dataesr/scientific-paragraphs-categorization huggingface dataset:
As body composition and chemotherapy tolerance are closely linked, patient selection should be improved to enhance chemotherapy results. This could be particularly applicable in clinical practice and in therapeutic decision making, where an integrated approach could yield measurable results. Further, better patient selection would allow a more tailored treatment and provide a wider data set for patients under-represented in clinical trials. Better patient selection may also decrease impact of overtreatment and serious adverse events such as hospitalizations related to severe toxicity and disability. Finally, this study will also assess the feasibility of DXA scanning in elderly cancer patients and could change our frailty geriatric approach in older cancer patients by adding a new predictive biomarker of negative health outcomes.
Abstract One consequence of the intensive use of glyphosate is the contamination of rivers by the active substance and its metabolites aminomethyl phosphonic acid (AMPA) and sarcosine, inducing river eutrophication. Biofilms are the predominant lifestyle for microorganisms in rivers, providing pivotal roles in ecosystem functioning and pollutant removal. The persistence of glyphosate in these ecosystems is suspected to be mostly influenced by microbial biodegradation processes. The present study aimed to investigate the tripartite relationship among biofilms, phosphorus and glyphosate in rivers. The first part consists of a co-occurrence analysis among glyphosate, AMPA and phosphorus using an extensive dataset of measurements  (n = 56198)  from French surface waters between 2013 and 2017. The second part investigated the capacity of natural river biofilms to dissipate glyphosate, depending on phosphorus availability and the exposure history of the biofilm, in a microcosm study.
g. , phosphonate activation and C-P bound cleavage). The genes encoding the corresponding enzymes are assembled into the phn operon, which is widespread among bacterial species. To date, the effect of phosphorus on glyphosate biodegradation has mostly been studied for isolated microbial strains  (Krzysko-Lupicka et al. , 2015; McMullan and Quinn, 1994)  rather than for natural biofilm communities  (Klátyik et al. , 2017). The present study investigates the tripartite relationship among biofilms, phosphorus, and glyphosate in rivers. The first part of the study consists of the analysis of an extensive dataset of glyphosate, AMPA and total dissolved phosphorus concentrations recorded in French surface waters between 2013 and 2017 (n = 56198). The co-occurrence, seasonality and influence of trophic status on glyphosate and AMPA fate in rivers were investigated.
Meta-analysis of glyphosate, AMPA and total phosphorus concentrations in surface waters from France Data on glyphosate, AMPA and total phosphorus concentrations in surface waters in France were downloaded on 2018/02/16 from the NAIADES public database available at http://www. naiades. eaufrance. fr/acces-donnees#/physicochimie (NAIADES, 2018). This database contains the results of analyses performed by water agencies and environmental consultancies, with a total of 4733 (glyphosate), 4716 (AMPA) and 9326 (total phosphorus) sites located throughout France (excluding French overseas departments and territories). Within this huge database, the research criteria imposed for data selection in our study were as follows: time period: "2013/01/01 -2017/12/31"; data qualification: "correct" (i. e. result of the water analysis validated by the freshwater agencies); and fraction: "raw water".
Valid data obtained separately for each parameter (glyphosate samples N = 72298, AMPA N = 72277, total phosphorus N = 215462) were then grouped together (N = 56198) and filtered again by cases in which quantification of each compound was > average of their corresponding limit of quantification (LQ). This resulted in N = 31041 for glyphosate (LQ = 0. 03 µg L -1 ), N = 45552 for AMPA (LQ = 0. 02 µg L -1 ) and N = 199833 for total phosphorus (LQ = 0. 01 mg P L -1 ). Co-occurrence analysis was therefore performed in cases where three compounds were detected at the same station AND on the same date. The quantification of glyphosate and AMPA was also classified by trophic status (according to the concentration of total phosphorus in surface water, µg P L -1 ): oligotrophic (< 25, N = 1018), mesotrophic (25 -75, N = 6988), eutrophic (75 -100, N = 3147) and hypereutrophic (> 100, N = 11670).
The temporal evolution of glyphosate, AMPA and total phosphorus concentrations was assessed on a monthly basis from January 2013 to August 2017. The average concentration of each parameter was calculated for each month independent of the station (N = 56). The relationship between the AMPA/glyphosate ratio and the concentration of total phosphorus was assessed for a monthly averaged dataset using Spearman's rank correlation test ρ (P < 0. 05).
Modelling glyphosate dissipation and AMPA formation in microcosms The dissipation kinetics of glyphosate in water were fitted using OriginPro 2016 software (Origin Lab Corporation, USA) to a simple first-order exponential model (Exp2Mod1), characterized by the following equation: = (Equation 1) where t is the incubation time, Ct the glyphosate concentration at time t, C0 the initial concentration of glyphosate and k the rate constant in day -1. DT50 is the time required for the concentration to decline to 50% of the initial value. The kinetics of AMPA formation in water were fitted with the sigmoid function (Boltzmann) of OriginPro 2016, characterized by the following equation: = - 1 + ( ) + (Equation 2) where t is the incubation time, Ct the AMPA concentration at time t, C0 the initial concentration of AMPA, Cf the final concentration of AMPA, k the rate constant in day -1 and h50 the time required for the AMPA concentration to reach 50% of (Cf -C0).
Statistical analyses Statistical analyses were carried out using OriginPro 2016. The differences in various parameters linked to glyphosate dissipation kinetics (C0 and DT50), AMPA formation kinetics (Cf), glyphosate and AMPA content in biofilms, phosphorus uptake (Puptake), cell abundance, phosphorus content and phosphatase activity were assessed using three-way ANOVA followed by separate post hoc comparisons (Tukey's test, P < 0. 05). The three factors tested were site (Ups vs. Dws), phosphorous in water (LowP vs. HighP) and glyphosate initial concentration in water (LowG vs. HighG). The normality and homogeneity of variance were checked prior to ANOVA analysis (Kolmogorov-Smirnov's and Levene's tests, respectively, P < 0. 05) and data that were not normally distributed were transformed using logarithmic functions.
Concentrations of glyphosate, AMPA and phosphorus in French surface waters between 2013 and 2017 The analysis performed on the public database revealed that phosphorus concentrations are relatively high (mean of 0. 1 mg P L -1 , maximum 18 mg P L -1 ), corresponding to sites with high levels of eutrophication. Phosphorus was detected in all the samples analysed (N = 215462) and was properly quantified (LQ = 0. 01 mg P L -1 ) in 93% of the samples. In parallel, glyphosate and AMPA were also detected in almost all water samples (99. 9% each). Our analysis was specifically focused on samples containing at least one of the analytes above its LQ. The Venn diagram showed that co-occurrence (i. e. , when compounds were quantified at the same station AND on the same date) was mainly found for glyphosate -AMPAphosphorus (42.
Numerical implementation and discussion Numerical implementations of the previous relations have been performed through Matlab programs. These implementations aim at plotting the temporal derivative of the effective index ( The other parameters are the initial value of the height of the guiding layer 2ℎ (=0) = 1µ, the height of the first upper cladding 2 (=0) = 0. 4µ and the set of indices ( 1 ;  2 ;  3 ;  4 ) = (2; 1. 8; 1; 1. 5). The plot figure  4  clearly yields that the ratios between the different values of    is equal to the ratio between the corresponding ℎ  , which is easily predictable from equation  (17). Concerning the values of the times at which the mode disappear, their ratio are equal to the inverse of the ratio between the corresponding ℎ. In addition, a simulation under COMSOL on a specific structure corroborates this slope, this time considering two close structures to calculate the rate of change n eff (Fig.
The simulations have thus to be performed at every time step which take a great amount of time, whereas the proposed TDO formulation (eqs. (  8 ), (  17 ) and (  23 )) can be implemented on a regular computer via Matlab or Python for example, and give results within a few seconds. Furthermore, in a theoretical view, an extended and comprehensive study could be performed in order to determine the overall dynamics of the effective propagation constants in case of the temporal variation of both the above-mentioned layers at the same time. By considering the temporal variation of both 2h(t) and 2e(t) in the 4-layer family waveguides, the time-derivation of equations (  10 ) and (  18 ) should lead to a system of two coupled equations whose resolution should be equivalent to the displacement associated with a set of 2D dispersion maps vision instead of a set of 1D dispersion curves.
1 µ. -1 ; -0. 5 µ. -1 ; -1 µ. -1. The other parameters are 2ℎ (=0) = 1 µ, 2  = 0. 2 µ and ( 1 ;  2 ;  3 ;  4 ) = (2; 1. 8; 1; 1. 5). For example, by TDO    23 )) and for different sets of indices :( 1 ;  2 ;  3 ;  4 ) = (2; 1. 5; 1; 1) ; (2; 1. 8; 1; 1. 5) ; (2; 1. 8; 1. 5; 1. 5). The other parameters are 2ℎ  = 0. 4 µ, 2 (=0) = 1 µ and   = -0. 1 µ. -1. For example, by TDO formulation, the slope at t = 4s (or 2e = 0. 2μm) is respectively ∆  ≡0. 007 and 0. 048 for both cones of light. 1 : Compared results of the variation of the eigenvalues n eff with our variational method based on Time derivation Operator (TDO), that is eq. (  17 ) and Fig. 4 , and COMSOL software. As an example, this opto-geometric guiding structures is chosen at fixed parameters: optical indices (n 1 ; n 2 ; n 3 ; n 4 ) = (2; 1.
All STXM data analysis was 274 performed using aXis2000 (http://unicorn. mcmaster. ca/aXis2000. html). 275 In this work, in contrast to typical XMCD measurements in materials science, the intrinsic 276 magnetic properties of a sample are measured without using any applied magnetic field. There 277 are strict geometric requirements to measure magnetization by XMCD. As outlined in Fig. 1 , the 278 magnitude of the XMCD signal is determined by the dot product of the magnetization vector of 279 the sample and the polarization vector of the photon. The polarization vector of circularly 280 polarized photons points either forward (CR) or backward (CL) along the X-ray propagation 281 direction. In order to have a measurable XMCD signal there must be a non-zero projection of the 282 magnetization vector of the sample along the photon polarization.
Data analysis -particle size distributions from statistical evaluation of TEM images 294 The average number of particles and their typical size in cells of the glutaraldehyde-fixed 295 Batch B samples were determined by TEM. Individual cells were randomly selected on the grids 296 and imaged at 40,000x -60,000x magnification. This study was performed after the STXM 297 measurements, to avoid damaging the samples with high magnification TEM. 20 cells were 298 imaged per sample, and a minimum of N=18 cells in each sample were subjected to detailed 299 image analysis. Particles were counted and measured manually. Large variations in the contrast 300 of the particles (size dependency and inhomogeneous background) made it difficult to use an 301 automated procedure. The oval selection tool of the ImageJ software was used to draw a shape 302 that best matched the projection of each particle in the focal plane.
Acknowledgements The authors are grateful to Faivre A. , Raffin J. , Saunier A. and Gay L. for their help in growth measurements and biochemical analyses. Many thanks to  Dumas P. -J. for seed collection in the Marseille area. This study was funded by the  French Research National Agency  ( ANR Marséco 2008 CESA 018 ), and financially supported by the  National Innovative Cluster on Risks Management. Many thanks to the  Calanques National Park , and particularly to Le Mire-Pécheux L. , for advice and authorization for seed and soil harvesting, and to  Michael Paul  for English proofreading of the paper.
Evidence of the mediation of glycan molecules in the interaction between viruses and their hosts is accumulating and is now partially reflected in several online databases. Bioinformatics provides convenient and efficient means of searching, visualizing, comparing, and sometimes predicting, interactions in numerous and diverse molecular biology applications related to the -omics fields. As viromics is gaining momentum, bioinformatics support is increasingly needed. We propose a survey of the current resources for searching, visualizing, comparing, and possibly predicting host-virus interactions that integrate the presence and role of glycans. To the best of our knowledge, we have mapped the specialized and general-purpose databases with the appropriate focus. With an illustration of their potential usage, we also discuss the strong and weak points of the current bioinformatics landscape in the context of understanding viral infection and the immune response to it.
This effect is referred as to the glycan shield and the best example of this feature are the HIV virus, known to have the highest density of glycans attached to its surface proteins  [12] , and the Lassa virus  [13]. All of these functions are critical for viral replication of most pathogenic viruses and could be exploited to create antiviral compounds  [14]. Nonetheless this remains a challenge because of the complexity of these interactions involving diverse host-virus hybrid molecules. The cellular machinery produces numerous sugars that can be combined into a broad variety of glycosylation types, allowing an extended heterogeneity of virion surface molecules. Studying such multifaceted interplay between glycans and viruses results in complex data that cannot be thoroughly understood without databases and bioinformatics  [15].
The interpretation of large-scale analyses depends on comparative and predictive approaches in which biological databases play a crucial role. This emphasizes the importance of collecting, storing, curating, and making these data searchable online. Next, crosslinking the various specialized databases brings the user a broader view to grasp the complexity of multi-organism interactions. In the present review, we suggest that bioinformatics and databases are key to harnessing the potential for new knowledge discovery in virus and vaccine research. We also show that integrating complementary information from multiple sources is not straightforward.
Overview A handful of bioinformatics resources include the relevant pieces of information that can be put together to study glycan-virus interactions at different stages of infection. To follow the outline of a recent review highlighting the roles of glycosylation in virus biology  [16] , we have mapped the corresponding databases as shown in Figure  1. Overall, these roles pertain to immunity, attachment, entry, and exit, as well as glycoprotein synthesis. ViralZone  [17]  is a web resource that bridges sequence data to virus knowledge thereby providing the means of exploring the diversity of viruses. It links to viral proteome sequences contained in UniProtKB  [18] , the Universal Protein Knowledgebase. UniProtKB is also a reliable source for describing host glycoproteins that are further characterized in neXtProt  [19]  when the host is human.
Irrespective of their origin, knowledge of glycoproteins and the glycans they carry is stored in GlyConnect  [20]. In fact, most viruses use the host glycosylation machinery composed of enzymes that are categorized in the CAZy database of Carbohydrate-Active enZymes  [21]  upon sequence similarity and biosynthetic or degrading activity. Glycans are the substrates and products of these reactions. GlyTouCan  [22]  is the most comprehensive glycan structure repository. ViralZone, includes detailed information on virus taxonomy, virion structures, as well as their interactions with other molecules. This latter aspect highlights the frequent occurrence of glycan mediation in receptor binding. This is shown, for example, with cross-references to SugarBindDB  [23]  that collects information regarding host surface glycan recognition by pathogenic agents such as viruses.
Other instances of viral protein recognition via heparan sulfate proteoglycan (co)-receptors are cited in ViralZone though do not yet referr to MatrixDB  [24] , a database that maps experimentally proven interactions between ECM (extracellular matrix) proteins, proteoglycans, and glycosaminoglycans (GAGs). More generally, carbohydrate-binding proteins of all origins are compiled and classified in UniLectin  [25] , a recently released platform for the study of lectins. Major molecules involved in immunity (antibodies and antigens) are extensively covered in two very popular and long-standing databases, namely: the International Immuno-Genetics information system (IMGT)  [26]  and the Immune Epitope Database (IEDB)  [27]. More recently, a Database of Anti-Glycan Reagents (DAGR)  [28]  focused on anti-glycan antibodies was launched.
Unfortunately, DAGR is currently devoid of any cross-reference to existing databases. In fact, the obvious advantage of mapping the resources in relation to virus cycle as shown in Figure  1 , is to emphasize the potential for exploring knowledge via cross-references. The connectivity of databases is shown in Figure  2  where the renowned UniProtKB and Protein Data Bank (PDB)  [29]  obviously play a central role. As such, they provide general knowledge that can then be refined through consulting the more specialized resources featured in the figure. The content description and relevance to virology of each resource featuring in Figure  2  are provided in Table  1. The creation date was added as an indication of not only the reliability of sources, such as UniProtKB, PDB and IMGT, but also the profusion of recent effort in glycoinformatics.
Note that each and every of these resources is pointing to PubMed  [30] , the biomedical literature search engine that provides access to bibliographic information. This systematic link is not pictured to avoid crowding the figure with arrows, nonetheless the existence of shared PubMed IDs between databases is an asset for knowledge mining. We illustrate this point with PubMed ID 12825167  [31]  that is common between UniProtKB and SugarBindDB. A scenario of navigation across databases is shown in Figure  3.
Other viral lectins with this fold can be explored from there. UniProtKB and UniLectin both cross-link to PDB entries associated with capsid protein VP1, but the latter database favors PDB references with bound glycan ligands. This is the case of PDB 2ZL7 that details GalNAc(α1-3)[Fuc(α1-2)]Gal as a ligand. Note that PDB is preferred as it uses the LiteMol software  [35]  for visualizing 3D structures. LiteMol has a convenient app for showing glycan in 3D  [40] , consistent with the Symbol Nomenclature For Glycans (SNFG) notation  [41]  that is used in Figure  3  and in all glyco-related resources cited. Both glycan ligands proposed either in SugarBindDB or in UniLectin are matched to GlyConnect using GlyS 3 , an in-house substructure search engine  [36]  also available as a standalone tool from the Glycomics@ExPASy collection  [42].
The result is a list of full glycan structures containing the ligand substructures. Most of these full structures are attached to mucins, some of which are linked to UniProtKB. Another course of exploration can be followed through the reciprocal link between SugarBindDB and GlyTouCan. A type 1 A Lewis b is registered in GlyTouCan and the corresponding record includes a link to molecules in PubChem  [33]  which, as part of the large NCBI (National Center for Biotechnology Information) collection  [30] , opens many further lines of investigation. This example illustrates the potential for identifying protein interacting partners along with details of the glycan structures and glycan ligands that mediate this interaction. If databases were to be more complete, automated procedures could rapidly extract the relevant pieces of information.
Repertoire Mapping to Enrich Databases A large part of the knowledge regarding glycan-virus interactions remains in the literature which is screened by database curators to be made available online. This is a slow process which mainly explains the current lacks in databases. Furthermore, this incomplete picture leads to focus on general-purpose resources such as UniProtKB or the PDB with PubMed in the background. Figure  2 , where most arrows are concentrated on these resources, illustrates this point conclusively, as well as highlights the limited potential for glycan data integration in the immunological databases. Substantial knowledge is also contained in the results of glycan arrays that have been used to study virus-host interactions for close to two decades. For instance, this approach revealed the subtle differences that distinguish avian from human flu based on glycan recognition by hemagglutinin  [43].
As we write, a new platform designed to visualize, analyze and compare glycan microarray data is briefly introduced in a just released Application Note  [38]. This shows that bioinformatics applied to processing and analyzing glycan array data is still very much in the making. Another reference library is hosted in the Glycosciences Laboratory of Imperial College, London. It contains many virus screens (see Category D in https://glycosciences. med. ic. ac. uk/data. html). However, each experimental result displayed from the database does not provide any cross-reference other than a link of a cited published article to PubMed. Table  2  summarizes information on the software tools that have been cited in the text so far, mainly for visualizing structural or glycan-binding data.
Glycans as Virus Receptors in ViralZone Viruses initiate infection of a host cell by attaching to specific receptors. Binding to an entry receptor triggers a chain of dynamic events that will enable cell penetration by the virus. Many virus-host receptor interactions have been described, and those are reported in the ViralZone virus receptors table (https://viralzone. expasy. org/5356) which contains 254 expert curated interactions. An excerpt of this table is shown in Figure  4. Of those, 53 are interactions with glycans (carbohydrates) from 49 different viruses. All proteinaceous molecules are linked to UniProtKB, glycans are linked to small molecule entries of ChEBI  [32]  through Rhea, the biochemical reaction database [34] and to SugarBindDB. Representing glycan ligands bound by the influenza virus is challenging because it binds the sialic acid cap of those, and therefore can attach to many different molecules.
The field of glycosaminoglycanomics is budding, but data collection is still limited by the lack of standards to encode structural information  [50]. Progress in complying with standards is the first step to increasing connectivity  [51]. Furthermore, links between ChEBI and MatrixDB help bridging with chemical biology databases. GAG entries of MatrixDB are also cross-referenced with GlyTouCan. MatrixDB is designed and built to display interaction networks that provide a snapshot of direct and indirect physical interactions between proteins, GAGs, and small molecules with a particular focus on the precise definition of extracellular matrix multimers. Ultimately, the table shown in Figure  4  will be refined to include links to MatrixDB, which in turn will share data with SugarBindDB.