dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION Some of the solutions applied in eTOX for generating a predictive system usable in production environments involve the use of specific software, wrapping the scientific work developed by SMEs and academic partners into a "package" easier to deploy and integrate in corporate settings. The use of this kind of software, which is described in this article as a "modeling framework, " adds further advantages, like facilitating the consortium-wide adoption of standard modeling components, and simplifying key steps of the model life cycle, like the model retraining and maintenance. In eTOX, a new modeling framework was developed ad hoc for the project (eTOXlab). This software has been released as open source under GNU GPL v3. 0  (GNU GPL v3, 2007). The source code of eTOXlab is accessible at https://github.
com/phi-grib/eTOXlab. A fully configured VM including eTOXlab is also accessible at http://phi. upf. edu/envoy/. Hence, future projects aiming to develop similar predictive systems have now the option of reusing these resources, either as they are or customizing them to meet specific project needs. We consider that these resources have value on their own, but they have an additional value as a proof-of-concept, since they demonstrate that they are helpful for making software tools developed by academic and SMEs usable by pharmaceutical companies. Table  1  lists some of the key features that, in our opinion, such kind of frameworks must incorporate. Another key element required is the definition and consortium-wide adoption of protocols for labeling, documenting and verifying the models. These are important aspects, which must be negotiated with the end-users for providing fit-for-purpose solutions.
Many of the eTOX partners have continued their collaboration and now participate in a new IMI project (eTRANSAFE) 1 , which shares with eTOX the aim to develop predictive systems. The ideas and principles described in this article are being applied, extending and adapting them to meet the objectives of this new project. One part of this effort is the development of a new modeling framework (called Flame), inspired on the same principles of eTOXlab but technologically more advanced. The source code of this software, still in development, is distributed under  GNU GPL v3. 0 (GNU GPL v3, 2007)  and can be accessed at https://github. com/phi-grib/flame. Finally, a limited version of eTOXsys, including the modeling system described here and a few selected models has been made open to the scientific community and can be accessed at http: //etoxsys.
CONCLUSION Beyond the concrete database, predictive models and integrated computational system that have been developed, the eTOX project has demonstrated that the successful completion of ambitious industry-oriented collaborative projects requires not only the development and implementation of state-of-the-art scientific approaches, but also the careful implementation of adequate technical and organizational solutions. Among them, the adoption of adequate standards and protocols is a key component. The efforts done in eTOX in this respect are being extended to the new IMI eTRANSAFE project 1 , which will jointly exploit preclinical data and clinical safety information for a better prediction of potential human safety liabilities  (Sanz et al. , 2017). We hope this paper will contribute to save the readers' time and effort in similar public-private projects, as well as to improve the efficiency in the collaboration between the pharmaceutical industry and external parties in the development and application of computational tools supporting the drug discovery and development pipeline.
FUNDING The eTOX project (Grant Agreement No. 115002), was developed under the Innovative Medicines Initiative Joint Undertaking (IMI), resources of which are composed of a financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contributions. The authors of this article are also involved in other related IMI projects, such as iPiE (no. 115735), TransQST (no. 116030) and eTRANSAFE (no. 777365), as well as the H2020 EU-ToxRisk project (no. 681002).
(E) Functional composition of NOT-elements into NOT-modules, by nesting elements to obtain conjunction of NOT functions. For a 2-input NOT-module, the output gene is expressed only when none of the inputs has been present (corresponding to a NOR gate: NOT(A) AND NOT(B)). (F) Hierarchical composition framework for Boolean recombinase logic. ID-and NOT-modules are composed in series, following a priority rule in which the NOT-module is placed upstream the ID-module. The device shown here can be scaled to perform all functions based on conjunction of NOT and IDENTITY functions. An Automated Design Platform for Recombinase Logic. We then aimed at generating a software for automating the design of cellular consortia performing asynchronous Boolean logic. ] [36]  We thus designed an algorithm called CALIN (Composable Asynchronous Logic using Integrase Networks) based on two main steps (Figure  4A ).
First, the Boolean function of interest is decomposed into a disjunction of conjunctive clauses using the Quine-McCluskey algorithm (see Methods). Then, each clause is converted into a given computational device for which particular connections between integrases and inputs are generated. The CALIN script written in Python is available on Github and can be directly used for high-throughput generation of biological designs. Furthermore, the CALIN python script can design logic devices customized for specific organisms (E. coli, B. subtilis and S. cerevisiae) and can be tailored by the user to generate devices using fully customized DNA sequences. S1  for numbers). (D) Example of a biological implementation for a 4-input Boolean function. The function shown here is divided into a disjunction of conjunctive clauses (see Figure  2A ).
■ ACKNOWLEDGMENTS We thank  L. Ciandrini ,  G. Cambray ,  G. Labesse , members of the synthetic biology group and of the CBS,  P. Lemaire  and  P. Hersen  for fruitful discussions,  J. L. Pons  and  L. Bonnet  for help with the CALIN website. Support was provided by an  ERC   Starting Grant  "COMPUCELL", the  CNRS/INSERM Atip-Avenir program  and the  Bettencourt-Schueller Foundation  to J. B. S. G. was supported by  Ph. D. fellowships from the  French Ministry of Research  and from the  FRM: Fondation pour la Recherche Medicale  ( FDT20170437282 ). The CBS acknowledges support from the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01.
Data At the level of every city; we acquire, parse and combine GTFS (Google Transit Feed Specification) a datasets of every transportation mode. Google Transit Feed Specification is a format of data created to provide transit schedules and public transport information for specific geographical location. It is a "standard" developed by Google in order to help public transport agencies to publish and integrate their data with Google Maps. A typical GTFS feed includes information about multiple aspects of a transit system, such as stops, routes, trips, and schedules. Needless to say, the availability of these datasets is a key resource to study the dynamics of the transportation systems, e. g. , see  [59] [60] [61] [62]. In our study, we use GTFS datasets to represent the anatomy of the public transportation system in all cities except London, and build a multiplex urban transportation network for every city.
In order to process and transform the combined GTFS datasets to multiplex system, we perform four tasks: Merging GTFS dataset from different sources. Since the datasets come from various agencies and transportation companies which have adopted different indexes, the first step to reliably build transportation network after merging datasets is to re-index stop locations to avoid any conflicts. To do so, we join stations spatially (using latitude and longitude coordinates). We use text similarity matching techniques applied on stations' names to double check our results. Identifying and extracting routes. As we are interested in identifying connected locations, we start by filtering occasional trips, such as trips during national holidays, etc. , from our dataset. Then, for each trip, we order stop locations based on departure time to identify connected locations.
In the case of the city of London, we use both (1) OpenStreetMap (OSM) b and (2) The National Public Transport Data (NPTDR). c OSM provides an updated map of different bus and metro stations in the city, whereas NPTDR contains a snapshot of every public transport journey in Great Britain for a selected week in October each year. While NPTDR database covers Great Britain (England, Scotland, Wales), we focus only on London city. First, we filter all the stations from NPTDR that are inside the bounding box of London city. Second, we extract all the stop points and trajectories of the two modes of transportation considered, i. e. , bus and metro networks in this case. Then, we use these stop points and trajectories to build the graph of each layer. Next, we identify the inter-layer edges that connect all the same nodes residing in both layers.
Finally, we build a two-layer transportation multiplex for the city of London by merging both graphs and using the identified ordinal nodes. Note that the hardest part about collecting data, is to use two sources of data for two modes of transportation, for example the metro network and the bus network for the city of London. When we started the resilience analysis, the data was not available for the city of London. Thus, we used other reliable data sources, Open Source Maps and The National Public Transport Data (NPTDR), in this case. The summary of the characteristic of each network is given in Table  1.
We also see that the multimodal transportation network of Paris allows a higher coverage compared to New York and London. This is explained by the density of both "Metro de Paris" which is the second denser worldwide, and the great density of its corresponding bus network that has more that 38K edges (a complete graph of that size would have had approximately 45K edges). Another surprising yet interesting observation is the coverage achieved by the London multiplex network, which lays a little bit above 0. 2 at τ = 1000, way behind the performances achieved by the other three cities. This is all the more surprising that both London metro network and bus network are of the same size as Paris networks. The reasons of such under-performance might be due to the fact that London networks have been generated from a different dataset which might be incomplete for the bus network (the metro network has been thoroughly verified by us).
Availability of data and materials Detailed bus and metro network data are made available through open data portals. For metro networks, we parse GTFS (Google Transit Feed Specification) datasets for every city. For NYC, we used GTFS data from NYC MTA, see http://web. mta. info/developers/download. html. For Chicago, we used GTFS data from Chicago Transit Authority, see http://www. transitchicago. com/developers/gtfs. aspx. For Paris, we used GTFS data from RATP, see https://data. ratp. fr/explore/?sort=modified. For London, we used GTFS data from TFL Open Portal, see https://api. tfl. gov. uk/. These data sets generally are well maintained; however, many properties are often incomplete or missing entirely. For this purpose, we infer required road characteristics to build realistic and routable road networks using OpenStreetMap, an open-source crowd sourced mapping tool.
Introduction Poor air quality (AQ) is a global public health issue and AQ events can span across countries. Using emergency department (ED) syndromic surveillance from England and France, we describe changes in human health indicators during periods of particularly poor AQ in London and Paris during 2014. Methods Using daily AQ data for 2014, we identified three periods of poor AQ affecting both London and Paris. Anonymised near real-time ED attendance syndromic surveillance data from EDs across England and France were used to monitor the health impact of poor AQ. Using the routine English syndromic surveillance detection methods, increases in selected ED syndromic indicators (asthma, difficulty breathing and myocardial ischaemia), in total and by age, were identified and compared with periods of poor AQ in each city.
Retrospective Wilcoxon-Mann-Whitney tests were used to identify significant increases in ED attendance data on days with (and up to 3 days following) poor AQ. results Almost 1. 5 million ED attendances were recorded during the study period (27 February 2014 to 1 October 2014). Significant increases in ED attendances for asthma were identified around periods of poor AQ in both cities, especially in children (aged 0-14 years). Some variation was seen in Paris with a rapid increase during the first AQ period in asthma attendances among children (aged 0-14 years), whereas during the second period the increase was greater in adults. Discussion This work demonstrates the public health value of syndromic surveillance during air pollution incidents. There is potential for further cross-border harmonisation to provide Europe-wide early alerting to health impacts and improve future public health messaging to healthcare services to provide warning of increases in demand.
Air quality data The area studied here has been limited to London and the whole Paris region (Île-de-France), rather than a country level. In England, the Department for Environment, Food and Rural Affairs monitors and reports on levels of air pollution using monitoring stations and provides health advice using the Daily Air Quality Index (DAQI). 8 AQ in the Paris region is monitored by Airparif and reported using the Citeair index. 19 oth DAQI and Citeair systems monitor and report on multiple pollutants; however, each index is reported using different methodology. Therefore, the daily pollution levels across both London and Paris were standardised here, using the reported levels of PM 2. 5 and PM 10. The city-wide average value for each PM on each calendar day was calculated as a mean of the maximum values reported for each monitoring station on that day, in that city.
Each ED had a list of diagnosis terms created locally which was available for selection in the patient attendance record. These diagnostic terms have associated codes linked to them with each ED using one of three codesets: Commissioning Data Set (CDS) Accident and Emergency Diagnosis Tables,  23  10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10)  24  or Snomed CT. 25  EDs eligible for inclusion in this study were defined as those reporting using ICD-10 or Snomed CT diagnosis coding systems, which provide the level of detail required for the identification of the indicators of interest; EDs using the CDS coding system were not able to provide the coded diagnosis data in this detail. This investigation included five eligible EDSSS participating EDs in London (all located within the London PHE Centre).
More relevant codes may exist for each indicator than described here; however, only codes reported to EDSSS/OSCOUR in this study are included. Although each system was found to include different codes and even numbers of codes within each indicator, they would identify most of the same patients for inclusion within the indicators used here. For each syndromic surveillance system, attendances were aggregated by age group defined as 0-14, 15-44, 45-64 and 65 years and over. The epidemiological analysis of ED attendance data included construction of trends in attendances for each syndromic indicator, both for all ages and for each age group, and city. The daily percentage(s) of attendances for each indicator were calculated using the number of attendances within an indicator (numerator) and the daily number of total (all-cause) attendances with a diagnosis code within each surveillance system (denominator).
CC-S, TCH, GES, AJE contributed to the study design, critical revision and final approval of the manuscript. Funding This research received no specific grant from any funding agency, commercial or not-for-profit sectors. This surveillance is undertaken as part of the national surveillance functions of Public Health England and Santé Publique France. Competing interests None declared. Patient consent Not required. ethics approval Ethical approval for this work was not required. The anonymised EDSSS health data used in this study were routinely collected at part of the public health function of PHE. The collection and analysis of data provided by the OSCOUR network in the frame of public health surveillance and epidemiological studies has been authorised by the French National Commission for Data protection and Liberties (CNIL).
Items from each domain were evaluated by at least 30 patients from countries. Patient input on item relevance, clarity, coverage etc. was obtained using semi-structured interviews. Before the interviews, items were translated into the relevant languages by the Translation Office of the EORTC Quality of Life Department according to rigorous and well-established guidelines. 18 The list of items was revised based on the patient comments. In phase 4, the resulting item banks were field-tested in heterogeneous, international samples of cancer patients. The goal was to include a total of about 1,000 patients across countries. The patients completed the new items together with the QLQ-C30. The resulting dataset formed the basis for the final psychometric evaluations. These included: evaluating dimensionality using factor analysis for ordinal variables; calibrating the IRT model (the generalized partial credit model) and evaluating item fit; evaluating differential item functioning (DIF) to explore whether items function similarly across different groups of patients (e.
g. , men and women, patients from different countries); and evaluating the measurement precision of the CATs based on the resulting item banks using both observed and simulated data. 14 Items not fitting the unidimensional IRT model or exhibiting DIF were candidates for exclusion. Ethical approval for phases 3 and 4 was obtained in accordance with local ethical standards. CAT measurement requires specialized software for real time selection of items, for estimating scores, etc. Therefore the EORTC QLG has developed software for conducting online CAT assessment. The group has also developed software for selecting items for short forms. Short forms are static, fixed questionnaires composed of items selected from the item banks. Scores based on such short forms are directly comparable with those based on the dynamic CAT version and are particularly useful when (online) electronic data collection is infeasible.
Role of the funding source The funder of the study had no role in the study design, data collection, analysis, or interpretation, or writing of the article. The executive committee of the EORTC Quality of Life Group read the article and EO'TC Q L G T all the data in the study and had final responsibility for the decision to submit for publication. The EORTC Q L G PROMs can be used free of charge in academic settings, but the group receives compensation fees when its PROMs are used in commercial settings. The CHES system for electronic data capture is developed by the commercial company Evaluation Software Development (ESD).
Metabolome analyses Serum samples were analyzed with a UHPLC-QTOF-MS system (Agilent Technologies, Palo Alto, CA, USA) in randomized order as a single batch within study. The total number of molecular features was 11,909 and 5290 for SAPALDIA and EPIC Italy respectively. A detailed description of laboratory and preprocessing procedures can be found in Supplementary material. The features with non-missing values for at least 60% of the total sample were retained. The final data set contained 7089 and 2790 features for SAPALDIA and EPIC Italy respectively (1452 were in common). In EPIC Italy, additional missing values were imputed using the procedure implemented in the R package imputeLCMD.
Pathway enrichment analyses Metabolomics, given the high dimensionality and high dependency, benefits much from multivariate systems approaches like pathway enrichment tests. Yet, the challenge unique to metabolomics in this context is annotation. Unlike other omics, annotation of the features obtained from untargeted metabolomics requires laborious manual work. The Mummichog software offers an opportunity to bypass this step and to conduct pathway enrichment tests directly from untargeted MWAS results. Using Mummichog, we found various pathways enriched for AOA, CCVD, and air pollution exposures. Air pollution MWASs and pathway enrichment tests conducted in two cohorts served as each other's validation. Although we found no single overlapping features between the two cohorts when comparing validated pathways, lack of such overlap does not exclude the possibility that the pathways truly reflect air pollution-induced metabolic changes, involving different molecules.
Acknowledgments This work was supported by the grant  FP7  of the  European Commission  " Enhanced exposure 36 assessment and omic profiling for high priority environmental exposures in Europe" (EXPOsOMICS  grant  308610  to PV). The SAPALDIA cohort and biobank is funded by the  Swiss National Science Foundation  grant no  33CS30-148470/1&2  to NP. EPIC-Italy was financially supported by the  Italian Association for Cancer Research (AIRC). We thank  Mr. Vincent Cahais  for coordinating the data storage and transfer. The authors declare they have no actual or potential competing financial interests.
A 3° × 3° region is selected, because it closely matches the size of the domain over which enhanced observations were collected during MC3E and because most of the biases and bias changes seen in the models are of that size at least, allowing multiple similar points to be averaged together. For the two models that did not provide 1° × 1° gridded data (CNRM-NWP and CAM5-IPHOC), the time series are produced using data for the single model grid point nearest to SGP, although these data still represent an average over the spatial size of their model grid box. Statistical significance tests, performed at the 95% confidence level, are carried out to identify whether any T2M bias at SGP, or anywhere over the CONUS, is significantly different from zero. When comparing daily-averaged temperatures for the same day but at different lead times, a paired t test is used, which is used to identify whether there has been any significant change in the bias with lead time.
Results Eleven different models have simulated the T2M over the CONUS during the warm season of 2011 as part of the CAUSES project. Each of the simulations will be evaluated using common techniques. Unfortunately, not all models taking part in the project were able to provide all the variables with all the time and spatial resolution required for them to be evaluated using all of the methods being developed as part of the project. However, enough data were available from all of the models for at least some of the evaluation to be carried out on all of the models. In the figures in this paper, all available model data are shown. This section outlines the analysis techniques that have been applied to each model, and a more detailed discussion of the results will be presented in section 4.