dataesr/scientific-paragraphs-categorization huggingface dataset:
Lastly, this study specifically focused on the spatial characteristics of the RSNs; other characteristics such as their dynamics or the association with cognitive variability in late adulthood were not explored because the necessary data were not available. In particular, it will be important to investigate the cognitive impact on the spatial definition of the Atlas55+ RSNs, in cognitively impaired populations. In the current study, all individuals were identified as healthy and showed very low variability in their general functioning (Mini-mental state exam score: mean (std): 28. 6 (1. 5), across the CamCAN55+ and ADNI cohorts) which prevented further analyses to identify spatial variability in the RSNs as a function of cognitive ability.
Mental Health (R01MH113619 to S. F); the National Institute of Biomedical Imaging and Bioengineering (U54 EB020403 to P. M. T. ); the French government agency ANR (LABCOM Ginesislab, ANR 16-LCV2-0006-01 to M. J. ). LL is supported by a fellowship from the French Alternative Energies and Atomic Energy Commission (CEA). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.
of Mental Health (R01MH113619 to S. F); the National Institute of Biomedical Imaging and Bioengineering (U54 EB020403 to P. M. T. ); the French government agency ANR (LABCOM Ginesislab, ANR 16-LCV2-0006-01 to M. J. ). LL is supported by a fellowship from the French Alternative Energies and Atomic Energy Commission (CEA). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.
Methods: In this study, we will include 342 women with non-metastatic hormone receptor positive BC with an indication for treatment with ET. Patients will be randomized 1:1 by minimization and strati ed by: age, stage, type of ET prescribed and presence of comorbidities or not, in 2 arms. The intervention will consist of numerical HRQoL assessment using the CHES software before each consultation (with delivery of scores to clinicians) coupled with therapeutic information. Therapeutic information will consist on 3 workshops related to understanding the prescription, nutrition and fatigue. A reminder letter will also be send to patients every month. Patients in the control group will follow standard care. HRQoL will be assessed using a classic "paper-pencil" collection at baseline in both arms to ensure comparability between arms and at 12-month.
From the patient's point of view, the inclusion of numerical assessment in the process of data collection has shown bene ts in disease management and symptom control  [24] , improving survival  [20]  and patient-clinician communication  [19]. Moreover, it also potentially facilitates clinical decision through the systematic and continuous collection of symptom data  [25]. Since compliance is a multidimensional phenomenon, any attempt to improve it should encompass several potential contributors. Thus, we hypothesize that electronic measurement of HRQoL (before each consultation with scores communicated immediately to clinicians who can discuss it with patients), coupled with therapeutic information, will improve 12-month compliance rate with ET in patients with non-metastatic BC. At a later step, we will assess the role of social support and psychological distress as potential modulators of compliance with ET, the capacity of HRQoL to predict compliance with ET, patient satisfaction with care and physician's perception regarding the clinical utility of routine HRQoL evaluation.
Inclusion procedure The study will be proposed to eligible patients by their doctors (oncologists, surgeons, radiation oncologists). Patients will be included at the time of the rst prescription of ET (at the end of the treatments by surgery +/-chemotherapy +/-radiotherapy). Once included, patient will be randomized into one of the two study arms (interventional arm or control arm). Patients will then be referred to the clinical research associates, whose role will be to collect patient data (clinical and sociodemographic) in both study arms and to instruct patients in the interventional arm on how to use the CHES software  [26]. For patients who wish to access the platform from home, a username and password will also be provided. If needed, clinical research associates will also assist patients to complete HRQoL questionnaires.
Assessment of HRQoL The intervention will consist in an electronic measurement of patient HRQoL before each consultation with delivery scores to clinicians, who can discuss it with patients and couple it with therapeutic information. Patients will complete the EORTC-QLQ-C30 and the EORTC-QLQ-BR23 questionnaires before their consultation, via a touch pad or from their home via a secure web portal. Patients will complete the questionnaires via the CHES software  [26]. The CHES software was developed by ESD (Evaluation Software Development) in collaboration with the EORTC Quality of Life Group to facilitate the inclusion of HRQoL measurement instruments in research projects and daily clinical practice. This mode of collection provides clinicians with real-time results, and generates alerts in the event of clinically signi cant deterioration of patients' HRQoL scores, allowing them to tailor treatment in a personalized manner.
Secondary endpoints The secondary endpoints will be anxiety and depression assessed by the HADS questionnaire, social support assessed by Sarason's SSQ6 questionnaire and HRQoL assessed by the FACT-G questionnaire. HRQoL data using FACT-G questionnaire will be assessed at inclusion in both arms to ensure comparability between groups and at 12 months, to assess the predictive value of HRQoL on compliance to ET. Patient satisfaction with care will be assessed by the EORTC-SAT-C33 and EORTC-OUT-PATSAT-7 questionnaires. Physician perception regarding the utility of systematic HRQoL evaluation will be assessed using an ad hoc questionnaire derived from the work of Velikova  [19, 28] , including the perceived utility and satisfaction of routine assessment HRQoL, reasons for use/non-use and the intention to adopt this assessment in routine care.
Data collected Sociodemographic characteristics (age, sex, level of education, family situation, socio-professional category, work time), medical and surgical history, date of tumor diagnosis, tumor characteristics, previous treatments, patient clinical characteristics at inclusion and at each follow-up visit (weight, height, overall patient condition), concomitant treatment, type of ET received, HRQoL data (FACT-G ), anxiety and depression (HADS), social support (SSQ6), treatment modi cation (change in the type of ET), treatment-related toxicities and their grade will be collected. Patient satisfaction with provided care will be measured using the EORTC-SAT-C33 and the EORTC-OUT-PATSAT-7. Clinicians' perceptions of systematic health-related quality of life assessment (utility and perceived satisfaction, reasons for use or non-use, and the intention to adopt it in practice) will be collected via an ad hoc questionnaire.
CHES For this study, a French version of the CHES web-based solution will be available. Each patient will be represented by an identi er, to guarantee the data con dentiality and anonymous exploitation of database for statistical purposes. CHES offers a cross-sectional presentation (presentation of scores for each dimension) and a presentation of the longitudinal course of HRQoL for each dimension of the questionnaires. CHES also minimizes input errors, reduces the number of missing data related to data collection, and avoids ambiguities in the responses. This software also allows remote access via a platform that will allow patients, who desire (outside the consultations) to give an input on their HRQoL, in the event of a deterioration or improvement perceived at the clinical level.
Descriptive analysis A descriptive analysis of patients' clinical and socio-demographic characteristics at inclusion will be performed for each arm. Data will be expressed as number and percentage for categorical variables, and as mean± standard deviation (SD), or median and interquartile range [IQR] for continuous variables, as appropriate. The number of missing data will be speci ed. The number of patients with available data will also be speci ed for quantitative variables. Normality will be tested using the Shapiro-Wilk test. According to data distribution, independent Student t tests or non-parametric Mann-Whitney tests will be used to compare results between groups. The basic categorical characteristics will be compared using tests of Chi square or Freeman Halton according to the number of the variable categories.
HRQoL analysis All HRQoL scores will be calculated according to FACT-G guidelines and described according to the arm (interventional arm or control arm). A logistic regression model will be used to assess the capacity of HRQoL to predict 12-month compliance with endocrine therapy. The modulatory potential of social support on compliance will be assessed using an interaction term between the availability / satisfaction of social support that patients receive and HRQoL in a logistic regression model. The modulatory potential of psychological distress on compliance will be assessed using an interaction term between patient anxiety / depression and HRQoL in a logistic regression model. An analysis of missing HRQoL data pro les will also be performed. If a Missing Not At Random (MNAR) pro le is demonstrated / suspected, multiple imputation of missing data can be performed in sensitivity analysis, taking into account the variables associated with the occurrence of missing data.
Ethical considerations As a speci c research intervention is carried out, the protocol falls within the scope of interventional biomedical research and was authorized by the French Health Agency (ANSM-Agence Nationale de la Sécurité du Médicaments et des produits de santé", IDRCB number: 2019-A01323-54) in June 2019 and by the French Ethical Research Committee (CPP-"Comité de Protection des Personnes") in October 2019. The clinical trial has been registered at ClinicalTrials. gov with the identi er NCT04176809. This study will be done in accordance with the ethical principles of the Helsinki Declaration and the Good Clinical Practice of the International Harmonization Conference. Participants must provide informed consent. Subjects will be informed of the objectives of the project and the risks and bene ts of the explorations to be carried out.
manuscript submitted to Geochemistry, Geophysics, Geosystems Acknowledgments This work was supported by the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (grant number  716429 ). ISTerre is part of  Labex OSUG@2020  ( ANR10 LABX56 ). The authors declare no other sources of funding and no competing interest. The authors are very greatful to  Maxime Maurice  and  Guillaume Morard  for their fruitful comments. The authors thanks  Carolina Lithgow-Bertelloni  for the editorial handling of the paper and two anonymous reviewers for their comments.
The ganglioside was subsequently merged with the NTD domain of SARS-CoV-2 S protein as obtained from pdb file # 6VSB  [16]. Initial conditions corresponded to minimized structures obtained with the Polak-Ribière algorithm. Docked complexes were subsequently submitted to iterative cycles of molecular dynamics using the CHARMM36 force field optimized for carbohydrates  [17]. Interaction energies were calculated from stable complexes using the Ligand Energy Inspector function of Molegro  [13]. N-acetylneuraminic acid (Neu5Ac) was generated with the Hyperchem database. 9-O-acetyl-N-acetylneuraminic acid (9-O-SIA) was retrieved from pdb file 6Q06  [18]. CLQ is N-(7-chloroquinolin-4-yl)-N,N-diéthyl-pentane-1,4-diamine. Its three-dimensioanl structure was retrieved from pdb file # 4V2O  [19]. CLQ-OH is (RS)-2-[{4-[(7-chloroquinolin-4-yl)amino]pentyl}(ethyl)amino]ethanol.
Microbacterium phage sequencing and genometrics With the exception of the previously published Min1  [29] , all phages were sequenced at either Western Carolina University or the Pittsburgh Bacteriophage Institute. Genomes were annotated using a previously reported pipeline involving automated gene predictions followed by careful manual inspection and revision  [33]. GenBank accession numbers are shown in Table  1. Microbacterium phage genome sizes range from 17,032 bp (Miaurora) to 97,757 bp (Hendrix), and the viral genomes have a variety of types of termini, including defined cohesive ends (with 8-11 base single-stranded 3' DNA extensions), direct terminal repeats (DTRs) ranging from 203 bp (Hyperion) to 3,809 bp (Musetta), and circularly permuted headful-packed genomes (Table  1 ). The G+C% contents vary from 51.
4% (Count) to 71. 4% (Zeta1847); the host G+C% is 67-68%. The numbers of predicted open reading frames (ORFs) range from as few as 25 (BurtonThePup) to as many as 155 (Hendrix) (Table  1 ). The predicted gene products were assorted into phamilies (phams) using similar metrics to those described previously  [8, 10, 34] , and genome maps displayed using a browser-accessible version of Phamerator  [34]. Approximately one-third of the phages code for at least one tRNA, but none have more than four tRNA genes (Table  1 ).
Microbacterium phylogenetics Actinobacterium phylogenies were constructed using a Neighbor-Joining method with MEGA7  [36] [37] [38]. The optimal tree with the sum of branch length = 0. 59180754 is shown in Fig 4. The evolutionary distances were computed using the Maximum Composite Likelihood method in the units of the number of base substitutions per site. Codon positions included were 1st+2nd+3rd+Noncoding, and all positions containing gaps and missing data were eliminated. There were a total of 1216 positions in the final dataset.
Acknowledgments We thank  Christian Gauthier ,  Katie Wetzel ,  Krista Freeman , and  Monty Montgomery  for comments on the manuscript. We also thank  Rick Ellingworth  ( UWRF Biology Lab Manager ) for assistance with media and reagent preparation,  Jason Myers  ( University of Minnesota Characterization Facility ) for assistance with electron microscopy, and  Lisa Kroutil  ( UWRF Chemistry Department ) for assistance with phage purification of Burro. We are grateful for programmatic assistance from  Billy Biederman ,  Danielle Heller ,  David Asai , and  Crystal Petrone. We appreciate  HHMI  for hosting a summer 2019 bioinformatics workshop where much of this analysis was done, and the contributions of participants in a summer 2018 bioinformatics workshop for analyses of phages ArMAWen and Schubert.
3 3795bp DR Siphoviridae 112 1 lytic MH047631 ValentiniPuff Singleton Mpa NRRL B-14843 62517 67. 4 Cir per Siphoviridae 112 0 lytic MH825712 Zeta1847 Singleton Mpa NWU1 47921 71. 4 3' 11-b ext Siphoviridae 75 1 temperate MH271320 1 Mfo NRRL B-24224: Microbacterium foliorum NRRL B-24224; Mna ATCC BAA1032; Microbacterium natoriense ATCC BAA-1032; Mpa NWU1: Microbacterium paraoxydans NWU1; Mpa NRRL B14843: Microbacterium paraoxydans NRRL B-14843; Mae NRRL B-24228: Microbacterium aerolatum NRRL B-24228; Mne CBX102: Microbacterium nematophilum CBX102. 2  Genome length in base pairs 3  Genome termini. Cir per: circularly permuted; ext: extension; DR: Direct Repeat 4  Virion morphology, determined by electron microscopy 5  Lytic or temperate life style, as predicted bioinformatically 6  GenBank Accession number https://doi.
, 2015). The CRW tracks from each species were simulated within a different restricted area, based on the combination of all original tracks and increased by a buffer zone accounting for the error of the satellite tags (100 km from the closest original track;  Žydelis et al. , 2011). Various studies highlighted the importance of choosing pseudo-absences on the same geographical extent as the tracking data  (Barbet-Massin, Jiguet, Hélène Albert, & Thuiller, 2012). Moreover, selection of pseudo-absences can influence variable selection, model fit and predictions of SDMs  (Wisz & Guisan, 2009). To ensure selection of an appropriate pseudo-absence dataset, we assessed the similarity between the original and the simulated tracks based on the overall direction and distance, assigning a flag value between 0 and 4 (0 being the most similar tracks and 4 the most dissimilar).
merca tor-ocean. fr/eng), based on the ocean general circulation model configuration ORCA025 NEMO, at a spatial resolution of 0. 25° for the 1992-2009 period  (Bernard et al. , 2006)  and assimilated satellite and in situ data. From 2010, GLORYS. 2v1 was updated with the equivalent Mercator Ocean operational real-time system (PSY3). Biological variables, net primary production (NPP) and euphotic depth (ZEU) data, were derived from ocean colour satellite data (http://www. scien ce. orego nstate. edu/ocean ), using the vertically generalized production model (VGPM) of  Behrenfeld and Falkowski (1997). Both GLORYS-2v1 outputs and primary production data were interpolated onto a regular 0. 25° × 0. 25° grid with a weekly time step to be used as forcing for the SEAPODYM. In the model, three vertical layers are defined in relation to the euphotic depth, as micronekton diel vertical migration (DVM) is mainly induced by daylight variations, validated with biomass estimates from micronekton sampling cruises and acoustic backscatter data  (Lehodey et al.
Following the identification of time-lagged Chl-a concentration as an important predictor of the spatio-temporal distribution of baleen whales around the study area  (Prieto, Tobeña, & Silva, 2016) , we tested a time-lag of 1 month prior to the point location for the following variables (indicated by variable name_1m): LTL-bio, LTLprod, Chl-a and NPP. All environmental and prey-related variables were extracted for each original and simulated track point using the R software and the packages "raster"  (Hijmans & van Ettern, 2012) , "ncdf4"  (Pierce, 2019)  and "rgdal"  (Bivand et al. , 2019). Data points with incomplete information were removed from the final dataset, corresponding to 0. 02%, 0. 06% and 1. 60% of the total dataset for fin, blue and sei whale, respectively. Prior to running the models, we examined collinearity between pairs of covariates  (Zuur, Ieno, & Elphick, 2010)  using the Pearson's correlation coefficient and selected those variables less correlated (<0.
, 2016). For each of the 40 runs, we calculated the area under the receiver operating characteristic (AUC) curve and the variance explained (R 2 ). The AUC index ranges from 0 to 1, with values below 0. 6 indicating a discrimination ability no better than random, values between 0. 7 and 0. 9 considered as reasonable and values >0. 9 as excellent  (DeLong, DeLong, & Clarke-Pearson, 1988). Successive observations along the track of the same individual can be serially correlated with respect to associated habitat characteristics, which may underestimate parameter uncertainty and result in selection of inappropriate predictor variables during model selection  (Fieberg, Matthiopoulos, Hebblewhite, Boyce, & Frair, 2010). To check whether the serial correlation was an issue in our data, we subsampled the original dataset by selecting every fifth data point, representing a time interval of 60 hr, which was considered sufficient for the individuals to move anywhere with overall distinct environmental conditions  (Žydelis et al.
| Serial correlation evaluation Models based on subsampled and full datasets yielded similar results for all three species in terms of importance of predictor variables and robustness to control point selection (Table  S4 ). In the case of fin and blue whales, subsample models had slightly lower AUC values than full dataset models but AUC values were similar for sei whale models. Nonetheless, habitat preference predictions from these models were comparable to those from full datasets for all the species (Figures  S6-S8 ). Taken together, these results indicate that our analyses and predictions were robust to serial correlation.
ACK N OWLED G EM ENTS We are very grateful to  Cláudia Oliveira ,  Irma Cascão ,  Maria João Cruz ,  Miriam Romagosa  and many volunteers, skilled skippers, crew and spotters that participated in the tagging fieldwork. This work was supported by  Fundação para a Ciência e Tecnologia (FCT) ,  Azores 2020 Operational Programme and Fundo Regional da Ciência e Tecnologia (FRCT)  through research projects  FCT-Exploratory project  ( IF/00943/2013/CP1199/CT0001 ),  TRACE  ( PTDC/MAR/74071/2006 ) and MAPCET ( M2. 1. 2/F/012/2011 )cofunded by  FEDER ,  COMPETE , QREN, POPH,  ESF ,  ERDF ,  Portuguese Ministry for Science and Education, and Proconvergencia Açores/ EU Program. We also acknowledge funds provided by  FCT  to MARE, through the strategic project  UID/MAR/04292/2013. SPJ was supported by a postdoctoral grant ( REF.
GREENUP/001-2016 ), MT by a  DRCT doctoral grant  ( M3. 1. a/F/028/2015 ),MAS by an  FCT-Investigator contract  ( IF/00943/2013 ), FV by an  FCT Investigator contract  ( CEECIND/03469/2017 ) and RP by an  FCT postdoctoral grant  ( SFRH/BPD/108007/2015 ). LMTL modelling work has been supported by the  CMEMS Service Evolution   GREENUP  project, funded by  Mercator Ocean. We are grateful to  Elliott Hazen  for offering guidance and advice, and to two anonymous referees whose comments greatly improved this work.
MATERIALS AND METHODS The FAN Model Brief Description and Indicators Presentation "Flows in Agro-food Networks" (FAN) is an agent-based model that simulates and assesses farming activities based on processing and exchanging agricultural materials. FAN is a theoretical tool to explore and assess opportunities for a circular economy in small farming regions, such as the "Ribéracois", which we refer to as a "district" herein. At this scale, FAN may help to unravel interactions concerning material exchanges in farming activities by simulating recycling flows and assessing their degree of circularity. The FAN model mechanisms and submodels are described following the ODD (Overview, Design concepts, and Details) protocol for agent-based models in  Fernandez-Mena et al. (2019). The model script, the Ribéracois dataset and the scenarios parametrization are available in  Fernandez-Mena (2017a).
Greenhouse Gas Emissions The net greenhouse gas (GHG) emissions are estimated including both on-and off-site emissions linked to changes implemented in the scenarios, such as the emissions induced by material truck transportation, chemical fertilizer and feed off-site production as well as avoided emissions induced by bioenergy production. To estimate emissions in FAN, a simple soil mineralization model is applied  (Hénin and Dupuis, 1945; I. P. C. C. , 2006). Tier 1 default values are used for emissions from farming and the ADEME (2017) database is used for truck, biogas and feed imports emissions. In the net GHG estimation (expressed in CO 2 -equivalence), emissions are considered to be positive, whereas C stored in soils and CO 2 avoided are considered to be negative. Farm machinery and buildings are not included since they are considered to be invariable among scenarios.