dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations Concomitant approaches can be suitable for solving problems related to clinical and genomic datasets. However, none of these approaches provides an explicit recipe for dealing with heterogeneous data. 1  In particular, the longitudinal aspect is not taken into account in these kinds of approaches. In addition, most methods require new representations derived from the original space. Distorting the initial information may significantly complicate the posterior validation of the extracted features. The inherent limitation of methods based on dimensionality reduction was referred to previously. An additional difficulty arises with methods based on similarity matrices, such as kernel methods that implicitly map the data in a new feature space, since these methods require a pre-image problem to be solved for features to be approximated and, where possible, interpreted.
MIXTURES OF REGRESSIONS WITH CLINICAL AND GENOMIC DATA To take advantage of both the clinical and the genomic information, the two datasets can be used simultaneously via a mixture model. Mixtures of experts provide an elegant framework for including concomitant variables as secondary information alongside subtype data  (Gormley et al. , 2019). This section starts with a description of mixture-of-experts models, with a view to clarify the links between this framework and the approach that we are proposing.
Specificity Our model is designed to take into account the longitudinal aspect of the clinical data and the high-dimensional nature of the genetic data. Y comprises observed values of clinical variables over a series of follow-up visits indexed by j. The vth clinical variable observed during the j th visit of patient i is denoted y iv (j). Also, the number of variables in the genetic data G may be of the order of a few million after genotype imputation, so that dedicated metrics [such as CADD  (Rentzsch et al. , 2018) , used in our explanation concerning Parkinson's disease] or more general elimination techniques such as screening rules [see  (Ndiaye et al. , 2017)  for instance] may still be required beforehand. Note that even where this kind of prior processing occurs, we remain in a configuration where N ≪ L.
Implementation The implementation of the method proposed in this study, which we have named DiSuGen, and an R Markdown tutorial are available for download  (Courbariaux et al. , 2020). We build on the FlexMix R package  (Grun and Leisch, 2008)  which proposes an EM algorithm suitable for multinomial logistic mixture models. To implement our method, we developed an adapted concomitant variable driver making use of glmnet within FlexMix. For a faster convergence, we resort in practice to a Classification EM (CEM) algorithm in which τ (q+1) ik are replaced by the indicator variables z (q+1) ik  (Celeux and Govaert, 1992).
Clinical Data Clinical data were collected at inclusion and then at yearly clinical follow-ups between 1 and 7 years. They include scores evaluating the progression of the disease. Two of these scores are taken to be representatives of the evolution of the disease, namely UPDRS III (Section III of the Unified PD Rating Scale, a motor examination), and MMSE (the score from the Mini-Mental Status Examination tool kit, an evaluation of cognitive impairment). The higher UPDRS III and the lower MMSE, the greater the degree of impairment will be. These two scores were adjusted beforehand for gender effects and for treatment doses by considering the residuals of the linear regression with gender and treatment doses as (factor and quantitative, respectively) predictors. The time scale used is patient age.
ACKNOWLEDGMENTS The methodological reflections behind this work grew out of sustained exchanges with the members of this project, notably Pierre Neuvial. We would also like to thank  Agathe Guilloux  for her help and enriching discussions around this work, as well as  Franck Samson  for his work on the (intranet) web interface of the method. We would like to thank the  DIGPD study group  for collecting the data, and the patients who formed this cohort. The data have been adapted for this specific work by  J-CC , SB, FD,  Graziela Mangonne ,  Alexis Elbaz , and  Fanny Artaud.
Crippa et al. , 2018))ons were obtained from CAMS Global anthropogenic emissions (CAMS-GLOB-ANT v4. 2-S1. 1;Granieretal. , 2019). The emissions are based on the emissions provided by the Emissions Database for Global Atmospheric Research (EDGAR v4. 3. 2;Crippaet al. , 2018)and the Community Emissions Data System (CEDS; Hoestly et.
Acknowledgements Authors acknowledge the financial support of  Centre National des Etudes Spatiales (CNES , the  French Space Agency)  via the  SURVEYPOLLUTION  project from  TOSCA (Terre Ocean Surface Continentale Atmosphère ), the  Université Paris Est Créteil (UPEC) , and the  Centre National des Recherches Scientifiques-Institut National des Sciences de l'Univers (CNRS-INSU) , for helping in achieving this research work and its publication. We thank the French atmospheric data centre AERIS (https://www. aeris-data. fr,last access:  10 June 2022 ) for supporting the production of IASI+GOME2, data access and computing resources provided by the  ESPRI IPSL mesocentre  (https://mesocentre. ipsl. fr/,last access:  1 April 2022 ) and the  European Air Quality e-Reporting  for in situ surface measurements of ozone, EUMETSAT for GOME-2 level 1 data (provided by the  NOAA CLASS data portal).
IASI is a joint mission of  EUMETSAT  and  CNES. We thank the  Institut für Meteorologie und Klimaforschung (Germany)  and  RT Solutions (USA)  for licenses to use, respectively, the KOPRA and VLIDORT radiative transfer models. We also thank  Zhaonan Cai  from the  Chinese Academy of Sciences (China)  and  Xiong Liu  from the  Harvard-Smithsonian Center for Astrophysics (USA)  for their help to produce IASI+GOME2 data. The  Programme National de Télédétection Spatiale (PNTS)  and the  Agence Nationale de la Recherche  ( ANR ) also contributed in supporting the development of the IASI+GOME2 satellite product. Part of this work was conducted at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration (NASA). We warmly acknowledge all datasets provided for this study: CO satellite retrievals from IASI by  ULB/LATMOS  ( Université Libre de Bruxelles/Laboratoire Atmosphères, Milieux, Observations Spatiales ) laboratories (special thanks to  C.
Crippa et al. , 2018))ons were obtained from CAMS Global anthropogenic emissions (CAMS-GLOB-ANT v4. 2-S1. 1;Granieretal. , 2019). The emissions are based on the emissions provided by the Emissions Database for Global Atmospheric Research (EDGAR v4. 3. 2;Crippaet al. , 2018)and the Community Emissions Data System (CEDS; Hoestly et Anthropogenic Giglio et al. , 2016)2014) of Ancillary Data (ECCAD, https://eccad. aeris-data. fr/,lastaccess:26May2021). al. , 2018), and provided as monthly averages with a horizontal resolution of 0. 1° × 0. 1°. Soil NO emission was obtained from CAMS Global soil emissions provided as monthly averages with a horizontal resolution of 0. 5° × 0. 5° (CAMS-GLOB-SOIL v2. 3;Granier et al. , 2019; Simpson and Darras, 2021). Soil NOX is mainly emitted as NO. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is one of the most widely used biogenic emission model(Guenther et al.
, 2012). We obtained biogenic isoprene and monoterpenes emissions developed under the Monitoring Atmospheric Composition and Climate project (MACC), MEGAN-MACC Biogenic emission inventory (MACC-MEGAN,Sindelarova et al. , 2014). The emissions are provided as monthly averages with a horizontal resolution of 0. 5° × 0. 5°. The locations of fires are derived from the Terra and Aqua MODIS (Moderate Resolution Imaging Spectrometer) active fire products (MCD14ML Collection 6;Giglio et al. , 2016)distributed by the Fire Information for Resources Management System (FIRMS, https://earthdata. nasa. gov/firms, last access: 13 March 2020). This dataset provides the values of Fire Radiative.
Here we restrict the discussion to sources with a relative uncertainty on E pk IC lower than 50%. This condition favors sources with slightly harder gamma-ray spectra than average. The effect is more pronounced for FSRQs (mean photon-index difference relative to the whole sample: ΔΓ = 0. 08), and diminishes for BL Lac objects as n pk syn increases (from ΔΓ = 0. 05 for LSPs to ΔΓ = 0. 03 for HSPs). Overall, the fraction of sources with E pk IC meeting the above condition is 30%, 28%, and 24% for FSRQS, BL Lac objects, and BCUs, respectively. FSRQs preferentially exhibit E pk IC below 1 GeV, while most BL Lac objects have E pk IC above this value, reflecting the dichotomy seen in the photon-index distributions (Figure  1 ). Most values of E pk IC obtained for the nine radio galaxies meeting the above condition are notably larger than those reported from a combined fit to the X-ray and gamma-ray data (see, e.
This threshold was calculated as a polynomial fit to the data of blazars with ( ) D =  E 50 5 % pk IC. A detailed study of blazar populations is beyond the scope of this paper so we just outline some broad features. The synchrotron-peak frequency could be determined for 785 of these blazars, including 230 FSRQs, 362 BL Lac objects, and 193 BCUs. In a simple single-zone synchrotron self-Compton model and assuming inverse-Compton scattering in the Thomson regime, the peak Lorentz factor of the electron distribution g SSC pk most contributing to the electromagnetic emission can be directly assessed in a redshift-independent way from the two peak frequencies ⎛ ⎝ ⎞ ⎠ g = n n pk SSC 3 4 1 2 pk IC pk syn. These frequencies, evaluated in the source rest frame 77  , are plotted as a function of one another in Figure  6.
We emphasize that the estimates of n¢ pk syn and ¢ E pk IC are obtained independently, so nearempty regions in Figure  6  do not result from an observational or analysis bias. BCUs follow the same general trend seen for FSRQs and BL Lac objects. The correlation between n¢ pk syn and ¢ E pk IC is strong for BL Lac objects (Pearson coefficient = 0. 75) but weak for FSRQs (Pearson coefficient = 0. 08 for ( ) n¢ < log 13. 6 pk syn , comprising 90% of the sample). A fit of a linear function ( ) ( ) ( ) a n ¢ = ´¢ + E K log log 1 pk IC pk syn (with ¢ E pk IC in MeV and n¢ pk syn in hertz) to the BL Lac object data provides α = 0. 366 and K = -1. 71. A similar fit for the The Compton dominance is defined as the ratio between the peak νF ν for the high-and low-frequency SED components. This redshift-independent parameter has been intensively discussed in the context of the blazar sequence (e.
g. ,  Meyer et al. 2011; Finke 2013; Nalewajko & Gupta 2017; Paliya et al. 2021). The Compton dominance is plotted as a function of n¢ pk syn in Figure  7 , together with the threshold derived from that plotted in Figure  5. The trend is very similar to that seen when the Compton dominance is assessed using the prescription of  Abdo et al. (2010a). Fitting the BLLac object (log-log) data in Figure  7  with a linear function gives a slope of -0. 46 ± 0. 12. No clear indication of an upturn around ν pk ; 10 14 Hz manifesting the transition between synchrotron self-Compton and external Compton as the dominant emission process  (Finke 2013 ) is seen in Figure  7. However, the larger scatter in Compton dominance observed for FSRQs relative to BL Lac objects is likely due to this difference in main emission processes (external-Compton versus synchrotron self-Compton), in addition to different variability levels.
Summary The new release (4FGL-DR3) includes about 19% more blazars than the initial 4LAC-DR1, comprising 75 FSRQs, 117 BL Lac objects, 395 BCUs, and four radio galaxies. The large fraction of BCUs (two thirds) calls for new spectroscopic data to enable the classification. The BCU photon-index distribution suggests that the fraction of FSRQs within these BCUs is notably larger (by a factor ;2. 5) than that found in the set of classified blazars. This feature, not apparent in 4LAC-DR1, may result from a larger flaring propensity of FSRQs relative to BL Lac objects in the LAT energy range, as exemplified, e. g. , by the very different fractions (42% versus 6%) of variable 4FGL-DR3 sources seen in these classes. Both redshift and photon-index distributions of the new FSRQs and BL Lac objects are similar to the previously detected ones.
Astrofisica in Italy and the Centre National d'Études Spatiales in France. This work performed in part under DOE Contract DE-AC02-76SF00515. This research has made use of the NASA/IPAC Extragalactic Database (NED) (2019), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration, and of archival data, software, and online services provided by the ASI Space Science Data Center (SSDC) operated by the Italian Space Agency. D. Ga. andS. Ci. acknowledge support by ASI through contract ASI-INFN 2021-43-HH. 0 for SSDC, and the Istituto Nazionale di Fisica Nucleare (INFN) Appendix Description of the FITS Version of the 4LAC-DR3 CatalogTableA1provides a description of the FITS catalogs available in the online journal.
We also found moderate to high repeatability in individual differences in associative learning in each species. 4. We show that the OpenFeeder is a flexible tool to record performance in multiple cognitive and behavioural tasks in free-ranging animals across a variety of passerine species. The design, firmware and software are open source to facilitate use in a wide variety of species and thus allow continuous improvement of the system and development of new behavioural and cognitive tasks. In doing so, we hope that this tool will be used by a large community of cognitive ecologists and comparative psychologists for both within and across species studies. Furthermore, our system should facilitate replication of results across populations along large-scale environmental gradients to improve our understanding of the role ecology plays in the evolution of cognitive traits.
, 2019). 2. We wanted feeder systems to be able to conduct a large number of trials which meant the ability to have a large reward reservoir and function autonomously for a number of days or more. 3. To encourage the participation of a large number of individuals, we chose to focus on a simple design to produce tasks that require only perching as a response, but has the ability to measure a broad variety of cognitive abilities with this paradigm. To do this, we set initial tests with LED lights to present stimuli and provide software to easily construct new cognitive tests. 5. We also sought to keep the cost down to make the OpenFeeder accessible to more research teams by providing software under an open-source licence (Creative Commons Attribution ShareAlike 3. 0. CC BY-SA 3. 0) and plans for our initial feeder design using common materials for the main body with a few 3D printed components.
| Food access and RFID perch design Birds were given access to food through a 20 mm by 8 mm oval opening at the bottom of the food container. This design allows birds to fit their beak into the food storage section, but not their entire head to remove the risk of the door closing on the bird's head. The only way to obtain the food reward is to perch on a 3D printed perch, containing an RFID antenna, specially designed to force birds to perch in an optimal position for passive integrated transponder (PIT) tag detection (https://github. com/OpenF eeder/ mecha nics, Figure  1 ; Figure  S1  and Supporting Information).
| Cognitive test scenario software We created a user-friendly Matlab graphical user interface (GUI; https://github. com/OpenF eeder/ software, Figure  S13  (1) reward/food storage container, (2) electronic board compartment, (3) four light-emitting diode (LED) displays, (4) feeding hole protective cap, (5) passive infrared (PIR) sensor, (6) feeding hole, (7) 3D printed perch containing the RFID antenna, (8) 3D printed door to control access to the feeding hole, (9) servomotor compartment, (10) battery compartment. A complete tutorial on how to build the OpenFeeder is available at https://doi. org/10. 5281/zenodo. 6542026. a case study in the present paper (Figure  2 ). In this task, the four LEDs were switched on in blue for 5 s and the timing of lights turning on was determined at random with a probability of 0.
25 such that the feeder would reward birds one-quarter of the time and these reward periods were signalled by blue LED lights being on. Birds are rewarded by a 1 s door opening only if they perched while LEDs were on. To assess the repeatability of learning performance, individuals also performed a LEFT/RIGHT discrimination learning task (Supporting Information). We also use a collection of Matlab scripts that produces subtitles from log files that can be useful for quality control (i. e. estimating RFID detection rate) when responses are paired with video recordings of other behaviour in the vicinity of feeders. Finally, we developed a third Matlab GUI to allow serial communication between OpenFeeder boards and a laptop (Figure  S14 ). This GUI can be used for debugging and offers a number of other functionalities including uploading test scenarios, downloading data, OpenFeeder/PC timing synchronization, assessing battery charge level and RFID frequency (Figure  S13 ).
| DISCUSS ION In this paper, we present a new open-source tool for cognitive ecologists, behavioural ecologists or comparative psychologists interested in measuring cognitive performance of wild animals directly in their natural environment. The OpenFeeder relies on individual identification using RFID technology, self-administered tasks, weatherproof electronic and mechanical components as well as a flexible software environment to program multiple behavioural and cognitive tasks. Providing this new flexible cognitive testing device to work directly on wild animals, we hope to advance research in the ecology and evolution of cognition while prioritizing animal welfare and cognitive tests in natural environments. Automated cognitive testing devices that allow large sample sizes, such as the OpenFeeder, also provide an important tool for studies of contemporary selection on cognitive performance within species  (Cauchoix & Chaine, 2016; Morand-Ferron et al.
Furthermore, adaptation of the OpenFeeder to species of different sizes can be easily achieved by adjusting the angle and distance of perch containing the RFID antenna from the feeding hole and adjusting the feeding aperture for different beak or mouth morphologies. While we only describe results on associative learning here, more behavioural and cognitive tasks have already been implemented in the OpenFeeder system including spatial learning and memory, neophobia, colour associative learning, colour or spatial reversal learning and delayed gratification tasks (Supporting Information). As with other RFID systems, it can also be used to measure sociality and social structure  (Aplin et al. , 2012; Firth et al. , 2015). All these tasks can be programmed simply by changing configuration file parameters with our Matlab GUI (Figure  S13 ) or directly in a text editor.
Open access to the code and building plans along with the low cost (<150€/device) and the easy user interface for programming experiments will allow researchers interested in measuring cognition in the wild to use the OpenFeeder and to develop their own behavioural and cognitive tasks. By doing so, we hope to build an OpenFeeder international community sharing similar cognitive tasks, run on a common platform adapted to morphological and motor abilities of each species with a common webspace for exchanges and troubleshooting (https://www. reddit. com/r/ OpenF eeder/). Such widespread multi-species international collaborative projects have the potential to tackle important comparative questions, such as understanding past evolution of cognitive traits using comparative analysis of cognition  (MacLean et al.
, A. C. and J. M. -F. designed the study; A. B. and V. G. designed the electronics; V. G. produced the electronics; J. B. and A. B. coded the firmware; J. B. coded the software (Matlab scripts) and designed 3D printed parts; D. L. designed and built the feeders; N. E. K. collected the data; M. C. analysed the data and wrote the first version of the article; A. C. , G. B. J. , J. M. -F. and A. B. corrected and improved the manuscript. ACK N OWLED G EM ENTWe thank Marine Bely for her help with bird capture and banding and Guilhem Battistella for his help on testing of a previous version of the OpenFeeder. FU N D I N G I N FO R M ATI O NThis work was supported by the Agence Nationale pour la Recherche (ANR-18-CE02-0023 'SoCo'), Human Frontiers Science Program (HFSP; RGP 0006/2015 'WildCog'), the European Regional Development Fund (ECONECT project) and is part of the Laboratoire d'Excellence (LABEX) entitled TULIP (ANR-10-LABX-41) and IAST through ANR grant ANR-17-EURE-0010 (Investissements d'Avenir program).
To implement the spectral demixing data processing for multicolor SMLM, we developed a software, SplitViSu, which is freely available and is integrated into a workflow of image analysis, reconstruction and segmentation of SMLM data within the SharpViSu platform  23. We demonstrate the performance of our methods by imaging the nuclear pore complex (NPC), a reference standard for super-resolution microscopy  24  , revealing simultaneous localization of individual Nup62, Nup96, Pom121 and lamin B1 proteins within the nuclear envelope and providing new findings on their colocalization.
The simulated data contains two species of fluorophores with different ratios: r red = 0. 6, r blue = 3. 5. The true position is the same for both fluorophore species and is shown as a black dot. The total photon count detected from every localization is the same for all fluorophores and equals 2000 photons. The full width at half maximum (FWHM) of the microscope's PSF is 300 nm. The chromatic error between two fluorophores within the opposite channels of the image splitter is 24 nm (x-direction) and 8 nm (y-direction). Each species was detected 50 times within each channel. The circles represent the FWHM of the demixed distributions using the corresponding method for calculation of the output coordinates. Supplementary Figs. 8, 9 ). After the refinement, the localizations with low photon numbers can be additionally removed, which further improves resolution (Fig.
2e ). This method of processing of consecutive localizations is now implemented in the software suite SharpViSu  23. SplitViSu, a software for demixing of spectrally close fluorophores. To integrate different aspects of fluorophore colocalization analysis into a convenient tool, we developed SplitViSu, a software for demixing SMLM data acquired with an image splitter (Supplementary Fig. 10 ). The software has an interactive graphical user interface and can be used as a stand-alone application for Windows, or can be run under Matlab or from SharpViSu  23. See Supplementary Methods for download and installation instructions. The software interface guides the user through all the steps of demixing, starting from visualization of input data and flexible selection of the input regions for each channel of the image splitter.
The two input datasets can then be automatically or manually aligned and the localizations can be paired within a user-defined distance tolerance. The resulting paired localizations are visualized in the form of a bivariate histogram of photon counts, using either linear or logarithmic scales of axis and colormaps. The fluorophores are assigned using user-defined flexible sector regions on the bivariate histogram, and those sectors can be saved and reused for further experiments. SplitViSu estimates the cross-talk between demixed channels if data from a single-labeled sample is used together with sector regions for 2-3 output channels (Supplementary Fig. 6 ). SplitViSu allows to choose a method for calculation of output coordinates, e. g. using the coordinates from only one input channel or using their simple or weighted mean positions.