dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary and conclusions We presented a method based on ANN to classify J spectra into four categories: stars, galaxies, QSO at high redshift (z ≥ 2. 1), and QSOs at low redshift (z < 2. 1). The algorithms were trained   and tested in mock data developed by  Queiroz et al. (2022). We employed two different representations of miniJPAS photometry in order to train the algorithms. ANN 1 used as input photometric fluxes normalised to the detection band (r), while ANN 2 employed colours plus the magnitude in the r band. Therefore,  ANN 1 only has information about the shape of the spectrum, while ANN 2 also has access to the observed luminosity. f1 W score ANN 2 ANN 1 RF 1 RF 2 RF 1 mix RF 2 mix We enlarged the training set by mixing features from four different classes by adapting the mix up technique.
In these cases, the classifiers mostly yield QSO and galaxy as the two preferred classes. Nevertheless, the SDSS test samples is a relatively small set, and it is only representative of objects brighter than 22. 5. Therefore, the results should be treated with caution. The actual performance for fainter objects is still unknown, and the estimates we provided are based on our current physical knowledge of QSO, galaxies, and stars, together with our capability to generate simulated J spectra that mimic miniJPAS observations in the best possible way. A direct comparison of the results presented in this paper and other works in the literature is challenging because the data are divers and no standardised procedure is available to evaluate the algorithm performance. The capability of distinguishing between QSO, galaxies, and stars clearly depends on many factors, such as the S/N of the observations, the photometric filters, the wavelength coverage, and whether images or integrated photometry are used.
The algorithms presented in this work are part of a combined algorithm that unifies the outcomes of several classifiers  (Pérez-Ràfols et al. , in prep. b ). In the future, we will provide a QSO target list for a spectroscopic follow-up with the WEAVE survey  (Jin et al. 2023 ). This will give us valuable information about the strengths and weakness of our classifiers. WEAVE and the J-PAS collaboration will enter a feedback phase, where the knowledge acquired by one survey is to be transferred to the other in an interactive process. A natural extension of this work when enough spectroscopically confirmed sources are available is to use transfer learning and retraining the algorithms with observations in order to capture the structure of J-PAS data better.
Acknowledgements. This paper has gone through internal review by the J-PAS collaboration. G. M. S. , R. G. D. , R. G. B. , L. A. D. G. , and J. R. M. acknowledge financial support from the  State Agency for Research of the Spanish MCIU  through the  "Center of Excellence Severo Ochoa" award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ), and to the  AYA2016-77846-P  and  PID2019-109067-GB100. C. Q. acknowledges financial support from the  Brazilian funding agencies   FAPESP  (grants  2015/11442-0  and  2019/06766-1 ) and  Coordenação de Aperfeiçoamento de Pessoal de Nível Su-perior (Capes) -Finance Code 001. I. P. R. was suported by funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowskja-Curie  grant agreement no.
754510. M. P. P. and S. S. M. were supported by the  Programme National de Cosmologie et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES , the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program , managed by the  French National Research Agency (ANR) , and by  ANR  under contract  ANR-14-ACHN-0021. N. R. acknowledges financial support from  CAPES. R. A. was supported by  CNPq  and  FAPESP. J. C. M. and S. B. acknowledge financial support from  Spanish Ministry of Science, Innovation, and Universities  through the project  PGC2018-097585-B-C22. J. A. F. O. acknowledges the financial support from the  Spanish Ministry of Science and Innovation  and the  European Union -NextGenerationEU  through the  Recovery  and  Resilience Facility  project  ICTS-MRR-2021-03-CEFCA.
Introduction Staphylococcus aureus bacteraemia (SAB) is a severe condition whose incidence ranges from 38. 2 to 45. 7 per 10 0,0 0 0 personyears in the US. 1 , 2  In 10 to 25% of SAB cases, the infection localises to the valves or endocardial surfaces, leading to infective endocarditis (IE)  3 , 4  and thereby to enhanced morbidity and mortality. To exclude IE upon diagnosing SAB, the use of either trans-thoracic or transoesophageal echocardiography is recommended  5 , 6  but is far from being systematic: a recent study analysing 668,423 hospitalizations for SAB from the US National Inpatient Sample database (2001-2014) reported that only 11% patients in 2001 to 15% in 2014 had echocardiogram. 7 ] [10]  However, none of these scores take into account the possibility that certain strains of S.
Assembly and sequence typing Assemblies were processed using SPAdes v3. 11. 1,  28  enabling the automatic coverage cut-off and the mismatch/short indels reducing step. Contigs were broken in k-mers using Kraken v1. 1. 1  29  in order to remove contigs that did not share at least one k-mer belonging to S. aureus. Finally, contigs less than 300 nucleotides in length were manually removed. An assembly quality check was performed using Quast v4. 6. 1  30  and visualised by R v3. 6. 3. 31 Sequence types (STs) were determined using MLST v2. 9  32 , 33  software and STs were grouped in CCs.
Single nucleotide polymorphism (SNPs) analysis Variant calling analysis was performed on the entire data collection using TCH60 (ST30) as the reference genome (GenBank accession nos. NC_017342. 1 ). First of all, this genome was aligned against itself using MUMmer v4. 0. 0 34 in order to locate duplicate regions. Then, Snippy v4. 4. 5  35  software with a minimum of 10x variant site coverage and a new allele proportion set to 0. 5 was used on each dataset, producing a presence/absence matrix for SNP, insertion, or deletion positions for the whole genome. Genomic modifications detected in duplicate regions were removed. The remaining genomic changes were encoded as "1 when they were different from the reference. By combining the alignment of core SNPs purged of recombinations in nucleotide sequences by Gubbins v2.
Gene and non-coding RNA analysis Annotations were performed using Prokka v1. 14. 5  39  with NCTC8325 (GenBank accession nos. NC_007795. 1 ) GenBank annotation file as first protein database. Coding sequences (CDS) identification was reduced to a presence/absence matrix using Roary v3. 13. 0  40  with the split paralog option enabled. A specific database containing 659 genomic non-coding RNA sequences from five strains of S. aureus was constructed as follows: 538 ncR-NAs from NCTC8325 (http://srd. genouest. org/) were included in the main database; each ncRNA sequence from other genomes was mapped to the main database using blast v2. 2. 31  41  with parameters set to 90% identity and coverage; finally, 47, 51, 16, and 7 new genomic sequences were added from N315, JKD6008, Newman, and USA300_FPR3757, respectively.
Statistics All statistical analyses were performed using R v3. 6. 3. 31 Principal component analysis (PCA) and hierarchical clustering (HC) with complete method and Euclidean distance for both rows and columns were performed to screen for possible stratification of the data. Fisher exact tests were used for proportion comparisons, Wilcoxon-Mann-Whitney tests for distribution comparisons, one sample t-tests to compare the mean of our data to a known value, and Cohen's d to measure the effect size of the difference between two means. A threshold of significant p-value was set at 0. 05. A common heritable trait independent from the genetic background was measured by the Pagel lambda phylogenetic value; in this model, the lambda value varies between 0 (absence of phylogenetic signal on trait distributions) to 1 (strong phylogenetic signal; see Supplementary material Text S1).
Assemblies and sequence typing The entire data collection included 274 IE and 650 non-IE SAB samples from four geographical origins. An assembly summary is reported in Fig. S1  and in the Supplementary excel File. A total of 106 STs were identified amongst the 924 S. aureus strains in the collection. Each isolate was assigned to one of the 28 CCs (Table  S1 ). The data set was essentially composed of six clonal complexes, namely CC30, CC45, CC8, CC5, CC1, and CC15, each accounting for 15. 8 to 8. 0% of the entire collection and overall representing 74. 2% of this collection. The six largest CCs and the five largest ST distributions amongst the geographical origin are represented in Fig. S2.
SNPs and Pagel lambda phylogenetic analyses A total of 15,436 SNPs were identified among the 924 S. aureus samples using the TCH60 (ST30) as reference (GenBank accession nos. NC_017342. 1 ) after duplicated region and recombination corrections. 1127 and 1043 SNPs were present in more than 5% and 10% of the entire data collection, respectively. The phylogeny of all the 924 strains according to infection type, CCs and STs assignment is shown in Fig. 1. Discrimination between IE and non-IE SAB was primary searched through the Pagel lambda phylogenetic signal measurement (Supplementary Text S2) within the entire strain collection and within each of the four geographical origins (Table  S2 ). The lambda (phylogenetic signal) value of the analysis of the global data set reached 0. 9441 and was above the one calculated through random permutations (0.
Genome-wide association study IE and non-IE SAB assemblies were split in k-mers (i. e. , sequence substrings of length k) and tagged in order to search specific genomic signatures associated with IE through DBGWAS  43  including a correction for population structure. Nine overlapping kmer sequences issued from 3 analyses were found statistically significant between the two infection types. These nine-overlapping k-mer sequences were mapped on TCH60 (GenBank accession nos. NC_017342. 1 ) and all nine matched between a rRNA-5S and tRNA-Asn (1,092,881 -1,092,964 bp) genomic region. Each significant k-mer had other matches in TCH60 due to the number of rRNA-5S and tRNA-Asn present in the genome. To further investigate this result, the 9 k-mer overlapping sequences were concatenated to obtain a sequence of 84 nucleotides.
55 However, the inclusion of such a high number of patients will be challenging, and if genetic markers are identified, they are likely to be valid only for that one lineage (i. e. , a specific ST or CC), and thus difficult to exploit in a diagnostic context. Some concerns in genome-wide association studies are the use of only one reference genome and therefore the risk of missing SNPs on genes not present in this genome. To overcome this bias, we performed kmers analyses that did not require the use of a reference strain and that accounted for lineage effects in the linear mixed model by including the phylogenetic distance calculated from the variant calling analysis. To reduce the risk of false positives in GWAS, we agree with Young et al. that it would be desirable to use genomes from the same sequencing technology or even the same sequencing run to generate datasets with identical read sizes and relatively similar sequencing depth.
ALMA data In this work, we focus on QGs lying in GOODS-S  (Dickinson et al. 2003) , taking advantage of the rich multiwavelength ancillary data in the field that we combine with 1. 1 mm data from the GOODS-ALMA survey  (Franco et al. 2018; Gómez-Guijarro et al. 2022). As a second step, in order to maximise the sensitivity of our stacking analysis, we complement our ALMA dataset with the full suite of available ALMA Band 6 archival data in GOODS-S.
GOODS-ALMA GOODS-ALMA is a blind 1. 1 mm galaxy survey in the GOODS-S field, centered at α = 3 h 32 m 30 s , δ = -27 • 48 00. The survey covers a contiguous area of 72. 42 arcmin 2 with ALMA Band 6 and it is a combination of two observing campaigns carried out at different angular resolutions at a homogenous sensitivity. The high resolution dataset has an average sensitivity of 89. 0 µJy beam -1 and angular resolution of 0. 251 × 0. 232 (synthesised beam FWHM). The low-resolution dataset has an average sensitivity of 95. 2 µJy beam -1 and angular resolution of 1. 33 × 0. 935. The combined mosaic reaches a factor 1. 5 deeper sensitivity of 68. 4 µJy beam -1 at angular resolution of 0. 447 × 0. 418. The high resolution mosaic was presented in  Franco et al. (2018, GOODS-ALMA 1. 0). The low-resolution mosaic and the final combination of the two mosaics was presented in  Gómez-Guijarro et al.
ALMA archive We downloaded all ALMA Band-6 projects that cover (i. e. with a primary beam correction better than 0. 2) 1 of our 121 QGs (see Sect. 3) from the archive. This was done using the Python package astroquery. We then calibrated all these projects using the calibration scripts provided in the archive by the ALMA observatory (i. e. the so-called scriptForPI. py). Each QG is covered by at least two ALMA pointings (GOODS-ALMA 1. 0 and GOODS-ALMA 2. 0) with four ALMA pointings, on average. This implies that in the final stacking analysis, a total of 477 ALMA pointings were combined, each with a different depth and spatial resolution. Stacking in the uv-plane with appropriate weightings (see Sect. 4) makes it possible to combine all these different pointings.
Stacking analysis We performed a stacking analysis of the final sample of 121 QGs at hand in the uv-plane (see methodology in  Gómez-Guijarro et al. 2022; Wang et al. 2022) , which has been shown to improve the signal-to-noise ratio (S/N) of the stacked image compared to a stacking analysis performed in the image plane  (Lindroos et al. 2015) ; most importantly, it allows us to stack datasets of different angular resolution. In particular, we applied a mean uv-stacking to the QGs sample, concatenating the visibilities from the GOODS-ALMA observations. The main advantage of the GOODS-ALMA map is the homogeneous coverage of the field that results in a uniform contribution of the stacked sources in the final image. A166, page 3 of 7 Subsequent aperture photometry at the phase center yielded a 3σ upper limit flux of 47.
Conclusions We conducted a 1. 1 mm stacking analysis of a carefully selected sample of 1 < z < 3 QGs, using both the GOODS-ALMA survey and the full ensemble of ALMA archival data in the GOODS-S field. We recovered, at the phase center of the image, a signal of S 1. 1 mm = 11. 70 ± 3. 59 µJy, arising from the stacked ensemble of QGs with weighted mean values of z = 1. 47 and log M * = 10. 70 M. Using the empirical templates of M21, we estimated an average log(M dust /M ) = 7. 47 ± 0. 13 and log(M gas /M ) = 9. 42 ± 0. 14 that correspond to f dust = 0. 05 ± 0. 02% and f gas = 5. 3 ± 1. 8%. With these estimates, along with with previous stacking results and studies of individual high-z QGs, we reached the following conclusions: -The average population of QGs at z = 1-3 appears to retain a non-negligible amount of gas, with an average of f gas ,= 5.
3 ± 1. 8%. This is in agreement with previous stacking results, but considerably larger compared to the values -or upper limits inferred by studies of individual high-z QGs. -While still poorly constrained due to the scarcity of data in the low mass regime, the f gas of QGs at z = 1. 5 appears to remain flat up to log(M * /M ) ∼ 11 and then rapidly drop towards the largest stellar masses. This could alleviate the tension between the stacking results and the studies of individual sources that so far have predominantly drawn targets from the very massive end of the high-z population of QGs. The sub-arcsecond resolution of the ALMA observations exploited in this study has been instrumental into overcoming the main caveat of the previous stacking attempts of high-z QGs, namely, the uncertainties introduced by the coarse resolution of Herschel and other ground-based sub-millimetre and millimetre facilities.
Acknowledgements. G. E. M. and D. B. S. acknowledge financial support from the  Villum Young Investigator  grant  37440  and  13160  and the  Cosmic Dawn Center (DAWN) , funded by the  Danish National Research Foundation  under grant No. 140  P. D. and M. T. acknowledge support from the  NWO  grant  016. VIDI. 189. 162  (" ODIN ").
84). This study however concerned a single species, the bramble (Rubus fruticosus L. ), and four PTEs. Another study provided good accuracy to detect plants affected by several soil PTE (As, Cr, Cu, Sn and Zn) in mining area from airborne hyperspectral imagery and Random Forest algorithm  (Yang et al. , 2020). Most of the published studies were limited to in-field spectral measurements without addressing airborne measurements and have focused on the spectral range [400-900 nm] not considering the spectral bands beyond 1000 nm. Few studies concerned pine species, a priori sensitive to PTEs in soil, and polymetallic contamination by various metals and metalloids. To our knowledge, there is no published study based on a comprehensive database construction (including spectral measurements, soil metal contents, biophysical, biochemical and structural traits) to identify pine species traits altered by chemical contamination.
The objective of our study was to assess PTE contents in soil by the spectral signature of two pine species in the reflective spectral domain [400-2500 nm] measured by an airborne hyperspectral instrument. This study focused on nine PTEs: As, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Zn. The studied contaminated land is a former ore processing site which has been phytostabilized for several years ( § 2. 1). A complete database was built with needle-scale spectral signatures measured during an in-situ experiment completed by soil PTE contents, foliar pigment contents and other biophysical vegetation traits at needle and tree scales ( § 2. 2 and 2. 3). This database allowed to define the most sensitive vegetation parameters to each soil PTE and to select the best suited wavelengths establishing context-specific spectral indices.
DBH 26 pines (13 pines of each species) and 9 pines (5 Aleppo and 4 Stone pines) were sampled in the contaminated and control areas respectively (Fig. 2 ). The trees within the contaminated area were of comparable ages (about 15 years), and control pines were of variable ages estimated from 12 to 35 years. Their geographical coordinates were acquired using a differential GNSS. Needle samples from contaminated and control sites have been measured in situ (structural traits, spectral signatures) and in laboratory (pigment concentrations, biophysical traits). These measurements were completed by soil PTE contents. All these soil and vegetation measurements have filled out an exhaustive database.
Acknowledgments Airborne data was obtained using the aircraft managed by Safire, the  French facility  for airborne research, an infrastructure of the  French National Center for Scientific Research (CNRS) ,  Météo-France  and the  French National Center for Space Studies (CNES). The authors gratefully acknowledge  E. Buffan-Dubau  and  D. Lambrigot  for their assistance in pigment analyses,  M. Philippe  ( CNRS, GEODE ) and  G. Le Roux  ( CNRS, LEFE ) for p-XRF soil measurements and the site manager.
In the Black Sea, the phytoplankton and zooplankton indicators have all thresholds values. In Romania for phytoplankton biomass, the indicator value for GES is obtained by calculating the 90 th percentile value of the summer season values (June -August). This value is then compared with the averages of the reference periods 1956-1960 (GES) and 1980-1988 (no GES). For the mesozooplankton indicators (biomass, abundance), GES is established by the statistical analysis of data from 1960-2002 and expert knowledge. For each broad habitat types, GES is obtained by calculating the 90 th percentile of the indicator value from the cold and warm seasons (full set of data in six years) in each marine unit. These values are then compared with the average of 1960-1969 period (GES) and 1977-2002 (no GES).
Conclusions and summary of recommendations Monitoring data remain expensive and limited in space and time so that the Marine Strategy should optimize sampling to best explore the pressure-response relationships and spatial representativeness of GES assessment. The current sampling network raises two main problems: sampling gaps due to regional habitat variability and specificities, and lack of structural organization for the monitoring of pelagic habitats. The first issue is the coarse interpolation of the GES assessment for broad habitat types (Figures  1 2 3 4 5 ) and within Marine waters that does not ecologically reflect the variability of pelagic habitats. This lack of representativeness is due to the limited network of sampling stations and sampled data. For example, a coastal area experiencing a harmful phytoplankton bloom event may be missed by a bi-monthly sampling strategy during the riskiest season due to the possible shortness of events and the spatial heterogeneity.
Acknowledgements The authors would like to thank the  Pelagic Habitats Experts , part of the  MSFD Biodiversity Expert Network  for the fruitful discussions on the harmonisation of the MSFD assessment and monitoring for pelagic habitats. IV wishes to acknowledge support from the program "Monitoring and recording the situation of the marine sub-regions of Greece / Upgrading and functional updating of the  MSFD monitoring network ", funded by national and  EU  funds under National Strategic Reference Framework 2014-2020 ( MIS 5010880 ), and the European project  ABIOMMED: Support coherent and coordinated assessment of biodiversity and measures across Mediterranean  for the next 6-year cycle of MSFD implementation, funded by  DG Environment  ( 11. 0661/2020/839620/SUB/ENV. C2 ), coordinator  Dr.
Background and Purpose Use of unilateral cochlear implant (UCI) is associated with limited spatial hearing skills. Evidence that training these abilities in UCI user is possible remains limited. In this study, we assessed whether a Spatial training based on hand-reaching to sounds performed in virtual reality improves spatial hearing abilities in UCI users Methods Using a crossover randomized clinical trial, we compared the effects of a Spatial training protocol with those of a Non-Spatial control training. We tested 17 UCI users in a head-pointing to sound task and in an audio-visual attention orienting task, before and after each training. <br>Study is recorded in clinicaltrials. gov (NCT04183348). Results During the Spatial VR training, sound localization errors in azimuth decreased. Moreover, when comparing headpointing to sounds before vs.
e. , with or without hearing aid. We calculated them by computing the average between the thresholds available in clinical record for each subject for 250, 500, 1000, 2000, 4000, 8000 Hz. The study was approved by a national Ethical Committee (Ile de France X, N° ID RCB 2019-A02293-54) and recorded in clinicaltrials. gov (NCT04183348). Before starting the experiment, each participant signed an informed consent.
Statistical analysis Linear mixed-effect modeling was used for all statistical analyses. Statistical analyses were run using R (version 1. 0. 143). For the linear mixed-effect (LME) model, we used the R-packages emmean, lme4, lmerTest in R Studio  [32, 33]. The R-package car was used to obtain deviance tables from the LME models. For further details about "Materials and method", see  [22]  and Supplementary Results for details on each of the analyses described below. To study head movements, we extracted three dependent variables: number of head rotations, head-rotation extent and head-rotation bias  [18]. All the variables concern head rotations around the vertical axis. To calculate the number of head movements, we counted all the detected peaks of velocity in the head trace expect for movements smaller than 2°, which were removed to exclude movements which are not indicators of spontaneous head intentional movements and not related to the task (i.