dataesr/scientific-paragraphs-categorization huggingface dataset:
Column 3 gives the derived rest-frame UV magnitude of each galaxy. Column 4 denotes the sub-region of the UltraVISTA imaging within which each object has been found: 'U-D' refers to the ultradeep stripes while 'D' refers to the deep stripes, although as discussed in the text the difference in depth between these two regions has now been largely eliminated at J , H , K s in UltraVISTA DR5. The coordinates for each source are given in the following two columns. The final two columns list an alternative ID, as appropriate, for those (8) sources which were already detected by  Bowler et al. ( 2020 )  or were listed in the COSMOS2020 catalogue  (Weaver et al. 2022 ) , indicated by B20 and W22, respectively. Interestingly , and as largely anticipated, the new sources reported here almost all lie with the D (deep) stripes, where the UltraVISTA data have been most improved in depth between DR4 and DR5 (ef fecti vely doubling the area available in COSMOS for the selection of very high-redshift galaxies).
2  and tabulated in Table  6. The combined data set from the ground and space allows for increased dynamic range in our LF determination although this is only possible up to z ∼ 11 due to the limited wavelength range accessible from the ground. At z = 8 the new data contributing to the LF is purely from the COSMOS sample and therefore we place new constraints on the bright end alone. Our results are in good agreement with  Bowler et al. ( 2020 )  and show a clear deviation from the McLure et al. (  2013 ) Schechter function at the bright end. At z = 9 our results at the bright end are in also good agreement with  Bowler et al. ( 2020 )  and our faint-end bins determined using the JWST sample are in good agreement with  McLeod et al. ( 2016 ). Our JWST results at z = 9 are also in good agreement with  Bouwens et al.
( 2021 ). At z = 10. 5 our new source from the UltraVISTA imaging allows us to compute a bin at the bright end of this LF and our JWST sample provides a fainter bin. The z = 13. 25 LF was calculated from the JWST sample alone as this redshift range cannot be probed from the ground. This shows a modest evolution from z = 9 to z = 10. 5 and then further decline to z = 13. 25. Table  6. Computed UV LF data points using the derived sample of z > 7. 5 galaxies from UltraVISTA and JWST. The columns show redshift, the central UV absolute magnitude of the bin, the bin width and the source number density within the bin, along with uncertainties. MNRAS 518, 6011-6040 (  2023 ) z M UV M φ (mag) (mag) (10 -6 mag -1 Mpc -3 ) 8 -22.
AC  K N OW L E D G E M E N T S C. T. Donnan ,  D. J. McLeod ,  R. J. McLure ,  J. S. Dunlop ,  R. Be  gle y,  F. Cullen  and  M. L. Hamadouche  acknowledge the support of the  Science and Technology Facilities Council. A. C. Carnall thanks the  Leverhulme Trust  for their support via the  Leverhulme Early Career Fellowship scheme. The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant no. 140. Based on observations collected at the  European Southern Observatory  under  ESO  programme  ID 179. A-2005  and  198. A-2003 and on data products produced by  CALET  and the  Cambridge Astronomy Surv e y Unit on behalf of the UltraVISTA consortium. This work is based [in part] on observations made with the  NASA/ESA/CSA JWST. The data were obtained from the  Mikulski Archive for Space Telescopes at the Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc.
Nevertheless, although SNP phylogenetic core-genome analysis enables more detailed clustering between strains and better calculation of genomic distances between genomes, it requires complete genomes for processing the obtained data  (3, 5). To ensure good-quality, complete reference genomes, which is essential for these epidemiological association analyses, we recently developed an open-source tool (SalmoDEST) that can download wellcharacterized good-quality, complete reference genomes from the open-access GenBank database  (6). This tool can extract complete Salmonella genomes with a coverage higher than 50x and genome length over 4 Mb; it verifies the serovar to which genome belongs and identifies the corresponding MLST profile  (6). Nevertheless, although the number of compete genomes deposited in the open-access databases increases every year, a complete reference genome is still not available for several Salmonella serovars.
The protocol identifies the closest complete genome to use for SNP phylogenetic analysis among the ones available in the EnteroBase Salmonella database  (1, 8). We indicate how to query EnteroBase by searching for the closest hierarchical cluster (HC) 2,000 profile of the serovar under study and visualize results using the GrapeTree clustering analysis  (5, 9). Finally, when choosing the most suitable complete genome, polyphyletic serovars require special attention. A polyphyletic serovar derives from multiple independent ancestors  (1). For example, a study of the phylogeny of the Salmonella Derby serovar showed that strains displaying the same antigenic pattern S. 1,4,  [5] ,12: f,g:  (10, 11)  according to the White-Kauffmann-Le Minor scheme (12) -and, consequently, sharing the name Salmonella Derby -belonged to at least three distinct genomic lineages  (13).
e. , one list for the US, one for EU and one for FR) were compiled for the serovars isolated from humans and three other lists for those collected from the agri-food sector. The three lists for human cases and the three lists for the agri-food isolates were used separately for the Venn diagram analysis that was carried out using the ggVennDiagram R package (v. 1. 2. 1)  (20). Finally, the serovars selected for this study were chosen according to the following criteria: being common to at least two lists and belonging to the leading 20 serovars of each list (Table  1 ). For each of the serovars selected, the most common MLST profiles were identified using the data available in the EnteroBase Salmonella database on 19 July 2021. The MLST profiles with 10 or more genomes in the EnteroBase database were selected for this study.
For each of these MLST profiles, three good-quality genomes were downloaded. The complete or contig genomes were searched and downloaded using the SalmoDEST tool  (6)  and manually via the GenBank and EnteroBase Salmonella databases. Good-quality genome criteria were a length > 4 Mb, coverage > 50x and an analysis of how well genome matched the predicted serovar using SeqSero2  (21). When available, genomes from the Anses LSAl collection were selected and sequenced for this study. One genome of S. Javiana was obtained from the strain S11LNR1976 (renamed 2019LSAL01686) from the French National Reference Laboratory Collection (LNR-Anses) in Ploufragan-Plouzané-Niort Laboratory. Three genomes of S. Paratyphi B were obtained from the strains CIP 106179, CIP 55. 42 and CIP 106950 (renamed 2019LSAL01933, 2019LSAL01934 and 2019LSAL01936, respectively) of the French CIP collection (Collection de l'Institut Pasteut, Paris, https://www.
enterica subsp. enterica serovars, 639 genomes were collected from GeneBank, EnteroBase, CIP and the Anses Salmonella Network databases following the criteria described above. Eleven Salmonella samples from the other subspecies were also included. Genome ID, accession number, predicted serovar, MLST profile, genome length and coverage of the 650 genomes retained in this study is reported in Supplementary Table  1. Of the final total set of 650 genomes, 83 were complete genomes and 567 were contigs. Among the complete genomes, 77 belonged to the S. enterica subsp. enterica and 6 to other subspecies. Of the 567 contig genomes selected, 562 belonged to the subspecies enterica and 5 contig genomes belonged to other subspecies (Supplementary Table  1 ).
Reference genome panel available in public databases Given the selected set of high-quality complete genomes and the phylogenetic analyses carried out, we compiled a list of reference genomes with metadata and associated quality data (Supplementary Table  2 ). We selected 83 complete genomes from the initial set of 650 genomes, with 1 S. enterica subsp. salamae, 2 S. enterica subsp. arizonae, 1 S. enterica subsp. diarizonae, 2 S. enterica subsp. houtenae and 77 S. enterica subsp. enterica, representing 48 serovars and 71 MLST profiles.
From the first set of 650 genomes analyzed for these 58 prevalent serovars, the final taxon sampling genomes was composed of five outgroups and 214 ingroup S. enterica subsp. enterica strains. The cgMLST and pan-genome kmer phylogenetic analyses both uncovered a deep split that delineates four sister groups within S. enterica subsp. enterica, including the two previously partially described groups  (14, 25, 31). Although many of the relationships reconstructed in this study are consistent with previous reports, our taxon dataset provides a more thorough interpretation of polyphyletic serovars than any other study. The large selection of serovars and sequence-type profiles included allowed deeply appreciating the relationship between serovars, their genomic lineages and MLST profiles. For each serovar, we included a larger selection of sequencetype profiles than previously.
Further analyses are needed to investigate the other polyphyletic serovars identified in this study. Although 25 polyphyletic serovars have been identified in our taxon dataset, there are probably more. For example, the serovars Agona, Havana and Montevideo were not identified as polyphyletic in our study, but have been described as such previously  (14, 25, 36). The MLST profiles selection parameters applied in this study (i. e. , for each serovar, we included the MLST profiles with 10 or more genomes in the EnteroBase database on 19 July 2021) did not make it possible to highlight the polyphyly of these three serovars. Furthermore, the number of distinct lineages for a polyphyletic serovar also depends on the taxon dataset selected. For example, we previously described four distinct lineages for the serovar Derby  (37).
In the dataset selected for the study of the diversity of the serovar Derby in France, the genomes belonging to ST39, even if most closely related to ST40 genomes (i. e. , with an average of 3,962 SNPs and an SD of 20 SNPs), were identified as an independent lineage. The influence of the dataset on the results was also observed on the genomic groups identified. In our panel, we underlined strongly supported subgroups that confirm previous observations  (14, 25, 31, 34, 38). We called these subgroups "sections" to echo previous descriptions. For example, in this study, "section Typhimurium" comprises the genomes of Salmonella Typhimurium, Heidelberg and Saintpaul  (14)  and encompasses the genomes of Salmonella Typhimurium ST19, 34, 36, Heidelberg ST15, Saintpaul ST27, 49, 50, 680, 1,934, 3,602 as well as Coeln ST1995, 2,015 and Reading ST1628.
Acknowledgments We thank the  ANSES Salmonella Network team of the LSAl-Anses ,  Laetitia Bonifait  of the  LNR-Anses  and  Dominique Clermont  of the  CIP (Institut Pasteur)  for providing isolates for sequencing. We would like to thank  Corinne Danan  for the suggestions given during the proofreading of the manuscript.
Note that in these experiments, the ratio R 60S-80S/40S also increases from 2. 5 at 22°C to 4. 75 at 37°C, which is consistent with increased values obtained in the experiment shown in Fig. 7A. Then, we performed LC-MS/MS on 40S and/or 60-80S peak fractions (Fig. 7B , S4 and Supplementary Data 1). First, this analysis Figure  7. Ribosome profiles and LC-MS/MS analysis on ribosome subunits in response to heat stress. A) Extracts from two experimental replicates (black and grey) of non-treated (22°C), heat treated (37°C for 5 h and 24 h), and recovered (37°C for 24 h then 22°C for 5 h and 24 h) seedlings were fractionated through 15-60% sucrose gradients. The percentage of the full scale of absorbance was monitored at 254 nm. Peaks corresponding to 40S and 60S-80S ribosomal subunits/monosomes are indicated and controlled by the presence of mature 18S and/or 25S rRNAs (Figure  S7 ).
Northern blot and primer extension Total RNA extractions and 5' end-labelling of oligo probes (p23, p43, p5 and p6, oTIS, oP, pU3, and pU6; see Table  S1 ) were performed as described previously  [31]. Northern blots and primer extension gels were performed using 3 μg and 6 μg of total RNAs, respectively. Northern blot membranes were pre-incubated in PerfectHyb Plus Hybridization Buffer (Sigma) for at least 3 hours at 42°C. Labelled probes p23, p43, p5, and p6 were then added (1 µl at 10 µM) and incubated overnight at 42°C. Membranes were washed at 50°C during 15 min with 2X SSC 0. 1% SDS, then with 0,5X SSC, 0. 1% SDS, and finally with 0,1X SSC 0. 1% SDS. Primer extension and dideoxy sequencing reactions were performed according to  [10]. Northern blots and primer extension reactions were analysed on a Personal Molecular Imager (PMI, BioRad) and quantified using Quantity One software.
Circular RT-PCR and sequencing Total RNAs (5 μg) from non-treated (22°C) and heat treated (37°C for 5 h and 24 h) seedlings were circularized with T4 RNA ligase 1 (NEB M0204S). Then, the circular RNAs (1 μg) were reverse transcribed with primers p31, rt1, and rt3 to, respectively, hybridize 5'ETS, 18S, and 25S rRNA sequences. cDNAs were PCR amplified with primers r2, r5, r7, r8, r9, r10, p32 and p33 (Table  S1 ). PCR products were cloned into a pGemT easy vector (Promega) and sequenced with primer T7 by Eurofins Genomics (France). Sequences were analysed using reference sequence provided in Supplementary Information 1 and software Geneious 11. 0. 5.
Database searches were performed using Mascot (version 2. 6. 2, MatrixScience, London, UK) against an Arabidopsis thaliana protein sequences database downloaded from The Arabidopsis Information Resource TAIR site (TAIR10 version gene model), in which common contaminants and decoy sequences were added (2 x27534protein entries in total). Spectra were searched with a mass tolerance of 15 ppm in MS mode and 0. 07 Da in MS/MS mode. One trypsin missed cleavage was tolerated. Carbamidomethylation of cysteine residues was set as a fixed modification. Oxidation of methionine residues and acetylation of proteins' n-termini were set as variable modifications. Identification results were imported into Proline software (http:// proline. profiproteomics. fr/) for validation. Peptide Spectrum Matches (PSM) with pretty rank equal to one were retained.
False Discovery Rate was then optimized to be below 1% at PSM level using Mascot E-value and below 1% at Protein Level using Mascot score and more than one specific peptide was required. Protein abundances of RPS, RPL and RPP ribosomal proteins were estimated using weighted spectral counts. Detailed sample preparation and LC-MS/MS protocols and data analysis workflows are provided in Supplementary Information 3 and data 1.
Acknowledgments We thanks  F. Barneche  for detailed critical reading of the manuscript,  H. Moreau  for facilitating access to the BioPIC platform and  T. Roscoe  for comments and corrections of English. This work was supported by the  CNRS  and by grants from the  ANR , RiboStress  17-CE12-0026-01  and MetRibo  ANR-20-CE12-0024-01. This study is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ) and of the  "Ecole Universitaire de Recherche (EUR) "  TULP-GS  ( ANR-18-EURE -00019 ).
Data availability statement Authors responsible for the distribution of material integral for the findings presented in this article is Julio Sáez-Vásquez (saez@univ-perp. fr). All RNA-seq raw data used to generate Figure  S10  were submitted to the Sequence Read Archive (SRA), BioProject: PRJNA732814. https:// dataview. ncbi. nlm. nih. gov/object/PRJNA834587.
Remote sensors A delay sensor is a circuit capable of measuring the variation in the delay of an oscillatory digital signal. The propagation delay of an oscillator through digital components will fluctuate as a function of the temperature and voltage of the circuit. Therefore, as the chip performs different processing tasks, these will influence the delay propagation of the target oscillator. This delay will be quantified and then sampled as a digital signal. Both TDCs and ROs can be used to implement delay sensors. In this work, we use the ROS from  [Gra+19]  to perform the data acquisition. Figure  1    The main advantage of the ROS from  [Gra+19]  is their acquisition rate. When implemented in the reconfigurable fabric, these sensors allow to use sampling frequencies up to 250 MHz in the Zynq-7000 family of SoC-FPGAs, and greater in newer technologies.
Acquisition rate As illustrated in Fig. 4 , we assumed that the A1 application could retrieve the samples from the ROS. These two modules were connected through an AXI-HP channel clocked at 300 MHz; a typical link in Zynq Ultrascale+ chips. Recall from Subsection 2. 3 that the underlying operations of Photon-Beetle include RhoXor (one cycle), AddShift (one cycle), and SubMix (eight cycles). To determine the acquisition rate of the processor we sampled some digital triggers that correspond with the processing of these operations. The details for this experiment are shown in Fig. 6. By clocking the architecture under attack at 1 MHz we could retrieve five samples per cycle. These results indicated that the processor could retrieve 32 bits of data with a delay of 200 ns. Since we only included one sampler per transaction, our sampling rate was approximately 5 MSps.
We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400-800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leaveone-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e. g. , 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and a cdom (440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-ofsample datasets.
The objective of this research is to extend a previously developed machine-learning (ML) model, i. e. , Mixture Density Networks (MDNs)  (Pahlevan et al. , 2020; Smith et al. , 2021)  to concurrently estimate Chla, TSS, and a cdom (440) from the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. To the extent possible (owing to the differences in the spectral, spatial, and radiometric characteristics of these instruments), we aim to advance consistency among multi-mission products to deliver globally seamless WQ products. This development relies on an in situ database that contains hyperspectral radiometric measurements and co-located Chla, TSS, and a cdom (440). The model performance is gauged against that of select, topperforming state-of-the-art algorithms.
g. , the absence of specific IOPs in case of LMI). Among these methods, the Multi-Layer Perceptron (MLP) neural network  (Doerffer and Schiller, 2007)  trained with a Hydrolight simulated dataset  (Mobley and Sundman, 2008)  has been adopted as the standard processing of OLCI data over inland waters  (Toming et al. , 2017)  and applied to OLI  (Hafeez et al. , 2019)  and MSI  (Ansper and Alikas, 2019; Brockmann et al. , 2016; Soomets et al. , 2020). Instead of using simulated data, our study leverages a sizable database of in situ data for training and testing of an MDN for the simultanous estimation of Chla, TSS, and a cdom (440) at a global scale.
Development data A large portion of our database (R rs -Chla and R rs -TSS) has been described in previous publications  (Balasubramanian et al. , 2020; Jiang et al. , 2021; Pahlevan et al. , 2020; Smith et al. , 2021). For this study, we added new co-located samples from Australia, New Zealand, the Netherlands, Spain, Uruguay, and Brazil (Fig. 1 ). This dataset has been further augmented by in situ R rs -a cdom (440) measurements. Our most complete dataset includes paired R rs and Chla samples that correspond to 35 different sources, each encompassing data from one or multiple campaigns from a single principal investigator (PI). Our TSS and a cdom (440) datasets came from 26 and 17 sources, respectively. The details on the geographic scope of these sources are provided in Appendix A. Note that the data from a single PI delivered at different stages were treated as separate sources, leading to a total of 42 different sources (Appendix A).
% 37% Fig. 1. Continental distribution of the development data where in situ R rs and WQ indicators were measured. The percentage of contributed data from each continent is also provided for the data applicable to MSI (see text). Note that 63 samples from South Africa have not been illustrated (see Table  A ). processed via the Atmospheric Correction for OLI 'lite' (ACOLITE)  (Vanhellemont and Ruddick, 2021). These scenarios were selected because they represent a wide range of WQ conditions (e. g. , 0. 5 < Chla < 100 mg m -3 ). While no ideal atmospheric correction (AC) processor for generating seamless R rs from these missions has yet become available  (Pahlevan et al. , 2021a; Warren et al. , 2019) , ACOLITE (v20190326. 0) is a convenient processor for inland and nearshore coastal waters and allows for retrieving R rs under highly turbid or eutrophic optical regimes, thereby making it a suitable choice for demonstration purposes.
, 2017a) , SeaDAS did not return as many matchups as ACOLITE when MSI imagery were processed. This issue however does not apply to OLI  (Pahlevan et al. , 2021a). Here, median satellite-derived WQ estimates within 3 × 3-element windows were computed. In situ WQ data for this matchup exercise were obtained from various databases across North America. A majority of the Chla matchup dataset has been employed in previous studies  (Pahlevan et al. , 2021a; (Smith et al. , 2021)  ). This study added TSS and a cdom (440) matchups. The TSS data were mostly available through the U. S. Water Quality Portal (htt ps://www. waterqualitydata. us) and Geological Survey's National Water Information System (https://nwis. waterdata. usgs. gov/nwis) that contain data from small inland waters, as well as through the Chesapeake Bay repository (https://www.
chesapeakebay. net) with data mostly ranging from 0. 1 to 20 g m -3. These datasets were further augmented by the repository provided in  Ross et al. (2019). Several filtering techniques were executed to exclude duplicate data records. More Chla and a cdom (440) data were made available through the Natural Science and Engineering Research Council of Canada's (NERC) Lake Pulse Network (LPN), an extensive multi-year field campaign conducted across select drinking water supplies and lakes in Canada  (Huot et al. , 2019). Additional a cdom (440) datasets acquired in Lake Winnipeg (Manitoba, Canada) and across several lakes in Minnesota were contributed by Environment and Climate Change Canada (ECCC) and the University of Minnesota  (Olmanson et al. , 2020) , respectively. For Chla and TSS matchups, we permitted a +/-3 h time window from the satellite overpass for coastal matchups and a same-day overpass for inland waters.