dataesr/scientific-paragraphs-categorization huggingface dataset:
The processing of the Kepler data for the 2157 eclipsing binaries is described in §2. Production of an O -C curve for each system is discussed in §3, while an overview of our triple star candidates is presented in §3. 3. Expressions for the various effects that appear in the O -C curves are given quantita-tively in §4. Our approach to the analysis of the O -C curves, in order to extract as much information about the physical system parameters as possible, is described in §5. Our results for the 39 triples found in the search are presented in §6. We discuss the limitations on the determination of system parameters using only the Kepler eclipse timing data, without supplemental information that could be provided by ground-based spectral observations (and in some cases by the Kepler data themselves).
Kepler binary data set The data we use for this study are long-cadence (LC) lightcurves for all binaries published in the latest Kepler eclipsing binary catalog  (Slawson et al. 2011 ; see also  Prša et al. 2011). We used all the files from Quarter 1 through Quarter 13 which were available for retrieval from the Multimission Archive at STScI (MAST). The data used had all been reprocessed with the PDC-MAP algorithm  (Stumpe et al. 2012; Smith et al. 2012) , which removes much of the instrumental noise from the flux time series while retaining the bulk of the astrophysical variability in sources. For each quarter, we normalized the flux series to its median value, and then stitched the quarters together into a single file for each source.
Measuring Eclipse Times The baseline algorithm we utilized for determining the eclipse times consists simply of testing each flux point in the Kepler data set for a local minimum and fitting a parabola to the lowest three points in the local minimum. Then the fitted parabola is used to interpolate between Kepler samples to find a more accurate time of eclipse minimum. As we show, this algorithm is quite good for short orbital period binaries, but begins to lose accuracy for longer-period binaries when the eclipse duration may consist of a substantial number of Kepler long-cadence samples. To carry out our initial search for periodic variations in the O -C curves, we used this basic algorithm exclusively. However, after interesting systems were identified, we recalculated more accurate O -C curves using a better algorithm that involves more of the eclipse profile (T.
This is the next simplest shape for any symmetric eclipse profile. Because there are four parameters that describe a symmetric quartic, this would require four or more flux points to fit. Five is the minimum number of points in a symmetric arrangement which can have a lowest flux point with two higher-flux points on either side. However, we judged this to be too many to use for the shortest period binaries -in some cases, the eclipse is only a few Kepler cadence points wide. Thus, to get a flavor for how a quartic might fit, we utilized a function of the following form: F n = α(t n -δt) 2 + βα(t n -δt) 4 + F min (2) where the parameter β was fixed at a representative value of 0. 3. Thus, there are still only three parameters to fit analytically to three data points. Again, note that all the times are dimensionless (i.
This is due to the fact that the time of each long-cadence measurement was corrected to the Solar System Barycenter. As another caveat, we note that many of the contact binaries exhibit a pseudo-random walk in eclipse phase as well as quasi-periodic behavior with typical amplitudes of ∼300 sec rms  (Tran et al. 2013 ). In addition, the O -C curves for the secondary eclipses in these systems are often anti-correlated with the primary O -C curve  (Tran et al. 2013). The characteristic timescales for these cyclic changes in phase can range from weeks to many months. Therefore, one should be cognizant of the possibility that O -C periods of the order of the 3-year Kepler data interval might simply be the lowest prominent frequency of a random-walk process -especially for contact binaries. In this work we remain mindful of this possibility.
(  13 ), is longer than the timescales that characterize other perturbations that drive precession of the longitude of periastron in the binary, the Kozai cycle will not operate  (Eggleton & Kiseleva-Eggleton 2001; Fabrycky & Tremaine 2007). Moreover, effective damping from the two stars in the binary can terminate the Kozai cycles completely -preferentially leaving i m in the range of 35 • to 50 •  (Fabrycky & Tremaine 2007). The values of τ longterm for all of our triple star candidates are listed in Table  3. They range from ∼3 years to 5000 years, but with only 7 of the systems having τ longterm < 15 years. Therefore, the generally sinusoidal behavior of these long-term perturbations will look approximately linear or quadratic on the 3-year timescale of the Kepler data set. And, as a rough approximation for representing such behavior, we have included a quadratic term in our fit (see §5).
Most systems should lie to the left of this line which is taken from eq. (  16 ) with e = 0. 3. The horizontal green line is a rough lower limit to values of P trip that can be detected via the Roemer delay with the Kepler Q1-Q13 data set, given a sensitivity of ∼50 seconds in detectable amplitude (see eq. 7). Finally, the red line is a rough upper limit to values of P trip that can be detected via the physical delay with the Kepler Q1-Q13 data set given a sensitivity of ∼50 seconds in detectable amplitude (see eq. 10). An assumed value of e = 0. 3 was used to evaluate this latter limit. Systems to the left of the red line are typically detected via the Roemer delay. panions (via eclipse timing variations) in these systems. Finally, if we limit ourselves to seeing 1. 5 -2 orbital cycles of the triple system, then orbital periods greater than ∼900 days are nearly ruled out.
SUMMARY AND CONCLUSIONS We have analyzed the Kepler binary data set for eclipse timing variations, with the intention of identifying signatures of the presence of third bodies. We found some 39 plausible candidates for triple star systems, eight of which had been previously found by the members of the Kepler team, but only a few of these had been studied in any detail. Some were found via tertiary eclipses, while others were detected from systematic variations in their O -C curves (in the latter case using typically only ∼1/10 of the data in the current study). We have subjected all of the 39 systems in this study to an analysis which includes possible Roemer delays as well as physical delays. All the best fits are physically sensible, though revisions may be necessary when Doppler velocity measurements, for example, become available.
Data gaps were identified and a Delphi survey was carried out to obtain consensus on the detection and management of each co-morbidity. The expert panel members for the Delphi survey comprised 10 dermatologists with substantial clinical expertise in managing moderate-to-severe psoriasis patients, as well as a cardiologist and a psychologist (see appendix) with an interest in dermatology. Agreement was defined using a Likert scale of 1-7. Consensus regarding agreement for each statement was defined as ≥75% of respondents scoring either 1 (strongly agree) or 2 (agree).
Data analysis Following each round of completion of the questionnaires, responses were collected and analysed on a group basis. The percentage of respondents assigning each Likert score was calculated for each question. Summary statistics for each question included the minimum, median, mode and maximum scores, as well as an interquartile range. To further establish the degree of agreement or disagreement, responses to statements were categorized as 'Agree/strongly agree' (score 1-2), 'agree somewhat/neither agree nor disagree/ disagree somewhat' (score 3-5) or 'Disagree/strongly disagree' (score 6-7). Consensus regarding agreement for each statement was defined as ≥75% of respondents scoring either 1 (strongly agree) or 2 (agree).
76 TeV collected with the ALICE detector. The standard ALICE offline event selection criteria  [20]  were applied, including a collision vertex cut of ±7 cm along the beam axis. The collision centrality is estimated from the amplitude measured by the VZERO detectors  [17]. The data sample is divided into centrality classes which span 0-70% of the hadronic interaction cross section, with the 0-5% class corresponding to the most central (i. e. smaller impact parameter) collisions. Charged particles reconstructed by the TPC are accepted for analysis within |η| < 0. 8 and 0. 2 < p T < 5. 0 GeV/c. A set of requirements described in  [20]  were applied in order to ensure the quality of the tracks but also to reduce the contamination from secondary particles. To evaluate the systematic uncertainties in the analysis, events recorded with two different magnetic field polarities were analysed leading to an uncertainty below 7% for all centrality classes.
ACKNOWLEDGEMENTS The ALICE collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: Calouste Gulbenkian Foundation from Lisbon and Swiss Fonds Kidagan, Armenia ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ;  National Natural Science Foundation of China (NSFC) , the  Chinese Ministry of Education (CMOE)  and the  Ministry of Science and Technology of China (MSTC) ;  Ministry of Education and Youth of the Czech Republic ;  Danish Natural Science Research Council , the  Carlsberg Foundation  and the  Danish National Research Foundation ; The  European Research Council  under the European Community's  Seventh Framework Programme ;  Helsinki Institute of Physics  and the  Academy of Finland ;  French CNRS-IN2P3 , the  'Region Pays de Loire', 'Region Alsace', 'Region Auvergne'  and  CEA, France ;  German BMBF  and the  Helmholtz Association ;  General Secretariat for Research and Technology, Ministry of Development, Greece ;  Hungarian OTKA  and  National Office for Research and Technology (NKTH) ;  Department of Atomic Energy and Department of Science and Technology of the Government of India; Istituto Nazionale di Fisica Nucleare (INFN) of Italy ;  MEXT Grant-in-Aid for Specially Promoted Research, Japan ;  Joint Institute for Nuclear Research, Dubna ;  National Research Foundation of Korea (NRF) ;  CONACYT, DGAPA, México, ALFA-EC  and the  HE-LEN Program (High-Energy physics Latin-American-European Network );  Stichting voor Fundamenteel Onderzoek der Materie (FOM)  and the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) ,  Netherlands ;  Research Council of Norway (NFR) ;  Polish Ministry of Science and Higher Education; National Authority for Scientific Research -NASR  ( Autoritatea Nat ¸ionalȃ pentru Cercetare S ¸tiint ¸ificȃ -ANCS );  Federal Agency of Science of the Ministry of Education and Science of Russian Federation ,  International Science and Technology Center, Russian Academy of Sciences, Russian Federal Agency of Atomic Energy, Russian Federal Agency for Science and Innovations and CERN-INTAS ;  Ministry of Education of Slovakia; Department of Science and Technology, South Africa; CIEMAT, EELA, Ministerio de Educación y Ciencia of Spain, Xunta de Galicia (Consellería de Educación ),  CEADEN, Cubaenergía, Cuba , and  IAEA (International Atomic Energy Agency );  Swedish Research Council (VR)  and  Knut & Alice Wallenberg Foundation (KAW) ;  Ukraine Ministry of Education and Science; United Kingdom Science and Technology Facilities Council (STFC) ; The  United States Department of Energy , the  United States National Science Foundation , the  State of Texas , and the  State of Ohio.
We first measured the profiles of phase, retardance and slow axis orientation (this will be reported extensively in another paper). From this database, we were able to plot the Fig. 2  including the phase change and the retardance for the two orientations ( + X and -X) for two different configurations i. e. Xx and Xy. As it is well known, we see on the phase curve that whatever the configuration (but not for the same energy range), there is an increase of the average index change (above T1 threshold) followed by a strong decrease above T2 threshold. It is important to note that the phase and thus the azimuthal average index exhibit an orientational asymmetry. It is larger on Xx than on Xy configuration. Asymmetry is also detected on retardance profiles across the lines as shown in Fig. 3. This has been mentioned previously by Beresna et al.
Acknowledgments This work has been performed in the frame of  FLAG (Femtosecond Laser Application in Glasses)  consortium project with the support of several organisations: the  Agence Nationale pour la Recherche  ( ANR-09-BLAN-0172-01 ), the  RTRA Triangle de la Physique (Réseau Thématique de Recherche Avancée ,  2008-056T ), the  Essonne administrative Department (ASTRE2007) , the  Ministry of the Foreign Affairs  ( PHC Alliance ) and  FP7-PEOPLE-IRSES e-FLAG 247635.
The candidate predictors were specified a priori and all the variables mentioned in previous models  [4]  were available in our database. The model calibration (goodness of fit between observed and expected (predicted) values of the observed response, irrespective of prediction level) was assessed by Hosmer and Lemeshow goodness of fit test  [14]. Goodness of fit was determined by the C-statistic (the area under the ROC curve). Prognostic parameters are presented as the odds ratio (OR) [95% confidence interval (CI)] relative to the indicated reference category. For sensitivity purposes and facilitating clinical interpretation, we compared the logistic model, with an ordinary least squares OLS linear model adapted for binary endpoints, where effects can be interpreted as absolute risk difference per unit.
Study limitations Our dataset was not a random dataset and not all the patients with at least four CFCs were treated, a part of these couples dropped out and renounced to their fertility project. It is probable that more favourable prognosis were treated, thus our estimate compared with the strict population should be over-estimated on a strict Intent to Treat basis. In that sense, our research has not the objective to determine an estimate of LB rate in the population of couples with multiple CFCs. Malizia et al. [22]  clearly underlined the difference between patient returning to IVF, and those interrupting their project, in showing the poorer prognosis of the latter group. As a consequence, for optimistic and conservative estimates, we calculated in censoring non-returning patients or assimilating them as failures, respectively.
Data collection Data from institutions participating in GIPAP have been collected in several databases. Axios maintains an Access to Treatment Online Management System (ATOMS) database, which captures detailed information on institutions and countries where Axios programs operate. The Max Foundation also maintains an institution, physician and a Patient Assistance Tracking System (PATS). All data analyses were retrospective, based on existing patient records collected by physicians as part of the patient's routine clinical care and transmitted to Axios and the Max Foundation without any identifiers. Given these parameters, an Institutional Review Board (IRB) exemption was requested and received from the Western IRB. Analyses were conducted under the authorization from Novartis and the Max Foundation.
In the present paper, data were available only for programs managed by Axios (44/81 countries). Consequently, programs directly managed by Novartis were not included in the analysis. Patients' inclusion and quarterly follow-up data were systematically collected for each hospital participating in GIPAP. All individual data were initially collected by the physicians in charge of the patients and then anonymously transmitted to Axios and the Max foundation. Axios actively monitored the quality of those individual data by conducting regular audits. At the patient level, socio-demographic and health characteristics were collected, including gender, age at enrollment, initial diagnosis (GIST or CML, along with the stage of disease: chronic, accelerated and blast crisis), and the hospital at which the patient was enrolled and treated.
The date of inclusion in the cohort was the date of initial patient approval between 2003 and 2010. The end point was the date of the last reapproval in 2010 for active patients or the date of closure for other patients. The reasons for closure were: lost to contact, clinical reason, death, and other. The duration of follow up was measured from the date of inclusion to the end-point date and was considered as a proxy for survival. Only patients for whom complete institutional information was available were included in the analysis. At the institutional level, institutions run by the government were classified as public institutions, while hospitals operated by non-governmental organizations (NGOs), or private or other organizations, were classified as private institutions. Data on core competencies related to operations research, clinical trials, and training activities were also collected.
Institutions with activity in at least one of these competencies were classified as having institutional research ability. Data on institutions' technical competencies were also collected and comprised Ph + chromosome testing for CML or CD 117 testing for GIST diagnosis, and the use of bone marrow biopsy or aspiration. Data on the availability of hematologists or oncologists at the institutions were also collected. In addition, institutions were stratified by location within the WHO region (Africa, America, Eastern Europe, Pacifica and South East Asia) in order to assess the independent role of the factors previously described, while adjusting for supranational features shared by countries from the same WHO regions; those features are related to health care system factors such as the overall financial or organizational strength, and could confound the relationship between institutional factors and enrollment or survival.
The evaluation of performance at the patient level was performed using multivariate Cox models accounting for the clustered structure of the data within institutions and considering the activity status of the patient as a proxy for survival. Thus, closure for death or clinical reason was considered as the event of interest, with death determined by patient being defined as "dead" at the latest follow-up. Closure for other reasons lost to follow-up, and active status (patient still alive at latest follow-up) at the end of the study were analyzed as censored data. In an alternative sensitivity analysis, closure for any reason and lost to follow-up were also considered as events of interest. Results from the original and alternate analyses were similar, and the latter results are not included in this report.
These differences include the quality and number of health care facilities, access to health care, and availability of government or private insurance programs. In order to achieve optimal health outcomes for patients in GIPAP, greater emphasis should be placed on institutional factors that improve survival, regardless of the differences in health care infrastructures among different countries. This study had several limitations. First, a number of institutions included in this analysis had a small number of patients included, which may limit the utility of their datasets. Second, as previously described, the use of case closure as a proxy for survival is an imprecise method that may lead to incorrect assumptions about actual patient outcomes  [11]. The ability to capture more precise information about the reasons for patient drop-out or case closure should improve our understanding of how various patient and institutional factors impact health outcomes for patients in GIPAP.
Additional data on the impact of patient and institutional characteristics on patient outcomes continue to be collected. Further evaluation of this growing body of data should help to identify factors that improve outcomes for patients with CML or GIST. Incorporation of findings of such analyses into the structure of GIPAP may help to increase the program's effectiveness and may help improve outcomes for patients participating in the program. Identification of institutional characteristics associated with improved patient outcomes may enable criteria that could be used to select institutions for GIPAP participation. The ability to select institutions most able to improve patient outcomes would likely increase the success of an access program. This may be especially important when drug access programs are initially implemented, as early success should help them to gain traction and could facilitate their rapid and effective implementation.
(Biographic data taken from Mannweiller, 1998. ) 10 Max Hartmann was born in 1876. He trained in natural sciences at the University of Munich, obtaining his doctorate there in 1901. Between 1902 and 1905  he was Privatdozent at the Institute of Zoology of the University of Gießen. In 1903 he presented a thesis for full professorship on the modes of reproduction of organisms, a demonstration of what would be the main motif of his scientific production -theories of sexuality. Influenced by Fritz Schaudinn, he transferred in 1905 to the Institute of Infectious Diseases in Berlin, where he implanted the department of protozoology. In 1914 he moved to the same department at the Kaiser-Wilhelm Gesellschaft Institut für Biologie (Kaiser Wilhelm Institute for Biology) in Dahlem, Berlin, where he was director between 1933 and 1955.
Acknowledgements John Ngoyi  is also affiliated with the  Institut Supérieur des Techniques Médicales ,  Mbuji-Mayi ,  Democratic Republic  of the Congo and  Jean-Jacques Muyembe  is also affiliated with the  Faculté de Médecine, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo. Veerle Lejon's research  for this study was conducted in part at the  Department of Clinical Sciences of the Institute of Tropical Medicine in Antwerp, Belgium.
The approach is based on the full vector equations of the electromagnetic inverse scattering problem. The illposedness of the problem is faced by the application of an inexact Newton method. Preliminary reconstruction results are reported. The two papers by M. Alekhin et al. are concerned with the use of the microwave signals in order to detect and characterize vital signs, with a focus to the breathing activity characterization in medical applications. The focus is on the development of advanced data processing with the end of using this noncontact screening technique in real medical problems as sleep apnea syndrome. The paper by I. Arnedo et al. gives a review of different synthesis techniques for the design of passive microwave components with arbitrary frequency response. The authors provide the theoretical foundations based on inverse scattering and coupled-mode theory; several applications are shown, where the devices designed following those techniques have been successfully tested with a focus on advanced UWB radar and communications and on novel breast cancer detection systems.