dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. I. B. would like to dedicate this work to  Francesco Palla  who recently passed away. Part of this work was funded by the  Royal Society Wolfson Merit  award  WM090065 , the  Consolidated STFC  grant  ST/J001627/1STFC , the  French Programme National de Physique Stellaire (PNPS)  and  Programme National Hautes Énergies (PNHE) , and by the  European Research Council  through grants  ERC-AdG  No. 320478-TOFU  and  ERC-AdG  No. 341157-COCO2CASA. This work used the DiRAC Complexity system, operated by the  University of Leicester IT Services , which forms part of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk ). This equipment is funded by  BIS National E-Infrastructure capital  grant  ST/K000373/1  and  STFC DiRAC Operations  grant  ST/K0003259/1. DiRAC is part of the  National E-Infrastructure.
g. , parylene C, and wrapped onto a tissue, but additional fabrication issues such as long-term mechanical and lamination stability must also be considered; this is something we have not yet explored, however. For applications deeper inside the tissue or organ of interest, neural pixel systems could be constructed into implantable probes, a technique already demonstrated for basic OEIPs  (19, 22). The ability to sense electrophysiological signals and deliver neuroactive compounds at the same location represents a first step in constructing a closed-loop feedback system, capable of monitoring neuronal activity and adjusting local release of neurotransmitters accordingly. Indeed, the system presented above only requires minor modification to the control software to explore this functionality.
Device Characterization. Impedance spectra of the electrodes were measured using an Autolab potentiostat equipped with a frequency response analyzer (FRA) module. Commercially available Ag/AgCl and Pt electrodes were used as the counter and reference electrodes. The applied voltage was 0. 01 V, and the electrolyte solution was aqueous 0. 1 M NaCl(aq). The contacts of the OEIP were connected to a Keithley 2400 source/measure unit, and constant current (1 μA or lower) was sourced and voltage was measured using customized LabVIEW software. Electrode recordings were obtained using a commercially available voltage recording setup, RHD2000 Evaluation System. As the reference electrode, a grounded Ag/AgCl electrode was immersed into the target electrolyte. The recordings were acquired at a 20-kHz sampling rate and analyzed using MATLAB (Mathworks)-based software with a low-pass filter of 1 kHz and were down-sampled by 500.
Electrical Recording Data Acquisition. Electrode recordings were obtained using a commercially available voltage recording setup, RHD2000 Evaluation System (Intan Technologies) (Fig. S2 ). A 3D printed sample holder was fabricated, containing gold-coated pins in contact with the 32 gold electrode pads of the device. As the reference electrode, a grounded Ag/AgCl electrode was immersed into the target reservoir containing the brain slice. Recordings were acquired at a 20-kHz sampling rate and analyzed using MATLAB (Mathworks)-based software with a low-pass filter of 1 kHz and were down-sampled by 500. Hippocampus Preparation. All protocols have been approved by the Institutional Animal Care and Use Committee of INSERM. All experiments were repeated twice on different biological samples. After decapitation of anesthetized mice, brains were rapidly extracted (postnatal day 14 through 18).
Tungsten electrodes (with a tip resistance of 1 MΩ to 3 MΩ) were positioned on top of the OEIP outlets/electrode openings. External electrode recordings were made with a World Precision Instruments DAM80 AC amplifier, and were acquired using an analog-to-digital converter (Digidata 1322B; Molecular Devices). Analysis was performed using using Clampfit (Molecular Devices)-or MATLAB (Mathworks)-based software.
ACKNOWLEDGMENTS. We thank  Gaelle Rondeau  and the staff of the clean room in  Centre Microelectronique de Provence (CMP)  for technical support during fabrication. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement 602102 (EPITARGET) and Initiative of  Excellence Aix-Marseilles  project  MIDOE  ( A_M-AAP-ID-13-24-130531-16. 31-BERNARD-HLS ). Funding was also provided by the  Swedish Innovation Office  ( 2010-00507 ), the  Swedish Research Council  ( 621-2011-3517 ), and the  Knut and Alice Wallenberg Foundation  ( KAW Scholar ,  2012. 0302 ). The authors also thank the  National Science Foundation  Grant  DMR-1105253  for partial support of this work, the  French National Research Agency (ANR)  through the project  PolyProbe  ( ANR-13-BSV5-0019-01 ),  Fondation pour la Recherche Médicale  under Grant Agreements  DBS20131128446  and  ARF20150934124 ,  Fondation de l'Avenir , the  Önnesjö Foundation , the  Region Provence-Alpes-Côte d'Azur , and  Microvitae Technologies.
Estimates of catches The MPA partially monitors artisanal fishing effort and catches. Only available fishing catches within the model area were thus considered, including catches from fixed nets (trammel nets, gill nets, and combined nets), small purse seine, and recreational fishing. To obtain an annual picture of artisanal fishing catches in the model area, we had to attain from two main sources of data: 1. A data set of fishermen interviews  (MARTE+ project, 2011)  from which effort in the model area in 1 yr was estimated, computed as total number of boats and days of fishing for fishing metier. 2. The available logbooks from three boats (two boats monitored in 2012, one in 2013-2014), from which catches in kg/d for each fishing m etier were estimated. Thus, data from the logbooks (kg/d of fishing for each species and fishing m etier) were multiplied for the total number of fishing days/yr for the same m etier.
Recreational fishermen must fill logbooks concerning catches within the MPA boundaries. From the logbooks, we computed average catches expressed as kg/h within our model area and multiplied for the total number of hours of recreational fishing in the MPA in 1 yr  (Cappanera et al. 2013). To account for the uncertainty in the input fisheries data, we derived alternative data sets using two multiplier coefficients for artisanal fishing catches (0. 5 and 2) and seven coefficients (0. 5 and from 2 to 7) for recreational fishing catches (characterized by larger uncertainty). Resulting values were then proposed to the evaluation of the MPA staff fishing experts, responsible for the monitoring of artisanal and recreational fishing. For each species, the most realistic estimate according to the expert's knowledge was retained.
These expert-modified values were used to build our reference model. Two other models were then built with expert's estimates of catches both multiplied and divided by the coefficient 1. 5, and a fourth model was also built with the original logbook's estimates, to identify differences with experts' estimates and highlight data gaps. Accounting for uncertainty, we aimed at highlighting the potential of Ecopath and EcoTroph as a tool for advising MPA management, and thus stress on the urgent need to improve monitoring strategies and increase ecological and fisheries data quality and availability.
Finally, we analyzed direct and indirect fisheries impacts on species interactions by computing and cumulatively plotting the MTI index of each fleet. As data availability and quality is the main limitation to EwE, like for any ecosystem model  (Lassalle et al. 2014 , Prato et al. 2014 ), data quality was assessed applying the food web diagnostics proposed by  Link (2010)  and the Data-Reli toolbox developed by  Lassalle et al. (2014). Details on the data quality and sensitivity analysis are also provided in Appendix S2.
Building a trophic model in the context of Mediterranean MPAs Scientific ecological knowledge on Mediterranean MPAs, when available, is often dispersed among several sources of information, ranging from local or foreign universities or environmental agencies, to local/traditional ecological knowledge, historical archives, and expert opinions. Integration of this wealth of information is essential for a holistic understanding of protection effects on ecosystem functioning and thus a fully informed management of MPAs. The trophic modeling approach adopted here is a useful tool to accomplish such integration, allowing to fit largely scattered data into a coherent picture of ecosystem functioning for the Portofino MPA. In particular, a snapshot of the highly productive area surrounding the southern promontory of the MPA was provided, representing an average year between 2007 and 2014.
ACKNOWLEDGMENTS This study was supported by the  MMMPA-Training Network for Monitoring Mediterranean Marine Protected Areas , which has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement no. 290056. The authors wish to acknowledge all those colleagues from the  Centro del Mare, ARPA Liguria (S. Albanese, N. Melchiorre, P. Moretto) , the  University of Genova  ( J. Alessi ,  M. Chiantore ,  A. Covazzi Harriague ,  M. Montefalcone ), and from the  Univerist a Politecnica delle Marche (C. Cerrano)  for the provision of data and/or technical advice essential for the development of the model. We also wish to acknowledge  P. Campodonico  from the  Portofino MPA  for the expert advice on recreational fishing and the MPA Director  G.
Immunofluorescence Cells grown on glass coverslips were fixed in methanol for 3 min at -20 • C. After rehydration, cells were incubated with the primary antibody for 1 h, rinsed with PBS, incubated with the fluorochrome-conjugated secondary antibody for 1 hour, and rinsed with PBS, all steps being performed at room temperature. Slides were mounted in Citifluor (Citifluor, UK). Microscopy was performed on a Leica DMR microscope (Leica) using a 63 × 1. 32 oil immersion objective. Photographs were taken using a Micromax CCD camera 13 (Princeton Instruments) driven by Metamorph software. Images were processed with ImageJ. To quantitate Pbodies, we used the plugin Spot Detector of the open bioimage informatics platform Icy (http://icy. bioimageanalysis. org;  (41) ). Primary antibodies were goat 4E-T and rabbit EDC3 (Abcam), rabbit DDX6 (Novus), mouse FLAG M2 (Sigma).
Several of these interactions were subsequently confirmed in reverse immunoprecipitations (see Figure  4B  for FLAG-UNR, -unrip and -DDX6). All these interactions were RNA-independent, as they resisted RNase treatment (Figure  1D ). Furthermore, the novel 4E-T-UNR RNAindependent interaction was confirmed with endogenous proteins, using both 4E-T and UNR antibodies for immunoprecipitation (Supplementary Figure  S1 ). We were also interested to assess the binding of CCR4-NOT complex subunits to 4E-T. Previously we showed that GFP-4E-T can interact with FLAG-CNOT1 and FLAG-CNOT7, though binding of these endogenous CNOT subunits was undetectable by western blotting  (26). In this study, low levels of CNOT7, CNOT8 and CNOT11 subunit peptides were detected by mass spectrometry (one of each in Experiment 1, data not shown).
Moreover, yeast two hybrid screens indicate that Drosophila 4E-T also binds Me31B, as well as UNR  (58). However, C. elegans does not possess UNR  (43)  nor unrip genes, according to database searches. Subsequent mapping of these protein binding sites led to the conclusions summarized in Figure  4  and Supplementary Figure  S3. First, DDX6 interacts with the CHD motif shared by vertebrate, fly and worm 4E-T homologues, in agreement with recent in vitro and in vivo data  (23, 36). Secondly, motif I in human 4E-T binds UNR, which in turn interacts with unrip. Motif I is conserved in vertebrate and fly proteins, but, interestingly, is absent in C. elegans IFET-1, accompanied by the lack of UNR/unrip proteins. Third, LSM14A binds 4E-T sequences spanning residues 440-694 while PAT1B interacts with residues 717-845, and CNOT4 with sequences downstream of residue 694.
ACKNOWLEDGEMENTS We are grateful to  Jens Lykke Andersen ,  Emma Anderson ,  Witek Filipowicz ,  Richard Jackson ,  Reinhard Luhrmann  and  Anne Willis  for gifts of plasmids and antibodies. We thank  Jo Rees  of the  Cambridge Centre for Proteomics  for help with ProteinCenter analysis, and  Witek Fillipowicz ,  Elena Conti  and  Ben Luisi  for discussions.
FUNDING BBSRC [ BB/J00779X/1  to N. S. ];  CNRS PICS  (to D. W. );  Agence Nationale pour la Recherche  [ ANR-14-CE09-0013-01ANR  to D. W. ];  Gates Cambridge Foundation  (to A. K. );  Fondation Wiener -Anspach  of the  Université Libre de Bruxelles  and the  Cambridge Newton Trust  (C. V. ). Funding for open access charge:  BBSRC.
Acknowledgment: JA acknowledges funding support from the  Spanish MICINN  with the grant " Salvador de Madariaga ". SJ is grateful for partial support via the  Australian Research Council   DP130101205   Discovery  project. EB and DH thanks the financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the 'Investments for the future'  Programme IdEx Bordeaux -LAPHIA  ( ANR-10-IDEX-03-02 ). Authors are grateful to  Drs. Achim Nadzeyka  and  Sven Bauerdick  for discussions on technical implementation aspects of IBL and ion sources. This work was performed in part at the  Melbourne Centre for Nanofabrication (MCN)  in the Victorian Node of the  Australian National Fabrication Facility (ANFF).
Different optimization thresholds and hypersphere radii (r) for the MEPs have been computed to assess their influence in the PEH topography. For geometry optimizations, a standard convergence threshold of 10 -5 and 10 -3 (in a. u. ) for the energy change and the norm of the gradient have been used, respectively. Additionally, a tighter threshold characterized by an energy change of 10 -8 and a norm of the gradient of 10 -6 has also been employed. On the other hand, several values for the hypersphere radius (r) defining the MEP have been computed, ranging from 0. 09 to 0. 3 (in a. u. ). CIs are characterized at the CASSCF level with the GAUSSIAN 09 package  [103]  and employing the projection technique of Bearpark et al. [104] , while CASPT2/MS-CASPT2 crossings are directly taken from the minimum energy crossing points found along the MEP.
Phylogenetic analysis Thirty-four reference strain sequences were selected and used in the analysis (Supplemental Table  3 ). Sequences were manually edited and trimmed using BioEdit 7. 0. 5. 1  (Hall 1999 ), then aligned using Clustal x  (Jeanmougin et al. 1998). Phylogenetic analysis was performed using neighbor-joining (NJ) and the algorithm of Jukes and Cantor with 1000 bootstrap resamplings in MEGA version 5  (Tamura et al. 2011 ) along with reference sequences from NCBI. Sequence extremities were trimmed on the basis of sequence quality and reading frame as previously described  (Deberdt et al. 2014; N'Guessan et al. 2012; Wicker et al. 2012) , giving final egl sequences of 666 nucleotides and mutS of 651 nucleotides. All egl and mutS sequences from the R. solanacearum strains used in this study were deposited into the GenBank (accession numbers of egl: KT961427 -KT961523, accession numbers of mutS: KT961524 -KT961620).
Acknowledgments This research was supported by the  Key Project from China National Tobacco Corporation  ( 110201202002 ) and the  Key Project from China National Tobacco Corporation Chongqing Branch  ( NY20130501070005 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Acknowledgments We thank all presenters and attendees of the Workshop '' Pathogen-Informed Crop Improvement '' for sharing their knowledge during the presentations and discussions. The workshop was organized by the  COST Sustain Action FA 1208  (https://www. cost-sustain. org). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits.
Literature search and the applied intersectionality framework We searched for literature on climate change, rural livelihoods and gender on 12 March 2013 using several databases (Web of Knowledge, SCOPUS, Wiley, Science Direct, Agricola, Agris, CAB Abstracts, Econlit, Pascal, and Francis). We used queries composed of five groups of keywords related to climate, rural livelihoods, mitigation, adaptation and gender (Table  1 ). The OR operator connected keywords within groups and the AND operator connected keyword groups. After removing duplicates, the search of all databases resulted in 345 papers. After screening titles and abstracts, we selected 196 potentially relevant papers. After reading through the papers and removing irrelevant papers (e. g. papers mentioning gender, climate change adaptation and mitigation only in passing, or papers that are conceptual but do not include case studies), we selected a final list of 41 papers on adaptation and 15 on mitigation.
For example, pastoral communities saw seasonal herding of livestock as the most important strategy, whereas agricultural farming communities did not share the same priority. Adaptive strategies differed, as women from the pastoral community were keepers of sheep and goats, whereas women from the agricultural farming community invested in charcoal production  (Brockhaus et al. 2013). Although they do not formally use intersectionality as a framework, many papers disaggregate data according to different social categories in their analysis. We analysed the papers to identify the social categories included in the vulnerability analyses. The results indicated that most papers use two categories (i. e. men and women), while the other papers include at least one other social distinction in their analysis.
The remaining statements were based on theoretical backgrounds and analysis. Although the vulnerability of women appears to be obvious-because of the social, political and economic marginalization experienced by women in various contexts-little is known about the local gender dynamics of vulnerability. There is a lack of data and local context analysis to reinforce and elucidate these assumptions. Adaptation as emancipation: How resilient are social and gender boundaries? In order to understand the transformational processes of adaptation, we aimed to analyse (using the intersectionality framework) whether, and to what extent, adaptation processes reinforce or challenge hierarchical, unequal social structures. We analysed articles and case studies that document changes in social structures associated with environmental change and adaptation to it.
A probabilistic representation of the resulting mesoscale elasticity random fields is then introduced and subsequently used as a surrogate model that can be calibrated on a limited number of realizations. In the next section, the model microstructure, as well as the computation of the mesoscopic and macroscopic properties, are presented. In addition, a statistical characterization of the filtered elasticity field is provided. Note that in the remainder of this paper, the set of generated microstructures and mesostructures will be referred to as the experimental database.
Statistical characterization of mesoscopic stiffness tensor This section is devoted to the characterization of a few statistical properties for the filtered elasticity, based on the experimental database. In accordance with the multiscale framework (in a periodic setting), the mesoscopic elasticity random field { C(x, θ), x ∈ R d } is identified with the periodized version, in the almost sure sense, of { C(x, θ), x ∈ Ω}. Note that in the sequel, such an extension will be used with no specific notation for any decomposition of the elasticity random field.
( 32 ) Each of the above correlation function is assumed to have a separable structure and (from the above statistical characterization on the simulated database) to depend on a single parameter denoted by α, no matter the Gaussian field or the direction involved. Consequently, it is assumed that R Ξ κ (x, y) = R Ξ µ (x, y) = d k=1 r(|x k -y k |) (33) for all (x, y) in Ω × Ω, where the one-dimensional correlation function r is here chosen as the so-called periodic correlation function  (Rasmussen and Williams, 2005) : ∀τ ∈ [0, L], r(τ) = exp - 2 α 2 sin 2 πτ L. (34) It is worth noticing that due to the nonlinear mapping defined by Eq. (  29 ), the correlation functions of the bulk and shear moduli random fields do not coincide with those of the associated Gaussian random fields. However, numerical evidences show that the nonlinear transformations generally induce similar shapes for the correlation functions of the image random fields, with a slight modification of the correlation length.
Calibration of the probabilistic model 3. 4. 1. Identification through statistical estimators From the previous section, it follows that the stochastic model depends on the parameters defining the first-order marginal distributions, as well as on parameter α (which controls the correlation lengths of the fields). The former can be readily obtained by using statistical estimators on the simulated fields. Specifically, it is found that κ = 1. 02µ m , µ = 0. 78µ m (35) and δ κ = 0. 321, δ µ = 0. 329. ( 36 ) We denote by τ → R mod κ (τe i ; α) (see Eq. (  20 ); note that the dependence on α is made explicit for subsequent mathematical consistency) the correlation function for the bulk modulus random field defined by the stochastic model, whereas τ → R data κ (τe i ) is the correlation function for the bulk modulus that is estimated from the experimental database -similar notations are used for the shear modulus random fields.
Validation on mesoscale quantities of interest Here, we investigate to what extent the probabilistic model can represent some quantities of interest defined at mesoscale. The probability density functions of bulk and shear moduli at a given point (recall that these quantities are invariant under translation in R d ) are shown in Fig. 11  and Fig. 12 , both for the experimental database and for the model-based simulations. A good match is observed, regardless of the modulus under investigation and no matter the calibration strategy (in the present case, the parameters obtained from statistical estimators or from the maximum likelihood principle coincide). Next, the experimental and model-based correlation functions are compared in Fig. 13  for the bulk modulus random field. It is seen that the correlation function calibrated by using the statistical estimator reproduces almost perfectly the experimental curve, whereas a small discrepancy with the one based on the likelihood estimator is observed.
Validation based on effective properties Below, we compare the statistical averages (over 100 realizations) of effective properties as obtained, either by solving a set of mesoscale corrector problems involving the calibrated stochastic model, or by directly solving the corrector problems defined at the microscale. The results (within the 99% confidence interval) are summarized in Tab. 1. It is found that the effective properties computed from the model are in very good agreement with the ones derived from the experimental database.