dataesr/scientific-paragraphs-categorization huggingface dataset:
Despite the absence of Haglr transcripts, growth deficit was not observed in homozygous mice, indicating that variation of Haglr RNA level had no appreciable consequence in organismal growth. Shorter transcripts were nevertheless still initiated from the anti-Hox strand in the Del(CpG114) chromosome (although at a much lower level) and terminated exactly at the position of the termination site of the Hoxd3 UTR (Fig. 6 ). These transcripts, which were also named "Haglr" in the Ensembl database even though they have an independent start site, initiated within the last exon of Haglr. It is not known whether such transcripts are still present in Midget mice after the large targeted deletion and lacZ reporter replacement  (3). If these additional RNAs were not maintained in Mdgt embryos, it is possible that their absence would perturb the transcription of Hoxd3, thus leading to growth retardation.
ACKNOWLEDGMENTS. We thank members of the D. D. laboratories for sharing material and discussions, the histology platform at  Ecole Polytechnique Fédérale (Lausanne) (EPFL) , the genomics platform in Geneva,  Olivier Schaad  for advice on statistical analyses, and  Thomas Alexander McKee  for discussions. This work was supported by funds from the  EPFL , the  University of Geneva, Swiss National Research Fund  Grant  310030B_138662 ,  European Research Council Grants SystemHox 232790  and  RegulHox 588029 , and the  Claraz Foundation  (D. D. ).
Fourier-Transform Infrared Spectroscopy (FT-IR) FT-IR experiments were conducted using a Thermo Scientific Nicolet IS10 spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) by a reflection technique (ATR). The absorbance data were processed for the wave number range of 400-4000 cm -1 using the software OMNIC Spectra 8. 3. 103 (Thermo Fisher Scientific, Waltham, MA, USA). Each film sample was respectively deposited directly between the two crystals. FT-IR absorbance spectra were also obtained for pectin and indomethacin powders for comparison with plasticized and indomethacin-loaded films.
The samples were withdrawn at different time intervals up to 24 h and replenished with an equal volume of Tris buffer solutions at each time interval. The absorbance of the withdrawn samples was measured at 320 nm using UV spectrophotometer (Cary 50Bio UV-Visible Spectrophotometer, Orsay, France). All the dissolution runs were performed in triplicate. Non-plasticized film without indomethacin was used as control to assess that no other compound absorbs at 320 nm. To analyze the drug release rate kinetics and mechanism of drug release, the in vitro dissolution data were fitted into Zero-order, Higuchi matrix model and Korsmeyer-Peppas empirical power law in order to obtain the best-fit model. Polymers 2017, 9, 289 5 of 14 (a) Zero-order kinetics: The release rate data were fitted into the following equation, Q t = Q o + K o × t (5) where Q t is the amount of drug dissolved in time (t), Q o is the initial amount of drug in the solution, and K o is the zero-order release constant.
Treatment group assignments were not masked. The primary endpoint was overall survival, defined as time from randomisation until death from any cause, analysed by modified intentionto-treat. A 12% higher overall survival at 2 years in the once-daily group versus the twice-daily group was considered to be clinically significant to show superiority of the once-daily regimen. The study is registered with ClinicalTrials. gov (NCT00433563) and is currently in follow-up.
An additional 5% was added to the sample size of 506 patients to allow for ineligible patients, giving a total recruitment target of 532 patients. The primary survival outcome was analysed using the modified intention-to-treat principle, because four cases provided no follow-up data and hence were censored at time zero. Further details about the statistical analysis are available in the protocol. 15 All randomly assigned patients who were treated with at least one study dose of chemotherapy and who were alive at the time of the first toxicity assessment were included in the safety analysis. Data were collected at each study site and monitored by the independent data monitoring committee. We submitted reports to the independent data monitoring committee on an annual basis, commencing 12 months after the first patient was randomly assigned.
More than 95% of patients overall had a performance status of 0-1. Less than 2% of patients were never smokers, almost two-thirds were former smokers, and just over a third were current smokers (table  1 ). 312 (57%) of 547 patients were staged with PET/CT, and 426 (78%) of 547 were stage III according to the Union for International Cancer Control classification (table 1). The number of planned cycles of chemotherapy was four for most patients (table 1). Almost 60% of patients actually received four cycles and a further 20% received six cycles of chemotherapy (table 2). At the data analysis cutoff in March 1, 2016, the median follow-up was 45 months (IQR 35-58) for those still alive. and case report form in three cases at baseline; in all three cases, the performance score was recorded as 0-1 on the randomisation form.
Number at risk (number censored) Once-daily Twice-daily HR 1•13 (95% CI 0•92-1•39); p=0•24 B C groups might be explained by several changes in the management of small-cell lung cancer since the publication of the Intergroup study, including PET/CT staging in more than half of patients, the use of modern and precise radiotherapy techniques, and improvements in supportive care. ] [9] [10] [11]  One of the systematic overviews also identified that time from the start of any treatment to completion of radiotherapy is a key variable in predicting outcome. 20 Although not significant, 2-year overall survival was slightly higher in the twice-daily group than in the once-daily group, which could possibly be a result of improved delivery of treatment in the Data are n (%). The radiotherapy toxicity population was used to analyse the prevalence of these adverse events because it would not be possible to report radiotherapy-related toxicity in patients who did not receive radiotherapy.
Radiation pneumonitis was not specifically reported in the Intergroup 0096 study, but in this trial very few (<3%) patients had severe radiation pneumonitis or severe pulmonary The lower than anticipated toxicity rates and rates of local failure reported in this study suggest that radiotherapy treatment delivered concurrently with chemotherapy could be intensified further-for example, by means of dose escalation or hypofractionation. A limitation of this study is that although we did not mandate an upper age limit-with the aim to gather much needed evidence about the outcome of elderly patients treated with concurrent chemoradiotherapyonly 15% of the patients included were older than 70 years. Data for patients older than 70 years participating in CONVERT was presented at the International Association for the Study of Lung Cancer 17th World Conference on Lung Cancer in Vienna, Austria, in 2016, and the results of this analysis will be presented in a separate report.
The key to completing accrual was to include a large number of recruiting sites. Furthermore, by contrast with US practice, concurrent chemoradiotherapy is not always adopted as the standard of care for limited-stage smallcell lung cancer in Europe, and the study provided an incentive for centres to adopt and set up concurrent treatment protocols. Given the importance of keeping overall treatment time as short as possible, future studies could investigate doseescalated twice-daily or hypofractionated radio therapy concurrently with chemotherapy. Further data for the outcome of patients treated with high-dose 2 Gy per fraction treatment will be provided by the ongoing CALGB 30610/RTOG 0538 study (NCT00632853). The upcoming analysis of the CONVERT translational studies, including the prognostic role of baseline circulating tumour cells, could provide data for relevant biological stratification factors that can be used prospectively in future studies.
Acknowledgments This study was funded by the  Cancer Research UK Clinical Trials Awards and Advisory Committee  (grant reference number  C17052/A8154 ),  French Ministry of Health ,  Programme Hospitalier de Recherché Clinique  (grant reference number  NAT 2007-28-01 ),  Canadian Cancer Society Research Institute  (grant reference number  021039 ), and the  European Organisation for the Research and Treatment of Cancer (Cancer Research Fund ,  Lung Cancer , and  Radiation Oncology Groups). The authors would like to acknowledge the support of the  National Cancer Research Institute Radiotherapy Trials Quality Assurance team  ( Nicki Groom  and  Elena Wilson ); the  Manchester Academic Health Science Centre Clinical Trials Unit ;  Sally Falk  and  Amy Bossons  (CONVERT trial coordinators);  David Ryder  ( Manchester Academic Health Science Centre Trials Co-ordination Unit statistician ); the  National Institute for Health Research (NIHR) Christie Clinical Research Facility ;  David Girling ,  Steve Roberts ,  Christian Manegold , and  Robert Huddart  (Independent Data Monitoring Committee Members); and  Dirk De Ruysscher  and  Jason Lester  ( Independent Members of the Trial Steering Committee ).
Results All statistical data analyses (including classical ANOVAs and regressions) were performed using the VBA freeware (http://mbb-team. github. io/VBA-toolbox/), [46, 52]. Here, we proceed with analyses of increasing sophistication. We start with model-free analyses, which rely upon simple descriptive statistics of peoples' aggregate behaviour in both Decision and Prediction phases. We then perform another series of statistical data analyses, in the aim of directly evaluating the qualitative predictions of our computational model. These analyses exploit inter-individual differences to provide an empirical reproduction of Fig 2. Finally, we report the results of Bayesian model comparisons, given trial-by-trial choice sequences in all experimental phases.
Validation of qualitative model predictions So far, we have only looked at qualitative differences between the Same and Different conditions in order to assess the existence of the false-consensus and influence biases (irrespective of cost type). In what follows, we aim at evaluating the predicted relationships between one's own attitude and one's prior on the Other's trait (false-consensus), and between one's prediction error and one's attitude change (social influence), for each type of cost, separately. We assess these relationships through the analysis of inter-individual variability in prior/posterior guesses (Prediction phase) and initial/final cost-susceptibilities (Decision phases). We estimate the former by fitting the BPL model (cf. Section 1. 2) to trial-by-trial data in the Prediction phase for each participant, in all (Same, Different and Noisy) conditions.
For several years, many researchers tried to semantically integrate geospatial datasets into the semantic web. Although, there are many general means of integrating interconnected relational datasets (e. g. R2RML), importing schema-less relational geospatial data remains a major challenge in the semantic web community. In our project SemGIS we face significant importation challenges of schema-less geodatasets, in various data formats without relations to the semantic web. We therefore developed an automatic process of semantification for aforementioned data using among others the geometry of spatial objects. We combine Natural Language processing with geographic and semantic tools in order to extract semantic information of spatial data into a local ontology linked to existing semantic web resources.
For our experiments, we used LinkedGeoData and Geonames ontologies to link semantic spatial information and compared links with DBpedia and Wikidata for other types of information. The aim of our experiments presented in this paper, is to examine the feasibility and limits of an automated integration of spatial data into a semantic knowledge base and to assess its correctness according to different open datasets. Other ways to link these open datasets have been applied and we used the different results for evaluating our automatic approach.
Introduction Integration of heterogenous datasets is a persisting problem in geographical computer science. Many classical GIS approaches exist making use of relational databases to achieve a tailormade integration of geospatial data according to the needs of the current task. In the SemGIS project we are aiming at integrating heterogenous geodatasets into a semantic web environment to take advantage of the flexibility of semantic data structures and to access a variety of related datasets that are already available in the semantic web. We intend to use the so-formed geospatial knowledge base in the application field of disaster management in order to predict, mitigate or simplify decision making in an event of a disaster. As in our project we are facing a large number of heterogenous geodatasets of which we often do not know the origin nor intention nor the author and therefore lack an appropriate domain expert to help us understand data fields, we as non-domain experts are be left with a manual integration approach of said data.
Dataset descriptions, if available, are often in natural language only which may give us hints but are hard to process in general and contain often hard to resolve ambiguities. However, despite mentioned obstacles we believe that a at least rudimentry classification and interlinking of our given datasets by means of the data values and data descriptions, is feasible. To achieve this goal we will in this paper present our fully-automated approach to analysing and integrating geospatial datasets into the current semantic web ecosystem and we will highlight its success as well as shortcomings of its various steps.
State Of The Art Our work is based on Natural Language Processing which is a major field of computational linguistics. In particular our approach is using but not limited to Language Recognition approaches to recognize keywords and terms in our datasets to relate them to already existing concepts in our semantic web knowledge base. We mostly rely on the following established techniques of the NLP community: Part-Of-Speech Tagging on limited language resources in common languages, the usage of several versions of Wordnet  (Miller, 1995)  along with BabelNet  (Navigli and Ponzetto, 2010)  and the multilingual labels of ontologies like DBPedia  (Auer et al. , 2007)  and Wikidata  (Vrandečić and Krötzsch, 2014).
Related Work Related work has been done on the integration of interconnected database table and published by the W3C as the R2RML 1 standard. This standard automatically creates a local ontology of a given database schema once a given mapping is provided. Further research has been conducted by  (Bizid et al. , 2014)  in which they use GML schemas to convert GML datasets to local ontologies and provide automated interlinking strategies for similarly structured database resources. In contrast to our work, this work focuses on similar datasets of a similar format only and does therefore not take into account a wider range of possible input formats. In a more general context, the task we are approaching can be seen as an interlinking or a link discovery task in a specific geospatial domain, whereas we try to link a generated local ontology to existing resources in the semantic web and try to identify concepts that represent the contents of our respective datasets.
(Nentwig et al. , 2015)  gives an overview on tools in the link discovery domain. Among the different tools, some projects, as the Karma project  (Knoblock et al. , 2012)  or the Silk Framework  (Volz et al. , 2009)  have a semi-automatic linking approach for a variety of domains with a geospatial reference. In contrast to our work most of these tools described in the references above require human assistance in the form of experts or administrators of the corresponding databases. Another project very close to ours, is the Datalift project  (Scharffe et al. , 2012). This project allows for taking many heterogeneous formats (databases, CSV, GML, Shapefile,. ) as Input in order to convert them into RDF and link them to the semantic web. Their goal is exactly the same, as ours but the approach not.
Semantic Extraction from Geodata Sets A geodataset describes in our context, a database table including one column for the to be described geometry and n Featurecolumns in which values related to the geometry are stored. Every feature (Fe) comes with a feature descriptor, which is usually present in a one-worded string description. In the real world, a geodataset can take on many vector and raster data formats (e. g. GML 2  (+dialects), KML 3  , SHP, POSTGIS Database Table ,.
GEOTIFF 4 ). For a more detailed description on data integration please refer to  (Homburg et al. , 2016). Even though in reality schema descriptions for example in GML 3. 2 might reveal a columns schema type by delaring a non-native type description in the XML schema, we assume for our experiments that no such information is given. We proceed in this manner because in our experience a significant amount of spatial data is schemaless and sometimes even their origin or authors are not known. In reality we could in some cases take advantage of (XML) schema type information if we create a local ontology out of (XML) schema descriptions as described in  (Homburg et al. , 2016). However even if we find a data type in a schema we are still left in relating this type to other types in the semantic web, as URIs from types in XML schemas do not usually refer to URIs of the semantic web.
Categorizing features When receiving a geodata set from an unknown source, little is known about how the features semantically relate to the given geometry. Features can describe a geometry better e. g. a column "height" of a dataset "Tree". It could also, be database related information such as ID columns or a relation to a different concept, e. g. column "partner city" of dataset "City". In conclusion: If we can determine the semantic functions of feature columns of the given dataset, we are able to relate them to the given SpatialObject and extend our knowledge base. For this purpose we intend to define classes of columns of our relational datasets that describe us information about the geometry itself (e. g. address information), metadata information such as IDs of databases or describing elements, hints about foreign key relations, hints about semantic descriptions of the geometry and in the case of numerical values the context of the numerical value in accordance with its unit if available.
Relation to existing datasets We might encounter that the dataset we are about to import into our knowledge base already exists in another knowledge base. However, its description in the already existing knowledge base might vary in detail, contain additional information or even contradicting information to the dataset we are about to import. The challenge therefore is to recognize an already existing dataset, to extend it accordingly and to determine false information entered into the knowledge base in order to correct it. Management of contradicting statements of data will be discussed in a later section.
Geometry and Dataset Specification Chances are, that the geometries we intend to import from our dataset are already present in the semantic web or through the import of a previous datasource (Fig. 1 ). In this case we can make use of already existing geospatial ontologies to verify our assumption. By using the Geonames 6 and LinkedGeoData Ontology 7 we can ask for existing concepts in a small enough buffer around the centroid of the geometry we are about to analyze as shown in Listing 1. If the buffer is set small enough and the coordinates are precise, we will get a unique result of a representing class for the geometry, if it exists. By analyzing each geometry of our given dataset in this way we can increase the chance to get the correct class if the same class occurs with many matches.
Feature Value Analysis Several kinds of information appear frequently in our dataset, that is why, a first step is to identify common information thanks to a Feature Value Analysis (Figure  2 ). During this step, a sequence of process specific to each kind of information searched are applied. The explanation of these processes is presented below. • Address components: The specificity of geodatasets is that they contain a geometry for each spatial object. The usage of the spatial object geometry with a geocoding service (in our case, Google Map API), allows for address enrichment which has been explained previously (cf. 4. 2. 1). The information which has been retrieved is compared with the different value of the cell, in order to know which column contains information concerning the geographic address of the object.
Feature Descriptor Analysis The Feature Descriptor Analysis (Fig. 3 ), in the case of our datasets the column name, can give us valuable information about Properties and Classes in Ontologies that represent the columns content. However, column names are represented in natural language and with a limited context to parse from, which can limit disambiguation methodologies if needed. In addition before an analysis of the feature descriptor can be conducted, the following preprocessing steps need to be done: • Recognition of common abbreviations and replacement of those with their long form • Detection of the language being used in the columns name.
Combination of the different steps and creation of the local ontology • If a class has been detected using the geometry detection, this class (or the highest ranked class) will be taken to describe the dataset • If a class has been detected via analysis of the files name, this class will be taken if no appriate geometry class has been detected • Properties and their respective ranges as detected by the Feature Descriptor Analysis and for address columns as determined by its respective analysis are created • Individuals are created according to the recognized classes and values that can be resolved to URIs will be created as the corresponding individuals.
Datasets For testing our approach, we need to apply it on a set of data which provide a diversity of information with different quality. Our project SemGIS being in the context of disaster management, we have chosen five files: two files about schools, two files about hospitals and a file about rescue organisation which will serve as buildings to be evacuated in case or emergency unit buildings respectively. Files about schools and hospitals come from two different regional sources, one of each concerns the city of Cologne and the other concerns the region of Saarland in Germany. The two different files have some similarities, but don't provide the same type of information. They allow to evaluate the integration of a similar dataset (with a same subject) from different data sources and with different content.
Experiment 1: Manual approach by non-expert This manual approach consists in creating an ontology for each file of the dataset. There exists several ways to create an ontology and to understand the meaning of a file when you are nonexpert 8. That is why, we have chosen to realize this approach two times with two different persons. For doing so, we have provided the dataset to these two persons and asked them to create an ontology which contains all information of the file. During the experiment, we have evaluated the similarity between these two manual approaches and then, each of them with our automatic approach. The comparison between a manual approach and our approach allows to compare a human method with a computational method.