dataesr/scientific-paragraphs-categorization huggingface dataset:
The occupational carcinogens included in the projections were asbestos, arsenic, benzene, beryllium, cadmium, chromium, diesel exhaust engine, formaldehyde, nickel, polycyclic aromatic hydrocarbons, second-hand smoke, silica, and sulfuric acid. Information on industry patterns was extracted from National Population and Housing censuses in  1986, 1996, 2006, 2011 , and 2016, and exposures by industry were derived primarily from the European CAREX database  [59, 60]. The results of this study showed that exposure to particulate matter, gases, and fumes, and asbestos was among the highest contributions to the national attributable DALYs in 2015 among occupational risks. In addition, from 1990 to 2015, the increase in total DALYs attributable to occupational carcinogens (112%) was higher than that for other occupational risks.
A few of the studies used previously established exposure databases, e. g. , CAREX to project the occupational cancer burden  [58]. CAREX is a database which contains estimates of numbers of workers occupationally exposed to carcinogens by European industries (exposure data from 1990-1993). As shown by the two studies applying CAREX and GBD data to estimate the occupational cancer burden in Iran, the burden appears relevant, but these estimations have large uncertainties and do not provide insight into targeted prevention measures, as they are based on extrapolating exposure from other countries, workforce, and risk data instead of structured representative (exposure and risk) data from Iran. This review provides the first overview of the status of occupational cancer research in Iran. A major limitation of the review is that carcinogenic exposures summarized in non-cancer studies are not captured here.
EXPERIMENTAL PROCEDURE For both DMS and MultiGLODS, we use the Matlab implementation, provided by the authors. For DMS, we compare version 0. 3 with the updated initialization of the poll set, as described above, to ensure the origin is included in the initial poll set also in even dimension. The parameter x_ini was set to the search space origin first and to a randomly chosen point in (︀-5, 5⌋︀  when a restart happened. The lower and upper bounds were set to -100 and 100 for each variable to create the initial poll set as indicated by COCO functions' lower_bound and upper_bound. The maximal number of function evaluations per restart is 20,000. All other parameters and algorithm components of DMS were kept as default. For MultiGLODS, we used version 0. 1 and the default parameters except for the same maximal number of function evaluations per restart of 20,000 and the same upper and lower bounds of -100 and 100 for the initial poll set.
Compared Algorithms DMS and MultiGLODS have been compared with the following five algorithms from the available COCO data sets. NSGA-II, as implemented in the platypus platform  [1]  is one of the most commonly used stochastic multiobjective algorithms and serves as a baseline, especially because it has been included in the original DMS paper already (but not in the platypus implementation). UP-MO-CMA-ES  [9]  is a multiobjective CMA-ES variant with unbounded population size, HMO-MO-CMA-ES  [10]  a hybrid variant of it which runs multiple different algorithms (including BOBYQA on weighted sums in the very beginning) in parallel. The portfolio algorithm HMO-CMA-ES is considered the best performing data set among all 32 currently available bbob-biobj data sets of COCO. It follows mostly the performance of the artificial reference algorithm "best 2016" that has been generated by extracting the best runtime for each function and target precision out of 16 algorithms from the BBOB 2016 workshop, see  [2]  for details.
Incidence, mortality and population data Incidence data  were provided by the French population-based cancer registries (Francim network). Depending on the cancer site, the network is currently covering 19 to 22 French districts (Département); that is, 21 to 24% of the metropolitan population. The oldest registry started collecting data in 1975 and the most recent in 2009 (Supplementary Table  S1 ). All malignant tumors (except non-melanoma skin cancer) are included and grouped according to the International Classification of Diseases for Oncology, 3rd Edition (ICD-O3) (Supplementary Table  S2 ); the term "All cancers" refers to all malignant tumors, including hematological malignancies. Mortality data  were provided by the Centre d'épidémiologie sur les causes médicales de Décès (CépiDc-Inserm).
In this database, the causes of deaths are coded according to the International Classification of Diseases (8th to 10th revision, depending on the period). Data are available for all French metropolitan districts. The numbers of person-years by annual age, year (year of diagnosis for incidence and year of death for mortality), and district were calculated from population census data (1975 to 2018) provided by the Institut national de la Statistique et des Études Économiques (Insee). Data were analyzed from 1985 for incidence (to stabilize estimation in 1990) and from 1975 for mortality (to estimate long-term cohort indicator not presented in the present paper).
A negative percent changes indicates that the sex gap narrowed and vice versa, except for cancers with female predominance (e. g. anus, skin melanoma, and thyroid gland) for which this is the opposite. In addition to relative sex differences, we calculated absolute differences by taking into account the difference between male and female agestandardized rates in 1990 and in 2018. Points of change in rate differences were calculated to quantify the sex gap over 1990-2018 on absolute scale (the 1990 rates served as the reference). This novel methodology allowed accurate analyzes by age as well as incidence estimates by anatomical or histological subtype (see full results in Ref. [8] ). All analyzes were performed in R, release 3. 4. 3, using gam function from mgcv package  [28].
Due to the high proportion of "uterus, not otherwise specified" in nearly 60% of death certificates, a specific statistical procedure was necessary to obtain the "observed" numbers of deaths for cervix and corpus uteri cancers  [8, 29]. The proportions of cervix and corpus uteri respectively among all cancer uteri deaths were first estimated by age and year from registry data (by convolution of incidence and survival) and then applied to the observed number of cancer uteri death in France (corpus, uteri or unspecified). Once the numbers obtained, they were modelled like those of other sites, using bidimensional penalized splines.
Moreover, we had to down-sample the WDPA dataset to analyse the protection coverage of priority areas at the resolution they were identified (i. e. 96. 5 km × 96. 5 at 30°North and South), which caused the loss of spatial information. While this approach is useful to highlight the priority areas that completely lack protection coverage, it is not sufficient to analyse the quality of protection in priority areas that are partially protected. Priority areas with a low percentage of surface protected may actually be well protected if they contain small, numerous, well-connected and well-managed protected areas. Contrarily, priority areas with a high percentage of surface protected may not be well protected if they contain only one large protected area, not connected to others and not well managed.
We computed four phylogeny-based scores for each of the 5477 extant mammal species. We first calculated evolutionary distinctiveness (ED)  13, 61  using the function distinctTree from the R package 'adiv'  62, 63. Second, we computed evolutionary distinctiveness and global endangerment (EDGE)  13  expressed as follows: (1)EDGE = ln (1 + ED) + GE × ln (2), where GE is the Red List category weight [least concern = 0, near threatened = 1, vulnerable = 2, endangered = 3, critically endangered = 4]. Third, we calculated heightened evolutionary distinctiveness and global endangerment (HEDGE)the φi' score in Steel et al. 16  corresponding for each species to the gain in expected PD if this species was secured (probability of extinction goes to 0). Finally, we computed a score corresponding for each species to the loss in expected PD if this species went extinct (probability of extinction goes to 1)the φi" score in Steel et al.
16. We will refer to this as the 'loss-significant evolutionarily distinctive globally enduring' score (LEDGE); while the LEDGE concept focuses only on secure species  20  , we use the term here to describe the calculation applied to any species. HEDGE and LEDGE scores were computed using the self-written function HED2 (see R script "new_functions" in the GitHub repository  64  ), with the IUCN designations projected to 50 years to derive probabilities of extinction from Red List categories  54. For all scores, we repeated the calculations over the 100 trees and computed the median species score over the 100 calculations. We tested the correlation between each pair of scores using a Spearman correlation test. For each score, we identified the 25% of species having the highest scores following Stein et al.
For nine remaining problematic names, we made manual edits (see R script "figures_tables" in the GitHub repository  64  for details) based on the taxonomic information in IUCN Version 2019-2  65  and in the third version of Mammal Species of the World 67. We obtained the list of conservation measures using the function rl_measures from the R package 'rredlist' 66 and only kept their first level of classification with 6 categories: "Land/water protection", "Land/water management", "Species management", "Education & awareness", "Law & policy", and "Livelihood, economic & other incentives". Calculation of scores by grid cell and identification of areas of interest. We overlaid the PHYLACINE gridded range maps for each species  37  to record their presence and absence in the 51,120 cells of a reference gridded map having, by construction, the same spatial extent, resolution and projection than each individual gridded range map.
69  as phylogenetic endemism and permitting to identify areas with a high concentration of spatially-restricted PD, here calculated by updating the function calc_PE (original function: https://github. com/DanRosauer/ phylospatial/tree/master/PhyloEndemism_in_R; see the R script "calc_PE" in the Github repository64 for the updated function). We also computed: (ix) the number of species in the TOP 25% HEDGE to identify the areas with a high concentration of priority species; (x) the number of species in the TOP 25% LEDGE to identify the areas with a high concentration of loss-significant species. Finally, we computed two new scores that represent respectively best and worst scenarios of expected PD in the next 50 years (Fig. 1 ): (xi) the gain in expected PD if all species present in a grid cell were secured (GexpPD), corresponding to the gain in expected PD if a local conservation action in the grid cell was able to transform the current global probability of extinction of all species present in the cell to 0; (xii) the loss in expected PD if all species present in a grid cell became extinct (LexpPD), corresponding to the loss in expected PD if a local threat in the cell was able to transform the current global probability of extinction of all species present in the cell to 1.
Matching the global network of protected areas and the priority areas. We downloaded the public version of the World Database on Protected Areas (WDPA) for November 2019 from Protected Planet (https://www. protectedplanet. net/). The WDPA is managed by the World Conservation Monitoring Centre (WCMC) of the United Nations Environment Program (UNEP) in collaboration with the IUCN  71. We excluded protected areas (i) whose status was "Not Reported" or "Proposed", (ii) designed as "UNESCO-MAB Biosphere Reserve", and (iii) provided as points with unknown boundaries and without a reported area. However, we included protected areas provided as points with unknown boundaries but with a reported area, using a geodesic circular buffer with an area equal to the reported value. We solved problems of invalid geometries in the selected files, and then dissolved all polygonsremoving this way overlaps and redundancy within protected areas.
We then transformed this re-processed WDPA dataset to fit the extent and projection of the reference gridded map. Finally, we calculated the percentage of area covered by the re-processed and transformed WDPA dataset for each grid cell of the reference gridded map. We carried out all these steps with a combination of opensource tools (GDAL 3. 0, GRASS GIS 7. 8, PostgreSQL 12/PostGIS 3. 0). We overlaid this map of percentage of area protected with the map of priority areas to derive the percentage of area protected for each of the priority areas identified.
Acknowledgements This work was supported by the  French State  through the  Research National Agency  under the  LabEx BCDiv  [ ANR-10-LABX-0003-BCDiv ], within the  framework of the program  ' Investing for the future ' [ ANR-11-IDEX-0004-02 ], and by the institutional activities of the  Global Observatory for Biodiversity and Ecosystem Services (GLOBES)  project of the  European Commission.
Data availability This study used datasets that are publicly available from PHYLACINE (https://doi. org/ 10. 5061/dryad. bp26v20) and VertLife (https://data. vertlife. org/). It also uses data from IUCN version 2019-2 that are available from the corresponding author upon request and with permission of IUCN. The coastline dataset used to make Figs. 3, 4, 6 , and Supplementary Figs.
Remote sensing According to availability and cloud coverage, satellite images were chosen for each decade, representing the wet (November to March) and dry period (April to October) between 1972 and 2016, totaling 23 images previously radiometrically corrected. The images, whose information is described in Table  S1 , were obtained from the Earth Explorer USGS database, accessed at http://earthexplorer. usgs. gov, courtesy of the U. S. Geological Survey. We have explored the vegetation coverage around the lagoon based on its spectral signature. Vegetation exhibits high absorption in the visible region and high reflectance in the near-infrared properties used in remote sensing to define reflectance indices. Herein, we have used the Soil Adjusted Vegetation Index (SAVI)  (Huete 1988) , which takes into account the normalization of soil color and exposure variations, setting the constant parameter "L" and therefore highlighting the vegetation cover.
S1  for details). We have also measured the inlet opening width through the years, using control points as a reference. To investigate the impact of winds on the lagoon geomorphology, remote sensing derived data were used, according to datasets available variables. Wind speed data at 10 m derived from ERA-40  (Uppala et al. 2005 ) and ERA-Interim  (Berrisford et al. 2011)  were accessed through the ClimateReanalyzer. org portal of the Institute of Climate Change of the University of Maine (USA). ERA-40 (1. 125 Â 1. 125 global grid) and ERA-Interim (0. 75 Â 0. 75 global grid) data were used to understand the regional impact of the winds in the coast given their spatial resolution. We have also used sea surface winds data derived from the NOAA/NCDC blended daily global 0. 25 Sea Surface Winds archive  (Zhang et al.
Regional responses to climate changes From wind databases derived from remote sensing, we observed an intensification of trade winds from the northeast to the Khnifiss region during the 1960s when wind speed on the continental shelf in front of the inlet was 5. 0 m s À1 on average. During the 1970s, it increased to 5. 5 m s À1 , and from the 1990s onward, it turned to be 6. 0 m s À1 on average (Figs. S9, S10 ). Due to the east-to-west alignment of the coast and an oblique action of the northeast trade winds, a high frequency of these winds favors the opening of the Khnifiss Lagoon inlet. It, therefore, provides an increase in the marine influence in this coastal environment. Thus, in a situation of wind strengthening under the above conditions, the seawater input in the lagoon would be favored, which corroborates the interpretations derived from both sedimentary records and satellite data.
65 g C m À2 yr À1 , were observed in the most recent sediment layers. These estimations indicate the great potential of the Khnifiss Lagoon as a carbon burial ecosystem and highlight the need for more carbon flux studies in tidal flat-saltmarsh ecosystems. A relation between the NAO index and the wind speed in the continental shelf in front of the Khnifiss Lagoon was observed; in periods of NAO+, winds are strengthening in an east-to-west direction. An intensification of northeast trade winds was observed since the 1970s through wind databases, and it is interpreted as the main cause for the observed inlet dynamics. Wind intensification in the coast is also related to intensification in the upwelling and may have favored carbon flux increases. We believe that modern sea-level rise has a synergistic contribution to the local hydrological dynamics.
Acknowledgments The authors acknowledged the Labgis system at the  Rio de Janeiro State University , which together with local  PCI Geomatica representation , supported the development of the remote sensing methodology. Thanks are also due to the  French Research Institute for Development (IRD)  for the facilities, personal, and financial support in the sample analysis, and field excursion. We also appreciate  Ibn Zohr  for hospitality and support during the development of this thesis. Elemental and isotopic analyses were performed at the ALYSES platform, at the  Institute of Research for Development -IRD (Bondy, France). Thanks are also due to the  Czech University of Life Sciences Prague  for the financial support regarding the publication of this manuscript. Finally, the authors thank for the valuable inputs of the anonymous reviewers.
Because the χ 0 1 is present in the decay chain of top squarks, it provides a powerful experimental probe for signal events: a large momentum imbalance in the plane perpendicular to the beam axis. Dedicated searches for top squarks in proton-proton (pp) collision events at √ s = 13 TeV have been carried out by both the ATLAS  [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36]  and CMS  [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48]  Collaborations. In this paper, we present a search for production of top squarks in fully-hadronic final states. This search is interpreted in R-parity conserving SUSY models in which the top squarks are produced in pairs or are produced via cascade decays of pair-produced gluinos. The data were collected by the CMS detector at the LHC in 2016-2018 and correspond to an integrated luminosity of 137 fb -1 of pp collisions at a center-of-mass energy of 13 TeV.
Collision data that are highly enriched in QCD multijet events are included in the training. These data are included using domain adaptation via gradient reversal  [123, 124]  to discourage the network from learning features of the simulation that are not present in data. With this method an additional output is added to the neural network, connected to the hidden layer in the same manner as the primary neural network output. This additional output is tasked with distinguishing between trijet candidates from QCD multijet simulation and trijet candidates from collision data. The primary neural network output is trained to minimize the ability to discriminate based on observables that are not well modeled in simulation. This yields a network that is able to discriminate between signal and background almost as well as a network trained without domain adaptation but with minimal reliance on features that exist only in simulation.
3  and demonstrates the complementary nature of these two algorithms over the full range of relevant p T. The top quark and W boson taggers exhibit slightly different performance in data and in simulation. We derive scale factors to correct the performance of the taggers in simulation to match their performance in data. For both the DeepAK8 and DeepResolved algorithms, the tagging efficiency is estimated in both data and simulation using a dedicated sample of events containing a single charged lepton, selected to be enriched in top quark and W boson production. For the DeepAK8 algorithm, the misidentification rate is estimated in a sample of events containing a single photon, which is selected to be depleted of top quark and W boson production. For the DeepResolved algorithm, the misidentification rate is estimated in a sample of events containing jets but no charged leptons and small p miss T.
Event selection and search regions Events used for the search regions in this analysis were collected with a trigger that requires both p miss T and H miss T larger than a threshold that varied between 100 and 140 GeV depending on the LHC instantaneous luminosity and data taking period, where H miss T is the magnitude of the vector p T sum of jets reconstructed at the trigger level. The trigger efficiency is greater than 95% after application of the event selection criteria described below, including the requirement of p miss T > 250 GeV. All events are required to pass filters designed to remove detector-and beam-related noise and events that suffered from event reconstruction failures  [107]. The data set used in this analysis is defined broadly by the exclusive presence of multiple jets of strongly interacting particles along with large p miss T.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NK-FIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CIN-VESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
AIM OF THE STUDY In the past year, key structures of SMN complex components and functional subcomplexes have been solved. These structural data in combination with biochemical work allowed already detailed insight into the functional architecture of the assembly machinery. Structural insight into the architecture of the entire SMN complex, however are still limited. This is mostly due to the fact that the SMN complex displays a large degree of heterogeneity in vivo, which prevents its isolation and structural determination by either X-ray or electron cryomicroscopy (cryo-EM). The aim of this thesis is to investigate the structure of the SMN complex using Schizosaccharomyces pombe as a model organism. This organism expresses a minimalistic SMN complex consisting of SpGemin2, SpSMN, SpGemin8, SpGemin7 and SpGemin6 only.