dataesr/scientific-paragraphs-categorization huggingface dataset:
First report of Neoseiulus reductus in Turkey Neoseiulus reductus has been identified morphologically and molecularly for the first time in Turkey. BLAST results of the two N. reductus sequences herein obtained (ITS) showed 99. 3 and 99. 6%, similar to the reference sequence in GenBank (GU966582 posted by Pham and Van der Linden in 2010), confirming the identity of the two specimens collected. A COI sequence (859 bp) has also been deposited in GenBank (accession nr. MT439329). There is no COI sequence in this database, and the Blast analysis only showed that the nearest COI fragment belongs to P. persimilis (accession nr. GQ222414). In addition, re-description of N. reductus is provided based on one female (Fig. 3 ). All measurements are provided in micrometers. Length and width of dorsal shield is 338 and 142, respectively.
Conclusion Accurate identification of phytoseiid mites is essential to design proper pest control programs for agriculture. DNA-based approaches may allow mite identification even by nonexperts under two conditions (i) the existence of a complete reference database, and (ii) clear rules for molecular identification. For this, determination of intra-and interspecific variation is essential to reveal the gaps for molecular identification. The present study correctly assigned the specimens to the species they belong to in the phylogenetic tree. However, we confirm here that ITS sequence is not very variable in phytoseiid mites and intraand interspecific distances are sometimes very close if not overlapping to those of species of different genera  (Navajas et al. 1999; dos Santos and Tixier 2016).
This clearly shows that a simple and universal decision rule based on distance overlap cannot be applied at family level. More probably, decision rules proposed would have to be different for some genus with more rapid DNA rate evolution (as Phytoseiulus)  (Kanouh et al. 2010b; Okassa et al. 2010). Mitochondrial DNA fragments seem more appropriate for separating morphologically close species and the study of intraspecific variation  (Okassa et al. 2010 ; dos Santos and Tixier 2016) because of higher genetic distances within and between species. In the public GenBank database, only one ITS sequence of N. reductus was available so far. Now, we provided the sequences of ITS and COI of two specimens that will help species identification. One important issue is the correct morphological identification.
Acknowledgements We would like to thank to  Dr. Sibel Yorulmaz Salman  ( Isparta University of Applied Sciences ),  Canberk Ay  ( Ankara University), Naciye Sena Çağatay (Ankara University ),  Banu Çiçek Tokgöz  ( Republic of Turkey Ministry of Agriculture and Forestry ),  Tuba Albayrak  ( Isparta University of Applied Sciences ),  Kenan Erdoğan  ( Ankara University ),  Esengül Özdemir  ( Ankara University ) and  Mehmet Ali Temur  ( Hacettepe University ) for providing plant samples.
PPD Score The evaluation of PPD in cassava roots is a destructive procedure. It was carried out according to the method developed by Wheatley in 1982, with some modifications  (Sánchez et al. , 2013). Roots were stored on shelves at room temperature under roof, protected from the sun and rain, but without walls. The environmental conditions during storage were monitored continuously with temperature and relative humidity data loggers. Every day (including harvest day, or day zero), roots of each genotype were selected randomly and evaluated for PPD. The duration of the storage period (DAY) was one of the independent variables considered in this study. For evaluation, roots were transversely cut into seven slices (thickness 0. 02 m), starting at the proximal end. Each slice was assigned a PPD score between 0 and 10, corresponding to the percentage of the cut surface that showed discoloration of the parenchyma (0 = 0%, 1 = 10%, 2 = 20%, etc.
Statistical analysis The arcsine transformation for binomial proportions was applied to PPD data for the analysis of variance  (Snedecor & Cochran, 1980). Regression analyses were performed on the data generated. The dependent variable was PPD, and the independent traits were DAY (i. e. duration of the storage period), DMC and scopoletin expressed on a fresh-(SFW) or dry-weight (SDW) basis. Scopoletin quantifications were included in the regression analyses in different ways. In Experiment 1, PPD scores were related to the daily evolution of scopoletin. For example, PPD measured in roots that had been stored for five days was linked to SFW and/or SDW also measured on the fifth day. The impact of initial levels of scopoletin (e. g. as soon as roots were harvested) on the onset and evolution of PPD through the different lengths of storage periods was also tested.
Experiment 1 During storage, several roots began to show symptoms of microbial rotting, and hence, the number of roots remaining for evaluation seven days after harvest was limited. Therefore, data from roots stored for seven days were not considered in order to maintain the balance of the data. Scopoletin data in year 2015 were only taken in one root per storage day and clone rather than the six roots evaluated in the previous years. There was a wide variation in the levels of PPD observed through the duration of this experiment. Environmental conditions during the storage period were correlated with PPD responses. There was a clear and negative association between PPD levels and average air relative humidity during the storage period. The strongest correlation (-0. 997) was observed with relative humidity measured at 13:00 PM.
Acknowledgments This work was supported by the  CIAT Cassava Program (Colombia) ,  CIRAD Qualisud Research Unit , and funded mainly by the  CGIAR Research Program on Roots, Tubers and Bananas (RTB)  with support from  CGIAR Trust Fund contributors  (https://www. cgiar. org/funders/) and the grant opportunity ID: OPP1178942: Breeding RTB products for end user preferences (RTBfoods), to the  French Agricultural Research Centre for International Development (CIRAD), Montpellier, France , by the  Bill & Melinda Gates Foundation (BMGF) https://rtbfoods. cirad. fr. The editorial comments and corrections by  Clair Hershey  improved greatly the quality of this manuscript. The valuable suggestions and corrections by reviewers also contributed significantly for the quality of this article.
T he North Atlantic warming hole (NAWH) is a robust, observed feature of the North Atlantic temperature trends in the past century  1. Various observational datasets generally agree on the warming pattern over the North Atlantic with surface waters south of Greenland warming less than the surrounding oceans, although actual temperature trends depend on dataset (Fig. 1 ). A similar NAWH pattern also emerges in global climate simulations using historical forcing or future CO 2 emission scenarios  2, 3. The causes of the NAWH however remain debatable. In principle, sea surface temperature (SST) changes could result from both oceanic and atmospheric processes. A number of previous studies argue that the slowdown of AMOC and the resultant weaker ocean heat transport convergence into the North Atlantic, could potentially explain the NAWH seen in climate simulations  [2] [3] [4] [5] [6] [7] [8] [9].
Moreover, based on our model estimates, the fast response to the relative TIO warming during 1950-2015 can explain most of the observed NAWH anomaly (~87% on average, see Fig. 9a , but this fraction varies with the observational dataset used). In terms of surface zonal winds over the subpolar North Atlantic, about 55% of the wind intensification can be attributed to the relative TIO warming (Fig. 5c ), but again this number would differ for other datasets. Thus, the general similarities in the spatial patterns and our rough estimates above suggest that atmospheric changes related to the Indian Ocean warming can at least in part account for the observed NAWH. However, our results also indicate that on multidecadal and longer timescales atmospheric and oceanic effects of the TIO warming on the NAWH would nearly compensate each other, unless other factors act to weaken the AMOC.
Methods Observational datasets. We use the Extended Reconstructed Sea Surface Temperature (ERSST) v4  37  , developed by National Oceanic and Atmospheric Administration (NOAA). For data comparison, we also use other SST datasets, including NOAA ERSST v5  38  , Hadley Centre SST dataset (HadSST3)  39  , and Kaplan SST v2  40. For atmospheric variables, we use National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1  41. In Fig. 2a , the climatology is calculated as a 30-year average for 1948-1977. Definitions of climate indices. To quantify the impacts on the warming hole, we defined a NAWH index as average SST in the subpolar North Atlantic (45°N-65°N, 50°W-20°W) minus average SST for the broader extratropical North Atlantic (30°N-65°N, 70°W-10°W); the two regions are marked as blue boxes in Fig.
Acknowledgements The authors thank  Kaylea Nelson  for her help in setting up the computational environment. Discussions with  Richard Seager ,  Mark Cane , and  Amy Clement  are acknowledged. We also acknowledge computational support from the  NSF/NCAR Cheyenne Supercomputing Center. This research was supported by a  Lamont-Doherty   Postdoctoral Fellowship  to S. H. , and by grants to A. V. F. from  NSF  ( AGS-1756682 ,  OPP-1741841 ) and by the  Guggenheim Fellowship. Additional support is provided through the  ARCHANGE  project ( ANR-18-MPGA-0001 , France).
Data availability ERSST v4 is publicly available at: https://psl. noaa. gov/data/gridded/data. noaa. ersst. v4. html. ERSST v5 is publicly available at: https://psl. noaa. gov/data/gridded/data. noaa. ersst. v5. html. HadSST3 is publicly available at: https://www. metoffice. gov. uk/hadobs/hadsst3/. Kaplan SST v2 is publicly available at: https://psl. noaa. gov/data/gridded/data. kaplan_sst. html. NCEP/NCAR Reanalysis 1 is publicly available at: https://psl. noaa. gov/data/ gridded/data. ncep. reanalysis. html. The data used to plot the figures in this study are available at https://doi. org/10. 5281/zenodo. 3964465.
The N -point function results show excellent consistency between the CMB temperature maps estimated using the different component-separation methods. Some differences between results for the 2015 and 2018 temperature data sets are caused by the use of different masks in the analysis, and the adoption of the pseudo-inverse matrix in the computation of the χ 2 statistic, as described by Eq. (  11 ). In the case of polarization, some scatter is observed between the functions computed for different methods, which is a consequence of the relatively low signal-to-noise ratio of the polarized data on large angular scales. Interestingly, a tendency towards very high probability values is observed for the pseudo-collapsed T T Q r 3-point functions for all methods, and for the equilateral T Q r Q r functions in the case of Commander and SEVEM.
As an alternative to the Stokes parameters, we also consider N -point functions computed from the temperature and E-mode polarizations maps. The probabilities of obtaining values of the χ 2 statistic for the Planck fiducial ΛCDM model at least as large as the observed temperature and polarization values are provided in Table  4. Here, we see that the most significant deviations between the data and the simulations occur for the T T E 3-point functions for all component-separation methods. Nevertheless, we conclude that no strong evidence is found for statistically significant deviations from Gaussianity of the CMB temperature and polarization maps using N -point correlation functions. Finally, we note that the results for the T T correlation function confirm the lack of structure at large separation angles, noted in the WMAP first-year data by  Bennett et al.
Peak locations and amplitudes, and various derived quantities, such as their correlation functions, have previously been used to characterize the WMAP maps in  Larson & Wandelt (2004 , 2005)  and  Hou et al. (2009) , and Planck data in PCIS15. We consider peak statistics from the Planck componentseparated temperature and polarization maps at N side = 1024, with E-mode maps reconstructed by the purified inpainting method described in Appendix A. The maps are pre-whitened by convolving them with an isotropic function derived from the isotropic best-fit CMB power spectrum, combined with a diagonal approximation to the instrumental noise covariance. Then, a confidence mask is applied, and weighted convolution is performed with a 2D-Gaussian smoothing kernel (that we label as "GAUSS"), as described in Appendix A of PCIS15.
Specifically, we consider the SEVEM foreground-cleaned map at a HEALPix pixel resolution of N side = 1024 convolved with a Gaussian beam of 10 , and a noise-weighted combination of the WMAP V and W bands at a HEALPix pixel resolution of N side = 512 convolved with a Gaussian beam of 30. Note that when the signal-to-noise is sufficient, the stacking procedure tends to provide an image with azimuthal symmetry about its centre, due to the almost uncorrelated orientations of the temperature peaks. Indeed, the Planck analysis for maxima above ν = 3 clearly reveals two rings, while the WMAP data are noise dominated at the same resolution. Furthermore, when maxima are selected above ν = 0, the enhanced resolution of the Planck data allows additional inner rings to be observed compared to WMAP. We now consider the consistency of the Planck nonoriented results with the predictions of ΛCDM by focussing on the mean value of the angular profiles µ(θ) estimated as the average of the profiles around all hot (cold) peaks above (below) a certain threshold ν.
The corresponding p-values for the comparison are presented in Table  7. We see that all component-separation methods yield consistent results. No significant differences for the intensity profiles µ T (θ) are observed with respect to the results found in the Planck-2015 analysis. As in the latter case, a systematic deviation between the data and the mean value of simulations is present. This was previously interpreted in PCIS15 as an effect connected with the deficit in the observed power spectrum at low multipoles. PCIS15 also demonstrated that the χ 2 statistic is suboptimal when considering the systematic shift between data and simulations, as seen in the intensity profiles µ T , and may lead to misleading p-values. We therefore consider an alternative quantity, the integrated profile deviation ∆µ T (W ), to evaluate the consistency between the data and the model.
Since similar p-values are found when comparing the mean angular profiles from the HMHD and OEHD data splits to corresponding simulations, we consider that it is unlikely that this discrepancy for the minima is cosmological in origin. We find that the noise level traced by the U r profile seems to be slightly higher than was seen in PCIS15. In addition, for ν = 0 and low values of θ (< 0. • 5), the mean determined from the simulations does not tend to a null value, especially for the NILC and SMICA maps (although this is not observed in the HMHD and OEHD analyses). Both effects may be related to noise correlations introduced by changes in the raw data processing, and variations in the weights ascribed to the frequency maps by the different component-separation methods. In addition, it was shown in Planck Collaboration Int.
19. Mean radial profiles of T , Q r , and U r in micro-kelvin obtained for Commander (red), NILC (orange), SEVEM (green), and SMICA (blue) at N side = 1024. Each individual panel contains the mean radial profiles (top part) and the differences between the mean profiles of the data and those computed from the ensemble mean of the simulations (denoted "Diff," lower part). Results based on stacks around temperature maxima and minima are shown in the upper and lower rows, respectively. The left three columns present results for peaks selected above the null threshold, while the right three columns show the equivalent results for peak amplitudes above (maxima) or below (minima) 3 times the dispersion of the temperature map. The black dots (connected by dashed lines) show the mean value from simulations and the shaded regions correspond to the ±1 σ (68 %) and ±2 σ (95 %) error bars estimated from SEVEM simulations.
20  and 21 , respectively. The stacking procedure is identical, except for the data sets used. The top rows of these figures show stacks of SMICA component-separated CMB maps at N side = 1024, with a 10 FWHM beam. The middle rows show stacks of the LFI 30-GHz maps at N side = 1024 (with leakage correction applied), further convolved with a 10 FWHM Gaussian beam, and corrected for the CMB contribution by subtracting the SMICA CMB map (at 10 FWHM resolution) itself beam-convolved with the LFI 30-effective beam, thus representing the total lowfrequency foreground emission. The bottom rows show the HFI 353-GHz polarization-sensitive bolometer maps convolved with a 10 FWHM Gaussian beam and degraded to N side = 1024, then CMB-corrected using the SMICA map further beam-convolved with the HFI effective beam, thereby representing the total high-frequency emission.
All stacks are presented in units of µK CMB , and cover square patches of size 4 • by 4 •. The CMB data show a characteristic ringing pattern in the temperature stacks, associated with the first acoustic peak, a high signal-to-noise pattern in the E-mode stack (as expected from the CMB T E correlation), and no evidence of detectable patterns in the B-mode stack (reaching  To investigate the quantitative agreement of the Planck component-separated maps with fiducial cosmological and noise models, we compare the radial profiles (Eq. 39) of oriented temperature and polarization stacks for all component-separation methods with identically processed FFP10 simulations. We inpaint temperature T within the common intensity mask, and reconstruct E and B modes using purified inpainting within the common polarization mask, as described in Appendix A.
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES , and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planckcollaboration. This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement numbers  687312 ,  776282 , and  772253.
Transmission measurements There is a large number of experimental data from transmission measurements available in the international EX-FOR database, using natural and enriched samples. The datasets selected for the evaluation are of high resolution and contain a sufficient number of points in order to properly describe the s-, p-and d-wave resonances. For each experimental dataset the specific characteristics needed for the RRR analysis were determined (contaminations, resolution function, normalisation factor, etc). In general, the Resolved Resonance (RR) parameters from JEFF 3. 1. 1 have been confirmed, even for resonances of > 0. Some small issues have been identified and are currently under investigation. Furthermore, the reproduction of the total cross section minima (i. e. transmission maxima) of natural Fe transmission experiment datasets was investigated, using the JEFF 3.
1. 1 RR parameters. For each dataset, only the normalisation factor and the time offset were fitted and the reproduction of the minima was checked. These regions are mainly sensitive to the elastic cross section of 56 Fe and of the minor isotopes. It has already been proposed that the elastic cross section of iron should be increased (for example with the integral measurement described in  [7] ). When dealing with data from microscopic total cross section measurements the statistics in the minima is generally poor. Nevertheless, high-resolution microscopic data from a very thick target are available in the EXFOR database, from Harvey (1975, EXFOR entry 13765001), which were very useful for this work. An example of the reproduction of these datasets is shown in Fig. 1. A total cross section "correction" was estimated by the difference of the evaluated and the experimental value in the minima up to 850 keV, from all the datasets, and the weighted average value was obtained as the final result.
Capture measurements Two high resolution capture measurements were used for the evaluation process, one found in EXFOR from Borella The normalisation factor was obtained from the fitting of the well known resonance at 1. 15 keV. The capture width of the resonance at 28 keV had to be increased, in order to match the transmission experimental data and a better reproduction of the capture data was achieved, as can be seen in Fig  3. Another issue pointed out within the CIELO project  [1]  was that the capture cross section is unphysically low and an artificial background had to be added in order to better reproduce a criticality experiment  [1]. In the context of the present work, the addition of such a background, potentially coming from a direct capture component, was checked with data from the integral measurement PERLE, performed at the EOLE reactor of CEA, Cadarache.
INPN Espèces A genuine tool for raising awareness and knowledge, building on the French biodiversity, INPN Espèces allows everyone to discover, from their mobile phone, the diversity of the species around us and to participate in the biodiversity inventory in France. It is the only app in France that allows us to investigate all the French Fauna and Flora species (mainland and overseas) through their characteristics, distribution or conservation status. Thanks to a data entry eased by images and geolocalisation from one's mobile, anyone can, with one click, improve the knowledge on the surrounding wildlife. To take part in the country's species surveying by transmitting our records to experts, only a few clicks are needed to share the sightings. The first identification by the observer is then checked by experts of each group involved in the project.
inaturalist. org One of the world's most popular online wildlife databases, iNaturalist helps everyone identify the plants and animals around us. It allows connection to a community of over 750,000 scientists and naturalists who can help us learn more about nature. Moreover, by recording and sharing our sightings, people will create research quality data for scientists who are working to better understand and protect nature. iNaturalist is a joint initiative by the California Academy of Sciences and the National Geographic Society. The systematics of Mantodea used here follow the latest update  (Schwarz and Roy 2019). In parallel, research was initiated on the forum of the website insecte. org, as well as on inaturalist. org. Alien Mantodea's records from the south of France were found on  Specimens from Arles: On 27 July 2019, a nymph Mantodea was observed in a garden, on mint.
Acknowledgements I would like to thank all the people who, in one way or another, participated in this initiative by making observations, sharing their data or discussing the subject:  Didier Aurelle ,  Roberto Battiston ,  Sylvie Bucci ,  Vincent Derreumaux ,  Jean-Claude Jamoulle ,  Bruno Meriguet ,  Guillaume Paulus  and  Mattias Perez ; my parents for their passionate dedication to look for specimens in the locality of Mouriès, with success. I wish to thank  Samuel Danflous  for revision of the manuscript with valuable suggestions and information. Finally, I.