dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodological considerations The strengths of our study include: first, the longitudinal design which allowed us to examine the associations between sickness absence and subsequent changes in health functioning. Second, the sickness absence data were derived from complete national registers, which contributes to the validity and reliability of our measure of sickness absence. Furthermore, our measure of sickness absence allowed us to examine the effect all-cause as well as diagnosis-specific sickness absence on physical and mental health functioning. Third, to ascertain physical and mental health functioning, we used the validated SF-36 physical and mental component summary scores. Despite the observed group differences were fairly small, many of them were close to or greater than 3 points, which has been previously suggested as a clinically significant difference  [24].
This study had also limitations. Firstly, when assessing the generalisability of the results, some characteristics of the data need to be considered. We studied an occupational cohort from the public sector with the majority of participants being women which limits the generalizability of the results. However, the participation was broadly similar among women and men, although slightly more women than men responded to the surveys  [16]. Moreover, the gender distribution reflects that within the City of Helsinki and the Finnish municipal sector in general. Furthermore, as we restricted our analyses to those who remained employed until the end of the sickness absence follow-up and those who did not retire due to disability during the subsequent follow-up for health functioning, the data are likely to be affected by the 'healthy worker effect' as is typical for occupational cohorts.
Acknowledgments We would like to warmly thank the members of the  Theory Division of CERN , and especially  Dr. Gian Giudice ,  Dr Wolfgang Lerche ,  Prof. Ignatios Antoniades  and  Dr Luis Alvarez-Gaume , as Theory group leaders, and  Ms. Elena Gianolio ,  Ms. Anne-Marie Perrin ,  Ms. Michelle Connor  and  Ms. Jeanne Rostant  for their support. We also thank  Prof. Moshe Moshe  from the  Technion University in Haifa, Israel  for the information on the paper [30].
Study Population We used data from the NutriNet-Santé study, a large web-based observational cohort launched in France in 2009 with a scheduled follow-up of 10 years. It was implemented in a general population and targeted internet-using adult volunteers. Briefly, eligible participants were recruited by a variety of means. Initially a vast multimedia campaign (television, radio, national and regional newspapers, posters, and internet) called for volunteers and provided details on the study's specific website (http://www. etude-nutrinet-sante. fr). Then, multimedia campaigns were repeated every six months. Further information is maintained on a large number of websites (national institutions, city councils, private firms, web organizations). A billboard advertising campaign is regularly updated via professional channels (e.
Assessment of Weight and BMI Height and weight data were collected at baseline and each year thereafter by a self-administered anthropometric questionnaire  [33]. BMI was calculated as the ratio of weight to the square of height. Participants with BMI < 25 kg/m 2 at baseline were classified as non-overweight (including 332 underweight participants in our study) and participants with BMI ≥ 25 kg/m 2 were considered overweight (including obese) in accordance with WHO reference values  [34]. The relative weight change over five years was calculated using five-year weight data subtracting the baseline weight data, divided by baseline weight data.
Assessment of Psychological Characteristics Psychological data were collected in 2010 using the Three-Factor Eating Questionnaire, which is a self-assessment instrument of eating behaviour (TFEQ-R21)  [43]. It covers three eating behaviour domains: the cognitive restraint scale (six items) assesses control over food intake to influence body weight; the emotional eating scale (six items) measures the propensity to overeat in relation to negative mood states; and the uncontrolled eating scale (nine items) assess the tendency to lose control over eating when feeling hungry or when exposed to external stimuli. The three scales range from 0 to 10 [44]. 2. 2. 6. Assessment of Socio-Demographic, Economic and Lifestyle Characteristics Socio-demographic (age and sex: women and men), economic (education: primary, secondary, undergraduate or postgraduate; household income per consumption unit), and lifestyle data (smoking status: never, former, or current smoker; history of dieting: never, former, or current dieter) were collected in 2010.
Acknowledgments: This work was supported by the  French National Research Agency (Agence Nationale de la Recherche)  in the context of the  2008 Programme de Recherche "Alimentation et Industries Alimentaires " ( Epipref  project,  ANR-08-ALIA-006 ), by the  INRA   Metaprogramme Did'It (Diet Impact  and Determinants Interactions and Transitions), and by the  Regional Council of Burgundy France  ( PARI Agral 1 ) and the  FEDER (European Funding for Regional Economical Development). The  Nutrinet-Santé study  is supported by the following institutions:  Ministère de la Santé (DGS) ,  Institut de Veille Sanitaire (InVS) ,  Institut National de la Prévention et de l'Education pour la Santé (INPES) ,  Fondation pour la Recherche Médicale (FRM) ,  Institut de Recherche en Santé Publique (IRESP) ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Institut National de la Recherche Agronomique (INRA) ,  Conservatoire National des Arts et Métiers (CNAM) , and  Université Paris 13.
S1 ). Initially, two individual homozygous mutant plants from each line have been selected for analyses and because their growth phenotypes did not significantly differ, the data obtained with the selected individual mutants have been pooled in this study. The initial phenotyping of the three LORE1 mutant lines included measurements of shoot lengths and fresh weights of 4-week-old plants with/out inoculation with M. loti. As shown in Fig. 3  in the absence of N sources (no N) or in the presence of KNO 3 concentrations (100μM; 1 mM) compatible with full nodulation capacity, the three lines did not present significant differences when compared to wild type plants, in terms of shoot biomass and nodules number (Fig. 3A-C ). In order to test whether LjNPF8. 6, which is also expressed at a significant level in the root tissue, could be involved in the nitrate dependent inhibitory pathways controlling the nodule formation process, nodulation capacity has been also tested in the presence of high external concentrations of KNO 3 (10 mM).
2 This work was supported by grants from the  Italian Ministry of Education (Progetti di Rilevanza Nazionale ,  PRIN 2010/2011 , PROROOT, Prot. 20105XLAXM), from  Rete delle Biotecnologie in Campania, Progetto Bio Industrial Processes -BIP -CUP   B25C13000290007 , from the  Institut National de la Recherche Agronomique  ( CJS PhD Fellowship  to SL) and from the  Agence Nationale de la Recherche  ( ANR-14-CE34-0007-01-HONIT  with a post-doctoral fellowship to MN). LMA was supported by a  Short-Term Fellowship  from  EMBO.
Introduction Penalized-likelihood approaches are becoming more and more popular in numerous domains in statistics: density estimation, variable selection, machine learning, etc. Here we consider a multiple changepoint detection setting for univariate datasets where we are interested in the estimation of the number of segments K. While earlier works have focused on variables distributed from specified distributions, for instance Gaussian  [1] , or either Poisson or negative binomial distributions  [2] , we consider here a more general framework of exponential family distributions. More precisely, the model is the following: Y t ∼ G(θ t ) = s(t), 1 ≤ t ≤ n where G is a distribution from the exponential family with parameter θ t ∈ R d. In the context of changepoint models, we want to consider that θ t is piece-wise constant along the time-line 1,.
7. 1. Simulation study with exponential distribution. We simulated 400 datasets of length n = 10 5 for which the number of segments K was drawn from a Poisson distribution with mean K = 50, and the K -1 change-points were sampled uniformly on {2,. , n -1} subject to the constraint that segments had to be of length at least 10. We considered 4 sets of values for the rate parameters. In all scenarios, odd segments had a rate of 0. 01 while the rate on even segments was chosen randomly with probability 0. 4, 0. 2, 0. 3 and 0. 1 among the values (0. 05, 0. 1, 0. 02, 0. 005) for datasets 1 through 100, (0. 02, 0. 05, 0. 015, 0. 005) for datasets 101 through 200, (0. 015, 0. 02, 0. 0125, 0. 007) for datasets 201 through 300, and (0. 015, 0. 02, 0. 011, 0. 008) for datasets 301 through 400. This resulted in datasets similar to those shown in Figure  1.
This genome has been used for the comparison of segmentation methods (see  [20]  and references therein) such as HMM (  [21] ,  [22] ) or penalized quasi-likelihood  [23]. The data and its annotation are publicly available from the National Center for Biotechnology Information (NCBI) pages at the url adress http://www. ncbi. nlm. nih. gov/. From a computational point of view, the large size of the Lambda genome hampers the direct use of the classical Dynamic Programming (DP) algorithm. Here we propose a hybrid algorithm that consists in first selecting a small number of relevant change-points using the CART algorithm  [24] , and then using dynamic programming on this set of candidate change-points. As in the simulation study, the penalty constant is calibrated using the slope heuristic (see  [13] ).
A search for high-energy neutrino emission correlated with gamma-ray bursts outside the electromagnetic prompt-emission time window is presented. Using a stacking approach of the time delays between reported gamma-ray burst alerts and spatially coincident muon-neutrino signatures, data from the Antares neutrino telescope recorded between 2007 and 2012 are analysed. One year of public data from the IceCube detector between 2008 and 2009 have been also investigated. The respective timing profiles are scanned for statistically significant accumulations within 40 days of the Gamma Ray Burst, as expected from Lorentz Invariance Violation effects and some astrophysical models. No significant excess over the expected accidental coincidence rate could be found in either of the two data sets. The average strength of the neutrino signal is found to be fainter than one detectable neutrino signal per hundred gamma-ray bursts in the Antares data at 90% confidence level.
Principle of the search Neutrino signatures are searched-for in an angular cone around the direction of, and within a maximum time offset from the time of each GRB. For any such space and time coincidence, the time difference with respect to the GRB alert is recorded. In order to avoid any boundary effect such as an artificial asymetry of neu- trino candidates around a GRB alert close to the beginning or end of neutrino telescope data taking, GRBs detected during a period of half the considered maximum time offset at the beginning and end of the neutrino data sample are excluded. The collected time differences are stacked in a common timing profile. In the case of no signal, only purely accidental spatial coincidences of the neutrino candidates with the defined search cones around the GRB positions would be expected.
Final GRB Sample Having chosen the maximal search time window and the largest angular search cone that should be taken into account, the final samples associated with the neutrino telescope data set were determined. The initially selected GRB catalog comprised 1488 bursts that had occurred between 2007 to 2012, which gives a detection rate of 0. 68 bursts per day. Out of these, 563 have been selected for the search of associated neutrinos in theAntares data using the criteria outlined above, of which 150 have a measured redshift z.
Results and Discussion The data collected by theAntares telescope from the years 2007 to 2012 were analysed to search for neutrinos within the predefined angular and timing search windows associated with the GRB catalogue. None of the neutrino candidates in the data matched these search windows, where 3. 9 would have been expected from background (0. 7 coincidences were expected for the GRBs with measured redshift z). The measured values of the test statistics are thus zero, and the ratio r = n + /n -is undefined. The probability to observe no events coinciding with all GRBs Test Statistic Sensitivity at 90% CL Sensitivity at 99% CL  Assuming totally uncorrelated neutrino data, the mean numbers of coincident events that would be expected within the GRB's search windows n coinc are also given.
Conclusion A powerful method has been presented to identify a neutrino signal associated with GRBs if it is shifted in time with respect to the photon signal. The signal is distinguished from randomly distributed data as a cumulative effect in stacked timing profiles of spatially coincident neutrinos in the data from the Antares and IceCube neutrino telescopes. Estimating the behaviour of the search for a large number of simulated measurements using randomised sky maps of the neutrino events, and comparing these with the actual neutrino telescope data, significances of the observations were derived. Using data from the Antares neutrino telescope between the years 2007 and 2012, a deficit of spatially coincident neutrinos with the selected gamma-ray-burst catalogue was reported, with 98. 8% of the randomised data leading to more coincidences between the neutrino data and the GRBs.
The application of the method to the public IceCube data in its 40 line configuration gives results compatible with the expectation from background. The presented approach could have identified an intrinsically time-shifted signal even if only of the order of one in a hundred GRBs would have given rise to a single associated neutrino in the Antares data. This is above the de-ANTARES 07-12 The number of data events coinciding spatially with the respective GRB samples n coinc are also given. The probabilities P (> meas) and the p-value, P (≥ meas) give the fraction of background-only pseudo experiments that yield test statistics above and at and above the measurement. Being 10 times the logarithm of two definite positive quantities, the ψ type test statistics are usually expressed in dB  [29].
acknowledgements The authors acknowledge the financial support of the funding agencies:  Centre National de la Recherche Scientifique (CNRS) ,  Commissariat  à l'énergie atomique et aux énergies alternatives (CEA),  Commission Européenne (FEDER fund and Marie Curie Program) ,  Institut Universitaire de France (IUF) ,  IdEx program and Uni-vEarthS Labex program  at  Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ),  Labex OCEVU  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ),  Région Île-de-France (DIM-ACAV) ,  Région Alsace  (contrat CPER),  Région Provence-Alpes-Côte d'Azur, Département du Var and Ville de La Seyne-sur-Mer, France ;  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ;  Istituto Nazionale di Fisica Nucleare (INFN) ,.
Bark Classification For the bark classification, we have a database of 2559 bark image belonging to 101 tree species from the data bank Pl@ntView 1. Pictures of bark have the same orientation as the tree (portrait mode). In our practical application, we have re-sized these images in order that the picture contain only the texture of the bark, not the background, and no other elements such as branch, leaves, or lichen. As a matter of fact, we can make the assumption that the interface of the application can guide the user to take such a king of photos. However, we have kept the bark images which have a gradient of light or shadows (see Figure7). This allows us to have an algorithm which should work with images shot in non-optimal conditions by beginners. We may also note that the number of bark images is not the same for all the species (20 in average, but can go up to 100 for some species and 2 or 3 for others).
Train is performed on half of the database, and tests are performed on the second half. We obtain a classification rate equals to 30,7%. If we observe the results of the plant task of the LifeCLEF challenges  (Goëau et al. , 2015) , plant recognition via the stem gives the worst classification rate compared to the other other organs. The state of the art give around 30% of good identification. So, with our method, we achieve the same level with a quite short feature vector and without heavy classification process. In the case of a smart-phone application, we can propose to the user a list of tree species which most likely match the tree he photographed. The Figure  8  shows the evolution of the classification rate according to the number of top answers returned. As said before, for a query bark image, we compute the probability of belonging to a species for each 1-vs-all binary SVM.
univ-lyon2. fr/reves/folia cies. The SVM classifier already returns a confidences value c SV M (F, S) and a similar value can be computed from the Folia classifier: c GNN (F, S) = e -d GNN (F,S) 2. Then, the combination is performed as a multiplicative fusion of the confidence values for each species: The resulting confidence ⊗ c is used to rank the species and the classification answer is the species corresponding to the highest combined confidence. We tested these two combination approaches on a dataset of couples of bark and leaf images corresponding the same species, for which we extracted respectively the bark features presented in this article and the leaf shape features from the folia application. The leaf images are also from the Pl@ntView database. However, some tree species have leaf image but no bark image, and vice versa.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. jpclett. 7b01295. Experimental details: single-crystal ice growth and temperature cell, experimental SFG setup and data analysis, effect of laser-induced heating, and measurement of bulk Raman and infrared spectra; Simulation protocols: MD simulation, calculation of SFG spectra, effects of intermolecular vibrational coupling on the SFG spectra, decomposition of SFG spectra, and hydrogen bond definition (PDF) The Journal of Physical Chemistry Letters Letter.
Study design Inclusion/exclusion criteria According to the inclusion and exclusion criteria summarized in Table  1 , the study investigators will confirm patient eligibility. All patients who are referred for TAVI in our center and who meet the inclusion criteria will be successively included and randomized. After verification of the eligibility criteria and obtaining informed consent, the patients will be randomized by an investigator to receive either BAV or direct implantation of the valve. For this purpose, the investigator will connect to the secure and dedicated CLINSIGHT website (CS Online) with his username and personal password. The random allocation sequences will be computer-generated by an independent statistician using a 1:1 ratio and permuted blocks of 4 and 6. Data collection and follow-up (see Fig.
2 ) Baseline data collected from enrolled patients will include demographics, past medical history, previous cardiac investigations and current medication. Patients will undergo transthoracic echocardiography at baseline, post procedure and at 1-month follow-up. Prospective monitoring of adverse and clinical events starts at randomization and continues until 1 month. All major adverse cardiovascular and cerebrovascular events (MACCE) and other serious adverse events will be recorded in the electronic Case Record Form and reported to the coordinating center within 3 days of first identification. On receipt of notification of any adverse or clinical event, the coordinating center will request additional details, specific to the nature of the event. These episodes will be carefully monitored by the trial coordinator and will be part of the information provided at regular intervals to the Clinical Events and Data Monitoring and Ethics Committees.
2  The transcatheter aortic valve implantation without prior balloon dilatation (DIRECTAVI) trial incidence of post dilatation, radiation exposure, contrast volume injection), and adverse events (VARC-2 criteria) at 30-day follow-up. Adverse events considered included life-threatening/major/minor bleeding, vascular access complications, acute kidney failure (RANKIN classification stage 2 or 3), pacemaker implantation, neurological events or new hospitalization for cardiac causes. We will also evaluate duration of hospital stay, echocardiography data (transvalvular gradients, aortic regurgitation quantification, left ventricular systolic and diastolic parameters). All the definitions used are in accordance with the VARC-2 guidelines  [17].
Ethics The Regional Ethics Committee has approved the trial (Comité de Protection des Personnes Sud Méditerranée, Montpellier, France) and all patients will provide oral and written informed consent. The trial is conducted according to the World Medical Association Declaration of Helsinki and will conform to the ICMJE Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. The trial has a Steering Committee, an independent Event Adjudication Committee, and an external Independent Data Monitoring and Safety Committee (DMSC) (registered number: 2015-A01824-45). The DMSC is composed of an interventional cardiologist, a cardiac surgeon, a neurologist, and a statistician. The trial is registered at ClinicalTrials. gov (NCT02729519).
Statistical analysis A detailed plan of analysis will be elaborated and finalized before the database is frozen (i. e. , after completion of the data management). After verification that patients' characteristics are clinically similar between study arms, the noninferiority of the HFNC device will be assessed by the one-sided Farrington-Manning confidence limit for the risk method using the noninferiority margin of 7%. The noninferiority will be declared if the success rate of the intervention arm (no prior dilatation) will be at most 7% higher than the success rate of the control (prior dilatation) arm. The main analysis will be based on an intention-to-treat analysis whereby all patients randomized in their original arm will be included in the analysis. Given that the primary outcome will be measured during the patient hospitalization, we do not expect any loss to follow-up nor any missing data for the primary outcome.
However, any such missing data will be dealt with according to standard approaches, depending on the nature of the missing data  [19]. This approach is, therefore, equivalent to the perprotocol analysis recommended for noninferiority trials. Fisher, chi-square and Wilcoxon-Mann-Whitney tests will be used as appropriate to compare secondary outcomes between groups with a superiority approach. Protocol violations will be reviewed case by case by the investigators to decide whether the patients can remain in the study and in the analyses. For the secondary outcomes, the data of patients who will withdraw or drop out from the study will be retained in the analyses until the time that they leave the study. Statistical significance will be set at 0. 05. Statistical analyses will be carried out with SAS (SAS Institute, Cary, NC, USA).