dataesr/scientific-paragraphs-categorization huggingface dataset:
statistical analysis Data are presented as number (percent) and mean ± standard deviation for qualitative and quantitative variables, respectively. Anxiety and depression variables were dichotomized with a cutoff at 8 for absence of anxiety or depression symptoms vs possible/certain presence of anxiety or depression symptoms. 17, 25, 26 Hereinafter, these two conditions are designated as absence and presence of anxiety and depression, for simplification. Characteristics of COPD and control subjects, stratified by professional status (dairy farmers vs non-farmers), were compared using Student's t-test and chi-square test for quantitative and qualitative variables, respectively. Characteristics of DF-COPD and NF-COPD subjects were compared using the same methodology. The prevalence of anxiety and depression was compared in the four groups using the chi-square test.
Logistic regressions adjusted for post-bronchodilator FEV 1 (% predicted value) were performed to compare the prevalence of anxiety between dairy farmers and non-farmers. The same methodology was used to compare the prevalence of depression between dairy farmers and non-farmers. To identify factors associated with anxiety among dairy farmers and non-farmers, bivariate logistic regressions for all candidate factors were performed. Odds ratios (ORs) were presented using forest plots. The same methodology was used to identify factors associated with depression. A p-value ,0. 05 was considered statistically significant. Analyses were performed using SAS software (version 9. 3; SAS Institute, Inc. , Cary, NC, USA).
Acknowledgments The authors thank  Stéphane Jouneau  ( Service de Pneumologie, CHU de Rennes, Rennes, France ),  Henri Simon  ( Mutualité Sociale Agricole, Besançon, France ) and  Martial Botebol  ( FeMaSaC, Besançon, France ) for their intellectual contribution. This study was supported by  Novartis Pharma ,  Mutualité Sociale Agricole, Fédération des Maisons de Santé Comtoises, Don du Souffle and Association Santé Éducation et Prévention sur les Territoires Franche-Comté Bourgogne.
25 μM of RNA or DNA, 4 μg of MBP, and 12 μg of MBP-Nowa1. DNA and RNA extraction, library preparation and sequencing Macronuclear DNA was isolated from post-autogamous cells as previously described  1. For PCR analyses of IES retention, DNA was extracted from approximately 800 cells with a Nucleospin Tissue kit (Macherey-Nagel). Total RNA was extracted from 400 ml of culture per the TRI Reagent BD protocol (Sigma) and was resuspended in RNase free water. A MirVana miRNA isolation kit (Ambion) was used for sRNA enrichment. MAC genomic DNA and small RNA libraries were produced and sequenced per standard Illumina protocols. Paired-end Illumina DNA-seq libraries were constructed and sequenced on either the Illumina GAIIX, HiSeq 2000 or HiSeq 3000 sequencers. Sequence data corresponding to the MAC DNA from NOWA1/ 2_a-KD can be obtained from the GenBank Sequence Read Archive (SRA) under the accession SRR646462.
MAC DNA sequence data for NOWA1/2_c-KD, DCL2/3/5-KD, PTCAF1-KD, PDSG2-KD, ND7-KD and control 1 (empty vector) is available from the European Nucleotide Archive, respectively under the accession numbers ERS1033672, ERS1033670, ERS1033674, ERS1033673, ERS1033676, ERS1033671. sRNA sequence data for NOWA1/2_b-KD and empty vector control can be obtained from the European Nucleotide Archive accessions ERS1455004, ERS1455005, ERS1455006, ERS1455007, ERS1455008, ERS1455009, and for DCL2/3/5-KD, from ERS1455358, ERS1455359, ERS1455360.
Reference genomes and IES retention scores The following reference genomes for Paramecium tetraurelia stock 51, obtained from ParameciumDB 34 , were used in the IES analyses and read mapping: MAC: http://paramecium. cgm. cnrs-gif. fr/download/fasta/ assemblies/ptetraurelia_mac_51. fa MAC+IES: http://paramecium. cgm. cnrs-gif. fr/download/fasta/ assemblies/ptetraurelia_mac_51_with_ies. fa (md5 checksums: dbcc54fb2987c8f60f8e765db7ed274c and 3e5b3fa65ebfaa484566 a1ffddf20239, respectively). IES retention scores (IRSs) were determined by ParTIES  35. For each IES, ParTIES counts mapped reads with unexcised IESs (IES+) and excised IESs (IES-) to determine an IES retention score, IRS = IES + ÷ (IES + + IES -). A table of all the IES retention scores used in this manuscript is included as Dataset 1 (see Data and software availability).
IES and OES retention score regressions To generate the IES and OES score regression matrices, shown in Figures  3A-C , Figure  4E  and Figures S1A-B , we provide the program "after_ParTIES. py" (see Data and software availability), which can be run on Dataset 1 and Dataset 2 (IES and OES retention scores, respectively). This was tested using Python3. 6 and relevant modules for scientific computing from the Anaconda software distribution from Continuum Analytics (version: Anaconda3-4. 3. 1). Three regressions are graphically shown for IRS pairs: an ordinary least squares (OLS) regression, a LOWESS regression (nonparametric) and an orthogonal distance regression (ODR) with a linear function initialized by the slope and intercept from the OLS. OLS and ODR used standard SciPy functions. LOWESS regressions used the Python statsmodels library.
tetraurelia MAC genome is currently missing from the reference assembly, this fraction was present in the ∼20 Mb assembly. To exclude the missing MAC genome regions, empty vector control (control 1) reads were mapped to the 7266 scaffolds with HISAT2 40 version 2. 0. 4 (parameters: "-X 600 -pen-noncansplice 0 --mp 24,8 --max-intronlen 1000), and then, using a custom Python script, only those regions with little or no sequence coverage (≤ 2. 0×; compared to a mean MAC coverage of ∼100×) were extracted (Figure  S5C  shows an illustrative scaffold from which OESs were extracted). Coverage was smoothed using a bi-directional average of two exponentially weighted moving averages (EWMA) with a 200 bp span. This procedure extracted 4799 sequence regions (12. 3 Mb). To minimize the effects of potential genome assembly chaff (short assembled regions, esp.
Shorter OESs are more weakly retained in most knockdowns affecting genome reorganization (Figure  S5D ) in Paramecium, but at least some of this effect may be due to boundary-related underestimation of retention. In future, when a complete Paramecium MIC genome is assembled, the relationship between OES length and retention can be fully assessed. OES retention estimates are provided in Dataset 2. Control OES retention was subtracted from the knockdown OES retention for each OES used in the regression analyses. sRNA mapping sRNAs were mapped and histograms of their genomic distribution were produced as previously  3  with the addition of OESs.
For the lower quantity of iesRNAs in NOWA1/2-KD cells, there are two possible explanations, both of which may be valid: (i) Nowa1/2 is directly involved in iesRNA production or stability; (ii) Nowa1/2 depletion inhibits scnRNA-dependent IES excision due to the inhibition of RNA scanning, which in turn inhibits iesRNA production. Teasing apart these possibilities is a possible future experimental avenue to gain additional insight into the function of the Nowa1/2 proteins. NOWA1/2 silencing was previously reported to lead to retention of maternally controlled IESs, and indeed maternally controlled IES retention levels (following microinjection of complementary IESs into the old MAC) correlate well with the IES retention scores (Figure  5D ). However, following NOWA1/2 silencing we also observe low levels of retention of some non-maternally controlled IESs by PCR analyses and in DNA-seq data (Figure  5D, E ; Table  S1 ).
Data and software availability Datasets 1 and 2, IES and OES retention scores, respectively, are available from Zenodo: DOI, 10. 5281/zenodo. 823766  54. The latest source code of after_ParTIES is available from GitHub: https://github. com/gh-ecs/After_ParTIES. The archived source code as at the time of publication is available from Zenodo: DOI, 10. 5281/zenodo. 832236 55. License: BSD 2-Clause License.
Acknowledgements This work was carried out with the contribution of the LIFE financial instrument of the  European Union  ( LIFE15 ENV/IT/183 ) in the framework of the  MOTTLES  project " Monitoring ozone injury for setting new critical levels " and published within the  International Union of Forest Research Organizations (IUFRO) Task Force on Climate Change and Forest Health. Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2017-74,2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 9 March 2017 c Author(s) 2017. CC-BY 3. 0 License.
These values extrapolate to free energies of activation at 25 °C of 107. 1/ 103. 3 kJ/mol and thermal half-lives of 7. 7/1. 6 days. High thermal stabilities are important for applications such as information storage, or in vivo uses. The slightly larger activation enthalpy for 2 (with the more hindered mesityl-subsituted azo moiety) is consistent with the notion that increasing the size of the orthosubstituents in azobenzenes leads to larger barriers. 17 For 2, thermal isomerization kinetics were additionally measured in toluene to probe the influence of solvent polarity. These data lead to activation parameters (ΔH ⧧ = 97. 6 ± 1. 6 kJ/mol and ΔS ⧧ = -31 ± 5 J•mol -1 •K -1 ) that are very similar to those in DMSO. The fatigue resistance of photochromic switches 2 and 3 was evaluated by repeating several isomerization cycles, which shows that they are robust: no photobleaching takes place (Figure  3 ), even upon irradiation over 20 days.
19 he absorption maxima do not change appreciably upon going from 2 to 4/5. Irradiation of E-4 at 365 nm in C 6 D 6 results in photochemical isomerization and formation of a PSS that consists of 75% of the Z-isomer of 4. The back-reaction was monitored by irradiation at 420 nm and shown to result in the reformation of 46% of E-4. Similar results are obtained for 5 (see the SI). Evaluation of the stability of Z-4 in DMSO solution leads to similar activation parameters for the thermal Z/E isomerization as those found for the precursor 2 (ΔH ⧧ = 99. 2 ± 1. 5 kJ/ mol and ΔS ⧧ = -26 ± 4 J•mol -1 •K -1 ; half-life at 25 °C of 7. 3 days). These data indicate that substitution of F for O-or S-alkyl groups does not significantly affect the photochemical and thermal switching behavior of this class of compounds.
2001). Third, selection-the underpinning of adaptive evolution-is often directional and well described with linear or linearized associations between phenotypic traits and components of fitness (King-ð8Þ solver et al. 2001). Parameters used for all models are provided in section A1 in the appendix (available online), as are expressions to calculate key statistics used to show ecological and evolutionary change from model outputs (sec. A2). Code to produce each figure is available in the Dryad Digital Repository (http://dx. doi. org/10. 5061/dryad. 4c117; Coulson et al. 2017)  as well as GitHub (https://github. com/tncoulson/QG -meets-IPM-figure-code/tree/master).
Under these conditions, a herpesvirus DNA polymerase gene fragment of the expected 733-nucleotides was amplified, including the primer sequences. Two cloned and eight uncloned DNA fragments were sequenced using standard Sanger sequencing procedures using the BigDye Terminator Sequencing Kit 3. 1 (Applied Biosystems), and their chromatograms were manually reviewed for potential misreading using the Chromas 2. 5. 1 software (Technelysium Pty Ltd), and exported to the SeqEd function of the Lasergene software (DNASTAR). The BLAST Nucleotide and BLAST X functions from the NCBI website were used to identify herpesvirus sequences most closely related to those derived from the beluga whales. All 10 sequences obtained from the DNA polymerase gene of the beluga whale alphaherpesviruses have been deposited in the GenBank database of the NCBI and their accession numbers are provided in Table  1.
sub-set of isolates from the ISR and Alaska belugas were tested targeting the DNA polymerase gene of known cetacean herpesviruses. Nucleic acids were extracted from infected and uninfected BWK cell cultures using the QIAamp DSP Spin Kit (QIAGEN) following the manufacturer's protocol. All PCRs were performed in a MJ Research, Inc. thermal cycler in 50 µl volumes. PCR assays targeting the DNA polymerase gene of cetacean herpesviruses contained 200 µM dNTPs, 2. 0 mM Mg SO4, 1 µl of Enzyme Mix Elongase (Invitrogen) and 0. 4 µM of each oligonucleotide primer (FP-Dpol_632: 5'-GAC TTY GCY AGY YTV TAC CCH AGC AT-3' and (RP-Dpol_633: 5'-TTG CKC ACS AGG TCS ACS CCY TTC AT-3'). These primers were designed after performing multiple sequence alignments (MegAlign, Lasergene, DNASTAR) of the complete open reading frames (ORFs) of the DNA polymerase genes of alpha-and gammaherpesviruses of bovine, ovine, and caprine species, available in the GenBank database of the National Centre for Biotechnology Information (NCBI), and selection of highly conserved D r a f t nucleotide sequences.
Over the years, these primers have been modified to their present composition, as homologous cetacean herpesvirus sequences became available (GenBank accession numbers: AY757301, AY949830, AY949828, AY952778, KJ406184, MF678601, KX424961, KX424962). The primers drive the amplification of DNA fragments that range in length from 728 to 733 nucleotides, including the primer sequences.
Acknowledgements Beluga samples from Alaska were collected under permit with the Marine Mammal Health and Stranding Response Network  932-1905-00/MA-009526  and  MMHSRP 932-1905-00/MA-009526  for Cook Inlet and permit with  National Marine Mammal Laboratory  permit  782-1719-07  and  14245  for  Bristol Bay  in conjunction with hunters with the  Bristol Bay Native Association. Cook Inlet samples collections were supported by  Prescott  grant  NA12NMF4390162. Beluga samples from  Canada  were collected under DFO permits and in partnership with the  Fisheries Joint Management Committee (FJMC)  beluga harvest monitoring program. The authors would like to thank the beluga harvesters and Hunters and Trappers We thank the  Fisheries Joint Management Committee  established under the  Inuvialuit Final Agreement , for funding some of the field work and laboratory analyses.
A sub-set of isolates from the ISR and Alaska belugas were tested targeting the DNA polymerase gene of known cetacean herpesviruses. Nucleic acids were extracted from infected and uninfected BWK cell cultures using the QIAamp DSP Spin Kit (QIAGEN) following the manufacturer's protocol. All PCRs were performed in a MJ Research, Inc. thermal cycler in 50 µl volumes. PCR assays targeting the DNA polymerase gene of cetacean herpesviruses contained 200 µM dNTPs, 2. 0 mM Mg SO4, 1 µl of Enzyme Mix Elongase (Invitrogen) and 0. 4 µM of each oligonucleotide primer (FP-Dpol_632: 5'-GAC TTY GCY AGY YTV TAC CCH AGC AT-3' and (RP-Dpol_633: 5'-TTG CKC ACS AGG TCS ACS CCY TTC AT-3'). These primers were designed after performing multiple sequence alignments (MegAlign, Lasergene, DNASTAR) of the complete open reading frames (ORFs) of the DNA polymerase genes of alpha-and gammaherpesviruses of bovine, ovine, and caprine species, available in the GenBank database of the National Centre for Biotechnology Information (NCBI), and selection of highly conserved D r a f t nucleotide sequences.
This can be, for example, work, school or a guitar class on Wednesday. • Flexible activities: these are less restricted in time and space, less planned. We can consider, for example, the action "go out for a beer at an outdoor café" because the sun is shining, or "go shopping" at the last minute because there is an ingredient missing for dinner. Typically, these activities are studied in household and activity-based travel surveys. Our objective is to reconstruct this type of data using alternative data sources that provide a direct measurement of mobility. For this, we use information obtained from Google and Twitter in order to construct a daily schedule for every agent giving time slot-wise probability for carrying out a given activity. Generating several thousands of individual schedules should make it possible to simulate daily rhythms of urban mobility in Bangkok.
Finally, even if cooperation is possible in one place, it is not evident for it to be replicated in another place where the market could be under the control of other telecom operators. One of our objectives is to develop a model of human mobility at the city-level, which would be as generic as possible and which could easily be applied to various geographic zones. This implies that we could use the same sources or information types in various places. In this context, we identified specific types of geographic data available on Internet. These rely on the combined growth of smartphones, social networks and online cartographic services which have led to a sharp increase in geographic information accessible online. Twitter's platform illustrates this phenomenon: users can send short messages publicly leaving digital traces that can be retrieved by others through APIs 5.
These messages are dated and are sometimes precisely geolocalized, revealing the GPS position of the user's mobile phone 6  -which is more precise than the telecom data, limited to the closest relay antenna. As Twitter geolocation feature was added in 2009, and rely on the growing availability of smartphones with a GPS chipset, we can consider this data as relatively new and make it a probable promising candidate for a study on daily mobility in a mega city. To create activity space profiles, we crossed these data sets with a land use typology of Bangkok generated using points of interest (POIs) listed in Google Places 7  , notably, shops, educational institutions and business entities (for a detailed methodology, see  Cebeillac et al. , 2017). The general idea is that by attributing categories of functions (restaurants, service, etc.
) to places visited by individuals, it is possible to reconstruct activity profiles over a typical week. If the number of individuals in the sample is large enough, these profiles can be used to generate a synthetic group of individuals with mobility patterns similar to the target group. The latter corresponds to the group that is most exposed to dengue, that is, people under 30 years of age living in Bangkok  (Limkittikul et al. , 2014). Even if it's difficult to estimate the age of Twitter users, as it's only declarative,  Sloan et al. (2015) , assessed that nearly 80% of the users in UK were below 30 years old and  Longley et al. (2015)  found that men below 30 and women below 25 years of age were overrepresented in London. Although Twitter's demographic data should vary from place to place, we assume that the order of magnitude is similar in Bangkok and that the use of Twitter is over-represented by the youth, which are also more exposed to dengue.
The technical side of this work is somehow linked with the study of  Noulas et al. (2011) , who collected Foursquare check-in data publicly shared on Twitter. As this data contains the name and the type of location (restaurant, train station, hotel etc. ), they were able to analyze the temporal patterns of check-ins in different kinds of places and the overall movement from one place to another. Huang et al. (2014)  inferred the potential activity linked with tweets by intersecting the location of the message by querying Google Place with a search radius of 20m. But their analysis focused only on one user with a large number of Tweets and cases with more than one POI in the searchradius are not evoked. Thus, our approach is more generic, as it doesn't focus on a subgroup of Twitter users (i.
e. , the one that use foursquare or just one user), and supported by a thematic land use classification from an independent spatial database. In the following sections we present (A) a typology of land use obtained from the Google Maps POIs in the city of Bangkok (Thailand) and (B) a database obtained from the social networking platform, Twitter. These data sources make it possible to characterize (C) the rhythms of daily mobilities in Bangkok, from the perspective of (1) macroscopic urban pulse and (2) rhythms and potential targets of individual movement. This data will be useful in calibrating a simulation model for studying the role of daily mobility in the spread of dengue, and their limitations will be discussed.
This typology of commercial areas and units in Bangkok is further complemented by another layer containing educational and religious places (schools, libraries, universities, temples, churches, etc. ) obtained by merging OpenStreetMap and Google Place data into a Boolean grid, and a transport layer (main roads, metros, buses, station and rails) obtained from OpenStreetMap. These socio-economic functions of Bangkok, paired with global and individual mobility patterns from Twitter will be analyzed in the following sections.
B. FROM GEOLOCATED TWEETS TO INDIVIDUAL ACTIVITY SPACES With more than 313 million active users in 2016, Twitter is one of the most popular platforms in the world for self-expression 9. It is a "micro-blogging" service launched in 2006, which makes it possible to send short messages containing 140 characters (tweets). Most of these messages are public, according to the user's choice, and hence accessible to everybody, and some of them are even geolocated (a feature added in 2009). This geolocation is called "general" when the user adds a location to his message. To do so, he chooses a place from a list of places, which is generally but not necessarily close to his real location. This geolocation is called "precise" when the user authorizes the Twitter application to access geolocation data of his phone and subsequently decides to share it in his message.
The precision of the localization then relies on the accuracy of the mobile phone's GPS, which may vary from a few meters to hundreds of meters, when the user is outdoors or indoors  (Zandbergen and Barbeau, 2011). Twitter gives free access to a part of its database and provides tools that allow collection of data, notably the API Stream 10  that provides, in real time, a maximum of 1% of the total public message flow. If a request returns fewer messages than the 1% threshold, which might be the case when we apply a geographical filter in Bangkok, all the public geolocated messages in the area should get recorded  (Morstatter et al. , 2013). To test these theoretical assumptions that rely on an opaque algorithm that could influence the data collection  (Quesnot, 2016) , we conducted an empirical test.
Raw data from the Twitter social network and pre-processing We recorded public and geo-localized data of Twitter users in Thailand in the extended area of Bangkok (13°5'-14°3'N, 99°8'-101°25'W) by limiting the extraction of data to the user's pseudonym (called unique identifier), time stamping and the precise coordinates generated at the time a Tweet is sent. Data was collected from 25 th June 2014 to 4 th December 2015. By the end of this period, we collected 20 126 426 tweets sent by 308 808 users within Bangkok and 25 752 072 tweets if we include messages sent by these users all over Thailand. Some of the messages sent on Twitter do not come from real persons. The company announced in 2014 that close to 8. 5% of its active users used other services. Therefore, a few of these users are potentially bots 11,12.