dataesr/scientific-paragraphs-categorization huggingface dataset:
To quantify the root biomass inside the sample holder, we performed an image analysis using the IMAGE J free software from the stack of 1074 slides obtained after the reconstruction. After adjusting manually the contrast over the whole stack to specifically select the roots, a binarization of the images was applied also on the whole stack. Then the toolbox "particle analysis" of Image J was used to quantify the number of pixels corresponding to the roots. Figure  9  presents the results of FIGURE 8 | Reconstructed tomography obtained for grapevine roots. The grapevine herbaceous cutting has been grown in a sample holder filled with sand. The dense 3D architecture of the roots is clearly observed in (A). A slide at the bottom of the sample holder is presented in (B). A threshold has been used to specifically contrast the roots from the sand and the sample holder (this 3D volume visualization is performed with the Avizo Fire 9.
2 software). FIGURE 9 | Image analysis for quantification of the root system. A contrast adjustment and binarization is performed on the whole stack of the 1074 slides obtained after reconstruction to specifically select the roots and quantify them by the particle analysis toolbox of Image J free software. these steps of image analysis for three different slides of the stack, one at the top (slide 0), one in the middle (slide 500), and one in the bottom (slide 1074). We found that over the reconstructed sample holder including the root system and the sand, 0. 16% of the total volume corresponded to the root system (0. 0486 cm 3 ).
, 2016)  for the same type of cuttings. Except WinRHIZO, all the softwares available nowadays are open source, but many of them are semi-automatic even manual like DART (Data Analysis of Root Tracings), SmartRoot, RootNav, and RootReader 2D. We thus proposed an automatic method for high-throughput and highresolution root images characterization, using a specific pipeline. Image segmentation is a crucial step in image processing, particularly in our Ascending Path method, and was used to identify adventitious roots of cuttings developed in rhizotron. It is an innovative result compared to previously published studies on grapevine with a similar device  (Dumont et al. , 2016). Several image segmentation methods have been reported in the literature. They can be divided in three main categories: segmentation by "Region" approaches, such as the Regiongrowing method  (Adams and Bischof, 1994)  or Split and Merge  (Horowitz and Pavlidis, 1974) ; segmentation by "frontier" approach, such as the Canny filter method  (Canny, 1986) , and Segmentation by Thresholding, as Histograms  (Jain, 1989).
In the case of 2D-rhizotron phenotyping, we have developed a new image analysis Matlab script adapted for rhizotron for identifying adventitious roots as a feature of root architecture. Calculation time for each image takes between 3 and 5 min automatically, whereas for the standard software generally available it takes more than 30 min. Thus, this method is automatic, fast and unsupervised, and allows high-throughput adventitious root parameters determination with high resolution images (144 Mpixels). Moreover, none of the other software can provide all the information on the RSA, as mentioned in Table  1. Even if image acquisition is not the same for the three devices tested and previously described, image processing could be similar using this new method proposed with "Ascending path" procedure and identification of the different points on the skeleton.
ACKNOWLEDGMENTS The authors are grateful to  C. Bernard ,  K. Palavioux ,  D. Gironde , and  F. Zenk , for their help during the plant cultivation and technical supervision of the 4PMI platform ( Phenotyping Platform  for Plant and Plant Microorganisms Interactions). We would also like to thank  David Correia  for his help in developing the hydroponics tubes and rhizotron growing systems. R. Gougeon was at the origin of the preliminary discussions about the application of the neutron imaging to the grapevine root systems. We really want to thank him for this idea and the put in touch of the two teams. Arnaud Helary has realized the neutron tomography cell design at the LLB and we are thankful to  CEA Saclay research center (Gif-sur-Yvette, France)  for hosting our neutron tomography work.
2003a; Kasperson et al. 2005; Adger 2006 ;  Eakin and Luers 2006; Brugère and De Young 2015). Building on the conceptual and practical foundations provided by these complementary approaches, the Intergovernmental Panel on Climate Change (IPCC) presented a definition that is now the foundation of many vulnerability assessments: "vulnerability [to climate change] is the degree to which systems are susceptible to, and unable to cope with, adverse impacts [of climate change]"  (IPCC 2001 (IPCC , 2007)). This formulation includes three key dimensions: exposure, sensitivity and adaptive capacity. As management and conservation move towards more holistic and integrative approaches, and more quantitative datasets and model outputs from multidisciplinary projects become available  (Guerrero et al. 2018) , efforts to assess vulnerability have gained attention in relation to SES.
Data collection How the data are collected, why, and by whom are all important considerations, as the data included in the vulnerability assessment will inform the outcomes, relevance, applicability, and use. It is therefore important to consider the most appropriate method and methodology for each indicator, and how this may influence the outcomes of the vulnerability assessment. Since each vulnerability assessment is context-specific, the most appropriate data may vary depending on scope, circumstances and resource availability. Possible data sources include data from primary research (ecological surveys, interviews, perceptions, model projections), secondary data sets (official databases, censuses, expert elicitation, spatial data), and grey literature  (Kittinger et al. 2014). It is of great value to incorporate local actors, decision-makers, and other stakeholders in the research.
g. principal component analysis;  Abson et al. 2012). Such data-driven "descriptive approach" is based on the structure (co-occurrence) of the indicators. Highly correlated indicators (e. g. wealth and education, or functional and taxonomic diversity) will tend to group together, and different principal components could be used to assess domains. Another type of deductive approach, include the use of equal weightings schemes  (Tonmoy et al. 2014)  under the assumption that all indicators contribute equally to a particular domain nested in a particular dimension. Choosing between a deductive and a descriptive approach is a decision based on the available data and whether it is known that some indicators are more important than others in influencing (negatively or positively) vulnerability. While it is likely that the influence of indicators is not equal, if the value and interactions among indicators are not well understood a decision should be made to treat them equally  (Allison et al.
Statistical Analyses All analyses were performed in R version 3. 4. 0 [22]. To estimate the probability of an individual displaying motility after an O 3 episode, non-parametric Kaplan-Meier curves were estimated using the "survfit" function (R package "survival"). Individuals that displayed abnormal motility or distress behaviors (including death) were the exact observations, while those that remained active or were not followed-up until the end of the survival tracking were the censored data. We used a log-rank test ("survdiff" function, R package "survival") to test for differences in Kaplan-Meier curves among the O 3 concentrations. Post hoc tests were conducted using pairwise log-rank tests adjusted for false discovery rate (FDR) ("pairwise_survdiff" function, R package "survminer"). Furthermore, we used multivariate generalized linear models (ManyGLM) to analyze the effects of O 3 exposure on fig wasp behavior in detail ("mvabund" R package  [23] ).
Acknowledgments: We thank  Candice Dubuisson  for her valuable help during the experiment, as well as the personnel of the  Platform TE (LabEx CeMEB , the  French National Research Agency (ANR)  under the " Investissements d'avenir "). We would like to thank  T. J. Wood  for proofreading and improvement of the English. We are further grateful to the reviewers whose comments improved the presentation of our study.
Funding: This research was partly supported by the programs International Research Project ( IRP)-CNRS-MOST ,  ANR  under the " Investissements d'avenir" program  (reference  ANR-16-IDEX-0006 ) and the  French National Research Program for Environmental and Occupational Health of ANSES  (reference  2018/1/138 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
This tool could be derived from a traceability tool. The idea is to first ask actors to implement a new inter-operable traceability tool based on the new GS1 standard (EPCIS). This implementation is often a first opportunity to accept to share electronic data between actors' information systems. As these exchanged data are not conflictual, the traceability system could be operational rather quickly. This first system increases the confidence between actors and could also create value for each and every one of them. Trustworthy and rapid information on ingredient origins and elaboration processes could be valuable for customers or local authorities. This traceability tool could also be upgraded in ways not typically associated with the SDSC approach. Besides traceability data, other sustainable data, such as economic, social and environmental data, could be recorded in the same EPCIS format.
The PL images/frames have been recorded every 5 min until signal saturation. Fig. 10c  shows the time-dependent average intensity of PL for the PAN/ZnO 20nm /APTES/ GA/Anti-AFB1&AFB1. It is seen that the PL intensity progressively increases over time. The PL images were analyzed using ImageJ software. The inset image indicates the change in average PL intensity during the APTES/GA/Anti-AFB1&AFB1 complex formation. The average PL intensity was about (~26%), and has a good agreement with the value (~25%) from PL-based real-time measurement for AFB1 10 ng/ml concentration (Fig. 9b ). A control experiment was also conducted on the PAN/ZnO 20nm sample. No significant non-specific adsorption of AFB1 was observed. The results confirm that the produced immunosensor provides a sensing capability with a high signal-to-noise ratio and diminished non-specific adsorption.
Acknowledgments I. I. , E. C. , V. M. , and M. B. acknowledge the financial support by project  H2020-MSCA-RISE-2017 , ' Novel 1D photonic metal oxide nanostructures for early stage cancer detection ' (Project number:  778157 ). V. M. acknowledge the partial financial support from the project " Środowiskowe interdyscyplinarne studia doktoranckie w zakresie nanotechnologii " No. POWR. 03. 02. 00-00-I032/16  under the  European Social Fund -Operational Programme Knowledge Education Development, Axis III Higher Education for Economy and Development, Action 3. 2 PhD Programme.
The objectives of this study were to quantify the effects on nitrogen metabolism and dairy performance of substituting soybean meal for proteaginous seeds or replacing raw with treated proteaginous seeds in dairy cow diets. This study was focused on three proteaginous seeds: faba bean, lupin and pea. Two databases were created, which gathered information on in vivo and in situ results, respectively. These were then used to analyze nitrogen ruminal degradability, nitrogen intestinal true digestibility, ruminal parameters, nitrogen partitioning and milk production and composition. A total of 32 and 36 articles were analyzed from the in vivo and in situ databases, respectively. Statistical analyses assessed the effects of substitution for each measured or calculated variable; t-tests were applied to compare the difference between the tested feed (i.
Selection of articles Publications of interest were identified by searching Google Scholar, Science Direct, Web of Science and PubMed using the key words "ruminants AND (digestibility OR degradability OR milk production) AND (faba bean OR lupin OR pea)". Two databases were created, the first gathered in vivo results about the addition of raw or heat-treated PS in ruminant diets, and the second collected in situ results concerning seeds ruminal degradability and intestinal true digestibility. All these results were published between 1974 and 2018 (except studies of  Mendowski et al. , 2019 and 2020). Two major axes were studied: comparisons between SBM and PS on the one hand, and between raw and treated PS on the other hand. To be included in the in vivo database, publications were required to describe studies carried out on dairy cows, focused on replacement of all or part of the concentrate protein source by PS (faba bean, lupin or pea), and contain results on milk performance, nitrogen digestion (in the rumen or in the intestine measured using duodenal and fecal flows), or nitrogen partition between milk, feces and urines.
To be included in the in situ database, publications had to contain results on nitrogen degradability in the rumen and/or nitrogen true digestibility in the intestine (measured using nylon bag in situ incubations) for faba bean, lupin or pea. In all cases, nitrogen was determined using Dumas or Kjeldahl methods. The steps involved in the selection of the publications are summarized in the Prisma flow diagram (Figure  1 ).
Creation of databases Two file templates were created with Microsoft Excel software  (Microsoft Corporation, Redmond, WA, USA, version 2016) , for the in vivo and in situ experiments, respectively, as methodologies and results differed between the two types of publications. For each publication, an individual sub-file, which gathered all the information contained in the publication, was completed using the corresponding template (in vivo and/or in situ) to be as exhaustive as possible. If one publication contained both in vivo and in situ results, two individual sub-files (one per template) were completed for this publication. Once all individual sub-files were completed, both databases (in vivo and in situ) were created by compiling their corresponding individual sub-files. For each database, a homogenization of qualitative terms used for some data was necessary before coding, and some units were converted when required.
Moreover, additional calculations useful for interpretation were performed when not directly reported in the publication. In the in vivo database, this concerned mainly the proportion of dietary CP provided by the tested experimental feed (i. e. the proportion of this feed in the diet × CP content of this feed/CP content of the diet). As the calculation of N in milk differed between publications, this variable was systematically recalculated as (milk yield × milk protein content / 6. 38 / 0. 95), assuming 6. 38 g of N in 100 g of milk proteins and 95 g of protein N in 100 g of total milk  N (DePeters and Cant, 1992; Spanghero and Kowalski, 1997). Nitrogen balance was systematically recalculated as N intake -(N in milk + N in feces + N in urine), using N in milk calculated with the abovedescribed formula.
In the in situ database, the effective degradability of N (ED6_N) was calculated using an outflow rate of particles from the rumen fixed at 0. 06 h -1 , to make all values from the different publications comparable. The ED6_N (g/g) was calculated according to  Ørskov and McDonald (1979)  as: ED6_N = a + (b × c) / (c + 0. 06), with a the soluble fraction (g/g), b the degradable fraction (g/g) and c the rate of degradation of b (h -1 ); or by the "step by step method" from the different points of N degradation kinetics  (Kristensen et al. , 1982). The content of dietary proteins entering the intestine (PIA6) and of dietary proteins truly digestible in the intestine (PDIA6) was calculated, respectively, as PIA6 = CP content of the seed × (1 -ED6_N) and PDIA6 = PIA6 × dr_N, with dr_N the intestinal true digestibility of N (in g/g) assimilated to the proportion of N apparently disappearing from mobile bags in the intestines  (Vérité et al.
Calculations and statistical analyses Statistical analyses were carried out using Minitab 17 software  (Minitab Inc. , USA, 2017). For each comparison, effects of treatments were assessed using t-tests applied to the difference between the tested versus control treatments (as described above), for each measured or calculated variable of interest (listed in Table  3 ). The differences were considered significant when below the threshold P-value of 0. 05 and a trend was considered at 0. 05 < P-value < 0. 10. When only one comparison was found, the P-value reported in the associated publication -when available -was indicated in the text.
In vivo responses Characteristics of cow milk production (days in milk and initial milk yield) and diets (nature and quantities of dietary forage, dry matter intake (DMI)) included in the in vivo database are summarized in Tables  2  and 3. Among PS, faba bean and lupin were the most represented, particularly in comparisons with SBM. Out of the 112 diets included in the database, 72 were distributed ad libitum, 12 were distributed sub ad libitum (i. e. at more than 90% of previously measured ad libitum DM intake, so that changes in intake reflect changes in choice feed between treatments), 11 were distributed in a restricted manner, and 17 were distributed in an unspecified manner. Only diets distributed ad libitum or sub ad libitum were considered for DMI analyses. Most of the diets were based on maize silage.
The N partitioning between urine and feces was not modified when SBM was replaced by raw PS, but very few data were available. The attenuation observed between in vivo and in situ responses could partly be explained by the risk of over-estimating the ED6_N of PS due to particle loss during incubation when using the nylon bag technique, which has been reported to be high with PS (Michalet-Doreau and Cerneau, 1992). Consequently, PDI values of PS may have been underestimated, which would imply a higher gap in dairy performance between SBM and PS. The low content of digestible methionine of PS (1. 48, 1. 44 and 1. 72 g/100 PDI for faba bean, lupin and pea, respectively, INRA 2018) should be noted. As very few publications, except for example  Mendowski et al. (2019 Mendowski et al. ( , 2020)) , reported supplementation with metabolizable methionine, diets containing PS could have had lower than recommended levels of digestible methionine, which could impair dairy performance, in particular MPC.
lupin (mainly Lupinus albus, and Lupinus angustifolius, considered without distinction because the two experiments with Lupinus angustifolius gave similar results to those with Lupinus albus) and pea (Pisum sativum). Only the following comparisons were considered in the present work (tested versus control): raw PS versus SBM; treated PS versus SBM; and treated versus raw for the same PS. This led to a selection from the in vivo and in situ databases, respectively, of 32 (representing 41 experiments) and 36 articles plus a nonpublished study, corresponding to the in situ measurements on the 11 experimental concentrates used in Mendowski et al. (2019 and 2020) by Chapoutot et al. (personal communication): 1 soybean meal, 7 faba bean-linseed blends (90:10) (raw, extruded at low or high temperature, with or without reducing sugars or an enzymatic cocktail), and 3 lupinlinseed blends (90:10) (raw or extruded at low or high temperature.
Acknowledgments: J. A. S. thanks the support of the program " Juan de la Cierva Incorporación" of the "Ministerio de Ciencia, Innovación y Universidades " (agreement no. IJC2018-034997-I ). M. G. -B. acknowledges support by the  Programa Ramón y Cajal  ( RYC-2016-21231 ;  Ministerio de Economía y Competitividad). We would like to thank all the authors who contributed their papers to this Special Issue and the reviewers for their invaluable revisions. We are also grateful to all the members of the Editorial Office of Microorganisms, and specially to  Josephine Xue , for giving us the opportunity of editing this Special Issue and their continuous support.
Quantitative real-time polymerase chain reaction (RT-qPCR) Primers designed using PrimerQuest® software were purchased from Sigma (Table  1 ). RT-PCR assays were performed as previously described  (Maruo et al. , 2018). The expression values of the genes of interest were normalized against three housekeeping genes PSMB6, ACTB and GAPDH validated with NormFinder software. Gene expression is expressed relative to the control group.
Next-Generation Sequencing (NGS) for Analysis of Gene Expression Profile In order to identify the functional mechanism of the HERV-K env gene in tumorigenic characteristics, the transcriptome of HERV-K env KO and over-expressing DLD-1 colorectal cancer cells were analyzed and compared with Mock cells through RNA sequencing (Table  2 ). The heatmap of gene expression changes is shown in Figure  4a  for the env KO group (108 downregulated and 70 upregulated: top panel) and env overexpression group (145 down-regulated and 50 up-regulated: bottom panel). The expression fold changes (FC) of HERV-K env KO and over-expressing DLD-1 colorectal cancer cells were compared with Mock cells using the ExDEGA program (Figure  4b ). To find a meaningful target of the HERV-K env gene, protein expression levels of target genes in HERV-K env KO, over-expressing, and KO re-expression transfected HERV-K env overexpression vector in KO DLD-1 colorectal cancer cells were analyzed (Figure  5a ).
These results suggest that reduced NUPR1 levels in HERV-K env KO cells activated RB levels to inhibit cell proliferation in DLD-1 colorectal cancer cells. The protein expression level of RB and phosphor-RB was significantly changed by HERV-K env KO cells but not changed at RNA level. These data suggest that HERV-K env KO and down-regulation of NUPR1 may regulate the protein stability of RB protein. The functions of NUPR1 have been reported to be highly related to ROS generation, apoptosis induced by autophagy, and ER stress  [26, 27]. Therefore, the ROS-NUPR1 pathway was analyzed via ROS levels and related protein levels in HERV-K env KO cells. As a result, ROS levels decreased in HERV-K env KO cells compared to that in Mock cells. The reduced level of ROS in HERV-K env KO cells was re-expression in HERV-K env re-expressed cells.
Plasmid Construct for Over-Expression The coding sequence of the HERV-K env gene was analyzed using the NCBI (https: //www. ncbi. nlm. nih. gov/, accessed on 24 February 2021) and UCSC Genome Browser Database (https://genome. ucsc. edu, accessed on 24 February 2021) for PCR. Amplified HERV-K env gene was inserted into pcDNA3. 1 (+) expression vector (Invitrogen, Carlsbad, CA, USA) and full sequences were confirmed by sequencing.
Immunofluorescence (IF) and Immunohistochemistry (IHC) For immunofluorescence analysis, the assay was performed as described previously  [32]. Cells cultured chamber slides were incubated in 10% normal serum in PBS for 1 h to block nonspecific antibodies. Slides were then incubated with an antibody against HERV-K Env (1:2000 dilution, Austral Biologicals) overnight at 4 • C. After primary antibody incubation, slides were washed three times in PBS for five minutes and incubated with secondary antibodies, including Alexa Fluor 546 anti-mouse antibody (Invitrogen, Carlsbad, CA, USA) for 1 h. Specimen epifluorescence was determined with the use of a confocal laser-scanning microscope (LSM510 META, ZEISS, Jena, Germany). Confocal images were analyzed using AlphaEase FC image analysis software (Alpha Innotech, San Leandro, CA, USA).