dataesr/scientific-paragraphs-categorization huggingface dataset:
Bluetooth low energy The Bluetooth Low Energy (BLE)  [32]  standard is widely used in IoT  [40]  applications to transmit sensor data in an energy-efficient way. In particular, BLE is used in video surveillance over Internet of Things (VS-IoT) devices to monitor security cameras when a movement is detected  [41]. To conduct transmission simulations over BLE channels, we use the Bluetooth Low Energy simulation with radio frequency impairments proposed in the Matlab Communication toolbox  [42]. We simulate different channel conditions by varying the Eb/No parameter. The Eb/No  In what follows, videos were encoded with this packet size limit in order to ensure that transmission was energy efficient and to maintain a constant and low header overhead. This encoding management will have an impact on error distribution within the video sequence, since an intra-coded frame requires many more packets to be delivered than an inter-predicted frame when the payload of each packet is constant, since it is less efficiently compressed in the former case.
Potential prey species were selected based on previous studies on the feeding ecology of killer whales in temperate and tropical waters (e. g. ,  Jefferson et al. , 1991; Fertl et al. , 1996; Pitman et al. , 2001 Pitman et al. , , 2003;; Visser, 2005) , particularly the Caribbean region (see  Bolaños-Jiménez et al. , 2014 , for a review), and based on availability of stable isotope data. These potential killer whale prey data were compiled from the literature (Table  1 ) or collected in the field. Short-finned pilot whale (Globicephala macrorhynchus) samples (muscle) were collected similarly to killer whale samples, off the coast of St. Vincent. Other cetaceans that were potential prey were collected from the French West Indies (Guadeloupe, 16 • 15 N, 61 • 34 W), in April 2015, 300 km north of St.
6415 • N, 61. 0242 • W) between December 2016 and February 2017, and between October 2017 and March 2018. Fishing surveys were carried out as part of an exploratory study led by the Regional Committee for Maritime Fisheries and Marine Farming. Stable isotope data from other species were gathered from the literature, including sea turtles  (Vander Zanden et al. , 2013; Tucker et al. , 2014)  and range of teleost species and oceanic sharks  (Madigan et al. , 2015 ; Table  1 ). TABLE 1 | Carbon (δ 13 C) and nitrogen (δ 15 N) isotope value (mean ± SD, ), sample sizes (number of individuals, n) and C:N ratios (mean ± SD, , when available) of killer whales (Orcinus orca) and their potential prey species in the Caribbean.
Stable Isotope Mixing Models Bayesian mass-balance stable isotope mixing models were built using the "MixSIAR" package in R  (Stock et al. , 2018)  to estimate the relative contribution of potential prey to the diet of killer whales from St. Vincent. Mixing models were run with three Markov chain Monte Carlo chains of 300,000 draws and a burn-in rate of 200,000 draws. Convergence of the models was checked using Gelmen-Rubin and Geweke diagnostics. The appropriateness of prey groups for killer whale diet and the TEF used here was evaluated by determining the likelihood that prey groups were included in a simulated killer whale mixing polygon  (Smith et al. , 2013). Mean (±SD) isotope values from muscle (tuna and other large pelagic fish, including sharks;  Madigan et al. , 2015) , mantle (oceanic cephalopods: this study), skin (sea turtles: Vander  Zanden et al.
Ecological Knowledge From Whalers Ethnographic data and information about the ecological knowledge held by Vincentian whalers were recorded during fieldwork conducted between 2008 and 2020 (under IRB oversight at Louisiana State University, University of Denver, and University of the South, United States). Throughout most of this period, four whaling boats operated from Barrouallie, each with a rotating crew of approximately twelve whalers. Seven whalers were interviewed repeatedly during this 12-year period and whaling activities were observed primarily during a 3month period of participant observation during the fall of 2009, and augmented by other regular interviews afterward. Informal interviews with whalers mainly consisted of lengthy, casual conversations, occurring during periods of downtime when the weather precluded going to sea, or with whalers who stayed ashore on days when their colleagues were at sea.
Sampling In St. Vincent, eight killer whale and 20 short-finned pilot whale samples were collected in 2015 and 2016. In addition, skin and blubber biopsy samples were also collected for three additional species as potential prey in April 2015 in the waters of Guadeloupe, in the French West Indies. These were sperm whale (Physeter macrocephalus, n = 10), Fraser's dolphin (Lagenodelphis hosei, n = 5), and pantropical spotted dolphin (Stenella attenuata, n = 29; Table  1 ). Other prey collected from fishing surveys include oceanic squids from Martinique (FWI), including neon flying squid (Ommastrephes bartramii, n = 7) and diamondback squid (Thysanoteuthis rhombus, n = 24). All potential prey data collected from the literature are provided in Table  1.
However, the magnitude of these interactions is not well documented. Predation attempts on other cetaceans could potentially be investigated from photographic records collected throughout the region, particularly on species that are resident in the Caribbean. The interpretation of stable isotope mixing models can be facilitated when patterns identified can be directly compared with other independent sources of information, such as behavioral observations  (Kiszka et al. , 2014)  or other dietary indicators such as stomach content analysis  (Jansen et al. , 2013). Interestingly, although sample size was limited to seven individuals, TEK data collected from whalers also suggest that killer whales mostly feed on odontocete cetaceans off St. Vincent and the Grenadines. Whalers base their perceptions on occasional stomach content observations when processing killer whales.
ACKNOWLEDGMENTS We would like to thank  Emma Smith , of the  University of the West Indies-Cave Hill , who assisted with storage and preparation of the samples. We would also like to thank the whaling community of  Barrouallie (St. Vincent ) for allowing us to collect cetacean tissue samples, and specifically  Diallo Boyea  for managing the collection program. We would further like to thank  Krishna Das  ( University of Liège, Belgium ) for running the stable isotope analyses for cephalopods, and  Cédric Pau  ( Regional Committee for Maritime Fisheries and Marine Farming ) for collecting the sampled squids off  Martinique. We would also like to thank  Valeria Paz  and  Tristan Normand  for preparing samples for stable isotope analysis. This is contribution # 243  of the  Coastal and Oceans Division in the Institute of Environment at Florida International University.
Vegetation monitoring Ten sampling transects, representing 125-m long ditch stretches, were selected in each of the 11 sites. In each transect, 25 0. 5 9 0. 5 m quadrats were randomly chosen each year, avoiding areas close to the banks ([ 1 m from the banks) to avoid sampling species having grown from the banks into the ditches. Sampling was performed once a year between mid-May and mid-June, when vegetation was well developed and no ditch had yet dried out. Monitoring consisted in (1) checking for the presence of vegetation in each quadrat by a single stroke of a rake in the whole water column, (2) identifying all plants to the species level following the taxonomic database taxRef v12. 0  (Gargominy et al. 2018) , and (3) visually assessing the plant species cover converted into six classes (0, \ 5%, 5-25%, 26-50%, 51-75%, and [ 123 75%).
Water quality To depict the water quality, we used data collected bimonthly by the Union des Marais de la Charente-Maritime (UNIMA) during the vegetation period (March to July) in one large representative drainage ditch of each site connected to all the sampling transects. Electrical water conductivity, pH and dissolved oxygen (expressed in % of saturation), were measured in situ in the top 30 cm of the water column using a portable electronic multi-parameter probe (WTW 3430, Thermo Fisher scientific Inc. ). Ammonium, nitrite, nitrate, total nitrogen (Kjeldahl), orthophosphate and dissolved organic carbon contents were measured in laboratory, based on water samples collected in the top 30 cm of the water column. Chlorophyll a was measured by spectrophotometry after acetone extraction. Water column turbidity was not included in the analyses because it was highly related to dissolved organic carbon and chlorophyll a content (data not shown).
Landscape characteristics The proportions of the major terrestrial land uses (meadows and crops) were measured in the whole area of each site using land-use maps provided by the E ´tablissement Public du Marais Poitevin  (EPMP 2015) , built from the agricultural database of the Institut National de l'Information Geographique et Forestiere (IGN) available at the scale of each agricultural plot  (IGN 2016). Additionally, the proportion of woodlands in each site was measured using a Rapideye remote sensing image (May 2014, 7 9 7 m resolution following  Rapinel et al. 2015). The length of the ditch network (expressed in km ha -1) was used as a measure of the quantity of aquatic habitats and of the extent of connectivity among ditches. It was measured for each site using the IGN topographical database, version 2008 (EPMP 2015) using QGIS 3.
Besides, a correlation matrix between all environmental variables was calculated (provided in Online Resource 1) including the relationships with the variables which were not implemented in the models. Models were run considering a Poisson error distribution for diversity indices, and a Gaussian error distribution for PCA scores. We tested all possible additive combinations of up to three variables from an initial set of 14 variables. Dredging and model averaging procedures were performed using the MuMin package  (Barton ´2016). Models were ranked by AICc, the Akaike information criterion corrected for small sample sizes  (Burnham and Anderson 2002) , and averaged estimates of the variables were calculated over a model selection with a cumulated weight of AICc of 0. 95. The effect of an explanatory variable was considered significant when its estimate (i.
Material and methods The EpSSG RMS2005 study was an investigatorinitiated prospective clinical trial conducted at 108 hospitals in 14 Countries (Argentina, Belgium, Brazil, Czech Republic, France, Ireland, Israel, Italy, Norway, Slovakia, Slovenia, Spain, The Netherlands, and the United Kingdom). The trial enrolled patients (aged 0e25 years) with localised RMS from October 2005 to December 2016 (EudraCT, number 2005-000217-35)  [1, 2]. Ethical approval was obtained prospectively in participating countries. Signed informed consent was obtained from each patient/parents according to national and institutional guidelines. Histological diagnosis was made by the local pathologist and reviewed by the EpSSG national and international Pathology Panel. Classification by histology was based on definitive histology.
5 Gy (range, 36. 0e55. 8). Overall, local AE nodal surgery was performed, at diagnosis or after neoadjuvant chemotherapy, for 96 of 164 patients (data missing: one case); all 36 IRS I-II and 60/128 IRS III. a One medulloblastoma, one undifferentiated sarcoma b One sudden death (cardiovascular cause) in complete remission off therapy after 2 months from the end of therapy.
This study confirms the importance of risk stratification for adapting treatment in HNnPM RMS. In addition, to better stratify patients, recent biological data have made it possible to distinguish among the non-alveolar forms of RMS, some more pejorative prognostic subtypes, in particular, those with a MyoD1 mutation, which nowadays may be considered as a high-risk tumour  [26, 27]. This study highlights the frequency of poor risk factors at diagnosis and the importance of adequate local therapy in the treatment of RMS frequently challenging in the head and neck area. This focus is continued in the future EpSSG protocol for RMS (FaR-RMS: An overarching study for children and adults with Frontline and Relapsed RhabdoMyoSarcoma; EudraCT Number: 2018-000515-24) in which there is a special emphasis on the optimisation of local treatment by investigating optimal delivery of RT, for example, dose escalation and timing of its delivery.
Statistical Analyses Data are presented as frequency (percentage), mean (AESD), or median (interquartile ranges [IQR]) when appropriate. T-test, Mann-Whitney U test, and c 2 test were used for comparisons between and within groups. There were very few missing values in the variables of interest, therefore we performed a complete case analysis without imputation. First, we fitted a univariate generalized additive model based on the Cox proportional hazards model and plotted the predicted values to visualize the dose-response relationship between predictors and the respective clinical outcomes. Based on these plots, we took the natural log for aPLA2R1ab titers. In the Cox proportional hazard model, we included serum creatinine and UPCR in a reference model. Next, we added baseline aPLA2R1ab and/or epitope spreading in the following models.
Analyses were performed with SPSS version 25 (IBM Corp. , Armonk, NY) and R with the RStudio shell (R Version 3. 5. 1, RStudio version 1. 1463), and packages foreign_0. 8-70, dplyr_0. 8. 0. 1, ggplot2_3. 1. 1, grid-Exrta_2. 3, tableone_0. 10. 0, mgcv_1. 8-24, crrstep_ 2015-2. 1, survival_2. 42-3, riskRegression_2019. 01. 29 and their dependencies. Differences were considered significant with P < 0. 05.
15 iterature data on biomarkers for patients with PMN must be interpreted with caution. Most studies were of limited size, and included patients treated and untreated with immunosuppressants. The Food and Drug Administration defines biomarkers and use the BEST (Biomarkers, EndpointS, and other Tools) glossary to define biomarker categories. 16 With respect to risk prediction, it is important to differentiate between prognostic and predictive biomarkers. A prognostic biomarker is used to identify the likelihood of a clinical event in patients with the disease. Such a biomarker thus predicts the natural course of the disease. In contrast, a predictive biomarker is used to identify individuals who are more or less likely to respond to drug therapy. Distinguishing between prognostic and predictive biomarkers can be difficult.
This was expected, as proteinuria and estimated glomerular filtration rate are components of the well-known Toronto risk score. 6 In univariate analysis, higher titers of aPLA2R1ab were associated with disease progression. However, in the proportional hazard model including UPCR and serum creatinine, aPLA2R1ab titers did not improve risk prediction. To confirm our findings, we evaluated the prognostic value of aPLA2R1ab on the risk of disease worsening of patients included in the control arm of the GEMRITUX clinical trial. The GEMRITUX data were similar to those observed in our cohort, with baseline aPLA2R1ab titers having no accurate prognostic value. Our findings showing that aPLA2R1ab titer has no added value over traditional biomarkers to predict progression seem contradictory to recent studies that also evaluated aPLA2R1ab as a prognostic biomarker.
This is in agreement with reported spontaneous remission rates of 30% to 40 %, suggesting that our clinical decision making was quite reasonable. Third, our treatment was often delayed, with treatment started >6 months after biopsy in 74% of patients. Last, the decision to start therapy was done without knowledge of aPLA2R1ab levels. One might also debate the endpoint of "nonprogressor. " In this respect, a lack of follow-up in patients who developed a spontaneous remission could pose a problem. If these patients all had a relapse, and would have received immunosuppressive therapy at the time of relapse, there would be no value of predicting prognosis. Therefore, we have gathered followup data of patients with spontaneous remission. There were 51 patients who developed spontaneous remission, with a median duration of follow-up after remission of 3.
5 [1-8] years. During this period of follow-up, only 2 patients (4%) received immunosuppressive therapy. We have confirmed our results with data from the control arm of the GEMRITUX clinical trial. Similar to CLINICAL RESEARCH Van de Logt et al. : Anti-PLA2R1 antibodies as prognostic biomarker data obtained from the original cohort, aPLA2R1ab titers had no prognostic value in the GEMRITUX patients. A limitation of this validation is obviously the small number of patients. However, these data are of clinical relevance in the individual patient. Improved outcome prediction will require a more complex model that would include repeated measurements of serum creatinine, UPCR, and aPLA2R1ab titer. Also gender and age should be incorporated into the model. Larger discovery and validation cohorts are needed to develop such risk models/calculators.
ACKNOWLEDGMENTS AL, CV, and JW acknowledge support from a grant from the  Dutch Kidney Foundation  (grant nr NSN  17PhD12 ). The work leading to these results received funding from  European Union Seventh Framework Programme   FP7/2007-2013  grant  305608 :  European Consortium for High-Throughput Research in Rare Kidney Diseases. GL and JJ acknowledge support from  CNRS , the  Fondation Maladies Rares  ( LAM-RD_20170304 ), the  National Research Agency  (grants  MNaims  ( ANR-17-CE17-0012-01 ), and Investments for the  Future IDEX UCAJedi   ANR-15-IDEX-01  and  Laboratory of Excellence SIGNALIFE, a network for innovation on signal transduction pathways in life sciences  ( ANR-11-LABX-0028-01 ) with allocated PhD fellowship for JJ), the  Fondation de la Recherche Médicale  ( DEQ20180339193  to GL and  FDT201805005509  to JJ), and the  Centre Hospitalier Universitaire de Nice  and the  Direction Générale de l'Offre de Soins of the French Ministry of Health  ( PLA2R1 autoantibodies in Membranous Nephropathy in Kidney Transplantation Programme ,  PRAM-KT PHRC2011-A01302-39 ,  NCT01897961 ).
We thank  Pierre Ronco  and  Karine Dahan  for sharing the results of the GEMRITUX study. The GEMRITUX study was funded by  Programme Hospitalier de Recherche Clinique ,  French Ministry of Health  grant  AOM10089 ;  European Research Council   ERC-2012-ADG_20120314  grant agreement  322947 ;  Agence Nationale pour la Recherche Programme Blanc   SVSE1 (2012 )  Decision grant   ANR-12-BSE1-0002-01 ;  Fondation pour la Recherche Médicale Equipe   FRM 2012  grant; and 7th  Framework Programme  of the European Community contract  2012-305608  ( European Consortium for High-Throughput Research in Rare Kidney Diseases ). The sponsor of this study was  Assistance Publique-Hôpitaux de Paris (Département de la Recherche Clinique et du Développement, Clinical Research and Development Department). Rituximab was given by  Hoffmann-La Roche.
Data Collection and Identification of Candidate Prophage Sequences in Complete Genomes of O. oeni A total of 231 publicly available genome sequences were obtained from the National Center for Biotechnology Information NCBI, https://www. ncbi. nlm. nih. gov (accessed on 2 January 2021) (Table  S1 ). Corresponding strains have been collected worldwide from different fermented beverages (red, dry and sweet white wines, sparkling wines and more recently cider and kombucha tea). Candidate prophage-like elements were identified using the RAST pipeline which provides an automated approach to phage genome annotation  [32]. As suggested by others  [33] , we performed a manual inspection of the sequences for the presence of signature sequences: attachment sites (att), gene(s) encoding integrase(s), terminases(s), transposases(s), genes coding for structural viral proteins and the sequences of prophage integration sites.
PCR Bacterial strains CRBO11105 and CRBO14210 (accession numbers LKSR01 and LKRV01) were obtained from the Centre de Ressources Biologiques Oenologiques (CRB Oeno; ISVV, Villenave d'Ornon, France). Genomic DNAs from were extracted from cultures by using the Whatman ® FTA Clone Saver card technology (Whatman, Sigma-Aldrich, France). PCR amplification reactions were achieved in a 25 µL final volume with 0. 2 µM of each primer. The Taq 5X Master Mix kit (New England, Biolabs, Evry, France) was used according to the manufacturer's recommendations. A BioRad i-Cycler was used for amplification. Bacterial DNA was introduced in the reaction mixture as 1. 2 mm FTA discs which were punched out of the center of FTA cards using a Uni-Core Punch (Qiagen Cat. No. WB100028, Whatman ® , Fisher Scientific, Schwerte, Germany) and transferred to PCR tubes.
The Genome of O. oeni Is Replete with Putative Prophages The systematic interrogation of 231 complete genomes assessed from GenBank (NCBI) resulted in the discovery of seemingly intact prophages in 134 strains of O. oeni (58% of the bacterial genomes studied) (    Lysogens originated from all types of beverages (wines, ciders, kombucha), and belonged to all four lineages described in O. oeni, including the less numerously represented phylogroups associated with cider (B and C) and kombucha (D). Lysogens mostly harbored a single prophage (64. 2%; n = 86). The 48 poly-lysogens contained two (n = 43) or three distinct prophages (n = 5) (Table  1 ). This first survey yielded a total of 187 prophages. They showed a genome length ranging from 35 kb to 46. 2 kb which, on average, and comprised between 2 and 6.
SSR of Int F and Int B Prophages During SSR, cross-activity of a recombinase of one phage with the attachment site of another is proportional to the degree of homology between their integrases and similarity between the core-and arm-type sequences in attachment sites of the respective phages  [53]. We therefore clarified the site preference pattern and sequence requirements for the SSR of Int F and Int B phages by analyzing their attP sites. To carry out the analyses, phage attachment core sequences were deduced from the attL and attR junctions within the host chromosome identified in lysogens (accessed from GenBank) and from the attP sequences from the genome of free Int B phages isolated from the enological environment  [18, 54]. For clarity, the phages belonging to the Int B group were subdivided into the Int B-F and Int B-B sub-groups, depending on the attachment site targeted in the bacterial chromosome (attB B or attB F ).
Even though the SSR unit of IntE phages has some peculiarities, the latter have not evolved completely independently. As seen in Figure  6 , IntE phages are closely related to the cos phages, together with IntC and IntF prophages. Corresponding genomes in this cluster are mosaics, whereby individual phages are constructed as assemblages of modules, many of which are single genes (result not shown). Of note, this cluster harbors prophages which are integrated on the same replichore in the chromosome of O. oeni. In contrast, the cluster corresponding to IntD prophages is less diverse and members share relatively few genes with the other cluster. Figure  6. Phylogenomic Genome BLAST Distance Phylogeny (GBDP) tree of representative oenophages from the CRB Oeno Collection. The tree was generated by VICTOR and visualized with Fig Tree  [36].
The cell suspension was recovered from the Medicon unit with a 5-mL disposable syringe (the chamber was rinsed twice to increase yield) and was filtered (70 µm Filcon ™ disposable filter device) and washed twice with RPMI 10% FCS. Cells were next split into three Eppendorf tubes and used for flow cytometry. Antibodies were mixed according to the 2 multicolor flow cytometry panels (P1 and P2) at the concentration advised by the manufacturers in PBS 10% human serum (Supplementary Figure  1  and Table  S1 ) plus viability dye (Fixable viability dye-eFluor 506, eBiosciences). Cells were stained for 30 minutes at 4°C (with P1, P2 Ab mixes or PBS) washed twice in PBS/EDTA and acquired using BD LSR fortessa, flow rate calibration method was used to calculate absolute cell numbers. Data were analyzed using FlowJo software (V10, Tree Star).