dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We gratefully acknowledge financial support from the following agencies and organizations:  Ministerio de Ciencia, Tecnología e Innovación Productiva (MinCyT) ,  Comisión Nacional 1234567890 CHEP IOP Publishing IOP  Conf. Series: Journal of Physics: Conf. Series 898 (2017) 052013 doi : 10. 1088/1742-6596/898/5/052013  de  Energía Atómica (CNEA) ,  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) ,  Argentina ;  State Committee of Science of Armenia, Armenia ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ,  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ,  Brasil ;  Croatian Science Foundation , Croatia;  Ministry of Education, Youth and Sports ,  MEYS LE13012 ,  LG14019 ,  7AMB14AR005  and the  Czech Science Foundation  grant  14-17501S ,  Czech Republic; Ministry of Higher Education and Research, CNRS-INSU  and  CNRS-IN2P3 ,  CEA-Irfu ,  ANR ,  Regional Council Ile de France ,  Labex ENIGMASS ,  OSUG2020  and  OCEVU, France ;  Max Planck Society ,  BMBF, DESY, Helmholtz Association, Germany ;  Department of Atomic Energy, Department of Science and Technology, India ;  Istituto Nazionale di Astrofisica (INAF) ,  Istituto Nazionale di Fisica Nucleare (INFN) ,  MIUR, Italy ;  ICRR, University of Tokyo, JSPS, Japan; Netherlands Research School for Astronomy (NOVA ),  Netherlands Organization for Scientific Research (NWO) ,  Netherlands ; The  Bergen Research Foundation, Norway; Ministry of Science and Higher Education , the  National Centre for Research and Development and the National Science Centre, Poland ;  Slovenian Research Agency , Slovenia; MINECO support through the  National R+D+I, Subprograma Severo Ochoa, CDTI  funding plans and the CPAN and MultiDark Consolider-Ingenio 2010 programme, Spain;  Swedish Research Council, Royal Swedish Academy of Sciences, Sweden; Swedish National Infrastructure for Computing (SNIC), Sweden ;  Swiss National Science Foundation (SNSF) ,  Ernest Boninchi Foundation, Switzerland ;  Durham University, Leverhulme Trust, Liverpool University, University of Leicester, University of Oxford ,  Royal Society, Science and Technologies Facilities Council, UK ;  U.
S. National Science Foundation ,  U. S. Department of Energy ,  Argonne National Laboratory, Barnard College, University of California, University of Chicago, Columbia University, Georgia Institute of Technology, Institute for Nuclear and Particle Astrophysics (INPAC-MRPI program ),  Iowa State University, Washington University McDonnell Center for the Space Sciences, USA. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement n o 262053.
For each protocol revision, consent was sought again. We obtained a single document, in an open format, that accounted for the whole consent collection process: a time-stamped consent status regarding each version of the protocol. This document cannot be corrupted and can be checked on any dedicated public website. It should be considered a robust proof of data. However, in a live clinical trial, the authentication system should be strengthened to remove the need for third parties, here trial stakeholders, and give participative control to the peer users. In the future, the complex data flow of a clinical trial could be tracked by using Blockchain, which core functionality, named Smart Contract, could help prevent clinical trial events not occurring in the correct chronological order, for example including patients before they consented or analysing case report form data before freezing the database.
In this context, the Blockchain emerging and promising technology (https://en. wikipedia. org/wiki/Block-chain_(database)) can bring a solid basis for transparency of the enrolment phase to all stakeholders of a clinical trial, especially in the context of obtaining participant consent. Three core functional principles of this technology can play a fundamental role, as follows: 1. Unfalsifiable time-stamping information: that is, proof of existence of any piece of data. When stored, these data are provable and immutable via a strong cryptographic protocol. Moreover, these proofs of existence can be checked on a public website (https://opentimestamps. org, https://arxiv. org/abs/1502. 04015). This transparency is of interest to any stakeholder. 2. Smart contract: a contract that is algorithmically implemented and binds any change in the protocol to patient consent needing renewal.
Blockchain comes into play At a clinical trial level. Blockchain technology can act as a SafeGuard for the complex and wide range of actors required in clinical trials. In practice, the proof of existence for consent is time-stamped and stored in Blockchains, thereby enabling clinical research stakeholders, such as sponsors, investigators and IRBs, which can be numerous in multi-center clinical investigations, to share consent-and re-consent-related data in real time and can archive and historicise consent sets, which can be matched with each revision of the protocol. At a patient level. Implementing a "privacy by design" technology and securely and transparently archiving any dataset that needs to be stored is a substantial step toward improving enrolment phase methodology. Of course, there are ways to achieve a certain level of security in data archiving.
However, even in that case, the attacker will not be able to corrupt data or steal money because it requires the private key attached to the Bitcoin account, and this attack will never occur successfully on the Bitcoin network. Even if it were the case, "double spend" will lead the non-accomplice nodes to distrust the Blockchain network. Also, in terms of attacks, because Blockchain is a shared database, anyone has a copy and no data will be missing or corrupted. We refer the reader to a thorough treatment in https://en. bitcoin. it/wiki/Weaknesses detailing the potential attacks with Blockchains. One step further, we can schematically consider two main issues regarding the consent process, the first related to the quality of the process itself and the second to the identity of the individual consenting.
ChainScript is an open standard. We updated the manuscript to precise this point and explain how the Chainscript improves transparency in reporting point by point and through an homogeneous document the event history of a process. (1) http://chainscript. io/ Further, authors state that "implementing a 'privacy by design' technology, and archiving securely and transparently any dataset that needs to be stored, is a game changer toward improving enrolment phase methodology". improving enrolment phase methodology". First, authors overstate the implication of how archiving securely is a game changer. Secondly, a secure and transparent archiving can be achieved without blockchain as well. The manuscript will improve further if authors circumspect such statements. When we enunciate that archiving functionality is a game changer, we had in mind all the issues related to the consent phase main of which are: failure to obtain written informed consent, failure to provide copies of the document to subjects, consent document not signed or not dated, missing pages in consent document provided to subjects, failure to re-consent when new information becomes available, use of wrong forms, use of expired, non-validated or unapproved forms, the only document last page kept in study files.
So, in this way, we estimate Blockchain is more robust, secure, in the sense that a distributed network circumvents the issue of "single node point of failure", and transparent. The wide adoption of Bitcoin, Ethereum or other community consensus driven technologies, is related to their open or so to say the so-called "permissionless" nature, which ensures that communities of peer users are not selected a priori by any central authority so that the validation of transactions through an open and inclusive proof-of-work process is robust. On a more broader view, what is commonly said about the core specificity with regards to the attributes that can be appended to the Bitcoin Blockchain technology, is that this distributed database helps achieve in the same time three goals that were not met altogether by precedent technologies: consensus about rules, consensus about history, consensus that coins are valuable.
And so the HASH ID ` 56c11d0. ` is not "flagrant errors" and is set as an example inside this merkel tree data-structure. So, if one wants to check the integrity of the transactions hashIDs, one should consider those appearing in the figures 4, 5, 6. We have sent to the only editor the production Chainscript file, according to the editor instructions. We have sent to the only editor the production Chainscript file, according to the editor instructions. Of course, the names and emails are blanked out, to respect data privacy as requested by F1000Research. 2) We insist with the fact that we identified linking digital identities and physical entities as an important issue and as explained in our series of responses to the reviews : -We recall that major and serious issues identified by the FDA corresponds, by a proportion of about 10% of trials, to failure to obtain written informed consent, consent documents not signed or dated, missing pages in consent document provided to subjects, failure to re-consent when new information becomes available, use of expired, non-validated or unapproved forms.
In the far future we envision, we would think of a standardization of some key aspects of clinical trials methods whose critical steps would happen through blockchained transactions. For example, in the model of clinical trials platforms, such as www. clinicaltrials. gov, whose information is in the model of clinical trials platforms, such as www. clinicaltrials. gov, whose information is mandatory before the study starts, a good practice would be then for patients to register on such a platform, they would then have their own key identifiers.
Bibliography We thank the reviewer for the precious remarks about bibliography and to have brought to the knowledge of the authors two of them, namely the one referred as `OriginStamp` and the Carlisle blogpost. We add and referenced them in the revised document. This is an important article, worthy of publication in F1000Research. There are some places in the article where the writing could be tidied up (e. g. references 1 and 10 are the same) but my main comments relate to questions that prompted in my mind, which the authors might wish to address if they revise it: 1.
Namecoin just directs to the Wikipedia homepage (https:// en. wikipedia. org/wiki). The authors should careful when citing the references. Thank you for your vigilance, we deleted this reference when taking into account one of your preceding remarks. No competing interests were disclosed Competing Interests: Version 3 14 November 2017 Referee Report doi:10. 5256/f1000research. 12989. r26847 Timothy Nugent Corporate Research and Development, Thomson Reuters, London, UK.
The general dynamic model of the mechanical system is described as: M (θ) θ + V m θ, θ θ + g (θ) + F d θ + Φ (θ, θ r ) = τ ω (9) where θ, θ, θ represent the positions, velocities and accelerations of the couplers, M (θ) is the inertia matrix, V m θ, θ is the centripetal-coriolis matrix, g (θ) is considered as the effect of gravity, F d is the viscous coefficient term, Φ (θ, θ r ) is the nonlinear hysteretic term, τ ω is the input torque applied to the manipulator joint by the SMA wire. The dynamic behavior of the couplers, gripper, load and links was directly obtained from the CAD design shown in Figure  1  (this CAD design was developed in the Autodesk/Inventor software tool). This model does not only include the exact geometry of each piece but also masses, inertias and centers of mass necessary for the dynamic analysis.
The CAD model is imported via the SimMechanics toolbox in order to obtain a continuous dynamic MATLAB/Simulink model of the mechanical system. On the other hand, the torsion springs and SMA wires torques were obtained from basic physical laws, where the SMA wire's force (F w ) is deduced by inversely proportional realtion to the stress (σ), which can be computed by integration of equation (  5 ): τ wi = F wi r i = Aσ i r i (10) where i = 1, 2 for SMA-1 and SMA-2, r is the coupler radius and A is the cross-sectional area of the wire. The torsion spring torque τ s for the single wire configuration is calculated as: τ s = k s θ 1 + b s θ1 (11) where k s is the spring constant and b s is the spring's friction factor, θ 1 is the angular position of coupler-1 with respect to X-axis. While for the second configuration (antagonistic wires) τ s is given by: τ s = k s (θ 1 -θ 2 ) + b s θ1 -θ2 (12) where θ 2 is the angular position of the coupler-2 with respect to X-axis.
This control represents a simple proportional control, which takes into account the direct and inverse dynamics of the one DOF robot arm. Equation (  28 ) shows the analytical Jacobian of the robotic system and the equation (  29 ) describes the direct kinematics as follows: J A (q) = ∂K op (•) ∂q = -a 1 sin (q 1 ) -a 1 cos (q 1 ) (28) K op (•) = a 1 cos (q 1 ) a 1 sin (q 1 ) -h (29) where a 1 is the length of the second link (150 mm), h is the length of the first link (100 mm) plus the base height (50 mm) and we define q = θ 1. The open and closed loop performance of the SMA actuated robotic arm was evaluated through simulations using Simulink/MATLAB. The open-loop test was performed by applying the maximum safe voltage (V 1H ) to the SMA-1 while no voltage was applied to SMA-2 and then vice versa.
Finite element modeling To obtain the strain field of the stretchable photonics, we have conducted numerical simulations with finite element method (FEM) using the commercial software ABAQUS v6. 14. The elastic properties of materials used in the simulations are either quoted from the manufacturer's specifications (for SU-8) or experimentally measured using dynamic mechanical analysis (for PDMS) or the ultrasound pulse-echo technique (for Ge 23 Sb 7 S 70 ). The stretchable photonic devices embedded in a PDMS matrix were modeled. Taking geometric symmetry into account, only half of the device and the matrix were needed in the simulation, which reduces the computational cost. Moreover, since SU-8 and Ge 23 Sb 7 S 70 glass have similar mechanical properties, the photonic device was modeled as a device made of SU-8 only.
ACKNOWLEDGEMENTS The authors gratefully thank  Lionel C Kimerling  and  Anu Agarwal  for providing access to device measurement facilities,  Qingyang Du  and  Derek Kita  for assistance with device processing and characterization, and  Mark Mondol  for technical support with electron beam lithography. Funding support is provided by the  National Science Foundation  under award numbers  1453218 ,  1506605 , and  1351875. The authors also acknowledge fabrication facility support by the  MIT Microsystems Technology Laboratories  and the  Harvard University Center for Nanoscale Systems , the latter of which is supported by the  National Science Foundation  under award  0335765.
The function of the valve mixing setup was verified by mixing fluorescein and rhodamine solutions and measuring fluorescence levels within the microfluidic chip (Supplementary Fig. 2 ). Microscopy and image analysis. Images were acquired using an Olympus IX71 microscope equipped with an Olympus 100× UPlanSApoPh3 oil objective, Cool-SNAP HQ2 CCD Camera, PI piezo Z-stage, and Prior H117 motorized XY stage. A HBO 103 W/2 mercury short-arc lamp and mEGFP and mCherry filter cubes were used for fluorescence imaging. μManager 28 was used for time-lapse acquisition and MATLAB for image analysis, real-time control and actuation of microfluidic valves. A custom image analysis program was written to track cells that exploits the properties of the 'mother machine' microfluidic topology. Fluorescence levels were measured within a small rectangular region of interest located at the end of each chamber where a single cell is expected to be trapped at all times.
The evolution of several individual cells could be precisely and robustly followed for extended periods of time ( > 15 h). Cells that filamented or did not grow were not considered for control experiments and analysis (pre-established criterion). In total, we run 7 single-cell control and 14 dynamic stimulation experiments. The experimental data and time-lapse microscopy images are available upon request. Models and numerical simulations. Models are based on pseudo-reactions that represent transcription, translation, and degradation/dilution due to growth. Depending on the problem, a deterministic or stochastic interpretation of this set of pseudo-reactions was employed. Mathematically, the deterministic interpretation of the above pseudo-reactions results in an ordinary differential equation (ODE) model, integrated with a stiff ODE solver (ode23s in Matlab).
Acknowledgements The authors thank their respective team members for critical reading of this manuscript. This work has received funding from the  Agence Nationale de la Recherche  ( ANR-10-BINF-0006 ;  ANR-13-BS02-0011 ;  ANR-16-CE12-0025 ;  ANR-16-CE33-0018 ) and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  724813 ).
e. , with a decreased expression in the absence of PRLR) and 251 repressed genes. Next, we sought to identify the common set of genes dysregulated in our model and in another murine model of prolactinoma, to increase the robustness and expand the signification of our findings. For this, we focused on the model of 9 week-old male ACI rats treated with implants containing 5 mg of diethylstilbestrol (DES), which develop prolactinomas after 12 weeks of treatment. In these animals, pituitaries enlarge up to 10-fold and PRL levels increase up to 220 fold  [18]. Specifically, we explored previously reported microarray data for this rat prolactinoma model (accession number GSE4028)  [19]  using the GEO2R program implemented in GEO (Gene Expression Omnibus). We focused on differentially expressed (DE) genes having adjusted p-values lower than 0.
05. This analysis allowed us to obtain a list of 5647 DE genes (transcripts) in the pituitary of DES-treated ACI rats compared with vehicle treatment, 2576 of which were upregulated in the prolactinomas and 3071 downregulated. The intersection of the rat and mouse datasets included an important number of common genes (303, which represent 36% of the mouse dysregulated genes). Then, we focused our attention only on the 218 genes displaying concordant variations (either up-or downregulated in both models, Supplementary Table  1 ). A gene set enrichment analysis showed that the concordantly upregulated genes were characterized by keywords such as cell cycle, regulation of cell proliferation, mitosis (i. e. , Top2A, Rrm2, Cenpi, Bub1, Ska1, Spc25). This result is consistent with an increased cell proliferation in Prlr -/- pituitaries, which manifests anatomically as enlarged lateral pituitary wings at 4 months.
Indeed, activation of Hippo signaling results in dysplastic growth and increased organ size. It has been shown that Lats1 -/-mice exhibit a pituitary hyperplasia  [38]. Thus, a dysregulation of the Hippo pathway may also contribute to pituitary tumorigenesis such as prolactinoma development. FOXO1 (forkhead box transcription factor 1) plays a role in normal somatotrope differentiation as well as gonadotrope function. However, no role of this transcription factor has yet been described in lactotroph cell or in pituitary tumorigenesis  [39]. That being said, it is worth mentioning that FOXO TFs are well-known targets of AKT1, and important regulators of cell-cycle and proliferation in many cell types. We have also explored the intersection between the rat + mouse dataset and DE genes from microarray data of human prolactinomas obtained from a previous study  [40].
Transcriptome analysis Two and four month-old wild-type (n = 6 and 6 respectively) and Prlr -/-(n = 6 and 6 respectively) mice were sacrified by decapitation. Pituitaries were removed, pooled three by three to form duplicates for each genotype, and frozen at -80°C before RNA extraction. Fifty ng total RNA was amplified and labeled with Cy3 using Low input Quick Amp labeling Kit (Agilent technologies, Santa Clara, California, USA) according to manufacturer's instructions. Labeled cRNA was then fragmented and hybridized over 1 Sureprint G3 Mm9 8 × 60 K array (Agilent technologies), allowing the study of the 8 samples in parallel and interrogating over 40K annotated transcripts of Mm9 assembly. Hybridization was performed according to manufacturer's instructions. Raw fluorescence data were extracted using Genespring software v12 (Agilent technologies) and normalized using the 75th percentile method.
Values were then log2 normalized and subtracted of the median value for each replicate. The whole experiment was replicated with another set of animals (12 wild-type and 12 Prlr -/-mice). Differential gene expression (DE) was assessed using Significance Analysis of Microarrays  [17]. The DE gene list was explored using the gene set enrichment analysis program Enrichr  [45]  to identify upstream regulators (kinases and transcription factors) for which known targets are significantly enriched in DE genes. This analysis was performed on 25 March 2016. The network was constructed using Cytoscape 3. 3 and the GeneMania plugin to automatically retrieve known direct protein-protein interactions (interactome) from databases and manually curated bibliographic sources  [46, 47]  and additional manual search.
ACKNOWLEDGMENTS The authors thank  Dr Gwennhael Autret  ( INSERM UMR 970 ,  PARCC-HEGP, Paris, France and Plateforme Imageries du Vivant, Université Paris Descartes ,  Sorbonne Paris Cité, Paris, France ) for MRI performance,  Dr Charles Roux  ( Département d'imagerie cardiovasculaire, Pitié-Salpêtrière, Université Paris VI, INSERM U1146, Paris, France ) for discussion on MRI imaging and  Dr Anne Wierinckx  ( Institut Universitaire de Technologie Lyon1, INSERM U1052, Lyon, France ) for discussion about microarray data analysis.
DISCUSSION In 2008, SLS-like gene clusters were discovered in clinically relevant Gram-positive pathogens (including S. aureus and C. botulinum) and other nonpathogenic bacteria  (7) , leading to the identification of the LLS gene cluster in L. monocytogenes. SLS is a potent membrane-damaging agent and a major virulence factor contributing to GAS infection through rapid destruction of eukaryotic cells and tissue damage  (6, 9-11, 15, 18-20). It has been proposed that SLS-like virulence factors from other Gram-positive pathogens also behave as potent cytotoxins  (6, 14). Interestingly, functional experimental data of LLS activity on eukaryotic cells are scarce (3), despite the fact that LLS is almost exclusively detected within lineage I strains (the most frequent lineage among L. mono- (C) The number of necrotic foci in spleen and liver tissues from the same infected mice was evaluated.
Three components were obtained at this stage: (i) the upper phase, a clear solution of blood plasma; (ii) a middle thin layer of platelets and leukocytes; and (iii) at the bottom RBCs. RBCs were collected and washed twice in cold 1ϫ Dulbecco's PBS (DPBS; Gibco). An equivalent MOI of 40 bacteria was incubated with 2 ml of RBC suspension (Ϸ10,000 RBCs) in 35-mm petri dishes (MatTek) at 37°C. Bacterial overnight cultures were directly added to the petri dishes during microscopy acquisition. Live-cell imaging was performed during 60 min on a Zeiss Axio Observer spinning-disk confocal microscope equipped with a 63ϫoil objective and driven by the MetaMorph software. Images were acquired every 5 s for 60 min. One hundred RBCs were counted for hemolysis for each of the strains tested during the duration of the experiment.