dataesr/scientific-paragraphs-categorization huggingface dataset:
EIB is supported by NVKP 16-1-2016-0017, OTKA 20237, OTKA 111958, and VEKOP-2. 3. 3-15-2017-00016. RK is supported by National Institutes of Health Grants HL091983, HL126186 and HL134608. CP is supported by the Italian Ministry of University, Research and Technologies and by Federico II University of Naples, Italy (STAR Grant). SL holds grants from the South African National Research Foundation. FBE is supported by the German Research Foundation (DFG Research Unit FOR2149). DdK is supported by Queen of Hearts program Dutch Heart Foundation 2013T084; BMRC CS-IRG, ATTRaCT SPF grant. CMB is supported by Insitut National de la Santé et de la Recherche Medicale, Université Paris Descartes and ANR-16-CE92-0032-02. 8. Conflicts of interest PF is the founder and CEO of Pharmahungary Group, a group of R&D companies.
Surveillance, Data Storage and Filtering: Levels of Infrastructure-Based Internet Control in Russia RuNet governance has developed upon several layers, with three main types of measures adopted since 1998: a) Surveillance measures of 'lawful interception', called System of Operative Investigative Measures (SORM), aimed at giving governmental services such as FSB (former KGB) access to private communications both by telephone and on the Internet; 2 b) Regulation of data storage, restricting important data flows to national borders; c) Filtering measures, restricting access to a growing list of websites (blacklist) 3 considered as extremist. These three layers are interconnected and show a global tendency towards a RuNet "balkanization"-hyper-localization and nationstate regulation of data and communication flows.
Maxim I. , hosting provider with Komtet, notes: "His revelations were not a surprise for specialists…. In Russia, and I am sure, in any country, it is possible to get whatever information about a user or his websites. Some RuNet users even joked about the role Snowden played in RuNet regulation: 'Why have they kept Snowden here?' Very simple. They just asked him about the downsides of the American system of surveillance and made a better one". 14 nowden's revelations attracted attention to existing surveillance practices and made it possible to compare SORM with the US system. However, the revelations' most important impact concerned the role of US cloud services and internet corporations. In response, the Russian government modified the "Law on storage and protection of personal data" and reconsidered data geopolitics to allegedly guarantee the "protection of Rus-sian citizens' data from US government surveillance".
Researcher-journalists Andrei Soldatov and Irina Borogan insist on the role that Snowden played in this: "Right on time, Edward Snowden appeared on the world stage. The NSA scandal made a perfect excuse for Russian authorities to launch a campaign to bring global web platforms such as Gmail and Facebook under Russian laweither requiring them to be accessible in Russia by the domain extension. ru, or obliging them to be hosted on Russian territory"  (Borogan & Soldatov, 2013). The law #242-FZ was adopted on 1 September 2014. It obliges providers to "store personal data of Russian citizens, used by internet services, on the territory of the Russian Federation". Providers must guarantee recording, systematization, accumulation, storage, updates, modifications and extraction of personal data using databases located on Russian territory.
15 Noncompliance with this new law may result in total blockage of the service. Thus, for example, in November 2016 LinkedIn was blocked in Russia (including mobile apps) for the violation of the new data storage policies. Web services are also required to build backdoors for Russian secret services to access stored data. Another way to put pressure on western companies is to block entire web services because they store "forbidden information". Thus, YouTube was blocked in Russia for hosting a video judged as extremist. Facebook removed a page called Club Suicide rather than seeing its entire network blacklisted. The repatriation of data illustrates the tendency of Russian internet governance towards "digital sovereignty"  (Nocetti, 2015, p. 112). Several resistance tactics have developed in response to these balkanization measures.
Now we have a piece of the Internet that is completely out of control by our own country, and it is not good. Because the data is being gathered globally, by someone who is beyond the borders of our state, and it is totally wrong". 17 According to our interviewees, this project is actually a response to the inefficient Yarovaya law and the growing popularity of encryption among RuNet users. Alexey P. emphasizes: "They understood that storing gigabytes of data will give no results, especially because it is en-crypted…. So the project to build a Man-in-the-middle attack on the governmental level is scary".
Users are also creatively adapting existing tools to achieve the bypassing goal. For instance, Russians actively use Net archives (archives. org, archive. is or cached versions of websites stored by Google), or activate the "turbo" mode in Opera or Yandex browsers, enabling a very high speed of data transfer. However, once again these tools merely give access to the blocked page, but do not guarantee any anonymity. Moreover, in November 2016, Roskomnadzor started negotiations with Opera representatives about the possibility of blocking access to forbidden content even in "turbo" mode. As of January 2017 no agreement has yet been reached, due to Opera having recently been sold to Golden Brick Capital, a Chinese investment consortium. As users can be tracked and eventually persecuted for their search of forbidden materials, a set of anonymizing network-layer tools is promoted through cryptoparties and online tutorials, starting with Tor.
On their end, activists are learning how to program message self-destruction or deactivate the tracking of search history and location. Encryption of mobile devices and usage of pass-paragraphs and double or triple authentication methods (combining fingerprints, password and a figure) are becoming popular alongside the use of anonymous search engines such as StartPage or DuckDuckGo, adblocking plugins and cookie controls. Activists advocate multilayered protection and encryption by combining virtual machines, VPN, TOR and encrypted mail and messaging clients. Finally, Snowden's revelations on NSA surveillance enabled a comparison of the Russian SORM and US intelligence strategies, showing common points and important differences between the two surveillance systems. Activists describe the Russian system as less efficient than its US counterpart, pointing to the geopolitical reasons behind this gap.
For example, Meduza actively informs its readers about recent updates in Russian Internet governance. Such expatriation also takes shape in a diaspora of infrastructures. In particular, what are called "mirrors" of forbidden websites are created, using platforms such as Amazon. 29 An increasing number of NGOs and other associations opt to transfer their hosting to outside of Russia. As Maxim I. , a hosting provider, observes: "Such websites as Children 404 30  or oppositional websites are progressively transferred to foreign servers and start using non-Russian gTLDs (generic top-level domains, such as. ORG). The reason for this is simple: without any court decision your page or the entire website can be blocked, sometimes even by mistake, as it happened with Google or GitHub. I remember also a mass exodus of clients in Belarus, after they have been obliged to work only in Byelorussian data-centers".
Conclusions As Edward Snowden is currently on (temporary) asylum in Russia, numerous scholars and journalists insist on the geopolitical significance of this act and emphasize its importance within the global context of a "Cold War 2. 0". Yet, far from supporting Snowden's fight for transparency of government data and Internet users' freedom, the Russian government is gradually centralizing its surveillance over the RuNet. However, the direct and indirect influence of the Snowden revelations is making itself visible in a number of other ways, by exposing censorship and surveillance at an unprecedented scale and encouraging creative responses to it. This article has explored how, in response to growing censorship, a variety of tactics are being developed and deployed by Russian users and content producers, ranging from infrastructure-based countermeasures and détournements to geopolitical reconfigurations involving the migration of hardware and people.
Although it is not within the scope of this paper to estimate the long-term impact of recent mobilizations (as we do not have enough data to measure this), we can conclude that Russian civic mobilization may be analyzed at two levels. The first is public and collective, for example the "anti-SORM" movement or petitions against Yarovaya law. Such movements appear to have limited impact beyond encouraging visibility and draw-ing public attention to the problem, and even so they remain limited to a small section of the population (with only around 600,000 signatures on Change. org and 2,000 people in attendance at the anti-Yarovaya law meeting), namely IT professionals, journalists, bloggers and Internet freedom activists. However, at the second level, that of so-called "evasion" tactics, mobilisation is far more successful: far from being a contentious means to criticize government or affect changes in legislation and Internet policy, these invisible or elusive techniques have a direct and immediate impact on the everyday practices of users and IT professionals.
Roskomnadzor is a Federal Executive Authority of the Russian Federation, performing the following functions: control and supervision of mass media (including electronic mass media), mass communications, information technology, and telecommunications; supervision and statutory compliance control of personal data processing; managing the Radio Frequency Service activities; supervision of production of copies of audiovisual works, computer software, databases and audio recordings on any media; accreditation of experts and expert organizations for content evaluation in order to ensure child information security. It is affiliated to the Ministry of Communications and Mass Media of the Russian Federation. The role of Roskomnadzor has recently expanded. The list of all its functions may be found on its official website: http://eng.
Acknowledgements. We thank  J. Stein ,  N. Merlet ,  F. Stoop  and  F. Auclair  for their contributions to the laboratory experiment version of Meso- NH  project which started several years ago at the  CNRM Geophysical Fluid Mechanics Laboratory in Toulouse. We thank  O. Thual  for a helpful discussion regarding the ideal flow used in section 3. 1. Experiments used in section 4 have been led by  M. Perrier  and  A. Butet  from the  CNRM Geophysical Fluid Mechanics Laboratory in Toulouse. We thank  Philippe Wautelet  for the 3D figures. Finally we thank  J-C. Canonici who designed the original scheme from which figure 6 was designed.
2G ; Supplemental Table  S2 ;  Mohammed et al. 2016) , indicating that at least a fraction of the TRs highlighted by our analyses directly coregulates transcription with NR1H4. We next sought to define whether differential TR recruitment to CRMs D-E and CRMs F-G could contribute to regulation of different biological pathways in the liver. With this aim, we first interrogated whether genes encoding TRs D-E or TRs F-G were genetically linked to distinct mouse phenotypes (MPs) using the mammalian phenotype ontology database  (Smith et al. 2005). While both were linked to MPs related to liver morphology/functions, TRs F-G were more specifically linked to altered metabolic homeostasis, while TRs D-E were more specifically associated with developmental defects (Fig. 2H ). In line with this, striking differences between CRMs D-E and CRMs F-G were also found when genes assigned to those CRMs as described previously (Fig.
5G ; Supplemental Fig. S21 , respectively). We found that TRs could be arranged into the same three main groups described in Figure  5A  (Fig. 5G ; Supplemental Fig. S21 ). A subcluster containing most TRs F-G was also evidenced by these analyses (Supplemental Fig. S21 ). To rule out a confounding effect of TRs preferential cobinding on these results, IGR data were further analyzed by using pairwise comparisons to define how frequently a given SNV impacts on the binding of two TRs by strictly focusing on CRMs where these two TRs are corecruited. Importantly, hierarchical clustering of these data produced similar results (Fig. 5H ). Altogether, these analyses therefore indicate that TRMs previously identified are independently evidenced by selective and coordinated modulation of TR chromatin recruitment.
Acknowledgments The authors thank  Dr. D'Santos C. and the proteomic core facility at  Cancer Research UK (Cambridge, UK)  for processing RIME samples. This work was supported by grants from the  Fondation pour la Recherche Médicale  (Equipe labellisée,  DEQ20150331724 ), " European Genomic Institute for Diabetes" (E. G. I. D. ,  Agence Nationale de la Recherche ,  ANR-10-LABX-46 ), and  European Commission. B. S. is a member of the  Institut Universitaire de France  and is supported by the  European Research Council  ( ERC Grant Immunobile , contract  694717 ).
Public functional genomics data recovery and TR ChIP-seq data processing Public functional genomics data used in this study were downloaded from public databases and are listed in Supplemental Table  S1. All raw data were processed similarly including FastQC analysis (http://www. bioinformatics. babraham. ac. uk/projects/ fastqc), read mapping to mm10 using Bowtie (version 1. 0. 0) (Langmead et al. 2009) , peak calling using model-based analysis of ChIP-seq version 2 (MACS2)  (Zhang et al. 2008) , and visual inspection of called peaks using the Integrated Genome Browser (IGB)  (Nicol et al. 2009). Replicates were managed using the irreproducibility discovery rate (IDR)  (Li et al. 2011) , and false positive calls repeatedly identified in inputs and IgG ChIP-seq were removed from all data sets.
Self-organizing maps analyses The self-organizing maps (SOMs) were generated using the R package "kohonen2"  (Wehrens and Buydens 2007). The input vectors from CRMs, optimal number of nodes, and parameters to train the SOMs were defined according to  Xie et al. (2013)  and are detailed in the Supplemental Material. Nodes were further grouped into classes based on hierarchical clustering performed using the hclust function of the R package "Stats" (R Core Team 2015). A planar projection of the toroidal map was used for data visualization.
Multidimensional scaling and hierarchical clustering analyses of TR co-occurrence TR co-occurence at CRMs from classes D, E, F, or G was used to calculate Tanimoto distance matrices, which were used for MDS and hierarchical clustering using the R packages "Stats" (R Core Team 2015) and "gplots" (https://cran. r-project. org/web/ packages/gplots/), respectively. Details are provided in the Supplemental Material. Gene ontology, mouse phenotype, and gene set enrichment analyses GO enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID 6. 7)  (Huang et al. 2009). ToppCluster was used to link TRs to MPs  (Kaimal et al. 2010). Gene set enrichment analysis (GSEA) was performed using the GSEA software developed at the Broad Institute  (Subramanian et al.
Institute for Theoretical Physics (MITP) for providing support during the completion of this work. This work has been performed in the framework of the ERC grant 291377 "LHCtheory: Theoretical predictions and analyses of LHC physics: advancing the precision frontier" and of the FP7 Marie Curie Initial Training Network MCnetITN (PITN-GA-2012-315877). It is also supported in part by the Belgian Federal Science Policy Office through the Interuniversity Attraction Pole P7/37. The work of FD and FM is supported by the IISN "MadGraph" convention 4. 4511. 10 and the IISN "Fundamental interactions" convention 4. 4517. 08. BM acknowledges the support by the DFG-funded Doctoral School "Karlsruhe School of Elementary and Astroparticle Physics: Science and Technology". The work of KM is supported by the Theory-LHC-France initiative of the CNRS (INP/IN2P3).
The work of MZ is supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodovska-Curie grant agreement No 660171 and in part by the ILP LABEX (ANR-10-LABX-63), in turn supported by French state funds managed by the ANR within the "Investissements d'Avenir" programme under reference ANR-11-IDEX-0004-02. Appendix A: The tW b and tW bH channels in the 4FS.
Acknowledgments We thank the LHCHXSWG and in particular the members of the ttH/tH task force for giving us the motivation to pursue this study. We are grateful to  Simon Fink ,  Stefano Frixione ,  Dorival Gonçalves-Netto ,  Michael Krämer ,  David Lopez-Val ,  Davide Pagani ,  Tilman Plehn  and  Francesco Tramontano  for many stimulating discussions, and to the MadGraph team (in particular  Pierre Artoisenet ,  Rikkert Frederix ,  Valentin Hirschi ,  Olivier Mattelaer  and  Paolo Torrielli ) for their valuable help. KM would like to acknowledge the  Mainz.
Stable carbon isotope ratios for individual fatty acids (FA) were calculated from FAME data by correcting for the one carbon atom in the methyl group that was added during the derivatization process. This correction was made according to  Gladyshev et al. (2012)  by taking into account the isotope ratio of the derivatized methanol (BF3methanol, -37. 9 in our study), and the fractional carbon contribution of the free fatty acid to the ester. δ 13 C FA = (δ 13 C FAME -(1 -f ) • δ 13 C CH 3 OH ) f (1) where δ 13 C FA is the isotopic composition of the free fatty acids, δ 13 C FAME is the isotopic composition of the fatty acid methyl ester, f is the fractional carbon contribution of the free fatty acid to the ester and δ 13 C CH3OH is the isotopic composition of the methanol derivatization reagent.
Chimeras were removed using Uchime (v4. 2. 40;  Edgar et al. , 2011). Gene sequences were compared to sequences in public databases with BLASTn  (Altschul et al. , 1997). The 16S rRNA gene sequences were aligned the SILVA SSUref N99 references (v. 128) using ARB aligner. The pufM DNA sequences were translated into amino acid sequences and aligned using the MAFFT E-INS-I algorithm. The resulting protein alignment was back-translated to nucleotide acid sequences using pal2nal  (Suyama et al. , 2006). Conservative values of 97% and 94% nucleic acid sequence similarity for 16S rRNA and pufM sequences, respectively, were chosen for clustering sequences into Operational Taxonomic Units (OTUs) using MOTHUR  (Schloss et al. , 2009). Representative sequences (defined as the sequence with the minimum distance to all other sequences in the OTU) were obtained using MOTHUR.
A pufM database and a consensus Bayesian tree were built as described in  Boeuf et al. (2013). Representative sequences of each OTU (245 pb) and short pufM environmental reference sequences were aligned as above and added to the backbone tree using the ADD_BY_PARSIMONY algorithm implemented in ARB software  (Ludwig et al. , 2004). Likewise, representative ssu sequences of each OTU (927 pb) were added to the SILVA reference tree using ARB. Non-informative taxa were removed from both final tree.
NanoSIMS Data Processing NanoSIMS data were then processed using the proprietary WinImage Software (L'image, CAMECA SIMS Image Processing). Purple sulfur bacteria were detected based on their typical coccoïd shape from the 12 C 14 N-images and each bacterial cell was defined as a region of interest (ROIs). ROIs data were exported as. csv files and processed using the R© statistical framework to calculate carbon and nitrogen stable isotope ratios as well as atom percent of 13 C and 15 N. r i = (R ini -R measured. i ) R ini -R final. i (2) where r i is a dimensionless ratio for incubation experiment i, R ini is the initial isotopic ratio of the bacteria (based on the mean of natural isotopic composition of the control), R measured. i is the measured isotopic ratio (NanoSIMS) of a given bacterial cell for incubation experiment i, R final.
ACKNOWLEDGMENTS This work was supported by  French National Program EC2CO-MicroBien  (project  BIOPOURPRE ). The authors want to thank  Prof. François Robert  and  Dr. Smaïl Mostefaoui  ( National Ion MicroProbe Facility at the Muséum National d'Histoire Naturelle, Paris, France ) for precious advices on C fluxes calculation and for help with NanoSIMS measurements, respectively. BJ acknowledges the  Station Biologique (Roscoff, France ) for hosting him in the frame of the  EU FP7 Integrating Activities  project  ASSEMBLE. cyanobacterial group as active diazotrophs in a coastal microbial mat using NanoSIMS analysis. ISME J. 6, 1427ISME J. 6, -1439ISME J. 6,. doi: 10. 1038ISME J. 6, /ismej. 2011. 200. 200.
(b) Recordings and playbacks Original field recordings of offshore pile-driving in Swansea Bay, UK, were made between 87 and 200 m from the sound source  [52, 53]. Pile-driving at this site involved a 1. 2 m diameter monopole being driven around 25 m into the seabed at a water depth of 6. 5 m. The recordings of this process were made with a Hi Tech Inc. HTI-99HF hydrophone with inbuilt preamplifier (manufacturer calibrated sensitivity 2204 dB re 1 V mPa 21 , 20-125 000 Hz frequency range) and a data logger (RTsys EASDA, 44. 1 kHz sampling rate). Recordings of ambient coastal sound were made at Portsmouth, Plymouth, and Gravesend, UK, using a Hi Tech Inc. HTI 96-MIN hydrophone with inbuilt preamplifier (manufacturer calibrated sensitivity 2164 dB re 1 V mPa 21 , 20 -30 000 Hz frequency range) and a digital recorder (Roland Edirol rspb.
(c) Acoustic analysis All sound recordings were analysed in MATLAB (v. 2013a and 2017a) using pamGuide  [59]  and paPAM  [60]. Spectrograms (see the electronic supplementary material, figure  S1 ) were calculated for 1 -2 000 Hz (the frequencies most likely to be of relevance to seabass  [61] ) using a Hann evaluation window, 50% overlap, 0. 1 s window length over 20 s recordings. Cumulative sound exposure level (SELcum) was calculated for the whole 5 min exposure period, whereas sound-pressure level (SPL) was calculated over 20 s recordings (electronic supplementary material, table  S1 ). Zero-to-peak level, 90% energy envelope, rise time, and single-strike sound-exposure level (SELss) were calculated using an average of five randomly selected pile strikes (electronic supplementary material, table  S1 ).
(e) Response measures The 10 min videos from each trial were converted to MPEG-4 with Handbrake 0. 10. 5 (https://handbrake. fr/). idTracker  [66]  was used to track the x and y coordinates of each fish throughout each trial. All subsequent analyses were performed using MATLAB (2016a) and followed similar methods to  [67 -69]. The parameters associated with the spatial and directional organization, as well as the movement dynamics of the fish, are detailed below. Measures were calculated separately for each fish in the group. All variables were calculated for the 1st (no playback) and 2nd ( playback) half of each trial separately.