dataesr/scientific-paragraphs-categorization huggingface dataset:
The co-culture induction patterns can be explored by interpreting the POChEMon competition model (Figure  3C )  (Jansen et al. , 2015). This representation corresponded to the unsupervised analysis through PCA of the co-culture sample residue when co-culture data were projected in the mixing model. Thus, the POChEMon approach allowed to identify co-culture related metabolome up-regulation in every sample (considering de novo induction as a specific case of upregulation). In Figure  3C , two co-culture induction patterns (in presence or absence of antibiotics) were observed and well separated according to the second axis. Interestingly, the overall variability among coculture replicates was 10 times lower in presence of antibiotics in comparison to their absence (based on PC1 and PC2 sample distribution).
Identification of co-culture up-regulated compounds highlighted Prorocentrum lima toxin induction As co-culture induction is a complex mechanism, different complementary supervised statistical approaches were used to highlight induced features  (Saccenti et al. , 2014). Data were analysed univariately using Student's t-test and multivariately using both OPLS-DA and the POChEMon approach  (Jansen et al. , 2015). The first two methods (univariate and OPLS-DA) were based on the comparison of the co-culture to the monoculture independently. Those two approaches selected variables that were significant in both comparisons. Alternatively, the POChEMon approach selected features responsible for the differences between an in silico mixture of both organisms and the co-culture profiles. This approach has the advantage to highlight features of specific groups of replicates  (Jansen et al.
ACKNOWLEDGEMENTS The authors would like to acknowledge the technical assistance of  Véronique Séchet  and  Florent Malo  for maintaining and providing P. lima cultures. The authors thank the  ThalassOMICS Metabolomics Facility-Plateforme Corsaire, Biogenouest, Nantes -for LC-HRMS analysis and metabolomics expertise. The authors would also like to acknowledge funding received from Ifremer and the regional research federation IUML for funding received for the Master studies of  Alizé Bagot  and  Maud Chaigné.
Thus, the reactive DESI-MS strategy described herein can most probably be extended to other types of materials (e. g. , solid surfaces) and in that case might even be easier to apply. Overall, the results of this publication evidence even further the relevance of sequence-controlled polymers for identification technologies. (project Euro-Sequences, H2020-MSCA-ITN-2014, grant agreement no. 642083), SATT Conectus Alsace (project Poltag), the University of Strasbourg, and the CNRS for financial support. The PhD position of D. K. was supported by the ITN Euro-Sequences. L. C. also acknowledges support from Spectropole, the analytical facility from Aix Marseille University, by allowing a special access to the instruments purchased with European funding (FEDER OBJ2142-3341). The first paragraph in the introduction was added on July 25, 2022, following a technical error wherein this was missing.
, 2021)  available at doi:10. 5281/zenodo. 4723409. This emphasised the need for multi-site or multi-location field trials as a crucial resource or enabler for crop science and breeding to tackle future production challenges (79% of respondents, n = 267). This included work on yield related traits as well as on plant growth, development, and physiological traits (Fig. 1B ). forefront for many years, as illustrated by the findings that the majority of co-authorships of scientific publications on plant phenotyping between 1997 and 2017 stem from European countries  (Costa et al. , 2019  Whilst field phenotyping is a global effort, this publication focuses on European field phenotyping due to its comparatively prominent position. Therein, we highlight the strong demand for an effective, coordinated Europe-wide field phenotyping network facilitating transnational access to field infrastructures.
plant-phenotyping. eu/lw_resource/datapool/systemfiles/elements/files/58cd4632-35 BCE-11e9-952e-dead53a91d31/current/ document/Infographic_report_on_the_2018_survey_FINAL. pdf). In 2020 a second broader survey of the international community was untaken using the SoSci Survey platform (www. soscisurvey. de) in collaboration with IPPN 2020. The target was to understand community demands, build a strong co-ordinated community and to engage new partners (e. g. private companies'). This survey reached 640 respondents in total, including 368 respondents from the European community. Dedicated questions regarding field phenotyping and modes of access were included. The full survey results are available via https://zenodo. org/record/4723409#. YWV2MZpBw2y  (Fahrner et al. , 2021).
For intensive installations, environmental variables (e. g. air and soil temperature, humidity, rainfall, solar radiation, soil water content) are measured at a similar frequency to the phenotypic traits (typically hourly). In lean field sites with minimal equipment, measurement is usually done via a local weather station with less frequent and broader scale (e. g. site-level) quantification of field environmental conditions. As phenotyping technologies are rapidly advancing, it is crucial for users to be able to identify the right technology for the different phenotyping purposes. installations across Europe, including indoor installations (in greenhouses and growth rooms, where environmental conditions can be partially or thoroughly controlled and automated system are available to evaluate the crop performance), field installations, data management systems and crops models (e.
ecofe. eu; Stützel et al. , (2016) ) with 12 partners conducting multi-location wheat trials. An identical common experimental design is deployed in all locations (replicates, nitrogen fertiliser treatments). A core set of cultivars is assessed (with each location providing the best cultivar for their location) with the same starting seed lot used for each cultivar and for all trial years. Low-cost predefined measurement protocols were used in all locations with common data sheets used to gather data and execute management operations (fertilizer applications, crop protection, sowing, harvesting). Specific post-harvest analyses were executed, based on samples from all locations, but analyzed in one central laboratory. Partners also used common protocols for the use of high-throughput field phenotyping using UAVs, i.
A key recommendation is that a sustained structural and long-lasting collaborative field infrastructure network is needed in order to provide greater collective benefit to the crop science and breeding community. Datasets collected at plant phenotyping installations have an associated cost and are labour intensive and difficult to reproduce (given the seasonal variation inherent in field trialling). Hence, it is essential that datasets can be used not only by the primary user but also re-used and reanalysed by the wider scientific community. At present, numerous datasets are lost due to a lack of proper data management. As open and accessible data is currently required by most journals, public research and funding institutions based on FAIR principles (Findable, Accessible, Interoperable and Reusable;  Wilkinson et al.
2016) , appropriate data management is not only the basis for sound scientific research but also a prerequisite for future data use. Deploying an information system in each European local infrastructure is a necessary step in order to reach the goal of FAIR datasets from field trials. Exchanging spreadsheets, as is current practise, is not compatible with the 'findable' and 'accessible' principles, and is plagued with problems such as software obsolescence and nonaccessible metadata (i. e. the information one needs to reanalyse an experiment). For example, the ontology driven 'Phenotyping Hybrid Information System (PHIS)'  (Neveu et al. , 2018)  enables sharing and integration of data from multiple sources and scales along with associated metadata. It could be possible to link this to the Genoplante Information System (GnpIS) for genetic and genomic data (member of ELIXIR)  (Steinbach et al.
, 2013)  via the Breeding API (BrAPI, https:// brapi. org), allowing further interrogation of datasets e. g. for genome-wide association studies. Starting in 2020, a pilot service conceptualised by the research infrastructure EMPHASIS data pilot is working towards making datasets collected at European installations, including field phenotyping sites, available to a large community of plant scientists, following the FAIR principles. The rationale is that a user can re-analyze published datasets and/or perform meta-analyses by compiling multiple datasets. This data pilot works in close collaboration with the genomics infrastructures ELIXIR (https://www. elixir-europe. org/excelerate/plants), via the Minimum Information About a Plant Phenotyping Experiment (MIAPPE working group;  Papoutsoglou et al.
, 2020 , http://www. miappe. org/) and AGMIP (modelling, https://agmip. org/), so the datasets can be used for different purposes by different scientific communities. The overall recommendation for ensuring best practice is that each group should install their local information system, e. g. PIPPA (https:// pippa. psb. ugent. be) or PHIS  (Neveu et al. , 2019). This would firstly ensure that local data management is set up. Then using ontologies and controlled vocabularies, following the MIAPPE guidelines will make the dataset interoperable, within and between groups, and enable their publication in dataverse such as Cyverse, used by the maize genomes to fields (G2F) project (https://www. genomes2fields. org/resources/).
, 2018; Salgadoe et al. , 2018). Further crop traits using satellite imagery such as vegetation indices, lodging, plant height, phenology, leaf area  (Zhang et al. , 2021)  are quantified at resolutions moving towards use for field phenotyping in larger breeding plots. On the practical side, a recent paper  (Jain et al. , 2019)  showed how microsatellite data management can have a big impact on agriculture sustainability in developing countries. Data were used to detect the impact of sustainable intensification interventions at large scales and to target the fields that would benefit the most, doubling yield gains. It is worth noting, however, that many satellite imaging solutions are affected by cloud cover. Especially in northern Europe, the number of cloud-free days can be few during the growing season, limiting data gathering at all critical plant developmental stages.
For example, for field experimental setups, statistical design, best practices in management and data production. (Poorter and Sack, 2012 ) defined a checklist for plant experiments and the MIAPPE principles also contain guidelines and lists of minimal information. A strategy for data integration and standardization was also developed in the EPPN 2020 project, including topics such as camera calibration, data interoperability and data analysis. For each topic, two levels were defined: level 1 was the necessary conditions for a platform to host transnational access and level 2 is the objective to reach for the future pan-European research infrastructure EMPHASIS (i. e. an advanced phenotyping community). Those standards were defined for controlled conditions but are directly adaptable for field experiments.
| Study design and data collection This was a retrospective, multicenter study using the dataset of the acute leukemia working party (ALWP) of the EBMT. The EBMT is a voluntary working group of more than 600 transplant centers that are required to report all consecutive stem cell transplantations, outcomes and follow-ups once a year. EBMT minimum essential data forms are submitted to the registry by transplant centers. EBMT centers commit to obtain informed consent according to the local regulations applicable at the time of transplantation in order to report data to the EBMT. Accuracy of data is assured by the individual transplant centers and by quality control measures such as regular internal and external audits. The results of disease assessments at transplant were also submitted and form the basis of this report.
Inclusion criteria were adult patients (≥18 years of age) at the time of transplant with AML in first hematological remission (defined as <5% blasts in the bone marrow)  20  achieved following 1 or 2 inductions who underwent a first peripheral blood (PB) ACT between 2000 and 2019. Patients with secondary AML were excluded. Data collected included recipient characteristics (age, gender), Karnofsky performance score (KPS), disease characteristics, number of induction chemotherapy courses, disease status at transplant, year of transplant, details of the conditioning regimen, and doses of PB total nucleated cells (TNC) and CD34 + progenitors. For this study, all necessary data were collected according to the EBMT guidelines, using the EBMT minimum essential data forms. The list of institutions contributing data to this study is provided in the Appendix S1 Cytogenetic risk at diagnosis was categorized according to the cytogenetic abnormalities included in the 2017 European LeukemiaNet (ELN) recommendations for AML.
DNA Extraction, Genome Sequencing, and Assembly The bacterial strains were grown overnight in BHI medium at 37°C, 200 rpm until mid-exponential growth phase and bacteria were pelleted. Total DNA was extracted as previously described  (Cadot et al. , 2010; Kavanaugh et al. , 2021)  and quantified with the Qubit ® Fluorimeter. DNA concentrations were adjusted to 30 ng/ml and sequenced by the MiSeq Illumina platform hosted at the Pasteur Institute, giving 2x150 bp paired-end reads. Sequencing analysis was performed as previously described  (Kavanaugh et al. , 2021)  and included quality control using FastQC and MultiQC, de novo assembly of draft genomes with SPAdes version 3. 13. 0. Raw WGS data are accessible through https://www. ebi. ac. uk/ena/ (accession number PRJEB46455). The genome sequences of the 3 strains were aligned against the pAH187_270 plasmid from the strain B.
Molecular Analysis M13 sequence-based polymerase chain reaction (M13-PCR), derived from a RAPD technique, allows differentiating between various strain patterns. M13 typing was performed as described  (Glasset et al. , 2016). The DNA profiles were analyzed with BioNumerics 7. 1 software (Applied Maths). The software compared the DNA profiles and clustered the strains according to their similarity. The toxin gene profiles were identified by assessing the presence of the cytK-1, cytK-2, HBLA, HBLC, HBLD, NHEA, NHEB, NHEC, hlyII and ces genes by PCR using specific primers  (Glasset et al. , 2016). The strains were then clustered into genetic signatures (GS) according to their different combinations of presence/absence patterns  (Glasset et al. , 2021). The strains were affiliated to one of the seven known phylogenetic groups according to the partial sequencing of the panC gene  (Guinebretière et al.
Proteomic Analysis of Matrisome ECM production was extracted using compartment protein extraction kit (Millipore). Proteomics analysis was performed using mass spectrometry, and the data were further processed using myProMS v3. 6 (work in progress; https://github. com/bioinfo-pf-curie/myproms). Protein functions and ontology were evaluated using Metascape-A Gene Annotation & Analysis Resource website https://metascape. org/ gp/index. html#/main/step1.
Extracellular Matrix Derived From Dental Pulp Stem Cells Exhibited Matrisome Proteins N-ECM and OM-ECM proteins from cellular compartment isolation were analyzed. Matrisome proteins were composed of 225 individual different proteins according to Human Matrisome Database (http://matrisomeproject. mit. edu/other-resources/ human-matrisome/) (Figure  2A  and Supplementary Table  S2 ). Detected proteins were classified according to different categories, the three core matrisome categories-9. 78% of collagens, 31. 56% of glycoproteins, and 4. 00% of proteoglycans-and the matrisome-associated protein categories-28% of regulators, 14% of affiliated, and 13% of secreted proteins (Figures  2B, C ). The proteins ratios were detected by quantitative label-free proteomics analysis in the N-ECM and OM-ECM.
2013 : 5). Mais cet argument pourrait être perçu comme paradoxal puisqu'ils démontrent parallèlement la présence de grains de sanidine très bien conservés sous grossissement MEB dans leur Figure  7  et qu'ils décrivent ces grains ainsi dans leur légende : « Les minéraux sont à arêtes vives et non altérés »  (Michel et al. 2013 : 8). Plus récemment, J. -J. Bahain et al. (Bahain et al. 2022)  ont publié de nouveaux âges par thermo-luminescence (TL) sur des silex brûlés provenant des niveaux 5 et 6 (bien qu'il ne soit pas expliqué́ pourquoi les niveaux 5a et 5b ont été combinés), et des recalculs des dates ESR/Useries de H. Masaoudi  (Masaoudi 1995)  sur des dents de mammifères. Ces recalculs de la série ESR/U ont été effectués sur la base de données dosimétriques in situ recueillies par des dosimètres installés dans les niveaux 4b, 5a, 5b et 6 (bien qu'aucune information ne soit donnée sur la date ou les conditions dans lesquelles ces travaux dosimétriques ont été effectués).
2013: 5). They make the latter argument despite showing very well preserved sanidine grains under SEM magnification in their figure  7  and stating in the legend that "The minerals are sharp-edged and unweathered"  (Michel et al. 2013: 8). 7. Two separate age models that can be constructed from published data for the Orgnac 3 deposits (background adapted from Khatib 1989: 10). Most recently, J. -J. Bahain et al. (Bahain et al. 2022)  published new thermoluminescence (TL) ages on burnt flints from Levels 5 & 6 (though it is not explained why Levels 5a & 5b were combined), and recalculations of H. Masaoudi's (Masaoudi 1995)  ESR/U-series dates on mammal teeth. These ESR/U-series recalculations were informed by in situ dosimetry data collected via dosimeters installed into Levels 4b, 5a, 5b, and 6 (though no information is given about when or under what auspices this dosimetric fieldwork was conducted).
The Upper Acheulean III is characterized by "a clear tendency for 'mousterianisation', despite having very pure [stylistically and technically real] Acheulean bifaces. The scrapers, whose representation by types barely changes, are better than in the former levels; the percentage of thick forms of Clactonian removals lessens, as do the scrapers, burins, and perçoirs"  (Combier 1967: 91). The Upper Acheulean II is different from the industries that surround it. "It is less rich, in total, in scrapers, while correspondingly the richest in notches and denticulates; they are everywhere in Level 4b"  (Combier 1967: 91). These Upper Acheulean sub-designations have not been widely discussed or used subsequently in the Paleolithic literature. Anglophone authors have included data from the Orgnac 3 lithic material in their analyses.
It is stated that bone preservation is excellent, with teeth making up less than 50% of the material, and that "all skeletal elements are well represented"  (Aouraghe 1992: 228). This directly contradicts H. Aourgahe's skeletal part representation data (fig. S4 ). The conclusion that "at Orgnac 3, prehistoric hunters, successively occupying the changing floors in the shelter, brought complete animal carcasses back to the site and processed them there" (Aouraghe 1992: 229) is not demonstrated by the data presented. Y. Sam's (Sam 2009 ) zooarchaeological study did not include identifiable elements from over 30 drawers of material (from which we identified ~1400 additional elements, see Supplement for details), nor any mention of this material or their taphonomic implications. The faunal data used were derived from the CERPT online inventory database, which contains 15 and 20% mis-identified elements, either to element, side, species, or a combination thereof (see Supplement for details).
Acknowledgements: We would like to thank  Dr. Richard Klein  for his support and guidance during our work on Orgnac 3. Thanks also go to  Dr. Henry de Lumley  and  Anne-Marie Moigne  for facilitating access to the Orgnac 3 material and associated publications at the  CERPT in Tautavel, France , and their hospitality during research visits. Dr. Évelyne Crégut-Bonnoure  provided helpful comments on a draft of the manuscript. This research was funded by a  NSF   Doctoral Dissertation Improvement Grant  (# 0925571 ) to JEL, the  Leakey Foundation ,  Stanford University's France-Stanford Center for Interdisciplinary Studies, Department of Anthropology , and  Stanford Archaeology Center. This article is dedicated to our dear colleague and friend  Jean Combier  who passed away in 2020, and whose life was dedicated to understanding the history of human origins in the Rhône Valley.
Hepatocyte IR is required for the reprogramming of liver gene expression rhythmicity in response to DRF Next, in order to further examine the potential consequences of such changes in the expression of core-clock genes, we performed a genome-wide analysis of the transcriptome IR hep+/+ mice and IR hepÀ/À mice fed AdLib or submitted to DRF (Table  S2 ). To analyze differential rhythmicity across such multiple conditions, we used the recently published Differential Rhythmicity Analysis in R (dryR) package  (Weger et al. , 2021). This analysis first evidenced that DRF tends to reduce the number of rhythmic genes and the rhythmicity of their amplitude both in IR hep+/+ and IR hepÀ/À mice when compared with AdLib feeding (Figure  4A ). Moreover, we found that the difference in rhythmic gene expression between IR hep+/+ and IR hepÀ/À mice is increased in DRF when compared with AdLib feeding (Figure  4B ).
RNA extraction and RT-qPCR Total cellular RNA was extracted using TRI Reagent (Molecular Research Center, Inc. , Cincinnati, OH, USA). Total RNA samples (2 mg) were reverse-transcribed using High Capacity cDNA Reverse Transcription Kits (Thermo Fisher Scientific, Waltham, MA, USA) for real-time quantitative polymerase chain reaction (qPCR) analyses; the primers used for the SYBR Green assays are listed in Table  S3. Amplifications were performed using a Stratagene Mx3005P system (Agilent Technologies, Santa Clara, CA, USA). The qPCR data were normalized to the level of Psmb6 (proteasome 20S subunit beta 6) transcripts, and analyzed using LinRegPCR. v2015. 3 to get the starting concentration (N0) which is calculated as follow: N0 = threshold/(Eff meanCq) with Eff mean: mean PCR efficiency and Cq: quantification cycle.
Microarray data analysis Microarray data were analyzed using R (R Development Core  Team, 2008)  and Bioconductor packages: www. bioconductor. org, v 3. 0  (Gentleman et al. , 2004) , as described in GEO accession GSE165156. Raw data (median signal intensity) were filtered, log2 transformed, corrected for batch effects (microarray washing bath and labeling serials) and normalized using quantile method  (Bolstad, et al. , 2003). A model was fitted using the limma lmFit function  (Smyth, 2004). Pairwise comparisons between biological conditions were applied using specific contrasts to extract genotype, Time and Genotype: Time interaction effects. A correction for multiple testing was applied using the Benjamini-Hochberg procedure (BH,  Benjamini and Hochberg, 1995; Benjamini et al. , 2006)  to control the False Discovery Rate (FDR).