dataesr/scientific-paragraphs-categorization huggingface dataset:
Image analysis Repeatability of automatic vessel density measurements in non-averaged images. We first analysed the repeatability of automatic VD measurements obtained using AngioVue OCTA software in non-averaged images. All non-averaged images were analysed separately. The intra-class correlation coefficient (ICC) between the 5 automatic VD measurements obtained in a same area (3x3 or 6x6 mm) and a same slab (SVP, DVC or full retina slab) in one eye was calculated. After analysing the overall ICC results, the ICC results were compared between patients and controls, scan areas (3x3mm or 6x6mm), and slabs (SVP, DVC or full retina slab). We also analysed the correlation between the Signal Strength Index (SSI, a quality index given by the Optovue HD device for each image) and the automatic VD measurement in the SVP and DVC, using the Spearman correlation coefficient.
All the statistical analyses were performed using R software. Impact of averaging on capillary detection. The aim of this study was to assess the change in vessel detection between non-averaged and averaged OCTA images. The original non-averaged images and the averaged images were compared using both quantitative and qualitative approaches. First, we performed a quantitative analysis of the change in capillary detection in the averaged images by comparing the pixel measurement between the single non-averaged images and the averaged images using ImageJ software. The methodology for this analysis is shown in Fig 2. We first constructed a pixel-by-pixel subtraction of a single non-averaged image (the first non-averaged image acquired) from the averaged image, that we called A-S (average-single).
Discussion In this study, we aimed to assess quantitatively the repeatability of automatic VD measurements and the impact of averaging on image quality and VD measures, but also to qualitatively and accurately analyse the impact of averaging on vessel and capillary segment visualisation. Our results confirmed that averaging allowed an overall enhancement of image quality by reducing the background noise and increasing vessel continuity, without any loss of visualisation of the vessel segments on the OCTA images both in healthy eyes and, in a greater proportion, in eyes with macular oedema. First, we confirmed the good repeatability of automatic VD measurements using AngioVue software (mean overall ICC: 0. 924) between 5 sequential non-averaged images. Interestingly, the repeatability was higher in healthy control eyes than in eyes with macular oedema (p < 0.
Breast cancer (BC) treatments induce vitamin D (VD) insufficiency and bone metabolism changes, resulting in osteoporosis and skeletal morbidity risk. We report the results of a bicentric phase II trial (ClinicalTrials. gov Identifier: NCT04091178) on the safety and efficacy of high-dose oral VD supplementation for VD deficiency correction in 44 patients with early BC treated with adjuvant chemotherapies. Patients received one dose of 100,000 IU 25-OH VD every 3 weeks from day 1 of cycle 1 to day 1 of cycle 5. The primary endpoint was the percentage of patients achieving serum 25-OH VD concentration normalization on day 1 of cycle 6 (D1C6). Secondary endpoints were safety, VD and calcium parameters at baseline and during chemotherapy, and identification of predictive biomarkers of VD normalization on D1C6.
In this study, two types of plasmids were used. Gene disruption-carrying cassettes were constructed using pCR-Blunt II TOPO vector (Invitrogen, Carlsbad, CA), whereas plasmids used for gene overexpression were based on a JMP62 plasmid  19. All genomic sequences were retrieved from GRYC database (http:// gryc. inra. fr/). The disruption cassettes were prepared as described in  27. Briefly, 1 kb fragments representing promoter (P) and terminator (T) sequences of YlHXK1 (YALI0B22308g) and YlGLK1 (YALI0E15488g) were amplified by PCR from Y. lipolytica W29 genomic DNA and subsequently fused using PCR-fusion technique. The obtained PT fragments were cloned into pCR-Blunt II TOPO vector. Resulting plasmids were then digested with I-SceI restriction enzyme and the I-SceI digested URA3ex (U) or LEU2ex (L) marker was inserted to obtain the disruption cassettes.
Bioinformatic methods. The amino acid sequences of YlHxk1, ScHxk1, ScHxk2 and K. lactis hexokinase (KlHxk1) were retrieved from the GRYC (http:// gryc. inra. fr/) and NCBI databases  45. The sequences of the hexokinases from the yeast species of the Yarrowia clade are available in Supplementary File S1; they have been predicted from the genome sequence published by Červenak et al. 29. All the amino acid sequences are numbered starting from the initiator methionine. The sequences were compared using Blastp  46  , aligned with ClustalOmega  47  and the resulting alignments were visualized using JalView 2. 11. 1. 0  48. The three-dimensional models of hexokinases were generated using SWISS-MODEL  30  and visualized with PyMOL (The PyMOL Molecular Graphics System, Version 2. 4. 0, Schrödinger, LLC).
A phylogenetic tree for hexokinases was constructed by using the Maximum Likelihood method with the JTT matrix-based model. A discrete Gamma distribution was used to model evolutionary rate differences among sites (4 categories (+G, parameter = 0. 9178)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 14. 63% sites). All positions containing gaps were removed. There were a total of 484 positions in the final dataset. Bootstrap values were calculated from 100 replicates. Evolutionary analyses were conducted in MEGA7  49. The potential NLS in YlHxk1 were predicted using SeqNLS  41.
Acknowledgements Authors would like to thank  Prof. Carlos Gancedo  for valuable discussions. PH acknowledges funding from  Wroclaw University of Environmental and Life Sciences ("Innowacyjny Doktorat ", Grant no. D220/0006/17 ); PS received the financial support in the form of " BioTechNan-Interdisciplinary Environmental Doctoral Program KNOW in the field of Biotechnology and Nanotechnology ". The project is co-financed by the  European Union-European Social Fund -3. 2Doctoral studies of the  Operational Program Knowledge, Education  and Development 2014-2020. The publication is financed under the Leading Research Groups support project from the subsidy increased for the period 2020-2025 in the amount of 2% of the subsidy referred to Art. 387 (3) of the Law of 20 July 2018 on Higher Education and Science, obtained in 2019.
QUANTIFICATION AND STATISTICAL ANALYSIS This section of the protocol offers indications for analyzing DNA content in comet tails (Figure  7 ). Use the following steps to analyze comet assay images using CometScore 2. 0, free software that has the ability to automatically score comets. 1. Launch CometScore 2. 0 and import an image to analyze. 2. Determine the cutoff to correct the background. Comet assay images often do not have uniform background illumination. Using a cutoff is useful for finding comet shapes but will not affect the calculation of measured parameters. 3. Score comets using one out of the three techniques (manual scoring, semi-automated scoring or completely automated scoring) according to the basal level of damages in the tissue of interest. For example, the colon being a tissue with a high background level of damages, we used the manual scoring technique.
ACKNOWLEDGMENTS This investigation was supported by grants from the  Swedish Cancer Society  ( CAN 2017/315  and  20 0699 PjF ), the  Swedish Research Council  ( 2018-02521 ), the  Kempestiftelserna  ( JCK-1826 ), the  Cancer Research Foundation in Northern Sweden  ( AMP20-993  and  AMP 17-884 ), and  Umea ˚University  (to T. F. ). We acknowledge the  Biochemical Imaging Center Umea ˚(BICU)  at  Umea  ˚University and the  National Microscopy Infrastructure (NMI)  ( VR-RFI 2016-00968 ) for providing assistance with microscopy.
Bioinformatics analysis of the metagenomics datasets The obtained reads were trimmed by quality in 5 ′ and 3' and filtered by mean quality using PRINSEQ (v 0. 20. 4) with a PHRED quality score of 20  (Schmieder and Edwards, 2011). The good quality reads were used to produce de-novo assemblies with Megahit version 1. 1. 1  (Li et al. , 2016). A taxonomic classification of the obtained contigs was performed by running Diamond (v 0. 9. 6)  (Buchfink et al. , 2015)  against the non-redundant protein database (release April 2019) and using the LCA algorithm from Megan 6 (v6. 11. 7)  (Huson et al. , 2007)  to visualise the classification of each contig. A taxonomic classification at the read level was also performed using Diamond (blastx + LCA, using the output format 102).
org)(database release 2019-03-18)  (Mokrejs et al. , 2010). We further queried both the entire 5 ′ UTR and the 5' UTR at 100bp intervals using parameter setting as indicated in  (Asnani et al. , 2015)  and in cases where this was unsuccessful, of the short regions of sequence identified in the output of IRESite. Amino acid sequences of the P1 region were aligned using MAFFT (v7. 2. 50) integrated in Geneious Prime (www. geneious. com) with the E-INS-I algorithm  (Katoh and Standley, 2013). Gaps and ambiguously aligned regions were stripped using trimAL v1. 4  (Capella-Gutierrez et al. , 2009). The most appropriate amino acid substitution model was then determined (here, LG + F + I + G4), and maximum likelihood trees were estimated using IQ-TREE (http://iqtree. cibiv. univie. ac. at/) with 1000 ultrafast bootstraps  (Nguyen et al.
Prevalence of four novel picornaviruses RNA was extracted from the 107 fresh faecal samples using QIAamp Fast DNA Stool mini kit (Qiagen), followed by cDNA synthesis using the High-Capacity cDNA Reverse Transcription (Applied Biosystems). Custom primers were designed for each of the four picornaviruses revealed (Table  S1 ). For Sphenifaro and Sphenigellan viruses a nested PCR approach was employed, and for Sphenimaju and Sphenilena viruses a semi-nested approach was employed. Detailed reaction methods are presented in the Supplemental Methods. Viral prevalence was calculated using the bioconf() package and statistically evaluated using a Chi squared test in R v 3. 5. 3 integrated into RStudio 1. 1. 463.
The 5 ′ UTR of Spenigellan virus shared 73% nt identity to Phopivirus (Hepatovirus B, GenBank accession number KR703607) over 51% of the query sequence (275 nt; positions 199 to 474) and further matched a number of other hepatoviruses. The 5 ′ UTR of Sphenimaju virus shared 73% nt identity to Red-crowned Crane Picornavirus yc-2 (Gruhelivirus A, GenBank accession number KY312541) over 64% of the query sequence (403 nt; positions 4 to 407) and further matched a number of other hepatoviruses. IRESsite, but not blastn, found a short match of the 5 ′ UTR of Sphenilena virus to Avian encephalomyelitis virus (positions 58 to 69 and 39-50). The 3 ′ UTR of Sphenifaro virus, Sphenigellan virus, Sphenimaju virus and Sphenilena virus was 84 bp, 317 bp, 221 bp and 7 bp, respectively. We identified a polyA tail at the 3 ′ end of the UTR in Sphenigellan and Sphenimaju virus but we did not identify the entire 3 ′ UTR for either Sphenifaro or Sphenilena virus.
As Ross virus is incomplete, no percentage identity values were calculated for the 2C. Similarly, Amundsen virus is incomplete and does not have sequence associated with any of the relevant peptides it is not included in panel B. Accession numbers for viruses included in B include: MT025069 Scott virus, MT025070 Ross virus, MT025064 and MT025063 Amundsen virus, KY312540 Crahelivirus A, KY312541 Gruhelivirus A, NC_003990 Tremovirus A, NC_001489 Hepatovirus A, KX156153 Rohelivirus A, MG600068 Fipivirus A. through PCR screening we showed that over 20% of the sampled penguins in this study were shedding at least one of these viruses, and in many individuals we co-detected two or three of these new viruses. The viruses revealed here, in addition to novel viruses from Antarctic penguins likely constitute a number of novel genera.
Juliette Hayer: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writingoriginal draft, Writingreview & editing, Supervision. Michelle Wille: Formal analysis, Investigation, Data curation, Visualization, Writingoriginal draft, Writingreview & editing. Alejandro Font: Investigation, Project administration, Resources. Marcelo González-Aravena: Project administration, Resources, Writingreview & editing. Helene Norder: Methodology, Investigation, Writingreview & editing, Resources, Supervision. Maja Malmberg: Conceptualization, Methodology, Formal analysis, Investigation, Writingoriginal draft, Writingreview & editing, Supervision. Acknowledgments Financial support was obtained from the DEANN project, Marie Curie Action funded by the European Commission -Grant agreement ID: 612583 and The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, Formas (grant number 221-2012-586).
During storm runoff events, the FRNAPH concentrations only showed a significant correlation with conductivity, turbidity and water discharge. Despite the uncertainty of the predictions, multi parameter regression models using hydro-meteorological variables were suitable to predict log transformed FRNAPHs' concentrations at low flows with a standard error of 0. 46. Model performance using the storm runoff events dataset was low. This study highlights different driving factors at low flows and during storm runoff events, and the need to measure at both time scales to better understand phage transport dynamics in surface water.
This study aimed at deciphering the interplay between FRNAPHs and suspended solids in the water column in relation to hydro-meteorological drivers and in contrasting hydrological conditions, i. e. , at low flow and during storm runoff events. The FRNAPH concentrations and genogroup distribution were measured in the Orne River (France) during two years at weekly intervals, and during four storm runoff events. The FRNAPH concentrations and the genogroups at different depths of a riverbank sediment core were also examined. The data collected were used to determine (i) whether FRNAPHs were transported in association with suspended solids, (ii) whether the FRNAPH and suspended solids transport was controlled by the same hydro-meteorological drivers, (iii) whether those drivers were different at low flow and during storm runoff events and, finally, (iv) whether it was possible to predict the FRNAPH concentrations from hydro-meteorological variables.
Experimental Set-Up and Sampling Water level was measured every 15 min at the Beth site using a bubble sensor OTT-CBS (OTT HydroMet, Kempten, Germany). Data were automatically sent to a local server using an autonomous station, DuoSens (OTT HydroMet, Kempten, Germany). Water discharge was calculated using gauging data obtained with an acoustic Doppler current profiler (Streampro ADCP; Teledyne Marine, Daytona Beach, FL, USA). A turbidity meter (YSI 600 OMS; YSI Inc. /Xylem Inc. , Yellow Springs, OH, USA) measuring turbidity, water temperature and conductivity was installed at the same site from October 2016. Global The economy of the Orne valley relied on steelmaking (and related, such as iron mining) activities during the 20th century. As a consequence of these activities, soils, sediments and water bodies were highly contaminated with the spreading of materials enriched in Water 2021, 13, 2250 4 of 18 iron, other metallic elements and persistent organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs)  [39].
Experimental Set-Up and Sampling Water level was measured every 15 min at the Beth site using a bubble sensor OTT-CBS (OTT HydroMet, Kempten, Germany). Data were automatically sent to a local server using an autonomous station, DuoSens (OTT HydroMet, Kempten, Germany). Water discharge was calculated using gauging data obtained with an acoustic Doppler current profiler (Streampro ADCP; Teledyne Marine, Daytona Beach, FL, USA). A turbidity meter (YSI 600 OMS; YSI Inc. /Xylem Inc. , Yellow Springs, OH, USA) measuring turbidity, water temperature and conductivity was installed at the same site from October 2016. Global solar radiation, air temperature and precipitation were measured ≈40 km south-east of Beth by Météo France (station Aéroport M. N. L. ; Goin, Moselle). The total rainfall and runoff at the catchment outlet were 718 and 347 mm during the first sampled year, respectively (September 2015-August 2016).
Statistical Analysis Data were analysed with the R software, version 4. 0. 2 [46]. The laboratory uncertainty associated with the results was estimated by error propagation. The outflow from the WWTP was estimated at approximately weekly intervals (when data are available  [43] ) by dividing the outflow ammonium daily load (kg/day) by the outflow mean daily ammonium concentration (mg/L). The FRNAPH loads from the WWTP were calculated by multiplying the weekly outflows by the average FRNAPH WWTP concentration. The hysteretic relationships between suspended solids and FRNAPH concentrations with discharge were assessed qualitatively for the sampled events following the procedure described by Williams  [47]. Clockwise (positive) hysteresis can be attributed to earlier suspended solids/FRNAPH peak occurrence, either due to suspended solids/FRNAPH increase at the earlier stage of the event or to a rapid decline at the latter stages before the peak discharge.
The non-parametric Mann-Whitney test was applied to determine whether there was a significant difference in the distributions of two groups of data based upon the ranking of scores from both groups using the 'wilcox. test' function in R. A regression model to predict log-transformed FRNAPH concentrations from the values of the hydrological and meteorological variables was build using the least squares method and the R package StatDA, version 1. 7. 4  [48]. A leave-one-out cross-validation was carried out using the 'caret' R package  [49]. FRNAPH concentrations below the detection limit were not considered in both the correlation and regression analysis. Turbidity, water temperature and conductivity values for the last two events were taken from the turbidity meter.
65; when comparing the same samples collected from February to September 2017). Figure  2b  shows the bulk water FRNAPH concentrations measured during the weekly sampling, the four sampled events and the discharge of the wastewater treatment plant (WWTP). Slightly higher concentrations were measured at the WWTP than at the bulk water weekly samples, showing a minimum impact of the water discharged from the WWTP. The FRNAPH concentrations measured during the four storm runoff events fell within the values measured during the weekly sampling campaign, with the mean event concentrations decreasing as follows: November 2015, December 2018, March 2019 and February 2016 (Figure  2b ). When merging all the event measurements, the data distribution was not significantly different than the distribution of the weekly sampling data (Mann-Whitney test, p = 0.
086). The riverbank sediment core data showed that the FRNAPH concentrations were below the detection level at all depths, but some genome copies were detected at the uppermost layer (Table  S2  in the Supplementary material). Figure  2b  shows the bulk water FRNAPH concentrations measured during the weekly sampling, the four sampled events and the discharge of the wastewater treatment plant (WWTP). Slightly higher concentrations were measured at the WWTP than at the bulk water weekly samples, showing a minimum impact of the water discharged from the WWTP. The FRNAPH concentrations measured during the four storm runoff events fell within the values measured during the weekly sampling campaign, with the mean event concentrations decreasing as follows: November 2015, December 2018, March 2019 and February 2016 (Figure  2b ).
The positive and negative coefficients for the log-transformed river discharge and water temperature, respectively, indicate that the log-transformed FRNAPH concentration increased with the river discharge and decreased with the water temperature. The leave-one-out cross-validation r 2 was 0. 54. Figure  6a  presents a graphical inspection of the fit between the measured and predicted values. The residuals versus the predicted values are shown in Figure  6b , where it is possible to observe that predictions across the range of the explanatory variables were made with similar precision. The residual standard error was 0. 46. The calculation performed with the runoff event dataset (Table  3 ; Figure  6 ) was able to predict only 30% of the variance in the measured FRNAPH concentrations. None of the parameters contributed significantly to the model.
The residual standard error was of the same order of magnitude (0. 43) as it was when predicting the log-transformed FRNAPH concentrations using the weekly dataset. However, the leave-one-out cross-validation r 2 value was significantly lower (0. 12), showing a very low model performance. Table  3. Regression model to predict the log-transformed FRNAPH concentrations from the hydrological and meteorological variables using the weekly dataset (n = 60) and the runoff events dataset (n = 74); the intercept is an estimated additive parameter of the regression model that ensures that the hyperplane passes through the average of both the response and the explanatory variables; the regression coefficients determine the tilt of the hyperplane  [48] ; significant values (p < 0. 01) are in bold.
FRNAPH Controlling Factors at Low Flows and during Storm Runoff Events Different hydro-meteorological factors affected the FRNAPH concentration at two complementary time scales in the Orne River. The FRNAPH concentration measured weekly over two years correlated to water and air temperature, whereas the runoff eventbased measurements correlated with turbidity, water conductivity and river discharge (Table  2 ). Similarly, different factors contributed significantly to the regression models to predict log-transformed FRNAPH concentrations at both time scales (Table  3 ). The most significant predictions were obtained for the weekly dataset (Figure  6 ), demonstrating their capability for FRNAPH concentration modelling. However, the regression model using storm runoff data showed a rather low performance when using a leave-one-out cross-validation approach.
Conceptualisation: N. M. -C. , L. O. and C. H. ; installation of field equipment: J. -F. I. ; sampling: N. M. -C. , L. O. , C. W. , C. M. , E. M. -P. and J. -F. I. ; FRNAPH protocols and FRNAPH data acquisition: L. O. , C. W. , C. M. and C. G. ; other laboratory work: N. M. -C. , E. M. -P. and C. H. ; data treatment: N. M. -C. and L. O. ; writing-original draft preparation: N. M. -C. , L. O. and C. H. ; writing-review and editing: N. M. -C. , L. O. , C. W. , C. M. , E. M. -P. , C. G. , J. -F. I. , H. -M. C. and C. H. ; funding acquisition and project administration: C. H. and E. M. -P. All authors have read and agreed to the published version of the manuscript. Funding: This research was mainly funded by the Luxembourg National Research Fund (FNR) and the French National Research Agency (ANR-14-CE01-0019) in the framework of the FNR INTER ANR research program (contract no.
Acknowledgments: The authors acknowledge feedback from all the members of the  MOBSIED  project team (https://mobised. liec. univ-lorraine. fr). Jérôme Juilleret ,  Cyrille Taillez  and  Jérémy Lepesqueur  are acknowledged for their help with field equipment and sampling. We also acknowledge the valuable assistance from  Romain Rivet ,  Remi Leblay ,  Delphine Collard  and  Magalie Labbe  for sampling and FRNAPH and genogroup analysis; from  Laurence Mansuy-Huault ,  Hussein Kanbar ,  Allan Bauer ,  Patrick Billard  and  Benoit Losson  for carrying out the core sampling; and from  Allan Bauer ,  Renaud Gley ,  Mathieu Le Meur  and  Laurence Mansuy-Huault  for carrying out the sampling of the first and second events. Luc Manceau ,  Benoit Losson  and  Allan Bauer  are acknowledged for installing the water level sensor and generating the discharge data.