dataesr/scientific-paragraphs-categorization huggingface dataset:
* This work was supported by  Agence Nationale de la Recherche  Grants  ANR-2011-BSV6-011-02  and  ANR-14-CE-10-0014-01  and funds from the  Institut Pasteur  and the  CNRS. The authors declare that they have no conflicts of interest with the contents of this article. □ S This article contains supplemental Data Set S1. 1 Supported by fellowships from the  Ministère de l'Enseignement Supérieur et de la Recherche  and from the  Association pour la Recherche contre le Cancer.
Data collection We reviewed cases of patients with cerebrovascular events and pancreatic cancer, who were managed in 4 stroke units in the Lorrain region (northeastern France) between January 1, 2009 and March 31, 2015. Reported cases were also identified in the literature using the database MEDLINE/Pubmed for articles published in the past 30 years, with the keywords "pancreatic cancer," pancreatic carcinoma," "pancreatic adenocarcinoma," "stroke," "cerebral infarction," "transient ischemic attack," and "cerebral vasculitis. " Demographic data (age, sex), information about pancreatic cancer (date of diagnosis, histologic type, stage), cerebrovascular events (type, radiological features), results of etiological investigations (electrocardiogram, echocardiography, cervical and transcranial Doppler ultrasonography, and angiography), presence of other systemic infarctions on abdominal computed tomography (CT) scan, laboratory analyses (complete blood count, coagulation tests, C-reactive protein [CRP], carbohydrate antigen 19.
Statistical analysis All analyses were descriptive and carried out by using IBM SPSS Statistics software, version 20 (SPSS Inc. , Chicago, IL). The study was observational and described clinical, radiological, and biological features of patients from our centers and in literature. Continuous variables are reported as median ± standard deviation and extreme values, and categorical factors as frequency and percentage. Statistical comparison was performed between characteristics of patients from our centers and from literature using the Mann-Whitney U test for continuous variables and Fischer exact test for categorical factors. A significant difference between both groups was defined as P < 0. 05.
Acknowledgments The authors thank  Sophie Marchal  ( Hospital of Verdun/Saint-Mihiel, Stroke unit, France ),  Raphael Demettre  ( Hospital of Barle-Duc, Stroke unit, France ), and  Alexandrine Larue  ( Hospital of Epinal, Stroke Unit, France ) for their help to collect data. They also extend their thanks to  Felicity Neilson ,  Matrix Consultants , for having reviewed the English language with scientific expertise.
Directly or indirectly, SarA influences the transcription of at least 120 genes in S. aureus  (16) , and it can either stimulate (with cna, fnbA, agr, hla, fnta, fnb and sec) or repress (with sarV, aur, sspa, spa, rot and SarS) target gene expression  (9, (17) (18) (19) (20). In addition to TFs, S. aureus expresses about 160 regulatory RNAs  (4, 21) , all recently compiled into a staphylococcal regulatory RNA database (SRD)  (22). sRNAs are expressed from both the core and variable accessory genomes, the latter including pathogenicity islands (PIs) and transposons  (23). Among these S. aureus sRNAs, only a handful has identified functions including certain ones that influence staphylococcal virulence in animal models of infection  (24, 25). In S. aureus, sRNAs usually pair with target mRNAs for regulation, influencing mRNA expression by modulating its degradation and/or its translation  (26).
Northern blotting Total RNA were separated on either denaturing polyacrylamide gels or on agarose gels, then transferred onto Zeta-Probe GT (Bio-Rad) or Nytran membranes (Schleicher & Schuell), respectively. All northern blots were revealed using the Digoxigenin method per the manufacturer's instructions (Roche). We used 20 nt probes previously marked with 3 -end DIG labeling. Signal acquisition was done using an ImageQuant LAS 4000 imager (GE Healthcare). Approximate quantifications were calculated using the ImageQuant software and normalized against transfer-messenger RNA (tmRNA) or 16S ribosomal RNA.
DNA probes were labeled with [␥ -32 P] adenosine triphosphate (ATP) using T4 polynucleotide kinase (New England Biolabs). Binding reactions were carried out as previously described  (40). Briefly, binding reaction medium (10 fmol DNA template, 20 mM HEPES-KOH, pH 7. 9, 20% glycerol, 0. 2 mM EDTA, 0. 1 M KCl, 0. 1 M MgCl 2 , 5 mM DTT and 0. 2 g poly(dI-dC)), was incubated when necessary with purified SarA or the E. coli RNA polymerase holoenzyme (New England Biolabs) for 30 min at 30 • C. Samples were loaded on native polyacrylamide gels. Detection was done with a Typhoon FLA 9500 (GE Healthcare). The dissociation constant (K d ) was determined by comparing the free versus the shifted DNA-protein bands (Im-ageQuant software), and apparent K d was derived from the computed concentration at half-maximal binding.
ACKNOWLEDGEMENTS We would like to thank our lab colleagues, particularly  Y. Augagneur ;  G. Pascreau , for reading the manuscript and giving advice;  M. Hallier , for the strains, plasmids and technical help with SarA purification; and  H. Le Pabic  for her shared interest in Srn 3610 SprC. We also would like to thank  A. Cheung  for the SarA family mutants and  E. Charpentier  for the pCN plasmids.
As of today, 10 short ragweed pollen allergens have been registered by the International Union of Immunological Societies (6). Among them, two are considered as major allergens, including the pectate lyase Amb a 1 and the cysteine protease Amb a 11, which are recognized by serum IgE of 90% (7, 8) and 66% (9), respectively, of ragweed pollen-allergic patients. We recently identified natural Amb a 11 as a 28-kDa glycosylated protein displaying amino acid sequence identities with other allergens belonging to the C1A cysteine protease family, such as Der p 1 and Der f 1 (from house dust mite Dermatophagoides sp. ; 24. 4 and 26. 0% identity, respectively), the actinidin Act d 1 (from Actinidia deliciosa; 37% identity), and papain (from Carica papaya; 34. 6% identity) molecules (  9 ). The Amb a 11 gene (GenBank TM accession number KF528831) codes for a precursor molecule of 364 residues encompassing both N-and C-terminal propeptides and preceded by a signal sequence of 22 amino acids (UniProt accession V5LU01_AMBAR) (9).
5, 150 mM Na 2 SO 4. Measurements were carried out on the DISCO beamline at the SOLEIL synchrotron (Gif-sur-Yvette, France). Four spectral scans were recorded in the 170 -265-nm range with 1-nm steps after subtracting signals obtained with the dialysis buffer. The resulting spectra were calibrated with D-10-camphorsulfonic acid using the CDtool software  (12)  and normalized to protein concentrations. Ratios of secondary structures were determined using the Selcon program  (13)  in Dichroweb  (14). For all samples, spectra were analyzed down to 180 nm, corresponding to a photomultiplier high tension below half of its total variation. Crystallization and Structure Determination of Pro-rAmb a 11-Crystals were grown at 20 °C using the vapor diffusion method. A first series of crystals was obtained after dialysis of purified pro-rAmb a 11 in 20 mM Tris-HCl, pH 8.
5-mm length obtained after 3 weeks under those conditions was used to establish a high resolution data set. Prior to data collection, crystals were soaked in a 20% (v/v) glycerol cryoprotectant solution and frozen in liquid nitrogen. Diffraction data were collected at 100 K on the PROXIMA 2A beamline at the SOLEIL synchrotron and then processed and scaled using the XDS program  (15). Crystal structures were determined by performing molecular replacements with Phaser (16) and using either the monomer of the vignain from Ricinus communis (Protein Data Bank code 1S4V)  (17)  or the propapain mutant from Carica papaya (Protein Data Bank code 3TNX)  (18)  as search models for the medium and high resolution data sets, respectively. Both pro-rAmb a 11 models were refined with non-crystallographic symmetry restraints and TLS (translation/libration/screw motion) using Buster 2.
10 (19). Electron density maps were evaluated using the Coot model building software  (20). The resulting medium resolution model contains residues Met-22 to Lys-352 and Pro-355 to Leu-386 in monomer A and residues His-21 to Ser-114 and Asn-120 to Pro-350 in monomer B. The high resolution model contains residues Ser-5 to Ser-12, His-21 to Ser-114, and Lys-121 to Asn-346 for monomer A and residues His-21 to Ile-115 and Asp-122 to Pro-348 for monomer B. Molecular graphic images were generated using the PyMOL software, version 1. 7. 6. The Protein Data Bank codes for the medium and high resolution models are 5EGW and 5EF4, respectively. Mice, Culture Media, Reagents, and Antibodies-BALB/c female mice (6 -8 weeks old) were obtained from Charles River (L'Arbresle, France). The experimental protocol was approved by Stallergenes' ethics committee, and animal handling was performed according to international regulations.
Measurements of airway hyperresponsiveness (AHR) were performed by whole body plethysmography (Buxco, Winchester, UK) after exposure to increasing doses (i. e. 6. 125, 12. 5, 25, and 50 mg/ml) of methacholine (Sigma). Results were expressed as enhanced pause indexes as described previously  (21). To assess inflammatory cells in BALs, mice were anesthetized with an i. p. administration of 50 mg/kg pentobarbital plus 10 mg/kg xylazine (Centravet, Maisons-Alfort, France). BALs were performed with 3 ϫ 400 l of Dulbecco's PBS, and samples were centrifuged at 500 ϫ g at 4 °C for 5 min. Cells were resuspended in Dulbecco's PBS and stained at 4 °C for 15 min with specific mAbs as mentioned above. Acquisitions and analyses were performed with a FACS Verse apparatus (BD Biosciences) and FlowJo software (Ashland, OR), respectively.
The N-terminal part of the longest helix (residues Cys-155 to Thr-172) contains the catalytic cysteine (Cys-155) in the vicinity of two additional residues forming the canonical catalytic triad of C1A cysteine proteases including both His-289 with 5. 7 Å between ␣-carbons and Asn-310 with 8. 6 Å between ␣-carbons (Fig. 3B ). Alignment of primary sequences of Amb a 11, Der p 1, and papain cysteine protease zymogens reveals conserved amino acid residues in the mature domain as well as, to a lesser extent, in the N-terminal propeptides (Fig. 4A )  (22). When crystal structures of pro-rAmb a 11, pro-Der p 1 (Protein Data Bank code 1XKG)  (23) , and propapain (Protein Data Bank code 3TNX)  (18)  were superposed (Fig. 4B ), only minor differences were observed in the N-terminal propeptide regions.
Of note, no hydrogen bonds similar to the ones reported herein between Cys-155 and Ser-99 have been observed in the published crystal structures of procaricain (Protein Data Bank code 1PCI) and procathepsin K (Protein Data Bank code 7PCK) that retain their catalytic cysteine  (36, 37). The potential role of the hydrogen bonds observed in pro-rAmb a 11 in maintaining the N-terminal propeptide in the vicinity of the active site cleft remains to be clarified. In our hands, mutation of Ser-99 in the propeptide did not impair the maturation capacity of pro-rAmb a 11. In addition, our failure to obtain a molecule with protease activity following expression and refolding of mature rAmb a 11 devoid of its N-terminal propeptide (data not shown) is consistent with a potential chaperone function of the latter in the overall folding of the molecule as demonstrated previously for other cysteine proteases  (38 -42).
Root genome expression Root gene expression was investigated by comparing 2x and 4x couples. Just a few genes presented a significant differential expression between 2x and 4x Trifoliate Orange (data not showed). In Cleopatra, mandarin very limited number of genes (< 0. 1%) presented a gene expression change > 2 fold (Figure  1 ). These results suggest that in these doubled diploid derive all of their alleles from a single species and may undergo fewer alterations in their regulatory networks, resulting in very limited changes of gene expression between 4x and diploid genotypes as previously proposed by  Stupar et al. (2007). However, large changes of expression were observed when comparing the 2x intergeneric hybrid Cleopatra mandarin × Trifoliate orange and its respective 4x, 2826 genes being over expressed and 2378 gene being down-regulated (expression change > 2 fold).
Data and simulation samples The data used in this analysis were collected by the ATLAS experiment at a centre-of-mass energy of 8 TeV during 2012, and correspond to an integrated luminosity of 20. 2 fb -1 recorded in stable beam conditions and with all relevant sub-detectors providing high-quality data. VBF and ggF Higgs boson signal events and Z boson background events are modelled by Monte Carlo (MC) simulations. The signal samples with a Higgs boson mass of 125 GeV are generated by Powheg  [23] [24] [25] , which calculates the VBF and ggF Higgs production processes up to next-to-leading order (NLO) in α S. Samples of Z boson + jets events are generated using MadGraph5  [26] , where the associated jets are produced via strong or electroweak (EW) processes including VBF, and the matrix elements are calculated for up to and including three partons at leading order.
05 0. 1 ATLAS -1 = 8 TeV, 20. 2 fb s data b b → Z b b → ggF H b b → VBF H (a) |) J2 η |,| J1 η w = max(| 0 1 2 3 4 5 6 (1/N) dN/dw 0. 05  signal region. The analytic forms considered are Bernstein polynomials  [54] , combinations of exponential functions, and combinations of Bernstein polynomials and exponential functions with various numbers of coefficients, and functions with a χ 2 probability greater than 0. 05, that do not introduce a bias, are selected. For each form, the minimum number of coefficients is determined by performing an F-test, and the corresponding function is chosen as a candidate function. The fitted signal strength is measured for each candidate function using toy samples. The function giving the smallest bias is used as the nominal distribution. The function giving the second smallest bias is taken as an alternative distribution, and is used to estimate the systematic uncertainty due to the choice of analytic function.
Summary A search for the Standard Model Higgs boson produced by vector-boson fusion and decaying into a pair of bottom quarks is presented. The dataset analysed corresponds to an integrated luminosity of 20. 2 fb -1 from pp collisions at √ s = 8 TeV, recorded by the ATLAS experiment during Run 1 of the LHC. Events are selected using the distinct final state of the VBF H → b b signal, which is the presence of four energetic jets: two b-jets from the Higgs boson decay in the central region of the detector and two jets in the forward/backward region. To improve the sensitivity, a multivariate analysis is used, exploiting the topology of the VBF Higgs final state and the properties of jets. The signal yield is estimated by performing a fit to the invariant mass distribution of the two b-jets in the range 70 < m bb < 300 GeV and assuming a Higgs boson mass of 125 GeV.
Perfusion culture hampers type I collagen production To quantitatively determine the biochemical composition of the neo-synthesized matrix, WB analysis were performed on sponges cultured in static or dynamic conditions in the presence of BIT. Observation of total collagen production revealed that type II collagen, the typical component of the cartilage collagen fibrils, was synthesized in both conditions, whereas a lower amount of type I collagen was detected in the sponges cultured under perfusion (Fig  6a ). The densitometric analysis of WB data confirmed a significantly lower production of type I collagen and showed a positive trend for type II/type I collagen ratio in sponges cultured in dynamic conditions (Fig  6b ). WB analysis revealed that type II collagen was present as unprocessed, intermediate, and mature chains in sponges cultured in both static and dynamic conditions (Fig  6 ).
This observation was confirmed by the densitometric quantification of WB data (Fig 7b ), showing that no significant differences were found in the production of type I collagen between CTR and BIT samples in both static and dynamic conditions. Importantly, both in the presence and in the absence of BIT, perfusion induced a strong decrease in type I collagen synthesis compared to static culture (  Fig 7). Altogether, our results indicated that a stop in type I collagen expression specifically occurred in dynamic conditions. Based on this, we examined cell morphology and collagen expression at different time points (7, 14, and 21 days). Special attention was given to the cells enclosed in the neo-synthesized ECM in the scaffold core. In static conditions, HACs maintained a fibroblastic shape from day 7 to day  21 (Fig 8).
Details of the biological data The following datasets are used in the analysis of biological data: • Multiple sequence alignements We used multiple sequence alignments (MSA) of sequences belonging to the Pfam Beta-lactamase2 family PF13354 for the inference of TEM-1 mutational landscape (resp. PDZ domain family PF00595 for PSD-95 mutational landscape) [30]. Raw data have been processed as described in [18]: we have used HMMer [29] to search against the Uniprot protein sequence database (version of March 2015); from the resulting alignments we have removed all sequences with more than 5 gapped positions; in order to take into account phylogenetic correlations and sampling biases we have associated to each sequence s m the following statistical weight: w m =   1 + m =m ′ Θ d m,m ′ -ϑN   -1 , (1) with d mm ′ being the Manhattan distance (number of mismatches) between sequences s m and s m ′ and Θ being the Heaviside step function whose value is zero for negative argument and one for positive argument.
The reweighting threshold is set to ϑ = 0. 8 as usually done in DCA inference [32]. The resulting MSA are N = 197 (resp. N = 81) sites long, and contain M = 2462 (resp. M = 3287) homologous sequences (average sequence identity ∼ 20% with the wild-type sequences). • Computational predictions of thermodynamic stabilities We used ∆∆G-predictions for all single amino acid mutations from the wild-type sequences as estimated by PoPMuSic webbased tool [31] taking as reference structure the PDB: 1M40 for TEM-1 (resp PDB: 1BE9 for PSD-95). • Experimental fitness measurements -TEM-1. Experimental measurements from [23] provide estimations of amoxicillin Minumum Inibitory Concentration MIC for n = 742 distinct single amino-acid mutations falling inside the part of the sequence covered by the corresponding Pfam model.
Introduction High-dimensional data characterizing the collective behavior of complex systems are increasingly available across disciplines. A global statistical description is needed to unveil the organizing principles ruling such systems and to extract information from raw data. Statistical physics provides a powerful framework to do so. A paradigmatic example is represented by the Ising model and its generalizations to Potts and continuous spin variables, which have recently become popular for extracting information from large-scale biological datasets. Successful examples are as different as multiple-sequence alignments of evolutionary related proteins  [1] [2] [3]  , gene-expression profiles  4  , spiking patterns of neural networks  5, 6  , or the collective behavior of bird flocks  7.
(3) However, the number of accessible mutant sequences remains small compared to the number of terms in this sum, and mutagenesis data alone are not sufficient to faithfully model fitness landscapes  27. In situations where no single dataset is sufficient for accurate inference, integrative methods accounting for complementary data sources will improve the accuracy of computational predictions. In this paper, (i) we define a generalized inference framework based on the availability of an equilibrium sample and of complementary quantitative information; (ii) we propose a Bayesian integrative approach to improve over the limited accuracy obtainable using standard inverse problems; (iii) we demonstrate the practical applicability of our method in the context of predicting mutational effects in proteins, a problem of outstanding bio-medical importance for questions related to genetic disease and antibiotic drug resistance.
An integrated modeling The inference setting. Inspired by this discussion, we consider two different datasets originating from a true model H 0. The first one, D eq = {s 1 ,. , s M }, is a collection of M equilibrium configurations independently drawn from the Boltzmann distribution P 0 (s). For simplicity, we consider binary variables s i ∈ {0, 1}, corresponding to a "lattice-gas" representation of the Ising model in Eq. (  1 ). This implies that energies are measured with respect to the reference configuration s ref = (0,. , 0). The standard approach to the inverse Ising problem uses only this type of data to infer parameters of H : couplings and fields in Eq. (  1 ) are fitted so that the inferred model reproduces the empirical single and pairwise frequencies, The second dataset provides a complementary source of information, which shall be modeled as noisy measurements of the energies of a set of P arbitrary (i.
e. not necessarily equilibrium) configurations σ a. These data are collected in the dataset D E = {(E a , σ a ) a=1,. ,P }, with E a = H 0 (σ a ) + ξ a a = 1,. , P. π emp i = 1 M M ∑ µ=1 s µ i , π emp i j = 1 M M ∑ µ=1 s µ i s µ j. (4) ( The noise ξ a models measurement errors or uncertainties in mapping measured quantities to energies of the Ising model. For simplicity, we consider ξ a to be white Gaussian noise with zero mean and variance ∆ 2 : ξ a ξ b = δ a,b ∆ 2. As schematically represented in Fig. 1 , datasets D eq and D E constitute different sources of information about the energy landscape defined by Hamiltonian H 0. Observables in Eq. (  4 ) are empirical averages computed from equilibrium configurations in D eq , providing global information about the energy landscape. On the contrary, configurations in D E are arbitrarily given, and a (noisy) measurement of their energies provides local information on particular points in the landscape.
A maximum-likelihood approach. To infer the integrated model, we consider a joint description of the probabilities of the two data types for given parameters J and h of tentative Hamiltonian H. The probability of observing the sampled configurations in D eq equals the product of the Boltzmann probability of each configuration, P(D eq |J, h) = exp - P ∑ µ=1 H (s µ ) -M log Z (J, h). (6) To derive an analogous expression for the second dataset, we integrate over the Gaussian distribution of the noise ξ a = E a -H (σ a ) obtaining a Gaussian probability of the energies (remember configurations in D E are arbitrarily given): P(D E |J, h) = P ∏ a=1 dξ a P(ξ a ) δ (E a -H (σ a ) -ξ a ) = 1 (2π∆ 2 ) P 2 exp - P ∑ a=1 [E a -H (σ a )] 2 2∆ 2 (7) The combination of these expressions provides the joint log-likelihood for the model parameters given the data: L (J, h|D eq , D E ) = log P(D eq |J, h) + logP(D E |J, h) (8) Maximizing the above likelihood with respect to parameters {h i } 1≤i≤N and {J i j } 1≤i< j≤N leads to the following self-consistency equations: p i (J, h) = π emp i + λ 1 -λ 1 M P ∑ a=1 σ a i [E a -H (σ a )] (9) p i j (J, h) = π emp i j + λ 1 -λ 1 M P ∑ a=1 σ a i σ a j [E a -H (σ a )] with p i (J, h) = σ i H , p i j (J, h) = σ i σ j H being single and pairwise averages in the model Eq.