dataesr/scientific-paragraphs-categorization huggingface dataset:
Adjusting the model to the data The IRS spectrum was acquired on 2 November 2008 around 18:15 UT, in less than 10 minutes. Unfortunately, the nucleus rotation period is not known with a sufficient accuracy to re-phase the spectrum with the HST or MIPS light curves, taken respectively 38 and 233 days after. As a consequence, we adopted a conservative approach and assumed two extreme solutions for the cross-section viewed by SST at the time of observation, corresponding to the minimum and cross-sections. As found in the previous section, only the HST shape model reasonably fits the MIPS light curve so we performed the analysis with this shape model. As explained in Section 3, there are five parameters to be constrained by the spectrum: γ, η and I for the nucleus and A and T dust for the dust coma.
Acknowledgements. This work is based on observations made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. We thank the SST ground system personnel for their prompt and efficient scheduling of the observations. We are grateful to  G. Faury  ( IRAP, Toulouse, France ) for his early contribution to the rotational analysis. I. Toth was supported by a grant from  CNES  ( Centre National d'Etudes Spatiales ) for his work at  Laboratoire d'Astrophysique de Marseille. H. Weaver  gratefully acknowledges support provided by  NASA  through grant number  GO-11226  from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy  under  NASA  contract  NAS5-26555.
Etymology: The prefix 'Eo' refers to the Eocene, 'geometer' to the family name. Species Eogeometer vadens nov. spec. Holotypus: Specimen SNSB-ZSM-LEP amb002 (Fig. 1 ) Locus typicus: Amber mine of Yantarni, Russia Stratum typicum: "Blaue Erde" Horizon (Upper Eocene -Lower Oligocene) Etymology: The species name 'vadens' -"walking" refers to the looping manner of locomotion. Repository: Bavarian State Collection of Zoology, Munich, Germany; accession number SNSB-ZSM-LEP amb002. Diagnosis of genus. The genus Eogeometer is established for this Eocene geometrid caterpillar presumably belonging to the tribe Boarmiini. A differential diagnosis will be given when more Eocene geometrids will become known. Attribution to any extant geometrid genus would be highly speculative. The amber larva shares the character combination of supposed conifer-feeding (see below) and a conspicuous semicircular anal plate with only a few extant geometrid larvae (see discussion), which, however, do not show a longitudinal stripe pattern.
Acknowledgements Michel Balke  and  Stefan Schmidt  ( SNSB -Bavarian State Collection of Zoology, Munich ) are acknowledged for providing the photographs of the larva. Sven Erlacher  (Chemnitz) helped by provision of literature. Michael Leipnitz  (Stuttgart) and  Egbert Friedrich  (Jena) helped with their expertise in geometrid larva identification. Manfred Sommerer (Munich) provided important literature references. We appreciate the efforts taken by  Conrad Labandeira  and one anonymous reviewer in improving the manuscript.
Sample We build our sample by combining two different datasets, as done for the study of the dependence of the luminosity function on the magnitude gap (Paper V). In the first sample (hereafter S1) we have the 34 FG candidates proposed by  Santos et al. (2007)  and analysed by the FOGO team in Paper IV. The spectroscopic completeness of the S1 sample is more than 70% down to m r = 17 and more than 50% down to m r = 18. For the S1 sample we have a total of 1244 available velocities (26 clusters), of which 579 turned out to be members of the respective cluster. We refer the reader to Paper IV for detailed information of the S1 sample. We were able to confirm that 15 out of 34 are genuine FGs, 7 are non-FGs, and the other 12 remain to be confirmed. We defined as "genuine fossil" a group or cluster of galaxies that accomplishes one of the two definitions that follow: i) it has a magnitude gap of at least 2 magnitudes between its two brightest member galaxies (∆m 12 ≥ 2) in the r band within half the virial radius or ii) it has a magnitude gap of at least 2.
The corresponding median M 200 masses (given in logarithmic units) are 14. 27±0. 38 dex, 14. 40 ± 0. 35 dex, 14. 33 ± 0. 40 dex, and 14. 21 ± 0. 48 dex. For each member galaxy, we have a velocity that comes from our own spectroscopy or SDSS and magnitudes from SDSS-DR7. In particular, throughout the paper we use the model magnitude in the r band and all absolute magnitudes are K-corrected following  Chilingarian et al. (2010). For each cluster of the S1 sample, R 200 was obtained from X-ray data and members were selected using a two-step procedure applied to the galaxies in the region within R 200. First, we used DEDICA  (Pisani 1993 (Pisani , 1996)) , which is an adaptive kernel procedure that works under the assumption that a cluster corresponds to a local maximum in the density of galaxies.
Dependence of the velocity dispersion profile on the magnitude gap We also study the cumulative and differential velocity dispersion profiles, σ (n) cum (< R) and σ (n) diff (R) respectively, in the different magnitude-gap bins. The differential quantities are computed at R = 0. 025 R 200 , R = 0. 05 R 200 , R = 0. 1 R 200 , R = 0. 2 R 200 , R = 0. 5 R 200 , R = 1 R 200. The cited quantities and the 68% uncertainties are computed using the biweight estimator of the ROSTAT package  (Beers et al. 1990 ). In Fig. 3  we present the results. In the very central part a trend seems to appear: the clusters with the largest ∆m 12 have a lower value of velocity dispersion in the centre. However, the trend is not as clear as it was for the velocity segregation, because clusters with 1. 0 < ∆m 12 ≤ 1.
Acknowledgements: The research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement n. 340519. Moreover, SZ acknowledges financial support from the  University of Trieste  through the program " Finanziamento di Ateneo per progetti di ricerca scientifica (FRA2015) " and from the grant  PRIN INAF2014-1. 05. 01. 94. 02. J. A. L. A. thanks the support from the  Spanish Ministerio de Economia y Competitividad (MINECO)  through the grant  AYA2013-43188-P. EMC  acknowledges financial support from  Padua University  through grants  DOR1699945 ,  DOR1715817 ,  DOR1885254 , and  BIRD164402. ED acknowledges the hospitality of the  Institute for Theory and Computation (ITC) at Harvard University , where part of this work has been completed.
In this paper we propose two methods to overcome the aforementioned limitations. First, we introduce a framework based on the singular value decomposition (SVD) to filter out the axial displacement of the sample from the local fluctuations linked to intracellular motility, enabling in vivo use of D-FFOCT. SVD based algorithms have been previously applied to OCT data, e. g. for smart OCT where the SVD is applied to the reflection matrix in order to extend the penetration depth  [14]. An SVD filtering method for D-FFOCT has been previously proposed for simulated data  [15]  but does not work on our experimental data mainly because the image formation model is different. Here we propose to find eigenvectors associated with axial motion and filter them out. Similar SVD based algorithms for spatio-temporal filtering have been used effectively in acoustics for Doppler acquisitions  [16, 17].
Removing artifacts using SVD In order to construct a D-FFOCT image, a stack of typically 512 direct images (1440 × 1440 pixels) is acquired with a standard FFOCT using our custom software  [18]. The FFOCT setup consists of a Linnik interferometer where both arms contain identical microscope objectives. The reference arm contains a silicon mirror mounted on a piezoelectric translation (PZT) used for phase modulation. In a typical FFOCT experiment, at least two images are acquired with different phase modulations and the FFOCT image is constructed by using appropriate phase-shifting algorithms  [4, 11]. For D-FFOCT experiments the PZT position is not modulated, fluctuations arise by scatterers motions inside the coherence volume. In this paper we used data acquired from two different setups.
Acknowledgments The author would like to thank  LLTech SAS  for sharing its raw data, especially  Émilie Benoit  and  Louis Dutheil  for carrying out in vivo and clinical experiments. The author is also grateful to  Olivier Thouvenin ,  Pedro Mecê ,  Kassandra Groux ,  Viacheslav Mazlin ,  Mathias Fink ,  Claude Boccara  and  Kate Grieve  for fruitful discussions and valuable comments regarding this paper. The data and algorithms used during the current study are available from the corresponding author upon reasonable request.
Study Sample The CARDIA study is a prospective, longitudinal cohort study evaluating the development of vascular risk factors in healthy young adults who provided informed consent to participate. The original cohort consisted of 5115 healthy black and white participants, 18 -30 years of age. 22  In the 25th year of follow-up, at a mean of 50 years of age, brain MR imaging was performed between August 19, 2010, and August 31, 2011, in 719 randomly selected subjects in the CARDIA study who had no contraindications to MR imaging. Structural (T1, T2, FLAIR), diffusion (MD, FA), and physiologic data (arterial spinlabeling perfusion, breath-hold fMRI) were acquired, and complete, analyzable datasets were obtained in 463 subjects (On-line Fig  1 ) from 2 sites: the University of Minnesota (n ϭ 250) and Kaiser-Permanente Division of Research (n ϭ 213), both using 3T Tim Trio scanners (Siemens, Erlangen, Germany).
4 ϫ 3. 4 ϫ 6 mm), and breathhold blood oxygen level-dependent (BOLD) fMRI (TR ϭ 2000 Processing of structural MR imaging sequences first involved histogram normalization and bias field correction of raw image data, which improve comparability of the uncalibrated, ordinal data, followed by template registration  23  and semiautomated tissue segmentation. 24  WML segmentation was performed using a previously validated supervised learning-based multimodal segmentation method. 24, 25  This support vector machine classifier was originally trained on multimodal MR imaging data from a separate training set with expert human manual segmentation of WMLs. It provides segmentation based on structural MR imaging (T1, T2, FLAIR) that strongly correlates with a definition of WMLs in the training set of a human observer.
From DTI, we calculated FA and MD scalar maps. CBF maps were calculated from pseudocontinuous arterial spin-labeling. 26  Maps of VR as measured by the percentage signal change in BOLD fMRI between breath-hold and rest states were generated using a method similar to that of Murphy et al. 27  To summarize, breathholding was used to elevate blood CO 2 and thereby induce cerebral vasodilation to effect changes in BOLD signal. During acquisition of BOLD fMRI, subjects were cued to follow four 16-second breathholds using E-Prime (Psychology Software Tools, Sharpsburg, Pennsylvania). Using FSL (http://www. fmrib. ox. ac. uk/fsl), we mo-tion-corrected the resulting images and smoothed them, and a whole-brain generalized linear model analysis was performed using a 9-second delay to account for hemodynamic lag.
ACKNOWLEDGMENTS The authors thank  Raymond Pomponio  for statistical assistance. Disclosures:  Ilya M. Nasrallah -RELATED: Grant:  National   Institutes of Health *; Support for Travel to Meetings for the Study or Other Purposes: National Institutes of Health*; UNRELATED: Grants/Grants Pending:  National Institutes of Health. *  Guray Erus-RELATED : Grant:  National Institutes of Health. *  John A. Detre -RELATED: Grant:  National Institutes of Health *; UNRELATED: Consultancy: Blackfynn,  CHDI Foundation ,  Dana Foundation ,  National Institutes of Health , Comments: I have provided expert advice on neuroimaging to the  University of Pennsylvania spinoff  startup company Blackfynn. I have received stock options. I have provided consulting to the  CHDI Foundation , a private foundation involved in Huntington disease research, but none in the last year.
Material and Methods The study conformed to scientific principles and national research ethics standards. Patients' care was not modified in any manner because of this study. Data were retrospectively collected by participants on their secured radiological information systems or on their institution's medical records. Data were sent to the investigators in an anonymous form. The investigators had no access to the participants' information systems and therefore had no possibility of reidentifying the patients. Consequently, informed consent of the patients was not required by personal data protection rules at the time of the study.
Patient radiation dose survey A national retrospective survey was conducted by the French Society of Medical Physics (SFPM) on patients aged ≥ 16 years who underwent CT examinations in France between 2015 and 2017. A list of 22 groups of clinical indications (GCIs) for diagnostic CT in 9 anatomical regions was initially defined by the SFPM, with the collaboration of the French Society of Radiology (SFR) and its associated organ sections, based on the frequency and the image quality requirements of the clinical indication. Seventeen GCIs were eventually included in the study (see Table  1  and next section for more details). A national call for volunteers for one or more GCIs was sent to the medical physicists' community. Data collection was performed by each participant independently, using one or more of the following means: picture archiving and communication system (80% of the participants), radiological or hospital information system (61%), dose archiving and communication system (50%) or paper medical records (9%).
Anonymised data were transmitted to the investigators with the help of a preformatted Excel datasheet. For each participant's CT scanner and specific GCI, 15-30 consecutive examinations were required. Collected data focused on the complete examination with details of each acquisition. Collected data included CT scanner characteristics (manufacturer, model, commissioning year and maximum collimation); patient information (age, and, optionally, gender, weight and height); acquisition parameters (use of contrast media, and, optionally, acquisition type -axial or helical-, scan length, kV, use of tube current modulation, use of patient restraint equipment and acquisition description); image reconstruction algorithm -Iterative Reconstruction (IR) or Filtered Back Projection (FBP) -; clinical information (clinical indication from the medical report and optionally, exam codification used for medical insurance) and dose data (CTDIvol, DLP and phantom type -16/32 cm diameter-for each acquisition without localizer radiograph acquisitions and without acquisitions related to the contrast media tracking as well as total examination DLP).
Exclusion criteria An essential point in this study was to have reliable data. Each participating facility was asked to check the clinical indication of the CT examination in the medical report. The working group of the SFPM and the SFR checked each collected examination data based mainly on the series description, contrast media use, phantom type (16 or 32 cm) and/or scan length. Examinations were excluded from the analysis based on the following criteria:  Patient's age < 16 years. Ultimately, sets of data counting less than 10 examinations per CT scanner and per GCI at the end of the exclusion process were excluded from the analysis. Likewise, GCIs having less than 10 contributing CTs were also excluded from the study. Consequently, 5 GCIs were abandoned (aortic dissection, suspicion of a lung cancer, screening for secondary liver cancer, suspicion of a kidney cancer and haematuria).
Results A total of 9667 acquisitions, corresponding to 7102 examinations included in the 17 selected GCIs, was collected from 88 CT scanners in 53 facilities. After the data validation process, 6610 examinations (8620 acquisitions, 86 CT scanners, 53 facilities) were analysed. This included 24% chest, 21% head, 14% abdomen and pelvis (AP), 11% chest, abdomen and pelvis (CAP), 9% pelvis, 6% ear, 5% mandible/maxilla, 5% cervical spine and 5% body CTexaminations (Table  1 ). 87% of participating facilities were public hospitals (59% academic and 41% non-academic), 9% comprehensive cancer care institutes and 4% private centres. All French regions were represented. Figure  1  shows the distribution of collected examinations by scanner manufacturer, commissioning year and GCI. The median commissioning year was 2013.
We assumed that CTDIvol values were in agreement with the national quality criteria (within ±20% deviation) checked annually and after tube replacement. Lastly, the manual process for patient size data and examination dose information in some departments not using a DACS, led to missing data in some cases. Moreover, the use of an Excel sheet to manually collect the data limited the number of examinations compared to an automatic collection with dedicated software, such as in the US DRLs study  [19]. However, the verification of the clinical indication was more feasible with the Excel sheet.
Acknowledgements The authors are very grateful to the  SFPM (Société Française de Physique Médicale)  for its support, to the  SFR (Société Française de Radiologie)  and its associated organ sections for their contribution in this study: CIREOL (Société francophone d'imagerie tête et cou),  SFNR  ( Société Française de Neuroradiologie), SIAD (Société d'Imagerie Abdominale et Digestive), SIFEM (Société d'Imagerie de la Femme ),  SFICV  ( Société Française d'Imagerie Cardiovasculaire) ,  SIGU  ( Société d'Imagerie Génito-Urinaire ),  SIMS (Société d'Imagerie Musculo-Squelettique) ,  SIT (Société d'Imagerie Thoracique ). The authors also wish to express their deepest gratitude to the staff (medical physicists, radiologists, technologists and radiation protection experts and officers) of the 53 radiology centres for their participation in this study and their efforts in providing the best data quality.
129X1-Gt(ROSA)26Sor tm1(EYFP)Cos /J; stock# 006148; The Jackson Laboratory; Bar Harbor, ME), thus allowing Cre-mediated STOP sequence deletion and EYFP expression in Kiss1 neurons of the double mutant offspring. MBH tissue encompassing the ARC of the Kiss1-Cre/YFP mouse at the pubertal stage (PND29) was microdissected and enzymatically dissociated using a Papain Dissociation System (Worthington, Lakewood, NJ) to obtain single-cell suspensions. FACS was applied to these suspensions using a FACS Aria III Sorter (BD Biosciences). The FACSDiva software (BD Biosciences) was used for the sort, and Kaluza software (Beckman Coulter) was used for the analysis. The sort decision was based on measurements of YFP fluorescence (excitation: 488 nm; detection: YFP band-pass 530/30 nm). First, cells were gated for forward scatter (FSC-A) and side scatter (SSC-A) to exclude cellular debris, and then aggregates were removed by selecting singlets using an FSC-A versus FSC-H plot (S4 Regarding reverse transcription (RT) and rt PCR (mRNA and miRNA) assays, RT-PCR was used to assess mRNA and miRNA expression in hypothalamic tissue and Kiss1 cells, as described below.
5 μL specific primer reverse (rv) 10 nm, 6. 25 μL Go Taq qPCR Master mix, and 2. 75 μL of Nuclease free water (final volume, 15 μL). Expression of Mkrn3 was quantified using the following specific primers pairs: rMkrn3 fw 5 0 -AGTTGGACG AAGCAAATCCTC-3 0 and rMkrn3 rv 5 0 -AGGTCGTGAGAGTAGCGACA-3 0 (accession number XM_218735. 10). For data analysis, relative standard curves were constructed from serial dilutions of reference cDNA samples from the whole hypothalamus or MBH, and the input value of the target gene was standardized to the levels of the ribosomal protein S11 (rRp-s11): rRp-s11 fw 5 0 -CATTCAGA CGGAGCGTGCTTAC-3 0 and rRp-s11 rv 5 0 -TGCATCTTC ATCTTCGTCAC-3 0 (accession number NM_031110. 1); or hypoxanthine-guanine phosphoribosyltransferase (Hprt): rHprt fw 5 0 -AGCCGA CCGGTTCTGTCAT-3 0 and rHprt rv 5 0 -GGT CATAACCTGGTTCATCATCAC-3 0 (accession number NM_012583.
3 μM gene-specific primers to amplify 12. 5 ng of cDNA template. Amplification of each target gene was performed using the following specific primers pairs: mKiss1 fw 5 0 -TGCTGCTTCTCCTCTGT-3 0 and mKiss1 rv: 5 0 -ACCGCGATTCCTTTTCC-3 0 (accession number NM_178260. 3); mMkrn3 fw: 5 0 -GAGAGGGAA ACGTGCTGTTTA-3 0 and mMkrn3 rv: 5 0 -CGGTCATCAGAGAAGG AAGAAC-3 0 (accession number NM_011746. 3); and mHistone3a fw: 5 0 -CGCTTCCAGAGT GCAGCTATT-3 0 and mHistone3a rv: 5 0 -ATCTTCAAAAAGGCCAACCAGAT-3 0 (accession number NM_008210. 5). Histone 3a served as a housekeeping gene. The PCR conditions used were 1 cycle of Hot-Start activation at 95˚C for 2 minutes, followed by 40 cycles of denaturation at 95˚C for 30 seconds; annealing at 60˚C for 30 seconds; extension at 72˚C for 20 seconds, and a final extension of 72 ˚C for 10 minutes.
Statistical analyses Statistical analyses were performed using Prism software (Graphpad Prism 6. 0 for Macintosh, GraphPad Software, La Jolla, CA, www. graphpad. com). The differences between several groups were analyzed by one-way ANOVA followed by the Tukey multiple comparison test for unequal replications. When comparing the influence of two different independent variables, experimental groups were subjected to a two-way ANOVA test followed by Sidak's multiple comparisons test. The Student t test was used to compare two groups. As a general procedure, the group sizes were selected based on previous experience with studies addressing molecular and neuroendocrine regulation of puberty, assisted by power analyses performed using values of standard deviation that we usually obtain when measuring parameters analogous to those examined in this study.
Acknowledgments We are indebted to  M. M. Malagon  ( IMIBIC, Cordoba, Spain ) and  Maria Giraldez  ( Instituto de Biomedicina de Sevilla -IBIS-, Sevilla, Spain ) for their helpful discussion about experimental work. We also thank  Esther Peralbo  ( Microscopy, Cytomics and Scientific Imaging Unit of the IMIBIC, Cordoba, Spain ) for her help with conducting fluorescence-activated cell sorting experiments.
Thereby, the Tokuyasu method yields better preservation of antigens, as well as fluorescence and photoswitching properties for SMLM  30. For the final registration of the SMLM and EM data sets, we tested different types of fiducial markers visible in both imaging modes. Because beads that were adsorbed to the back side of the formvar film could obscure the region of interest (latex-beads) or cause strong out-of-focus background (tetraspecks) (data not shown), we ultimately chose glutaraldehyde (GA)-fixed bacteria as fiducials. These exhibit a strong broadband, photo-switching auto-fluorescence signal, and provide a stably immobilized marker within the actual imaging plain and restricted to the space between cells. Our optimized superCLEM workflow is schematically represented in Figure  3. Briefly, after a 15-min pulsed uptake of cargo, cells were fixed, scraped, mixed with GA-fixed bacteria, pelleted and embedded in porcine gelatin.
We identified a number of ILVs within the globular part of the endosome, but only a fraction could be associated to the EGF SMLM signal (Suppl. Figure  S12B, E ). The analysis also showed that multiple Dronpa-Rab5c domains can be mapped to the same early endosome. In our superCLEM data sets we could also find instances, where Dronpa-Rab5c domains were located close to Tfn tubular bundles (Figure  7  B, Suppl. Figure  S12B ).