dataesr/scientific-paragraphs-categorization huggingface dataset:
In the analysis presented here, an additional LHCb data set, corresponding to 2. 0 fb -1 of integrated luminosity collected at 8 TeV, is added to the previous data set, and a number of new analysis techniques are introduced. The search for LFV in τ -decays at LHCb takes advantage of the large inclusive τ - production cross-section at the LHC, where τ -leptons are produced almost entirely from the decays of b and c hadrons. Using the bb and cc cross-sections measured by LHCb  [9, 10]  and the inclusive b → τ and c → τ branching fractions  [3] , the inclusive τ -cross-section is estimated to be 85 µb at 7 TeV. Selection criteria are implemented for the signal mode, τ -→ µ -µ + µ -, and for the calibration and normalisation channel, which is D - s → φπ -with φ → µ + µ -, referred to in the following as D - s → φ (µ + µ -) π -.
Different types of charged hadrons are distinguished using information from two ringimaging Cherenkov detectors (RICH)  [11]. Photon, electron and hadron candidates are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers  [12]. The trigger  [13]  consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. Candidate events are first required to pass the hardware trigger, which selects muons with a transverse momentum p T > 1. 48 GeV/c in the 7 TeV data or p T > 1. 76 GeV/c in the 8 TeV data.
The binning optimisation is performed separately for the 7 TeV and 8 TeV data sets, because there are small differences in event topology with changes of centre-of-mass energy. The optimisation does not depend on the signal branching fraction. The bins at lowest values of M 3body and M PID response do not contribute to the sensitivity and are excluded from the analysis. The distributions of the responses of the two classifiers, along with their binning schemes, are shown in Fig. 2. The expected shapes of the invariant mass spectra for the τ -→ µ -µ + µ -signal in the 7 TeV and 8 TeV data sets are taken from fits to the D - s → φ (µ + µ -) π -control channel in data. Figure  3  shows the fit to the 8 TeV data. No particle identification requirements are applied to the pion. The signal distribution is modelled with the sum of two Gaussian functions with a common mean, where the narrower Gaussian contributes 70% of the total signal yield, while the combinatorial background is modelled with an exponential function.
The reconstruction and selection efficiencies, R , are products of the detector acceptances for the decay of interest, the muon identification efficiencies and the selection efficiencies. The combined muon identification and selection efficiencies are determined from the yield of simulated events after the full selections are applied. The ratio of efficiencies is corrected to account for the differences between data and simulation in track reconstruction, muon identification, the φ(1020) mass window requirement in the normalisation channel and the τ -mass range. The removal of candidates in the least sensitive bins in the M 3body and M PID classifier responses is also taken into account. The trigger efficiencies, T , are evaluated from simulation and their systematic uncertainties are determined from the differences between the trigger efficiencies of B -→ J/ψ(µ + µ -)K -decays measured in data and in simulation, using muons with momentum values typical of τ -→ µ -µ + µ -signal decays.
The trigger efficiency for the 8 TeV data set is corrected to account for differences in trigger conditions across the data taking period, resulting in a relatively large systematic error. The yields of D - s → φ (µ + µ -) π -candidates in data, N cal , are determined from the fits to reconstructed φ (µ + µ -) π -mass distributions shown in Fig. 3. The variations in the yields when the relative contributions of the two Gaussian components are allowed to vary in the fits are considered as systematic uncertainties.
Setting This was a monocenter, prospective observational study. Patients were screened and enrolled between February 2011 and January 2013 in the general intensive care unit (ICU) at Estaing University Hospital, Clermont-Ferrand, France, after the protocol was registered on clinicaltrials. gov (NCT01270295). Patient management was based on our ICU standard protocols. Mechanical ventilation strategy (including weaning), sepsis management, and sedation were based on available guidelines  [5, 23]. Lung-protective ventilation was applied; a tidal volume of 6 ml/kg (predicted) body weight and a plateau pressure under 30 cmH 2 O were targeted  [5].
Clinical data Clinical and biological data were obtained for all living patients at all timepoints. According to our institutional protocol, a lung computed tomography (CT) scan was performed on day 0 in ARDS patients  [25]. Two independent radiologists performed the qualitative CT analysis according to the "CT-scan ARDS study group" criteria  [26]. Three patterns of loss-of-aeration distribution were identified: focal, diffuse, patchy. Nonfocal morphology was noted for patients with diffuse or patchy patterns  [27]. Clinical outcome was recorded until day 28.
Study population Thirty patients with ALI/ARDS  [24]  were enrolled; 30 consecutive age-and sex-matched patients without ALI/ARDS but under mechanical ventilation were included as controls. Data on the primary outcome were available for all patients. Baseline characteristics and main clinical outcomes are summarized in Table  1. Nine ARDS patients (30%) had baseline PaO 2 /FiO 2 between 200 and 300, and 23 patients (76%) were diagnosed with nonfocal lung morphology. First samples for the study were drawn a mean of 33 hours after intubation (SD 39), with no difference between groups (P = 0. 7).
This work was supported by grants from  Clermont-Ferrand University Hospital  (" Appel d'Offre Interne "  2010 ,  CHU Clermont-Ferrand )( Dr Jabaudon ), the  Auvergne Regional Council  ( "Programme Nouveau Chercheur de la Région Auvergne" 2013)(Pr Constantin ), the  Agence Nationale de la Recherche  and the  Direction Générale de l'Offre de Soins  (" Programme de Recherche Translationnelle en Santé "  ANR-13-0. wereassociated with severity as assessed by  PaO 2 /FiO 2. study design, data collection and analysis, decision to publish, or preparation of the manuscript.
INTRODUCTION The last fifteen years assisted to the arrival of a growing number of computational models of visual attention (saliency)  [1]. Yet, the functionality of the attention mechanisms and the specific domains of validity for each model remain elusive. This article focuses on the bottom-up visual attention models. The objective of these models is to extract regions that have a high probability to attract human interest (saliency maps) based on discriminative features  [2]. The performance of such models is evaluated by comparing the generated saliency map with a heatmap processed from collected human eye-tracking data. Another ground truth uses manually segmented masks of the most salient objects  [3]. In  [3]  it is already shown that depending on the ground truth (eye-tracking data or manually segmented objects), the saliency models ranking can be very different.
CONTENT BASED IMAGE RETRIEVAL Content-based image retrieval has seen considerable progress over the past years. Many challenges have been proposed to test the robustness of the proposed methods. One of the most popular challenges is the Visual Object Classes Challenge  [14]. VOC was proposed for the first time in 2005 with an objective: recognizing objects from number of visual object classes in realistic scenes. Since then, it has been organized every year and integrates a new constraints in order to offer a standardized database in object recognition domain. In this section, we describe VOC2007 challenge. Many papers used this challenge as basis to test the robustness and the performance of their algorithms  [15]  [16]  [17]. Furthermore, this challenge offers a dataset well designed to investigate the performance of object recognition methods on a wide spectrum of natural images  [14].
Acknowledgments Laboratoire Aimé Cotton  is " Unité Propre UPR 3321 du CNRS associée à l'Université Paris-Sud ", member of the " Fédération Lumière Matière " (LUMAT,  FR2764 ) and of the " Institut Francilien de Recherche sur les Atomes Froids " (IFRAF). R. G. F. gratefully acknowledges a Mildred Dresselhaus award from the excellence cluster "The  Hamburg Center for Ultrafast Imaging Structure, Dynamics  and  Control of Matter at the Atomic Scale" of the Deutsche Forschungsgemeinschaft  and financial support by the  Spanish Ministry of Science   FIS2011-24540  (MICINN), grants  P11-FQM-7276  and  FQM-4643  ( Junta de Andalucía ), and by the Andalusian research group  FQM-207.
Overview of the design We will conduct a parallel-armed, non-inferiority randomized trial comparing a new alternative format of SoF tables with current format. We will contact systematic review users by Email and ask them to fill a questionnaire developed using the 'Survey Monkey' online system. The data collection form will include questions about baseline information (demographic characteristics, background, familiarity with systematic reviews and the GRADE system, and so on). Then, participants will be randomly assigned to one of two SoF tables, either the one in the alternative format or the one with the current format (See Figure  1 ). Randomization will be stratified according to participants' background (health professional, guideline developer, researcher). They will be asked to answer questions to determine understanding, accessibility of information, and satisfaction with the table format to which they were allocated.
Randomization Once the potentially eligible participants have completed the general information and background questionnaire, those who meet the selection criteria will be stratified according to self-reporting data as clinicians, guideline developers, or researchers and randomly allocated to one of the 2 SoF tables in a 1:1 ratio. To minimize missing data, the 'Survey Monkey' system will randomly assign participants to one of the SoF tables immediately after stratification, providing them with a link to access the questionnaires and tables.
Blinding The data collection process will be conducted automatically by the 'Survey Monkey' web platform. The SoF table formats will be labeled as A, or C; thus, participants will be allocated to Table  A  or C without any other information about the nature of the tables. Participants will be blinded to whether the table they are exposed to was the one in the current or alternative format while the outcomes understanding, accessibility, and satisfaction, are assessed. The only exception will be the outcome preference, as the only way to measure it is comparing the table to which they are randomized with the table to which they were not allocated. Therefore, participant blinding will be broken only after they have fully assessed the SoF table to which they were initially randomized. Once the data collection process is completed, the database will be prepared for statistical analysis and interpretation in a blinded fashion.
Sample size calculation Sample size estimation was conducted using the software Stata/SE 10. 1 (StataCorp. , College Station, TX, USA) for Macintosh (Apple Inc. , Cupertino, CA, USA). Based on the primary outcome, the null hypothesis was that the proportion of participants who answer the understanding questions correctly is lower in the alternative format table group compared to the current format table group, while the alternative hypothesis was that the proportion of participants correctly answering the questions in the alternative format table group is at least the same or higher compared to the current format table group (See Equation  1 ): Null hypothesis H 0 : CF SoF tables -ANF SoF tables 3 NIM CF is superior to ANF ð Þ ð1Þ Alternative hypothesis H a: CF SoF tables -ANF SoF tables ≤ NIM ANF is not inferior to CF ð Þ ð2Þ NIM: non-inferiority margin; CF: current format; ANF: alternative or narrative format The proportion of participants correctly answering questions about understanding in similar randomized controlled trials that tested the current SoF table format was between 80 to 87%  [17, 18] , and we expect, at least, the same percentage in the group of participants randomized to the alternative format of SoF table.
Inferential analysis The primary outcome (understanding) will be estimated as the proportion of participants correctly answering each question per group. Data regarding this outcome will be analyzed using a multiple logistic regression per question, where the main predictor will be the arm to which participants were allocated (dichotomous -2 categories). Other potential predictors to include in the model are participant strata (nominal -3 categories), years of experience (nominal -5 categories), familiarity with the GRADE approach (dichotomous -2 categories), and previous education in health research methodology or epidemiology (ordinal -3 categories). To adjust for multiplicity, the P-value will be modified using the Bonferroni correction for 7 multiple comparisons (<0. 0035, CIs will be constructed with corresponding 1sided z score 2.
7). For the secondary outcome, accessibility of information, we will calculate means, SDs and 95% CIs per domain. Data regarding this outcome will be analyzed using multiple linear regression where the main pre. dictor will be the arm to which participants were allocated (dichotomous -2 categories). Other potential predictors included in the model will be participant strata (nominal -3 categories), years of experience (nominal -5 categories), familiarity with the GRADE approach (dichotomous -2 categories), and previous education in health research methodology or epidemiology (ordinal -3 categories). To adjust for multiplicity, the P-value will be modified using the Bonferroni correction for 4 multiple comparisons (<0. 0125). The secondary outcome, satisfaction, will be measured at an item level.
Since the 'Survey Monkey' system allows setting each question as requiring an answer before moving to the next section, missing data for a couple of questions within a questionnaire is not possible. However, as in any type of research, participants can leave the study whenever they want. When this happens, all the answers before leaving will be registered in our database, and all the ones that were not answered will be classified as missing from that point in the questionnaire until the end. If a participant leaves the study before randomization, the participant will be excluded and thus we will not consider this as missing participant data. On the other hand, if the person is randomized and does not complete the questions, an available case analysis will be used (that is using the data available until the participant left the study).
Acknowledgements The  Cochrane Methods Innovation Fund and McMaster University  funded this study. The SoF MIF authors group appreciate the valuable input provided by the  Cochrane Effective Practice and Organization of Care Group , the  Cochrane Consumers and Communication Review Group , the  Cochrane Public Health Group , the  Cochrane Hematological Malignancies Group , the  Cochrane Colorectal Cancer Group , the  Cochrane Airways Group , the  Norwegian Branch of the Nordic Cochrane Center , the  Cochrane Gynecological Cancer Group , the  Cochrane Risk of Bias Methods Group , the  Cochrane Oral Health Group , the  Cochrane Wounds Group , the  Cochrane Pain, Palliative and Supportive Care Review Group , the  Cochrane Patient Reported Outcomes Methods Group , the  Cochrane Musculoskeletal Group , the  Cochrane Screening and Diagnostic Test Methods Group , the  Cochrane Depression, Anxiety and Neurosis Review Group , the  Cochrane Applicability and Recommendations Methods Group , and the  Campbell and Cochrane Equity Methods Groups.
4. Tangible evidence of a commitment to collaboration both within the Cancer Centre's own country and internationally, as a single Centre usually will be less effective in developing and testing new approaches that lead to changes in clinical practice. 5. Establishment of shared resource facilities (Cores) to support the research Programmes. 6. National and international peer review systems (including evaluation by funding and government bodies) assess the Centre on a regular basis to help maintain and improve the overall quality of the programmes, leadership, shared facilities (e. g. -Opportunities for IT and e-hospital in which there in direct links to genomics and imaging and outcome databases across all histology (in addition to breast) 9. Ability and commitment to perform hypothesis-driven and hypothesis-generating clinical and population studies.
During that time, monthly samples of water were taken and concomitantly observation of the sponges was performed. Sponges could only be sampled during the six months when they were more robust (August 2010, and January, February, April, May and June 2011). A total of eight months of water samples and a total of six months of sponge samples were obtained. The water level was directly read from decimal meter scales painted on two PVC pipes placed at the bottom of the pond bottom at the dry period (Fig. 1 ). Data on precipitation was obtained from the INMET station (Instituto Nacional de Meteorologia, Platform Data Collection #83481, João Pinheiro, www. inmet. gov. br), located 21 km east of the study site. The most important variables related to the monthly input and output volumes of water in the system, the precipitation (P) and evapotranspiration (ETP) values were obtained from the INMET data.
M. spinata, as well as the other species formed abundant gemmules and contained all the four spicules categories seen in Lagoa Verde. However, no spicules of any of the sponge species were found in the bog bottom sediments. The lake had very poor seasonal supply of silica and thus all the available biosilica (from degrading vegetation, diatoms, and previous season sponge spicules) was certainly being retaken from the bog pond sediment. M. spinata was detected in sediments from Central Brazil, dated from at least 50. 000 yr BP  (Machado et al. 2014). When detected in past lentic sediments its spicules have been used as proxy data to support paleoenvironmental interpretations  (Cândido et al. 2000 , Parolin et al. 2007 , Almeida et al. 2009 , 2010 , Machado et al. 2014 ). However, these interpretations were mostly based on the available knowledge indicating that gemmoscleres/ beta megascleres would stand for water reduction in seasonal climates, whilst megascleres and microscleres taken both as supporters of the skeleton structures, allied to absence of gemmoscleres, would indicate absence of marked seasonality and sustained water input.
ACKNOWLEDGMENTS The authors are grateful to  Instituto Nacional de Meterologia (INMET)  for providing the meteorological data and to  J. P. P. Melasipo  for his cartographic support. This work was conducted as part of the Ph. D. dissertation of  M. C. Matteuzzo  (cotutela from  Universidade Federal de Ouro Preto (UFOP) ,  Brazil  and  Aix-Marseille University, France). M. C. Matteuzzo 's scholarship was funded by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/Brazil). The project was supported by the  CAPES/Comitê Francês de Avaliação da Cooperação Universitária e Científica.
Patients Patients were recruited from 14 participating French comprehensive cancer centers. These patients were older than 18 years and presented with extensive microcalcifications or multicentric foci (in two different quadrants) of the breast classified as American College of Radiology Breast Imaging-Reporting and Data System (ACR BI-RADS) categories four or five on mammography and a diagnosis of DCIS or DCIS-MI on VAB. The patients had an indication for mastectomy jointly determined by a radiologist and a surgeon because conservative treatment was not feasible. Patients with lumpectomy-diagnosed DCIS or DCIS-MI, previous ipsilateral radiation therapy or ALND, previous in situ or invasive ipsilateral breast carcinoma, or an indication for conservative breast surgery were excluded from this study.
Mastectomy with an SLN procedure was performed as described by Rodier et al. 9  For each SLN detected, an intraoperative evaluation of frozen sections was performed. For patients with positive SLN, an ALND was performed during the same intervention. This study was approved by the Committee of Protection of Individuals, Aquitaine, France and performed in accordance with the Helsinki Declaration. All the participants provided written informed consent (clinical trials NCT01841749).
ACKNOWLEDGEMENTS This work was partially supported by  Pfizer. The authors thank the  CINNAMOME Group ,  Pfizer  for research support;  Valerie Velasco  for TMA construction, immunohistochemical and  D-Dish  techniques; Beatrice Gonzalves for data collection;  Veronique Brouste  and  Carine Bellera  for statistical analysis and data managing; and  Pippa McKelvie-Sebileau  and  Jone Iriondo-Alberdi  of  Institut Bergonie  ´for medical editorial assistance with English.
, 2015). However, no studies to date have systematically examined whether enhanced f 82 is unique to IEPOX chemistry or whether it could also be enhanced in other sources. Nor has the range of f 82 been determined for IEPOX-SOA. Questions also have been raised about the uniqueness of this tracer and potential contributions from monoterpene SOA  (Anonymous Referee, 2014). In this study, the IEPOX-SOA results reported in various field campaigns are summarized and compared to predicted gas-phase IEPOX concentrations from a global model to help confirm the robustness of the AMS identification of this type of SOA. We then investigate the usefulness and limitations of the IEPOX-SOA tracers f C 5 H 6 O (i. e. , C 5 H 6 O + /OA) and f 82 by combining AMS data from multiple field and laboratory studies including a new data set from the 2013 Southern Oxidant and Aerosol Study (SOAS).
Experimental We classify the field data sets used in this study into three categories: (1) studies strongly influenced by urban and biomass-burning emissions: Los Angeles area, USA, and Beijing, China (urban); Changdao island, downwind of China, and Barcelona area, Spain (urban downwind); flight data from biomass-burning plumes and continental areas (NW and western, USA) in SEAC4RS and DC3 campaigns; and biomass-burning lab emissions (FLAME-3 study). (  2 ) Studies strongly influenced by isoprene emissions, including a SE US forest site (SOAS campaign); Two pristine forest site and one forest site partially impacted by urban plumes in the Amazon rain forest (Brazil). The latter site is classified in this category because (i) high isoprene concentrations (e. g. 3 ppb in average peaks in the afternoon) were observed during the study; (ii) the impact of biogenic SOA formed during 1000 km where the air travels over the pristine forest upwind of Manaus; (iii) PMF results indicate an important impact of IEPOX-SOA at this site  (de Sá et al.
IEPOX-SOA in a SE US forest during SOAS, 2013 We use the SOAS-CTR field study (SE US-CTR) as an example for the determination of IEPOX-SOA from AMS data via PMF analysis. The time series and mass spectrum of this component are shown in Fig. 2. The IEPOX-SOA PMF mass concentration is the sum of mass concentrations of all the ions in the IEPOX-SOA PMF mass spectra. The "mass concentration" of an ion is used to represent the mass of the species whose detection resulted in the observed ion current of that ion, based on the properties of electron ionization  (Jimenez et al. , 2003). An uncertainty (standard deviation) of IEPOX-SOA PMF mass concentration of ∼ 9 % was estimated from 100 bootstrapping runs in PMF analysis  (Ulbrich et al. , 2009)  (Fig. S1  in the Supplement). This uncertainty concerns only the PMF separation method.