dataesr/scientific-paragraphs-categorization huggingface dataset:
1 M ammonium acetate pH 7, before being centrifuged once again for 2 hours at 154,000 g, 4˚C. The final pellet was resuspended in 30 μL of 0. 1 M ammonium acetate pH 7. Cyrano was visualized by TEM using the same protocol as that used for the observation of Gally, except that the LF82 culture (OD 600 ~0. 3) was treated for 2 hours with 0. 09 μg/mL ciprofloxacin. Ten μL of each virion preparations were absorbed onto a carbon film membrane placed on a 300-mesh copper grid and stained with 1% uranyl acetate dissolved in distilled water. After drying at room temperature, grids were observed with Hitachi HT 7700 electron microscope at 80 kV (Elexience-France) and images were acquired with a charge coupled device camera (AMT). Finally, tails and capsids were measured using ImageJ software  [64].
The plate was incubated for 20 hours at 37˚C in a Tecan fluorimeter. Using the TECAN I-CONTROL software, OD 610 of each well was measured every 3 minutes after 15 seconds of orbital shaking of the plate at 158. 9 rpm and a wait time of 5 seconds. As previously done, 10 μL from cultures without any antibiotics were collected in order to verify the input bacterial titer. MIC values obtained from microplate cultures were similar to those obtained in tubes: 15. 63 μg/mL for gentamicin, 0. 24 μg/mL for cefotaxime, 0. 06 μg/ mL for ciprofloxacin and 0. 125 μg/mL for trimethoprim.
To quantify the packaged phage DNA, we used the Luna Universal qPCR Master mix from NEB (Ref M3003E) with primers described in Table  4  at 250 nM each, to target specifically phage genomes or to target the yersiniabactin biosynthesis salycil-AMP ligase protein encoding gene (ybtE) from LF82 in order to evaluate the bacterial DNA contamination of our samples. Nine μL of this mix was added either to 6 μL of diluted viral samples, 6 μL of LF82 genome for the qPCR reference, or 6 μL of H 2 O (negative control) and run in a StepOne Real-Time PCR System (ThermoFisher scientific) with the following program: 95˚C 1 min, (95˚C, 15s; 60˚C, 30s) 40 cycles, followed by melting curves. Results obtained were analyzed using the StepOne Software 2. 3.
Finally, time-point measurements were performed with FIJI/ImageJ  [70]. MCP-GFP expression in Lennox medium was analyzed on exponential growth cells (MAC 2606 strain) 60 min after addition or not of 0. 09 μg/mL of ciprofloxacin. Cells were examined on a slide covered with 1. 5% agarose in M9 minimal medium as described above and images were analyzed by counting dotted fluorescent cells (almost all fluorescent cells grown in Lennox medium supplemented with ciprofloxacin contained fluorescent foci) and non-fluorescent cells using the Image J software.
3 and then treated or not for 1 hour with ciprofloxacin (0. 09 μg/mL) was purified using the Wizard Genomic DNA Purification Kit (Promega). Amplification of the ybtE gene and attL, attB, and attP sites was performed by PCR with the following program: 94˚C 30s, (94˚C, 30s; 50˚C, 30s, 68˚C, 45s) 30 cycles using OneTaq polymerase (NEB) and the oligonucleotide pairs OPM80/OPM82, OPM75/Maj281, JC206/JC207, and Maj280/Maj281, respectively. PCR products were analyzed by 1X TBE 1. 5% agarose gel electrophoresis in presence of ethidium bromide. After migration, bands were revealed by the BioRad ChemiDoc MP imaging system and quantified with the Image Lab software (BioRad) using known DNA quantity of bands from the DNA ladder (SmartLadder, Eurogentech).
Genome and reads submissions The re-annotated genomes of the phages are available from the European Nucleotide Archive browser (http://www. ebi. ac. uk/ena/browser/view) with the following accession numbers: OV696608 for Gally, OV696612 for Perceval, OV696610 for Tritos, OV696611 for Cartapus and OV696614 for Cyrano. Raw data obtained from the virome sequencing have been deposited (accession number: ERR8973296).
This work was supported by the  Agence Nationale de la Recherche  https://anr. fr(Persist3R contract, reference:  ANR-18-CE35-0007 , Grant recipient: M-A. P. ). P. M. was supported by a fellowship from the  French Ministère de l'Enseignement Superieur et de la Recherche  and from the  MICA division of INRAE. E. B. was supported by a fellowship from the  French Ministère de l'Enseignement Superieur et de la Recherche. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study design and data sources We carried out a multicountry analysis of cross-sectional surveys in SSA countries, namely the Demographic and Health Surveys (DHS). The DHS are nationally representative surveys regularly conducted in the Global South collecting information on a broad range of indicators such as sociodemographic indicators, maternal and child health, malaria and HIV/AIDS. They are based on a two-stage sampling design, with Primary Sampling Units (PSU) selected in the first stage and households in the second. Women aged 15-49 years and men aged 15-54/59 years in participating households are eligible. Depending on the survey, data for men for the HIV indicators, biomarkers or both might be collected only from a subsample of the selected households. The information and serology on HIV/AIDS were collected in some DHS in which participants are asked for consent to be tested for HIV, which is done anonymously in most of the surveys.
Individuals who consented are interviewed face-to-face by trained interviewers who use a standard questionnaire. The agencies and research institutes that conducted the surveys were responsible for acquiring ethical clearance in each country. DHS Global Positioning System coordinates were obtained from the DHS database. These coordinates were intentionally and randomly displaced to ensure confidentiality of the respondents. Urban clusters were displaced between 0 and 2 kilometres (km), while rural clusters were displaced between 0 and 5 km. The spatial boundaries were also obtained from the DHS spatial database repository (https://spatialdata. dhsprogram. com/ home/). The DHS data were linked to these spatial data. For our analysis, we selected SSA countries with available DHS surveys between 2011 and 2019 that contained the variables of interest and spatial data.
Patient data and antibiotic records were retrieved from the Danish National CLL registry and from manual review of medical health records  (21). As previously described  (22) , fecal samples were collected using OMNIgene. GUT (DNA Genotek) stabilization tubes according to the manufacturer's instructions. Brie y, samples were immediately xated and subsequently frozen within 72 h. All samples were stored at - 80°C until shipment for sequencing. Samples underwent shotgun metagenomic sequencing on the Illumina Hi-Seq platform. Reads preprocessing and taxonomical pro ling were done using an in-house pipeline (Supplemental Methods).
Bioinformatics and Statistical analysis Descriptive analyses were performed for both the mouse and human CLL cohorts with relative bacterial abundance as input data. Unless stated otherwise, Wilcoxon rank-sum testing was used to identify signi cant differences between subgroups and the Benjamini-Hochberg (BH) method was used for multiple-testing correction; a BH adjusted p-value of < 0. 05 was considered signi cant. PERMANOVA was used to test variations in the microbial composition among different groups of murine samples (such as cage effect) or groups of patient samples (such as gender or antibiotic usage). Alpha diversity measures (richness, Shannon index) were calculated at species level using the vegan R package (26). A generalized linear model (GLM) was used to visualize the relationship between two independent binary variables -TCL1 AT and hygiene of the animal facilities, and one dependent variable -bacterial relative abundance.
We are currently undertaking such studies to extend on our previous exploration of a CLL gut microbiome signature  (10). Our study is limited by only assessing the microbiome on DNA level, whereas a more precise way would be the inclusion of metatranscriptomics and/or metabolomics focused on the microbiome. Another limitation of our study is the lack of consecutive samples per patient, which would allow us to describe the microbiome changes during disease progression more precisely. A study set-up where stool samples are collected before and after treatment initiation has been applied as translational studies adjoined to several clinical trials (NCT04008706; NCT04639362; NCT04608318); thus, elucidation of microbiome dynamics throughout treatment will be the focus of upcoming studies. Given the purpose of this study, while a descriptive overview of the mouse and patient data itself is insightful, a translational and functional comparison of our ndings in mice and human would be ideal.
However, profound taxonomical changes were not re ected in changes in the gut metabolic potential. It is thus appealing to further investigate whether the intestinal microbial composition and function could serve as a potential predictor for CLL development. Microbiome composition and diversity in mouse. A) Fecal alpha diversity in murine samples collected at two different timepoints (before and after TCL1 AT; TP1 and TP2 respectively). Mice with low quality of their microbiome sequencing upon quality control were excluded from the analysis (n = 3 from LH group, n = 5 from HH group). Alpha diversity measures include richness -representing observed number of genera, and Shannon index -representing evenness of species in a community. In the box plots, box edges represent the 25th and 75th percentiles, the center line shows the median and whiskers extend from the box edges to the most extreme data point.
Acknowledgements The authors would like to express sincere thanks to the patients providing samples for the study and making this kind of research possible. This research would not have been possible without our collaborator,  PERSIMUNE Centre of Excellence , which provided the infrastructure as well as nancial support and exceptional expertise. Special thank you to  Mette Jørgensen  for providing access and training for an in-house taxonomical pro ling pipeline. The staff at the  Hematology Department  and at the  PERSIMUNE biobank at Rigshospitalet  were essential for this study as they organized and collected the samples for our research. Computerome team provided computational platform and technical support. The authors would like to thank  Dr. Emma Philips  for editing the manuscript for English grammar and punctuation.
To allow the capture of very small specimens, the trawl is also equipped with a 7. 5 m long sock with a 12 mm mesh size. The complete raw biological data and metadata are available on the PANGAEA platform https://doi. org/10. 1594/PANGAEA. 959551  (Spitz et al. , 2023). Midwater hauls were conducted at night from 20 to 2000 m immersion depth. Each haul was made at a specific chosen immersion depth (i. e. sampling depth, Fig. 2 , Table  1 ). Once the trawl reached the pre-set depth it was towed horizontally (i. e. , constant immersion depth) for 1 h at 4 kn. A higher trawl speed on deployment and a low speed on retrieval were implemented to reduce bycatch at shallower depths than the target depth  (Kashkin and Parin, 1983; Sinclair et al. , 1999; Eduardo et al. , 2020a). The main disadvantage of a pelagic trawl of this size is the absence of an opening or closing mechanism generating potential contamination of the sample.
When weighing was not possible, an estimate of the average individual weight over the entire time series was calculated and the total weight by species was estimated. The term biomass refers here to the relative Catch-Per-Unit-Effort (CPUE). For this purpose, the biomass of each species within each trawl was divided by the volume filtered by the trawl during the fishing phase (vertical opening × horizontal opening × distance trawled). Thus, a comparison of the distribution along the water column can be made at the intraspecific level but comparisons of absolute biomass between species are not possible due to the difference in catchability between species which is not well known. The final biological dataset is thus translated into a species (biomass g. m -3 )-sites matrix.
e. the average of the depth between the beginning and end of the fishing phase). Three depthrelated parameters were used and defined as follows: immersion depth is the fishing depth, bottom depth is the bathymetry, and proximity to the bottom is defined as the difference between bottom depth and fishing depth. Bathymetry of the Bay of Biscay was taken from the General Bathymetric Chart of the Oceans (GEBCO, https://www. gebco. net/). From this data, different terrain parameters were calculated using the R package raster  (Hijmans, 2023). These parameters were: average bottom depth, proximity to the bottom, slope inclination in degrees, the terrain roughness defined as the ratio of the surface to the planar surface across the neighbourhood of the central pixel, the bathymetric Position Index (BPI) which indicates whether the pixel is part of a positive (ridge) or negative (trough) characteristic of the surrounding terrain and the terrain Ruggedness Index (TRI) which is calculated by comparing a central pixel with its neighbours, taking the absolute values of the differences, and averaging the result.
To capture strong terrain variations, we chose to use the 75% quantile value for each terrain parameter (i. e. slope inclination, terrain roughness, BPI, and TRI). The R package marmap was used to estimate the smallest distance between the sampling station and the 200 m-isobath representing the ocean shelf  (Pante and Simon-Bouhet, 2013). Some hauls being conducted above canyons and others out of canyons, the influence of this parameter on the distribution of the fish was verified. For this, a set of geo-referenced canyon characteristics was extracted (area, length, width and mean depth) and a typology of canyons was defined using an ascending hierarchical classification (AHC)  (Harris et al. , 2014). AHC was performed using the kmeans method for several k groups between 2 and 10. The optimal number of groups was estimated using the percentage of explained inertia.
, 2011). The number of nodes was selected using a cross-validation procedure with 100 iterations. The number of nodes minimizing the relative cross-error validation (CVRE) was therefore selected  (De'ath, 2002). The R package mvpart was used for this analysis  (De'ath, 2014). In this study, we built the MRT using standardized biomass (biomass per-unit-effort, i. e. g. m -3 ) data on which a square root transformation has been applied. Biomass is not evenly distributed among deep-sea pelagic species, with small species often being very abundant (e. g. Benthosema glaciale, Ceratoscopelus maderensis). However, ecological issues associated with ecosystem functioning and energy flows are more related to biomass than to abundance, which explains our choice to use this measure. To maximize the detection of dominant communityenvironment relationships only species with a percent occurrence ≥ 10% (=37 species) were retained  (Poos and Jackson, 2012).
, 2008). All these observations support the hypothesis that the nocturnal distribution of meso and bathypelagic fish species is modulated by the specific characteristics of the continental slope. In terms of species diversity, comparisons between studies are difficult due to differences in the fishing gear used. However, it seems that our results show a slightly lower number of species than the Atlantic studies in the open sea area  (García-Seoane et al. , 2021). Some limitations that could affect the interpretation of the results must be considered. In fact, in this study, we used data obtained in the fall over 17 years. Although the effect of time cannot be excluded, having a long data set allowed us to obtain a longitudinal picture of the composition of this community. Since the data for this study were only obtained at night, the definition of deep-pelagic fish community structure presented here cannot be extrapolated to daytime.
Mass Spectrometry The intact molecular weight analyses were performed using a MAXIS II electrospray mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany). The samples were first diluted to 0. 1 mg/ml in MilliQ water. The diluted samples were applied to an AdvanceBio Desalting-RP column (Agilent Technologies) followed by washing and elution from the column running an acetonitrile linear gradient and introduced to the electrospray source with a flow of 400 ml/min by an Ultimate 3000 LC system (Dionex). Data analysis was performed with DataAnalysis version 4. 3 (Bruker Daltonik GmbH, Bremen, DE). The molecular weight was calculated by deconvolution of the raw data in the range 20 to 60 kDa. The lipase sequence contains one N-glycosylation site (Asn-Xaa-Thr, sequence motif) that is glycosylated with a high mannose type glycan structure.
Small-Angle X-ray Scattering SAXS experiments were performed on the BioXolver L (Xenocs) placed at Department of Drug Design and Pharmacology, at the University of Copenhagen. During the measurements, the instrument was equipped with a 250 W liquid gallium alloy X-ray source (MetalJet) with a wavelength of k ¼ 1:34 Å. The scattering data were collected at 298 K in 20 x 30 s exposures with a sampledetector distance of d ¼ 654 mm. This provided a q-range of 0:011 À 0:50 Å À1 , with q defined as q ¼ 4p=k sinðhÞ, where 2h is the scattering angle. Data was averaged using RAW  [19]  and absolute scale calibrated using water as a reference. Pair distance distribution functions, pðrÞ, were obtained by indirect Fourier transformation performed using BayesApp  [20].
CHU de Bordeaux, Hôpital Pellegrin, Place Amélie Raba Léon, 33076 Bordeaux Cedex, France. Email: marine. cargou@chu-bordeaux. fr K E Y W O R D S HLA, HLA-C*12:384, novel allele, sequencing-based typing We report here a novel HLA-C*12 allele, now named HLA-C*12:384 that carries one nucleotide substitution in exon 2 when compared to the HLA-C*12:03:01:02 allele, identified in a volunteer bone marrow donor. The HLA typing was performed using Next Generation Sequencing (AllType NGS, One Lambda, Canoga Park, CA) on the Ion S5 system platform (ThermoFisher Scientific, Waltham, MA),  1  from exons 1 to 8. The reads were analyzed using the TypeStream Visual Software version 3. 0 (One Lambda). 1 ). This nucleotide change was confirmed using other NGS reagents provided by GenDX NGSgo-MX6-1 (Utrecht, Netherlands) run on the Illumina MiSeq system (San Diego, CA) and analyzed with the NGSEngine software (GenDX, version 2.
Indeed, the IPD-IMGT/HLA Database 3. 52. 0 release describes only a few other HLA-C alleles displaying an AAT sequence at codon 42. The analysis of the localization of this amino-acid and its antibody accessibility with the pHLA3D database  4   subject to the conditions stated in the most recent Nomenclature Report, 5 names will be assigned to new sequences as they are identified. Lists of such new names will be published in the following WHO Nomenclature Report.
indicated that this amino-acid is located into the peptide binding groove. As such this could have an importance in both allogeneic hematopoietic stem cell transplantation and solid organ transplantation. The coding nucleotide sequence of the new allele has been submitted to the GenBank database (Accession No. OQ606806) and to the IPD-IMGT/HLA Database (Submission No. HWS10065701). The name C*12:384 has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System in March 2023. This follows the agreed policy that, : 03:01:02 GC TCC CAC TCC ATG AGG TAT TTC TAC ACC GCC GTG TCC CGG CCC GGC CGC GGA GAG CCC CGC TTC ATC GCA GTG : 03:01:02 GGC TAC GTG GAC GAC ACG CAG TTC GTG CGG TTC GAC AGC GAC GCC GCG AGT CCA AGA GGG GAG CCG CGG GCG CCG : 03:01:02 TGG GTG GAG CAG GAG GGG CCG GAG TAT TGG GAC CGG GAG ACA CAG AAG TAC AAG CGC CAG GCA CAG GCT GAC CGA : 03:01:02 GTG AGC CTG CGG AAC CTG CGC GGC TAC TAC AAC CAG AGC GAG GCC G C*l2: 123 ----C*l2 : 384 ----F I G U R E 1 Alignment of sequences of exon 2 of HLA-C*12:384 and C*12:123 with the sequence of C*12:03:01:02.
CCC indicated that this amino-acid is located into the peptide binding groove. As such this could have an importance in both allogeneic hematopoietic stem cell transplantation and solid organ transplantation. The coding nucleotide sequence of the new allele has been submitted to the GenBank database (Accession No. OQ606806) and to the IPD-IMGT/HLA Database (Submission No. HWS10065701). The name C*12:384 has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System in March 2023. This follows the agreed policy that, : 03:01:02 GC TCC CAC TCC ATG AGG TAT TTC TAC ACC GCC GTG TCC CGG CCC GGC CGC GGA GAG CCC CGC TTC ATC GCA GTG : 03:01:02 GGC TAC GTG GAC GAC ACG CAG TTC GTG CGG TTC GAC AGC GAC GCC GCG AGT CCA AGA GGG GAG CCG CGG GCG CCG : 03:01:02 TGG GTG GAG CAG GAG GGG CCG GAG TAT TGG GAC CGG GAG ACA CAG AAG TAC AAG CGC CAG GCA CAG GCT GAC CGA : 03:01:02 GTG AGC CTG CGG AAC CTG CGC GGC TAC TAC AAC CAG AGC GAG GCC G C*l2: 123 ----C*l2 : 384 ----F I G U R E 1 Alignment of sequences of exon 2 of HLA-C*12:384 and C*12:123 with the sequence of C*12:03:01:02.
Analysis EEG was obtained using 16 electrodes (Fz/3/4/7/8, Cz, Fc5/6, T7/8, Pz/3/4/7/8, Oz) and evaluated independently by MV and SN. For quantitative analysis, we used MATLAB with EEGLAB  [9]  and MNE-Python  [10]. The first (last) two seconds of recordings were discarded and the adjacent first (last) 10 s were analyzed (reported TD-episodes lasted between 5 and 15 s in our experiments and recordings were stopped after reorientation). Two-by-two comparisons were made for section (start/end) * type of recording (with/ without TD) in the frequency and spatial domain (power spectra averaged across electrodes, topographical power maps for frequency bands). Statistical significance was assessed by cluster permutation tests.
, 2016; Kwiatkowski et al. , 2020; Jiang et al. , 2023a). The number and quality of ocean fCO 2 , A T , C T and pH measurements has increased substantially over the past few decades. Quality-controlled observations are now regularly assembled in global data syntheses such as SOCAT (Surface Ocean CO 2 Atlas,  Pfeil et al. , 2013; Bakker et al. , 2014 Bakker et al. , , 2016) )  and GLODAP (Global Ocean Data Analysis Project,  Key et al. , 2004; Olsen et al. , 2016 Olsen et al. , , 2019 Olsen et al. , , 2020;; Lauvset et al. , 2021). These datasets allow evaluation of properties trends in the global ocean, including the change of the ocean CO 2 sink (e. g. Wanninkhof et al. , 2013; Friedlingstein et al. , 2022; Watson et al. , 2020) , anthropogenic CO 2 inventories (e. g. Sabine et al. , 2004; Khatiwala et al.
, , 2016)). The last version in 2023 includes more than 40 million fCO 2 data with accuracy better than 5 µatm  (Bakker et al. , 2023). One important product from SOCAT is the use of data to estimate global air-sea CO 2 fluxes based on reconstructed pCO 2 fields (e. g. SOCOM project, Surface Ocean pCO 2 Mapping Intercomparison,  Rödenbeck et al. , 2015). Since 2015, these results are included each year for the global carbon budget  (Le Quere et al. , 2015; Friedlingstein et al. , 2022). On the other hand, following WOCE/JGOFS era in the 90s when almost all observations were started to be synthetized in a specific recommended format  (Joyce and Corry, 1994) , GLODAP focusses on water-column carbon observations (and other properties). Following the original GLODAP data-product  (Key et al. , 2004) , the project accumulated many new quality controlled observations.