follows: (1) brain stimulation (26. 2%), (2) symptoms (13. 5%), (3) coping (19. 8%), (4) social support (24. 2%), and (5) treatment innovation (16. 4%). A time series analysis of clusters indicated a change in the pattern of information presented in newspaper media during 1980-2017 (eg, more emphasis on cluster 5, focusing on treatment inventions). The Facebook dataset had 1569 texts. The cluster analysis resulted in 7 clusters, which were named as: (1) diagnosis (21. 9%), (  2 ) cause (4. 1%), (3) research and development (13. 6%), (4) social support (18. 8%), (  5 ) challenges (11. 1%), (  6 ) symptoms (21. 4%), and (  7 ) coping (9. 2%). A time series analysis of clusters indicated no change in information presented in Facebook pages on tinnitus during 2011-2016.
Study Design This study used a cross-sectional design based upon secondary analyses of publicly available data. The 2 datasets analyzed in this study were generated from US newspaper media and Facebook pages. The study did not require ethical approval as the data were gathered from publicly available sources. Only the publicly available Facebook pages (not personal pages) were included in the data extraction, thus maintaining the anonymity of the responses, and no personally identifiable information was included. Moreover, no individual dataset was discussed in the paper, again maintaining the anonymity of the data. Considering the minimal or no potential risk to individual participants, no ethical approval was required  [20].
Data Extraction To develop the newspaper media text corpus (ie, large and structured set of texts), we first explored the databases with the newspaper collection available at Lamar University, United States. Major Dailies by ProQuest was the database that had the largest newspaper collection. We then searched for articles related to tinnitus in this database between the years 1980 and 2017 and downloaded the results as text corpus. A python script was written to convert the text corpus to a format that was needed for data analysis and to preserve the metadata (ie, newspaper name, year of publication). A different python script was written to extract posts with Facebook pages dealing with tinnitus (during 2010-2016). In total, 20 Facebook pages with more than 100 likes were identified, and the postings were downloaded as a text corpus.
Data Analysis The text corpuses were analyzed using the Iramuteq software  [21] , which is an open source software. This software can perform various types of analyses on text data. The text corpus is composed of multiple newspaper articles. The software treats each of these articles as text (ie, it's the first unit). The first step of the analysis is to segment each article (ie, text) into smaller units called text segments (ie, each text is split into multiple text segments based on criteria of size and punctuation). The split of the text into segments decreases the granularity of the units and thus makes it possible to increase the precision of certain analysis, in particular the search of themes within the text corpus. The goal is to create segments of consistent size while trying to maintain the natural segmentation of texts marked by punctuation.
The segmentation process is based on a cutoff criterion that weighs the segment size by punctuation. The procedure does not fully respect the parameterized segment size if a strong punctuation (eg, a period, a question mark, and an exclamation mark) is present within a 15% margin of the planned cutoff. In the next step, the text corpus is lemmatized (ie, words are sorted by grouping inflected or variant forms of the same word) to their simplest forms, which are called lemmas (ie, group of words in a single form). Moreover, the software makes distinctions between "full words" (eg, verbs, noun, adjectives, and adverbs) and "tool words" (eg, pronoun, determent, and useful verbs such as-to be and to have). This distinction is done so that only full words are included in the main analysis. These steps are necessary to convert the large corpus into a more manageable dataset that is essential for further analysis.
To specifically analyze the text that is closely related to "tinnitus," the text segments related to this object were extracted and new corpuses were formed. It is important to note that the expression "tinnitus" was inside each of the text segments extracted. The subcorpus with more directed text segments was used for all further analyses. This was followed by cluster analysis made with the Reinert method used for textual data analysis  [22] [23] [24]. The Reinert method has been used frequently in French media studies. This method facilitates the investigation of links between topics and the metadata associated with the text (eg, dates to study chronology). A recent comparison between the Reinert method and Latent Dirichlet Allocation showed that topics from the Iramuteq software that uses the Reinert method (ie, a divisive hierarchical clustering) are more accurate  [25].
Second, the newspaper data extracted from the ProQuest database may not be a true representation of all the US newspapers. Although ProQuest has some of the most widely circulated newspapers in the United States, using a different search engine may have potentially resulted in a different corpus and perhaps different results patterns. Furthermore, some selection bias may have occurred while choosing Facebook pages, as we only included 20 pages with 100 "likes. " Different criteria may have resulted in a different text corpus. This study mainly focuses on the patterns of information found in newspapers and in Facebook pages but not about how appropriate or accurate the information actually is. Hence, future studies should explore the appropriateness and accuracy of content. Moreover, future studies should aim to validate the results of automated text analysis using predictive validity methods, which confirms results by examining the data looking at the identified trends over time  [31].
It would be interesting to study how the change over time may have occurred as a result of other metadata (eg, types of newspaper, focus of Facebook pages), which was not possible in this analysis due to limitations with software. Future studies should also consider studying the commonalities and differences among different types of media (eg, newspaper media, social media) using automated text analysis through more powerful software. Moreover, there is a great need to explore the role of the media on health behaviors, specifically in the tinnitus population.
Objectives: Thrombosis is a leading cause of morbidity and mortality in patients with Philadelphia negative chronic myeloproliferative neoplasms (MPNs). There are many thrombosis risk stratifications used in this patient group taking into consideration the age, thrombosis history and cardiovascular factors (hypertension, hypercholesterinaemia, hyper-trigliceridaemia, thrombocytosis, smoking and diabetes mellitus). In this work we evaluated the possible role of iron deficiency in thrombotic events (TE) of the polycythaemia vera (PV) patients. Methods: We considered the low mean cell haemoglobin (MCH <28 pg) value as a parameter to assess the iron deficiency in the multicentre database (15 Hungarian haematology centres) of our HUMYPRON GROUP (Hungarian MPN Working Group). The MCH values, recorded at the time of diagnosis of 296 patients with polycythemia vera, were retrospectively analysed.
The transferrin saturation, serum ferritin level, reticulocyte haemoglobin content and bone marrow iron content are the most used and suitable parameters to measure the body's iron status nowadays. On the other hand, more reports raised attention that red blood cell hypochromia and decreased red blood cell haemoglobin content indicate the presence of iron deficiency  [21] [22] [23] [24] [25] [26]. The low mean cell haemoglobin content (MCH) is an appropriate parameter which notes iron deficiency  [21] , and it is more available in retrospective databases in comparison to other iron status parameters. The aim of our retrospective multicentre study was to evaluate the impact of MCH, white blood cell count (WBC), platelet count (PLT), age and thrombotic events at the time of diagnosis and compare our findings to Tefferi and Landolfi thromboembolic risk stratifications in PV patients registered in the Hungarian Myeloproliferative Neoplasia Working Group (HUMYPRON GROUP) database  [27].
Conclusions Earlier publications proved that PV patients had an iron deficiency because of elevated red blood cell mass, and this condition could be corrected with cytoreductive therapy while phlebotomy might worsen it  [18]. However we did not find any data in the literature if iron deficiency enhanced TE risk in patients with PV. In the HUMYPRON database, we found low MCH in 95 cases out of 296 PV patients. After excluding patients with HD because of uraemia, thalassemia and myelofibrosis we considered the low MCH as a sign of iron deficiency  [21]. Comparing TE events in the two groups, the patients with low MCH were found to have significantly more thromboembolic events. Both of the internationally accepted risk stratifications, Tefferi and Landolfi, showed significant correlation with TE events in our analysis.
Rietveld Refinement and Analysis Rietveld refinement was undertaken for each diffraction pattern using TOPAS Academic Software  [19]  (4. 1, Bruker AXS Inc. , Madison, WI, USA, 2007). This software uses a linear least squares method to predict the measured X-ray diffraction pattern. The parameters used in the fitting calculation include the lattice parameters for each phase, the occupancy of each atom in the unit cell, the scale factor (relative intensity of each phase), and the effects of crystallite size (L vol ) and strain. The goodness of the fit was evaluated using the lower weighted profile R-factor, R wp  [20]. The relative weight fraction (W j ) of each phase (i. e. , precipitate type) observed in the diffraction pattern was calculated using Equation (3)  [21]. W j = S j • M j • Z j • V j n ∑ i=1 S i • M i • Z i • Vi (3) where S is the scale factor calculated by TOPAS, n is the number of phases, M is the mass of the unit cell, Z is the number of atoms in the unit cell, and V is the unit cell volume of the phase.
The authors would like to thank  T. Fülöp ,  J. Citrin ,  M. J. Pueschel , and  R. Bravenec  for helpful discussions, data, and feedback. Simulations were run on the CINECA Marconi cluster. GW was supported by the  Vetenskapsrådet (VR)   Framework grant for Strategic Energy Research (Dnr. 2014-5392 ), JET modelling task  T17-04 , and the  EUROfusion Researcher Grant. IP was supported by the  VR   International Career Grant  (Dnr. 330-2014-6313 ), and  Marie Skladowska Curie Action Cofund  (project  INCA   600398 ). This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
Actuator fabrication Clevios TM P Jet 700 PEDOT:PSS ink in aqueous solution (solids content 0. 6-1. 2 wt%, Heraeus Precious Metals GmbH & Co, Germany) was filtrated through syringe filters (Minisart ® NML Syringe Filters 17594-K) with a 5-m pore size. Inkjet printing was carried out using a jetlab ® II Precision Printing Platform equipped with a PH-46 drop-on-demand printhead (MicroFab Ltd. , U. S. A. ) and a piezo-electrically driven MJ-AT-01 dispensing device with an orifice diameter of 50 m. The jetting frequency was 800 Hz, with a customized waveform. The temperature of the substrate holder was set to 45 • C to enhance the evaporation of the solvent. Patterns were designed using the vector graphics software Inkscape (http://inkscape. org) and exported in a bitmap monochrome format to be uploaded by the printer software.
Since 2012, tick-borne encephalitis (TBE) is a notifiable in the European Union. The European Centre for Disease Prevention and Control annually collects data from 28 countries plus Iceland and Norway, based on the EU case definition. Between 2012 and 2016, 23 countries reported 12,500 TBE cases (Ireland and Spain reported none), of which 11,623 (93. 0%) were confirmed cases and 878 (7. 0%) probable cases. Two countries (Czech Republic and Lithuania) accounted for 38. 6% of all reported cases, although their combined population represented only 2. 7% of the population under surveillance. The annual notification rate fluctuated between 0. 41 cases per 100,000 population in 2015 and 0. 65 in 2013 with no significant trend over the period. Lithuania, Latvia and Estonia had the highest notification rates with 15.
Methods Since 2012, the European Centre for Disease Prevention and Control (ECDC) requires all 28 EU Member States, plus Iceland and Norway, to annually report their TBE data to the European Surveillance System (TESSy) database using the EU case definition (Box)  [18]. More detailed information on surveillance systems is available elsewhere  [10]. We included all cases reported during the years 2012-2016 meeting the EU case definition in the analysis. TBE Information received included age, sex, date of disease onset, probable place of infection, place of residence, importation status, hospitalisation status, vaccination status, and clinical outcome. Coded values for variables with geographical information (probable place of infection and place of residence) followed the nomenclature of territorial units for statistics (NUTS) of the EU  [19].
We used population denominator data provided by the Statistical Office of the EU (Eurostat) for calculating rates (data extracted on 22 September 2017). We compared continuous variables by the Mann-Whitney U test and categorical variables using the chi-squared test. We estimated annual rates of change and their 95% confidence intervals (CI) using a log-linear regression of notification rates over the period 2012-2016. We assessed goodness of fit of linear regressions using F statistics. We used Stata software release 14 (StataCorp. LP, United States) for all data management and statistical analyses.
Discussion The European TBE surveillance data suggest a stable trend over the years 2012-2016 with no reported changes in national surveillance systems; continuing the long-term trend observed in Europe since the mid-1990s  [12]. The number of TBE cases reported in Europe, excluding Russia, increased over the years 1990-1994, probably reflecting the start of surveillance in many countries  [12]. Over the following 15 years (1995-2009), the trend was stable with an annual number of TBE cases fluctuating between 2,000 and a peak in TBE cases, which resulted in the highest number of TBE cases (> 3,000) observed in Europe that year. An analysis carried out in eight European countries suggested that human behaviour in response to good weather conditions, e. g. increased outdoor recreational activities, was the main explanation for the 2006 spike rather than tick abundance  [13].
The overall stable trend observed in TBE surveillance data is mainly driven by a few countries reporting the majority of cases, potentially masking important disparities both between and within countries. For example, two countries (Czech Republic and Lithuania) accounted for 38. 6% of all reported TBE cases, although their combined population represented only 2. 7% of the population of the 25 countries included in this analysis. All countries with average annual notification rate above one TBE case per 100,000 population had a stable trend over the period. We only observed an increase in Finland and France. In France, the notification rate almost tripled in 2016 compared with previous years in the Alsace region where most cases occurred  [20] ; some newly identified foci such as the Alpine region could also have contributed to the upsurge in cases.
Yet, a general circulation model predicted that TBE transmission could be disrupted by climate change with a contraction of TBE areas to higher altitudes in central Europe and northern latitudes in Scandinavia  [25]. This would result in a decreased incidence in the coming decades but such change would probably not be captured over a 5-year period. Changes in human behaviour (e. g. increase of at-risk outdoor recreational activities) can put people at greater risk of exposure to ticks and thus TBE. However, with increased vaccine coverage such risk could be improved. Finally, better clinical awareness, testing and reporting would improve the ability of the surveillance system to detect cases. The geographical granularity of our data (at best NUTS3) does not allow fine monitoring of TBE foci, which countries are best placed to perform.
However, during the first effort to collect TBE data at the EU/EEA level most of the recommendations were followed  [11]. We implemented standard EU case definition for TBE and initiated routine collection of surveillance data from EU/EEA countries, to at least NUTS-3 geographical level for most of the countries. ECDC encourages all countries to report their cases at subnational level. The reported TBE cases followed a pronounced seasonality with most cases occurring during the warmer months May-October, which is likely due to human habits with people spending a greater amount of time outdoors in areas e. g. forests where ticks populations are high  [1]. Cases infected during colder months are possible, however, especially in central Europe. Cases of TBE are more common in older age groups, with the highest number of cases occurring in those aged 40-69 years.
Acknowledgements We would like to thank all people involved in the surveillance of TBE in the participating countries as well as the data managers at ECDC, without whom surveillance of TBE at EU/EEA level would not be possible. In addition, we would like to thank the following persons for their contribution to TBE surveillance in Europe:  Heidemarie Holzmann  and  Karin Stiasny (Austria) ;  Tinne Lernout ,  Vanessa Suin  and  Marjan Van Esbroeck, (Belgium) ;  Sanja Kurecic Filipovic ,  Iva Pem Novosel  and  Goranka Petrovic  (Croatia);  Bohumír Kříž Marek Malý  and  Helena Šebestová  ( Czech Republic );  Markku Kuusi ,  Jussi Sane  and  Pirjo Turtiainen (Finland) ;  Isabelle Leparc-Goffart (France) ;  Doris Altmann and Wiebke Hellenbrand (Germany) ;  Theano Georgakopoulou ,  Kassiani Gkolfinopoulou ,  Anna Papa , and  Danai Pervanidou  (Greece);  Jeff Connell  and  Sarah Jackson  (Ireland);  Giovanni Rezza ,  Caterina Rizzo  and  Giulietta Venturi  (Italy);  Antra Bormane ,  Tatjana Klemjacionoka  and  Irina Lucenko  (Latvia);  Saulius Čaplinskas ,  Aidas Spiečius  and  Milda Žygutienė  (Lithuania);  Eelco Franz  and  Agnetha Hofhuis  (The Netherlands);  Solveig Jore and Heidi Lange (Norway) ;  Cornelia Ceianu ,  Romana Rebreanu  and  Anca Sirbu  (Romania);  Edita Staroňová  and  Elena Tichá  (Slovakia);  Marta Grgic-Vitek , and  Maja Socan  (Slovenia).
Statistical analysis The OS and DFS rates were calculated from the time of transplantation by means of the Kaplan-Meier product limit method. Cumulative incidence was used to estimate the GvHD, relapse and nonrelapse mortality rates. The statistical analyses were performed with SPSS software (version 17. 0; SPSS Inc. , Chicago, IL, USA). Competing-risks regression analysis has been performed using the Fine and Gray (1999) approach.
Patients Twenty-five consecutive patients with high-risk leukemia had received haploidentical transplantation and were at least 100 days post-transplantation at the time the data were frozen for analyses (Sept 30, 2017). The median age at transplant was 12 years (2-17). Ten patients were diagnosed with B-cell acute lymphoblastic leukemia, 6 patients suffered from T-cell acute lymphoblastic leukemia, 7 were diagnosed with acute myeloid leukemia and 2 were diagnosed with biphenotypic leukemia. At the time of HSCT, a total of 12 patients were in first complete remission (three patients in morphologic remission but immunophenotypic or molecularly detectable disease); 9 patients in second complete remission; and 4 had advanced disease or morphologically detectable disease. All the patients received two haploidentical donor-cell products: the first was a source of hematopoietic progenitor cells and the second was administered to adoptively transfer a large number of donor memory T cells.
Results Total plasma N-glycan composition was determined by UPLC analysis of 2AB-labelled glycans as reported for HPLC  25  and more recently for UPLC where Saldova et al. 12  carried out extensive glycan analysis using exoglycosidase sequencing and MS to confirm all structures and populate a database. The chromatogram was integrated into 42 distinct individual peaks. The Oxford nomenclature has been used to annotate individual glycan structures  26  where A represents the number of antennae present, F indicates the fucose, B indicates the presence of a bisecting N-acetylglucosamine, G represents galactoses and S denotes sialic acids (Fig. 1 ). A representative chromatogram showing separation of the plasma glycome into predominant structures is shown in Fig. 2a  and Supplementary Fig. 1. Each glycan identified, its peak number and GU value is shown in Supplementary Table  1.
In total, this test produced an accuracy of 77. 8%, with a 100% and 50% specificity and sensitivity respectively. Adding the clinical variables produced a sensitivity of 62. 5% and a specificity of 90. 0%. CRP alone degraded the sensitivity greatly suggesting again that richer information is present in the relative abundance of the N-glycans (i. e. peak areas). At risk groups: As mentioned previously, high/medium family history contained higher levels of peak 40 (predominantly glycan A4G4S[3,3,3,3]4) and therefore may represent a biomarker to identify at risk groups. In addition, the same glycan peak (A4G4S[3,3,3,3]4) was increased in individuals who were sampled before they developed colorectal cancer (FINRISK dataset; P = 1. 10E-03; Supplementary Table  3 ). Further evidence that at-risk groups can be identified was confirmed by using all peak areas and age (allowed as both at risk and control groups were age matched) of individuals in our classification algorithm showing some discrimination power to identify at risk individuals (FINRISK dataset; Table  5 ; AUC 0.
This gene has been implicated in ulcerative colitis  40  , gastric  41  and small cell lung cancer  42. To the best of our knowledge, this is the first report of a specific glycan level being associated with colorectal cancer family history. Thus, increased levels of A4G4S [3,3,3,3]4 is a marker for not only CRC but high/medium cancer family history too. The ability to identify at-risk individuals before the onset of colorectal cancer through statistical analysis adds further credibility to this investigation, individuals who were sampled at a healthy stage from Finland who later developed CRC also had significantly increased levels of A4G4S [3,3,3,3]4 perhaps indicating that it is a marker for 'high potential to develop CRC'. Moreover, the at-risk group could be identified with discrimination significantly above random (FINRISK dataset; AUC 0.
Although unlikely it might be possible that part of the differences we observe may be due to slight technical variation. Environmental factors such as age and obesity are known to influence protein glycosylation. We have taken this into account in our analyses and adjusted accordingly for these contributing factors, however, it remains possible that glycosylation changes observed are due to the occurrence of cancer (reverse causality). We endeavoured to investigate this further by analysing a prospective cohort and we observed that CRC cases could be identified with discrimination significantly above random. In addition, GP40 in the SOCCS study and GP41 in the prospective study both contained increased levels of the glycoform found to be associated with family history. However, the pattern for the remaining glycoforms was different between the two datasets.