dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We presented data on the circulation of influenza viruses in France from 2003-2004 to 2012-2013 based on information collected within a countrywide, community based sentinel influenza surveillance network. While influenza A viruses circulated all seasons, influenza B viruses circulated (i. e. , accounted for at least 10 % of all influenza cases, according to the definition that we used throughout this paper) in half of the seasons. Influenza B accounted for around one fourth (23. 7 %) of all influenza cases in France from 2003-2004 to 2012-2013 and caused more than 50 % of all influenza cases in two of ten seasons  (2005-2006 and 2012-2013). No such historical data have been published in France but these results are in line with data of comparable quality from other European countries  [12] , for the totality of Europe  [13] [14] [15] [16] [17] , and for countries outside Europe as well  [18, 19] , despite differences in how the national influenza surveillance systems are organized in different countries.
In this experiment, the bilayers were hydrated from water vapor to avoid high absorption and background signals from bulk solvent hydration. 40 Solvent-saturated filter paper was placed above loosely packed balls of aluminum foil and cadmium shields were mounted carefully to avoid scattering of the filter paper and bulk water. The can was mounted inside of a temperature controlled cryostat with a temperature stability of AE0. 01 K. Data were taken while the membranes were fully hydrated and in their fluid phase, at a temperature of T = 30 1C (303 K). Due to the typically low scattering of the sample (B10%), we did not observe evidence of multiple scattering events. The membrane structure was determined via neutron diffraction in which a single Si wafer was mounted in an aluminum can, as shown in Fig.
nxspe (Nexus event-mode) format. A Matlab (Mathworks Inc. ) script tailored to stacked membranes used a cylindrical geometry where the components of scattering vector, Q, are the membrane plane, q xy , and the cylindrical axis, q z. Bulk water data were sliced using a powder geometry in DAVE (Data Analysis and Visualization Environment) software from NIST Center for Neutron Research. 54 All data were cut into ten equal slices in q z and up to 22 equal slices in q xy to make a grid of 220 squares in reciprocal space. For E i s of 0. 64, 0. 96, 1. 62, 3. 28, 9. 80 meV the widths in q z and q xy were 0. 027, 0. 033, 0. 043, 0. 062, and 0. 106 Å, respectively. Each square results in a spectrum of S(q (xy,z) , E) vs. E and was fit with functions that will be described in detail in the next section (Section 2.
27 Å À1 (0. 64 meV), 0-0. 33 Å À1 (0. 96 meV), 0-0. 43 Å À1 (1. 62 meV), 0-0. 62 Å À1 (3. 28 meV), and 0-1. 06 Å À1 (9. 80 meV). Resolution measurements for the membrane system were taken using a B15 K DMPC/H 2 O sample and bulk H 2 O resolution measurements using a vanadium sample. Resolutions and quasi-elastic spectra were cut to the same dimensions in reciprocal space. The fits are based on the Matlab nonlinear least-squares minimization algorithm ''lsqnonlin''. Fits were a numerical convolution of the resolution (Gaussian function(s)) with the d-shaped function and the model for the QENS, with the addition of a background. The resolution function of Let depends on the incident energy and scattering angle and may deviate from a simple Gaussian function. Therefore, the resolution spectra were fit with one Gaussian function (E i = 0.
Fitting more free parameters typically provides better fits with smaller w 2 -values. A clear distinction of which model to use is often difficult in QENS data because of the limited statistics in the data, such that different models fit the data equally well. We note that FCS suffers from the same issue and cannot unambiguously determine the basic character of lipid dynamics in membranes. 11 owever, we argue that motion of water molecules in anisotropic and heterogeneous environments is physically better described by a distribution of relaxation times (a KWW function) rather than a sum of individual Brownian processes (several Lorentzian functions). This interpretation is supported by the computer simulations, i. e. , by the linear scaling of intermediate scattering function in the log-log representation over several decades which is indicative of a single stretched exponential.
Acknowledgements Experiments were funded by the  Natural Sciences and Engineering Research Council (NSERC)  of  Canada , the  National Research Council (NRC) , the  Canada Foundation for Innovation (CFI) , and the  Ontario Ministry of Economic Development and Innovation. L. T. is the recipient of an  NSERC   Canada Graduate Scholarship , M. C. R. is the recipient of an  Early Researcher Award  from the  Province of Ontario. H. R. G. was supported by the  United States DOE, Office of Basic Energy Sciences  under Contract No. 46680. We profited from discussions with  Jean-Louis Barrat (Grenoble)  on the general interpretation.
Methods The EVRS Macular Edema Study was a nonrandomized, multicenter study in which the goal was to analyze the treatment of macular edema. The focus was on the results of varying treatments and treatment combinations for each etiology of the macular edema. This paper concentrates on cases of DME and their treatment outcomes. The members of EVRS contributed to the study by reporting on individual cases of macular edema and their management from 2008 to 2011. A portal was created on the EVRS website where reporting questionnaires were available to be filled out for each patient treated. At the conclusion of the reporting period, the study organizers received complete data on 2,603 cases of macular edema from 86 retina specialists. Follow-up for these cases ranged from 6 months to 2 years. The results were analyzed independent of investigators by the French INSEE (National Institute of Statistics and Economic Studies).
Reported data for each case included the type and number of treatment(s) utilized, the pretreatment and posttreatment visual acuities, and the specific dates of treatments and visual assessments. Lens status was also recorded. Information regarding complications was reported including an increased or new cataract, increased intraocular pressure, retinal detachment, vitreous hemorrhage, choroidal detachment, and macular hole. Macular optical coherence tomography (OCT) measurements were not reported by the surgeons in this investigation. After having cleaned the database, the global working sheet was sent to each contributor, masking the name of the other contributors, so that cleaning accuracy could be agreed upon. Considering that this study was performed in 29 countries, the regulations and institutional review board requirements were different in each location.
The second decision made was that, for each data point plotted to create the trend lines, a minimum of three cases would be needed for every follow-up interval. With this approach, only the averages of vision improvement with at least three cases would be included, and the impact of aberrant cases would be minimized. Additional analysis was performed in order to display final visual improvement (LogMAR) according to pretreatment visual acuity. A trend line combining the results for all treatments of DME was compared to plotted results for individual treatments and their average pretreatment visual acuities. Considering that this is not a randomized study, there is a risk of selection bias, with regard to the cases reported on by each physician. Based on recommendation by the institute of statistics, this risk was limited by always comparing the results obtained with different treatment modalities as opposed to presenting individual values of visual improvement at different time points along the trend lines.
As some of the most widely utilised intercellular signalling molecules, transforming growth factor β (TGFβ) superfamily members play critical roles in normal development and become disrupted in human disease. Establishing appropriate levels of TGFβ signalling involves positive and negative feedback, which are coupled and driven by the same signal transduction components (R-Smad transcription factor complexes), but whether and how the regulation of the two can be distinguished are unknown. Genome-wide comparison of published ChIP-seq datasets suggests that LIM domain binding proteins (Ldbs) co-localise with R-Smads at a substantial subset of R-Smad target genes including the locus of inhibitory Smad7 (I-Smad7), which mediates negative feedback for TGFβ signalling. We present evidence suggesting that zebrafish Ldb2a binds and directly activates the I-Smad7 gene, whereas it binds and represses the ligand gene, Squint (Sqt), which drives positive feedback.
By comparing published chromatin immunoprecipitation (ChIP)-seq datasets of genome-wide protein-DNA binding profiles for R-Smad1/3 and Ldb1  [8, 9, 21] , we have obtained evidence that Ldb1 co-localises with R-Smad1/3 at a substantial subset of R-Smad target sites across the genome, suggesting that Ldb1 might function together with R-Smads to implement transcriptional responses to TGFβ family signalling. In vertebrates, a paralogue, Ldb2, shares a high percentage of amino acid sequence identity and structural similarity with Ldb1  [28] , but its functions are largely unknown. In this study, we present in vivo functional and phenotypic data showing that Ldb2 regulates Nodal/BMP signalling and is required for early embryogenesis. Furthermore, we identify I-Smad7 and a Nodal ligand, Sqt, as direct target genes activated or suppressed respectively by Ldb2a, and show that the fine tuning of TGFβ family signalling requires both positive and negative control by Ldb2a complexes.
Ldbs and R-Smads Co-localise at a Subset of TGFβ Target Genes We compared published ChIP-seq datasets of Ldb1, the BMP effector, R-Smad1, and the Nodal/Activin/TGFβ effector, R-Smad3  [8, 9, 21, 29]. We found that the binding of Ldb1 overlaps R-Smad1 or R-Smad3 binding at a substantial subset of R-Smad targets across the genome (Fig. 1A  and 1B ), including at the known TGFβ target genes, I-Smad6 and I-Smad7 (Fig. 1C  and 1D ). Ldb1 binding at these loci was validated in murine cells by ChIP-quantitative PCR (qPCR) (Fig. 1E ). The ChIP-seq of Ldb1 had been performed in murine bone marrow cells or day 4 embryoid body (EB)-derived Flk1+ haemato-endothelial precursor cells  [21, 29] , whereas the ChIP-seq of R-Smad1 and R-Smad3 had been carried out in murine G1ER erythroid progenitor cells and murine pro-B cells, respectively  [8, 9].
Nevertheless, the widespread co-localisation of Ldb1 and R-Smads, albeit in different cell types, suggests the potential for functional cooperation between these factors. Ldb1 does not bind DNA directly but has been shown to assemble complexes containing Scl (Tal1) and Gata1/2 on DNA via motifs including Ebox, GATA, and Ets  [23, 24, 29]. Genome-wide comparison of ChIP-seq datasets suggests that Scl and Gata1/2 co-occupy a substantial subset of Ldb1-binding sites with R-Smad1 or R-Smad3 (S1 Fig. ). Indeed, the most enriched motifs identified in genomic sequences bound by R-Smads also include GATA, Ebox and Ets  [8, 9]. Taken together, these observations identify Ldb proteins as potential modulators of TGFβ superfamily signalling, possibly by associating with R-Smads to regulate TGFβ targets.
To further explore how Ldb2a regulates expression of these genes, we mined published protein partner and DNA binding site datasets for Ldbs. Most of our current knowledge of the Ldb family is from studies of Ldb1. Since Ldb1 and Ldb2 share highly conserved protein sequence and structure, they likely function through similar mechanisms. In haematopoietic lineages, Ldb1 functions as a bridging molecule, with Lmo2/4, to assemble TF complexes that bind DNA through SBE, E-box, GATA, and Ets motifs  [21, 23, 24]. LMO4 interacts with R-SMADs to mediate the TGFβ inputs in human epithelial cells  [27]. Other components of Ldb1 complexes, such as Gata1/2, have also been shown to modulate TGFβ family signalling by assembling and recruiting Smad complexes onto TGFβ target genes  [9, 26]. The Smad7 and Sqt genes contain conserved SBE, E-box, and GATA motifs (Fig.
Ldb2a Provides a Feed-Forward Control to the Transcriptional Activation of I-Smad7 by TGFβ Signals To gain further insights into the role of Ldb2a in TGFβ signalling, we studied the regulation of ldb2a expression by Nodal signalling. ChIP-seq datasets show an enrichment of R-Smad3 at the Ldb2 locus in various cell types and this enrichment can be stimulated by Activin/Nodal signalling (Fig. 6C )  [8, 9, 48]. To study whether Ldb2a is regulated by TGFβ signals, we treated zebrafish embryos with the Nodal inhibitor SB431542, from the MBT stage. We examined expression of ldb2a and other Nodal targets at 0. 5 and 3 hours after treatment, and showed that their expression was decreased by the blockade of Nodal signalling (Fig. 6D ). Thus, an Ldb2amediated coherent feed-forward loop delays the activation of Smad7 expression and the suppression of ligand gene expression.
Discussion We have compared published ChIP-seq datasets of R-Smads and Ldb1 complex components, and shown that they co-occupy a significant proportion of the genome in different cell types, which suggests potential roles for Ldbs in TGFβ signalling. This was validated by in vivo studies showing that Ldb2a does indeed modulate R-Smad/TGFβ family signalling during zebrafish development. Ldbs are non-DNA binding adaptor proteins, mediating the formation of TF complexes containing partners that are also crucial for TGFβ pathways. For example, LMO4, another non-DNA binding protein in Ldb complexes, interacts with R-SMAD1, 2, 5, 8, and Co-Smad4, in response to TGFβ signalling in human epithelial cells  [27]. GATA1, a TF in Ldb complexes, has been shown to assemble with SMAD1 on BMP response elements (BREs) in human HepG2 (liver hepatocellular) cells and is required for strong activation of a BRE in the first intron of Smad7  [26].
Analysis and Genome-wide Comparison of ChIP-seq Data The ChIP-seq datasets of each protein (Smad1, Smad3, Ldb1, Scl/Tal1, Gata2, and Gata1) were downloaded from the NCBI gene expression omnibus (GEO, http://www. ncbi. nlm. nih. gov/ geo). For Smad1 (ChIP-seq in murine G1ER cells), Smad3 (murine pro-B cells), and Ldb1 (murine bone marrow cells), their mapped reads on the MM8 genome (bed format) were used for peak calling analysis using MACS (version 1. 4. 2), while IgG was used as the negative control. Genome-wide comparison of ChIP-seq datasets was performed as previously described  [8]. Briefly, the location of Smad1/3 binding (query datasets, shown in x-axes) (Fig. 1A  and 1B ) in relation to Smad1/3-or Ldb1-enriched sites (base datasets, y-axes) was visualised by Java Treeview with the average reads density calculated in 100-bp bins ±2.
Real-Time Quantitative PCR Total RNA was isolated with the RNAeasy Microkit (QIAGEN). Quantitative PCR was performed with SybrGreen (Applied Biosystem). Data were collected with the ABI-PRISM 7000 or 7500 Sequence Detection system. β-actin1/2, EF1α, and GAPDH were used as internal controls. The relative abundance for each sample was computed by the comparative method (ΔΔCt). Statistical analysis was by the two-sample equal variance t-test. Error bars indicate the standard deviation. Primers are listed in S1 Table. Previously published primers as described  [59].
Chromatin Immunoprecipitation Followed by Sequencing or qPCR Analyses ChIP-seq and ChIP-qPCR of endogenous Ldb1 (using anti-Ldb1 antibody N-18, Santa Cruz) on murine Flk1+ BL-CFCs isolated from day 4 EBs was performed as described  [29]. 36-bp raw reads were mapped against NCBI build 37. 1 of the mouse genome with ELAND (Illumina). Uniquely mapped reads were extended to 200 bp and then transformed into the genome-wide reads density (coverage) with the ShortRead Bioconductor package  [62]. The coverage from ChIP and IgG control was visualized on a mirror of the UCSC genome browser. ChIP-qPCR analyses of Ldb2a in zebrafish gastrula embryos were performed as described  [63] , using two different methods for the IP: (a) inject low-level (50 pg) HA-Ldb2a mRNA that does not cause any defects on its own, and then precipitate HA-Ldb2a using HA antibody-coupled dynabeads (Anti-HA tag antibody: ChIP Grade, abcam ab9110; Dynabeads Protein A for Immunoprecipitation, Novex); (b) inject 50 pg Avi-Ldb2a (Avi: biotin acceptor peptide) together with NLS-BirA (bacterial biotin ligase), and then precipitate Biotin-Ldb2a using Streptavidin-coupled Dynabeads (Dynabeads MyOne Streptavidin T1, Invitrogen).
Acknowledgments We thank  Maggie Walmsley  for critical reading of the manuscript. We thank  Romualdo Ciau-Uitz ,  Arif Kirmizitas ,  Doug Higgs ,  Mary C. Mullins ,  Dominic Norris ,  Elizabeth Robertson ,  Alex Schier ,  Eric Soler ,  Fiona Wardle , and  Steve Wilson  for advice. SBE-luc and Id1-BRE2-luc constructs were kindly provided by  Peter ten Dijke  ( Leiden University Medical Center, Leiden, NL ). The zSmad7-PCS2 construct was kindly provided by  Hans-Martin Pogoda  ( Institute for Developmental Biology, University of Cologne, Germany ). We also thank  Lucy Wheatley  and  Upeka Senanayake  for providing the pMTB2-Avi-Tev-FLAG and NLS-BirA constructs, respectively.
Study Population The HIV-CAUSAL Collaboration has been described elsewhere  [9]. In brief, the collaboration includes several prospective cohort studies from 6 European countries and the United States: UK CHIC (United Kingdom), ATHENA (the Netherlands), FHDH-ANRS CO4 (France), Aquitaine (France), SHCS (Switzerland), PISCIS (Spain), CoRIS (Spain), VACS-VC (United States veterans), AMACS (Greece), UK Register of HIV Seroconverters (United Kingdom), ANRS PRIMO (France), and GEMES (Spain). All cohorts included in the HIV-CAUSAL Collaboration were assembled prospectively and are based on data collected for clinical purposes within national healthcare systems with universal access to care. Each cohort in the collaboration collects data on all CD4 cell counts, HIV RNA levels, treatment initiations, AIDS-defining illnesses, and deaths.
RESULTS The dataset included 10 969 individuals, of whom 6668 followed a lopinavir regimen and 4301 followed an atazanavir regimen. Table  1  shows the characteristics of the study population by regimen type at baseline. Women, individuals aged <35 years at baseline, those from non-Western countries, those with the lowest baseline CD4 cell counts, those with the highest baseline HIV RNA levels, and those starting treatment before 2008 were more likely to initiate lopinavir than atazanavir. In the mortality analysis, the median follow-up time was 40 (interquartile range [IQR], 20-61) months for the lopinavir regimens and 27 (IQR, 14-45) months for the atazanavir regimens. In the AIDS-defining illness or death analysis, the median follow-up time was 37 (IQR, 18-60) months for the lopinavir regimens and 26 (IQR, 13-44) months for the atazanavir regimens.
Acknowledgments. Writing Committee members:  Lauren E. Cain  ( Coordinating Center ),  Andrew Phillips ,  Ashley Olson  (UKRHS),  Caroline Sabin ,  Sophie Jose  ( UK CHIC ),  Amy Justice ,  Janet Tate  (VACS),  Roger Logan ,  James M. Robins ,  Jonathan A. C. Sterne  ( Coordinating Center), Ard van Sighem ,  Peter Reiss  (ATHENA),  James Young ,  Jan Fehr  (SHCS),  Giota Touloumi ,  Vasilis Paparizos  (AMACS),  Anna Esteve ,  Jordi Casabona  (PISCIS),  Susana Monge ,  Santiago Moreno  ( CoRIS/CoRIS-MD ),  Rémonie Seng ,  Laurence Meyer  ( ANRS PRIMO/SEROCO ),  Santiago Pérez-Hoyos ,  Roberto Muga  (GEMES),  François Dabis ,  Marie-Anne Vandenhende  (Aquitaine),  Sophie Abgrall ,  Dominique Costagliola  ( FHDH-ANRS CO4 ), and  Miguel A. Hernán  ( Coordinating Center ). Disclaimer. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction Thanks to advances in geometric modelling techniques and to the availability of cheaper, yet effective 3D acquisition devices, we are witnessing a dramatic increase in the number of available 3D data  [1, 36]. How to accurately and efficiently retrieve and classify this data has become an important problem in computer vision, pattern recognition, computer graphics and many other fields. Most methods proposed in the last years analyse geometric and/or topological properties of 3D models  [4, 22, 73] , that is, they focus on shape. Nevertheless, most sensors are able to acquire not only the 3D shape but also its texture; this is the case, for instance, of the Microsoft Kinect device. Also, image-based modelling and multiple-view stereo techniques enable the recovery of geometric and colourimetric information directly from images  [66].
In this context, we present here a comparative study on the retrieval and classification performance of six state-of-the-art methods in the field of textured 3D shape analysis. The present contribution builds on the dedicated SHREC'14 benchmark  [3] , and extends the associated track in three main respects: • Most of the algorithms tested in  [3]  have been reimplemented with some modifications for performance improvement. Additionally, a new method has been included in the comparative study in order to have a sufficiently detailed picture of the state-of-the-art scenario; • To help the reader in comparing methods beyond their algorithmic aspects, Section 4. 7 presents a taxonomy of methods highlighting the emerging shape structure, the scale at which the shape description is captured, the colour space that is considered to analyse texture shape properties, and how this information is combined with the geometric one; • The analysis of methods has been strengthened by exploiting the peculiar composition of the dataset, which has been populated by considering multiple modifications of a set of null shapes.
The dataset The dataset is made of 572 watertight mesh models, see Figure  1 , grouped in 16 geometric classes of 32 or 36 instances. Each geometric class represents a type of geometric shape (e. g. , humans, birds, trees, etc). Besides the geometric classification, models are also classified in 12 texture classes. Each texture class is characterized by a precise pattern (e. g. , marble, wood, mimetic, etc). The collection is built on top of a set of null models, that is, base meshes endowed with two or three different textures. All the other elements in the dataset come as the result of applying a shape transformation to one of the null shapes, so that a geometric and a texture deformation are randomly combined case by case. The geometric deformations include the addition of Gaussian noise, mesh re-sampling, shape bending, shape stretching and other non-isometric transformations that do not necessarily preserve the metric properties of shapes (e.
g. the Riemannian metric). As for texture deformations, they include topological changing and scaling of texture patterns, as well as affine transformations in the RGB colour channels, resulting in, e. g. , lighting and darkening effects, or in a sort of pattern blending. While the topological texture deformation has been applied manually, affine transformations admit an analytic formulation. A numerical parameter allows to tune each analytic formulation, thus making possible to automatically generate a family of texture deformations. In our dataset, texture transformations are grouped in five families, each family being the result of three different parameter values. Figure  2  illustrates some geometric and texture deformations in action. The added value in working with a dataset built in this way is that particular weaknesses and strengths of algorithms can be better detected and analysed.