dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work received a support from the  NATO  project  G5233  belonging to the  Science for Peace and Security Program (VAY and VVB). The authors appreciate the possibility to collect NPD data at the highresolution powder diffractometer SPODI at  FRMII, Munich, Germany. PV gratefully acknowledge the  Research Council of Norway  for providing the computer time (under the project number  NN2875k  and  NS2875k ) at the Norwegian supercomputer facility.
MATERIALS AND METHODS In this work, Total Column Ozone (TCO), Ultraviolet index (UVI) and Radiative Cloud Fraction (RCF) daily data were obtained from the Ozone Monitoring Instrument (OMI) sensor, that was launched in July 2004 aboard NASA (National Aeronautics and Space Administration) AURA satellite to continued the TOMS (Total Ozone Mapping Spectrometer) instrument recordings that ended yours activities in 2005. The OMI instrument employs hyperspectral imaging in a scanning mode to observe backscattered solar radiation in the electromagnetic spectrum ranges between 0. 270nm to 0. 314nm and 0. 306nm to 0. 380nm. The hyperspectral capabilities improve the accuracy of TCO measures and allow radiometric self-calibration of precise and long term wavelengths  (Levelt et al. 2006). The daily TCO, UVI and RCF data acquisition was performed for the central region of the state of Rio Grande do Sul, on a 1° x 1° latitude / longitude grid, comprising the surroundings of the Santa Maria city (29.
For this, the climatology of the TCO between 1992 and 2014 calculated by  Peres et al. (2017)  was used, being 290. 2 DU (Dobson Unit) the october climatological value, 8. 8 DU its respective standard deviation and 277. 0 DU the threshold value of climatological mean minus 1. 5 of the standard deviation. In addition, the evolution of UVI and RFC daily data were also analyzed in order to verify our impact during the mentioned TCO reductions. For the selected TCO reduction days, an stratospheric dynamics analysis was performed from meteorological fields of Potential Vorticity This stratospheric dynamics analysis starts with the observation of the ozone vertical profiles, comparing the 1986 to 2015 October average ozone profile with the daily average ozone profiles and respective anomaly calculations (1986 to 2015 October average ozone profile minus daily average ozone profile), in order to verify the height at this ozone reductions occur.
Acknowledgments This work is a  Bachelor in Atmospheric Sciences  course at the  Federal University of Western Pará  product, with financial support from the  Institutional Scientific Initiation Scholarship Program  ( PIBIC-UFOPA ). The authors express their thanks to the  MESO Project -Modeling and Prediction of the Secondary Effects of the Antarctic Ozone Hole , linked to the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES / COFECUB ), Process No. 88887. 130199/201701  and  Instituto Nacional de Ciência e Tecnologia Antárico de Pesquisas Ambientais ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico -CNPq  process no. 574018/2008-5  and  Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro -FAPERJ  process no. E-16/170. 023/2008. They also thank  NASA  for the OMI satellite data,  ECMWF/ ERA-Interim  for the daily ozone and PV data, and  NOAA  for the products generated through the HYSPLIT model.
(PV), wind magnitude (u and v) and Ozone mass mixing ratio, obtained for 15 potential temperature levels between 265 and 850 Kelvin, as similary used by Dos Reis et al. (2021) in identifying the Antarctic Ozone Hole Influence events over Southern Brazil. The ERA-Interim Pressure and Isentropic (Theta) Levels in Relation to a Standard Atmosphere can be verified in https://rda. ucar. edu/datasets/ ds627. 1/docs/Pressure_and_isentropic_levels/, as a function of the potential temperature equation, showing that the maximum in the ozone layer occurs at 25Km or 25. 492 hPa or 634. 5K. These meteorological fields are provided by ERA -Interim reanalysis from European Centre for Medium-Range Weather Forecast (ECMWF), detailed in Dee et al. (2011), for a 1. 0°x1. 0° latitude / longitude resolution grid.
Selection of the BCGs in KiDS One of the main goals of this work is to search for molecular gas reservoirs in distant BCGs and to characterize their stellar and star formation properties. To do this, similarly as C22a, we started by considering a parent sample of 1484 BCGs with spectroscopic redshifts in the equatorial KiDS-N area. Of these, 684 BCGs are at z > 0. 3. These BCGs were selected by  Radovich et al. (2020)  using the Data Release (DR) 3 of  KiDS (de Jong et al. 2017 ). As we are interested in actively star-forming and distant BCGs, we further limited ourselves to the BCGs with clear counterparts in the all-sky data release of the Wide-field Infrared Survey Explorer (WISE;  Wright et al. 2010)  at 22 µm in the observer frame (i. e. , the W4 channel). We found only six spectroscopically confirmed BCGs at z > 0.
The BCGs belong to the clusters J083727. 12+015642, J141534. 56-010806, and J143329. 76+020719  (Lesci et al. 2022) , which were detected with an S /N = 5. 8, 4. 0, and 4. 2 1 http://www. gama-survey. org/dr3/ A139, page 2 of 15  Castignani, G. , et al. : A&A 672, A139 (2023)   (Maturi et al. 2019; Bellagamba et al. 2019) , while they have an intrinsic richness λ = 36 ± 6, 28 ± 6, and 17 ± 3, respectively  (Maturi et al. 2019). These values correspond to richness-based cluster masses in the range log(M 200 /M ) = 13. 7-14. 3 (see Table  1 ), which are similar to those of the clusters associated with the three BCGs studied in C22a. These properties on average imply a low probability of 1% of the three clusters being false positives (see Fig. 12  of  Maturi et al. 2019). Furthermore, we searched by coordinates for our clusters in the literature using the NASA/IPAC Extragalactic Database (NED).
Both KiDS 0837 and KiDS 1433 BCGs are associated with clusters that are detected in the optical using independent datasets, as outlined in the following. KiDS 0837 matches the GMBCG J129. 35906+01. 94428  (Hao et al. 2010 ) and HSCS J083731+015837  (Oguri et al. 2018)  clusters. KiDS 1433 BCG matches the compact group SDSSCGB 22511  (McConnachie et al. 2009) , as the two have a projected separation of just 3. 9 arcsec (i. e. , 19 kpc).
Spectral energy distributions In Fig. 2  we show the ultraviolet to far-infrared spectral energy distributions (SEDs) of the three sources, taken from the GAMA database. The SED modeling reported in the panels of the figure corresponds to the MAGPHYS  (da Cunha et al. 2008 ) fits by  Driver et al. (2018) , which are publicly available in GAMA DR3. Photometric data include GALEX  (Martin et al. 2005; Morrissey et al. 2007 ) in the ultraviolet, SDSS  (York et al. 2000)  in the optical, and the VISTA Kilo-degree Infrared Galaxy Survey (VIKING;  Edge et al. 2013) , WISE  (Wright et al. 2010) , and Herschel-ATLAS  (Eales et al. 2010; Valiante et al. 2016)  in the near-to far-infrared. In Table  1  we summarize the main properties of the BCGs, including their stellar, star formation, and dust properties derived from the SED fits  (Driver et al.
Line diagnostics We now investigate the star formation properties of the BCGs using the spectra found in the GAMA database. We performed a similar analysis to that presented in C22a, as outlined in the following. The SFR is directly proportional to both Hα line and [O II] forbidden-line doublet (3726 Å, 3729 Å) luminosities L Hα and L [O II] , respectively  (Kennicutt 1998). We therefore used these relations to estimate the SFR, calibrated to a Chabrier (2003) IMF. We also corrected for dust attenuation using the empirical relation by  Garn & Best (2010) , which relates dust attenuation (A Hα ) to stellar mass as follows: A Hα = 0. 91 + 0. 77M + 0. 11M 2 -0. 09M 3 , (1) where M = log(M /M ) -10. With these conventions, the Hαand [O II]-based SFRs can be expressed as follows (see, e. g. ,  Gilbank et al.
We also simultaneously recorded the data with the fast Fourier Transform Spectrometers (FTS) as a backup at 200 kHz resolution. We encountered variable weather conditions during our observations. Overall, we had average system temperatures typical of the winter season and equal to T sys 107-116 K, 228-278 K, and 183-224 K at 3, 2, and 1 mm, respectively. As calibrators, we used the quasars PKS 1253-055 and OJ 287 for pointing, and PKS 1253-055 for the focus. We summarize our observations in Table  2. The data reduction and analysis were performed using the class software of the gildas package 2. Our results are presented in Sect. 4, and in the following, we describe our analysis of the IRAM-30 m spectra for the three targeted BCGs.
The chosen filters are thus optimal to detect the red sequence in the considered clusters. In the color-magnitude plots, the BCGs are denoted with green stars. We also distinguish field galaxies (gray points) from photometric cluster members (blue points), which were selected as galaxies with cluster membership probabilities greater than 50%, as was further discussed in Sect 5. 4 of C22a. Using both GAMA and SDSS spectroscopic databases, we did not find any other spectroscopic cluster member in addition to the BCG themselves. Visual inspection of the color-magnitude plots shows that our KiDS BCGs are the brightest of the selected cluster members. The one exception is a photometrically selected member with A139, page 10 of 15  Castignani, G. , et al. : A&A 672, A139 (2023)  Fig. 6. Excitation ratios r J1 plotted against the sSFR (left) and the depletion time (right) for intermediate-redshift BCGs with multiple CO(J → J -1) detections.
Following  Wiklind et al. (1997) , we modeled the stellar bulge contribution as follows: V rot,bulge (r) 2 = GM r r + r eff 1. 8153 2 , (6) where M = 1. 86 × 10 11 M (Table  1 ) and r eff = 7. 47 kpc are the stellar mass and half-light radius of the KiDS 1433 BCG, respectively. We estimated the latter in r band using the SourceXtractor++ (v 0. 16) package 7  , which is the successor to the tool SeXtractor  (Bertin & Arnouts 1996). We also derived a position-dependent model of the point spread function (PSF) with the PSFEx tool 8  , which we used as input in SourceXtractor++ to fit the galaxy with a Sérsic profile plus the model PSF. This analysis yielded r eff = (7. 47 ± 0. 40) kpc, which agrees fairly well with that inferred from the mass versus size relation of galaxies at similar redshifts (van der Wel et al.
Acknowledgements. We thank the anonymous referee for helpful comments which contributed to improving the paper. This work is based on observations carried out under project numbers  176-21  with the IRAM 30 m telescope. The research leading to these results has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 101004719  [ORP]. G. C. thanks IRAM staff at  Granada  for their help with these observations. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and IGN (Spain). G. C. , L. M. , C. G. , and F. M. acknowledge the support from the grant ASI n. 2018-23-HH. 0. L. M. and C. G. acknowledge the support from the grant  PRIN-MIUR 2017 WSCC32. C. G. acknowledges funding from the  Italian National Institute of Astrophysics  under the grant " Bando PrIN 2019 ", PI:  Viola Allevato  and from the  HPC-Europa3 Transnational Access programme   HPC17VDILO.
M. S. acknowledges financial contribution from contracts  ASI-INAF  n. 2017-14-H. 0  and  INAF  mainstream project  1. 05. 01. 86. 10. P. S. acknowledges support by the  ANR  grant  LYRICS  ( ANR-16-CE31-0011 ). D. T. acknowledges the support from the  Chinese Academy of Sciences (CAS) President's International Fellowship Initiative (PIFI)  with Grant  N. 2020PM0042. The research has made use of the  NASA /IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. The work uses GAMA data-products. GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo -Australian Telescope. The GAMA input catalogue is based on data taken from the  Sloan Digital Sky Survey  and the  UKIRT Infrared Deep Sky Survey.
On 06/19/2021, we conducted a search across four databases (PMC, Scopus, Web of Science, and PubMed). We included all studies that evaluated memory performance using the FCSRT in patients with bvFTD, as long as they also included either cognitively unimpaired participants or AD groups. We tested publication bias through the Funnel plot and Egger's test. To assess the quality of studies, we used the Newcastle-Ottawa quality assessment scale adapted for cross-sectional studies. We included 16 studies in the meta-analysis. The results showed that bvFTD patients perform better than AD patients (pooled effects between 0. 95 and 1. 14), as their memory performance stands between AD and control groups (pooled effects between -2. 19 and -1. 25). Moreover, patients with bvFTD present both genuine and secondary memory disorders.
Identification of Studies The study is pre-registered in PROSPERO (CRD4202126 5945). The literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  (Page et al. , 2021). On 06/19/2021, we performed a comprehensive search across four databases (PMC, Scopus, Web of Science, and Pub-Med) using the following search string: ("frontotemporal dementia" OR "frontal dementia" OR "Pick's disease" OR "frontotemporal lobe dementia" OR "frontal lobe dementia" OR "dementia of the frontal type" OR "behavioral variant frontotemporal dementia "OR "bvFTD") AND ("Free and Cued Selective Reminding Test "OR "FCSRT" OR "Grober" OR "Buschke"). To identify and remove potential duplicates from the retrieved records, we utilized the R package "Revtools" screening for identical titles or DOIs.
Data Extraction Each eligible full-text article was independently analysed by two reviewers of the authors' team to extract the following subscores in the FCSRT: Encoding (ENC, i. e. verbal encoding phase), Free ImmediateRecall (FIR, i. e. free learning phase), Cue ImmediateRecall (CIR, i. e. semantically cued learning phase), Total Immediate (TIR, i. e. free + semantically cued learning phase), Free Delayed Recall (FDR, i. e. free retrieval), Cue Delayed Recall (CDR, i. e. semantically cued retrieval), Total Delayed Recall (TDR, i. e. free + semantically cued retrieval), Recognition (R, i. e. recognition between distractors), Index of Sensitivity of Cueing (ISC, i. e. facilitation role of the semantic cue). We also gathered the following data: bvFTD diagnostic criteria and type of diagnosis (possible, probable, definite, or unknown) (see Table  1 ).
When the subtype of FTD was not defined, or the raw test scores were not available for the patients and at least one of the control groups, we contacted the corresponding author for the missing data. We reached out to 15 authors for additional information for 17 papers. We made three attempts before excluding the paper. Out of the authors contacted, nine authors responded, and two provided numerical data. In cases where multiple studies referred to the same cohort of patients, we asked the original authors to clarify any overlapping participants. If disambiguation was not possible, we included only the data from the study with the largest sample from the same cohort. To illustrate the search procedure, we have included a PRISMA Flow Diagram  (Page et al. , 2021)  in Fig. 1.
Data Synthesis and Analysis Effect sizes were calculated to determine the difference in test scores between (i) patients with bvFTD and healthy control participants and (ii) patients with bvFTD and AD. The test scores used for the analysis were Free Immediate Recall, Total Immediate Recall, Free Delayed Recall, and Total Delayed Recall. These specific subscores were considered crucial because the total score serves a proxy of genuine amnesia, while a potentially worse performance on the free score indicates the presence of unspecific memory impairments (i. e. genuine and secondary). It is worth noting that many studies have also focused selectively on these scores as well. To estimate the effect size, we calculated Hedges' g (the standardized difference between the groups) and its standard error using the R function "metacont" in the package "meta".
We chose Hedges' g because it corrects for bias that may arise from small sample sizes  (Hedges & Olkin, 1985). We employed a random-effects model for the analysis. Fitting a frequentist meta-analysis allowed us to check for the presence of outliers (by using the R function "find. outliers" in the package "dmetar") and influential points (by using the R function "InfluenceAnalysis" in the package "dmetar"). The "find. outliers" function considers a study as an outlier if its confidence interval does not overlap with the confidence interval of the pooled effect. Studies with extreme effect sizes may distort the pooled effect estimate leading to between-study heterogeneity. Therefore, if extreme effect sizes were identified, we removed those studies from the analysis and reevaluated the pooled effect.
We further examined the potential presence of publication bias through the funnel plot and Egger's test by using the R functions "funnel. meta" and "metabias" in the package "meta" (see Supplementary Material). Then, we conducted a Bayesian random effect metaanalysis using the R function "brm" in the package "brms" to calculate the pooled effect size and the heterogeneity between studies. Bayesian models have several advantages both theoretically and practically. They allow for the incorporation of prior knowledge into the analysis, leading to more precise estimates. Bayesian statistics is a theoretical platform for updating information, thus allowing the inclusion of additional data as soon as they are available. This meta-analysis can also be seen as a first benchmark that can be further developed and updated in the future.
In line with an Open Science perspective, we have made the data and analysis scripts publicly available (https:// osf. io/ eazck/? view_ only= 790c6 42312 2a458 3960b c9190 16792 4b) to facilitate transparency and reproducibility. The estimation started from a non-informative prior with a normal distribution (mean = 0, scale = 10) for the effect size and a half-Cauchy distribution (mean = 0, scale = 0. 5) for the heterogeneity. We also checked for the stability of the results (i. e. prior robustness check) by trying different prior distributions (see Supplementary Materials). We conducted posterior predictive checks to assess the model convergence and the overall validity (see Supplementary Materials). The direction of the effect size was negative if the performance of the bvFTD patient group was worse than the control or AD patient group.
Unfortunately, we identified only one study in our meta-analysis that reported neuropathologically proven cases of bvFTD. As a result, we were unable to conduct further analysis specifically focused on autopsy-proven cases. In the present meta-analysis, we are unable to differentiate memory impairment as a function of bvFTD or mixed bvFTD with other neurodegenerative syndromes (or its potential co-occurrence with other neurodegenerative syndromes). The studies we reviewed mostly reported clinical diagnoses, without accounting for pathology data or co-morbid pathology. Therefore, our results should be considered with a degree of imprecision due to the general lack of pathologically proven diagnoses. However, evidences ranging from pathological cases to group-studies have reported memory deficit associated with confirmed FTD (see for example  Hornberger & Piguet, 2012; Hornberger et al.
Unfortunately, the published studies did not report all the necessary information, and the authors we contacted were unable to supply sufficient information to construct a CVLT database. We have documented the results of the systematic review search in the Supplementary Materials. In conclusion, our study demonstrates that bvFTD is characterized by genuine amnesia as assessed by the FCSRT. The deficit is substantial and falls between the performance of healthy individuals and that of AD patients' performance, highlighting its clinical relevance for both the diagnosis of bvFTD and the differentiation between bvFTD and AD based on the FCSRT. Importantly, bvFTD patients exhibit evident signs of amnesia. Our meta-analysis confirms the presence of a memory deficit in bvFTD patients, supporting the coexistence of both genuine and secondary memory deficits.
A new method is presented to measure strain over a large area of a single crystal. The 4D-ED data are collected by recording a 2D diffraction pattern at each position in the 2D area of the TEM lamella scanned by the electron beam of STEM. Data processing is completed with a new computer program (available free of charge) that runs under the Windows operating system. Previously published similar methods are either commercial or need special hardware (electron holography) or are based on HRTEM, which involves limitations with respect to the size of the field of view. All these limitations are overcome by our approach. The presence of defects results in small local changes in orientation that change the subset of experimentally available diffraction spots in the individual patterns. Our method is based on a new principle, namely fitting a lattice to (a subset of) measured diffraction spots to improve the precision of the measurement.
Both Beche and coworkers and Hytch and coworkers applied dark field electron holography for strain measurement  [7, 8]. Although it can provide the distribution of strain over a large area, it requires hardware (holography), which is less frequent in TEMs. Pekin and coworkers mapped strain from scanning nanobeam diffraction and used a disk registration algorithm to determine the accurate positions of the individual disks (that have finite disk size due to the convergence of the used electron beam)  [9]. Commercial solutions are also offered for the mapping of strain  [10, 11]. Although several methods are available, there remained a need for a method that overcomes the above-mentioned limitations. We report such a solution here. A new computer program was developed to determine strain components and concentration of the substitutional dopant from 2D diffraction patterns collected in a STEM from a 2D area of a single crystal sample (TEM lamella), constituting a 4D-ED dataset.
The dataset contains n×m diffraction patterns, where n is the number of columns and m is the number of lines in the STEM image. The important point is the spatial distance between the locations where the individual diffraction patterns are recorded. By selecting the step size of the STEM (between neighboring pixels) in accordance with the probe size, we can maximize information content. In that way, both the spatial resolution of the STEM image and the spatial resolution of the strain map are determined by this step size that must be in accordance with the need, which spatial resolution strain map is adequate for the given sample. The number of columns and lines is determined by the size of the sample that needs to be mapped. The possibilities and limitations of this program are elaborated in the paper exemplified with application to silicon doped with boron well above the solubility level.