dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of gene network models Clustering and 'community structure' of modules within biological networks arise from variation in connectivity within the network and are known to be associated with function  [25, 26]. To rank these functional components within interactome models, we used the ModuLand plugin for Cytoscape 2. 8. 3 to determine overlapping modules and to identify hierarchical structure using the centrality property thus enabling the identification of key network elements  [27]. The central core unit of each network module (metanode) was defined as the ten most central genes. A list of the unique genes in each metanode was generated and used as a model of the functional core of the associated network for further comparison. Network topology was analysed using the CytoHubba plugin for Cytoscape  [28].
Analysis of epigenomic data Previously published methylation profiles of six GHD patients were used to assess the relationship of changes in DNA methylation in relation to response to r-hGH  [30]. The data from GSE57107 were re-analysed in Qlucore Omics Explorer 3. 3 and a median-based gene level summary of methylation was determined (n = 20,618). The relationship between gene level DNA methylation and response to r-hGH was determined using rank regression.
Classification of growth response All analysis was performed using the statistical software R 3. 3. 2 [31]. The relationship of baseline gene expression to potential predictive value (classification of low and high quartiles of response) was performed using Discriminant Analysis of Principal Components (DAPC)  [32] , Partial Least Squares Discriminant Analysis (PLS-DA) (mixOmics 6. 1. 1R package  [33] ) and random forest (RF) with 1000 trees  [34]. Class size imbalance was corrected for using Synthetic Minority Oversampling Technique (SMOTE)  [35]. Feature selection from RF data was performed using the BORUTA algorithm  [36]. The area under the curve of the receiver operating characteristic (AUC) was used to present the probability of a randomly selected sample being classified correctly. In RFs, about one third of the cases are left out of each iteration and can be used as a test set to perform crossvalidation and to get an estimate of the test set error, the out of bag (oob) error estimate.
A Similarities in the interactome models of the response of GHD and TS to r-hGH were identified by overlap at each year of therapy. Genes were selected that were significantly related to growth response in either or both GHD and TS. The genes related to each year of therapy were combined into a set of 58 uniquely identified genes and this set was used to generate an interactome module (reactome plugin for Cytoscape 3. 6. 0). Genes with a dark border also have a genetic association with growth response in either GHD or TS. Connecting lines represent known protein:protein interactions, size of the node is proportional to the number of connections made. B The clustering coefficient of the group of genes in the network module associated with each year of therapy was determined and presented as a histogram (average ± standard error of the mean).
TS LoQ TS HiQ Importance Gene Probe sets Gene Probe sets Gene Probe sets Gene Probe sets Importance 'area under the curve' up to 97%, providing the basis for a predictive test. Little overlap between GHD and TS was observed between the gene expression data that was associated with each year of growth response. We therefore investigated whether GHD and TS were interacting with similar functional units of genes using network models  [46]. We generated network models of growth response (as determined by height velocity) at each of the 5 years of treatment using baseline gene expression. Functional modules of genes within these models were ranked according to their network centrality. The measure of network centrality is known to be associated with mechanism  [26, 47]  and we used this measure to define the functional hierarchy of the modules of genes whose expression was linked to r-hGH response at each year of therapy.
Acknowledgements The following were PREDICT local centre principal investigators who enroled patients into the study:  S-W Yang ,  H-W Yoo ,  T-J Wang ,  S Cabrol ,  J Weill ,  R Pfaeffle ,  F Antoniazzi ,  A Pilotta ,  D Veimo ,  V Peterkova ,  A Ferrandez Longas ,  I Gonzalez ,  S Quinteiro ,  M Rodriguez Arnao ,  L Bath ,  M Colle ,  Y Brusquet ,  J-P Salles ,  J-W Hou.
Molecular approaches have greatly facilitated research across a broad diversity of parasitic taxa and may help unravel the huge and largely undescribed diversity of bird parasites  (Carlson et al. , 2020). Metabarcoding in particular has become a standard approach to describe biological communities in a variety of contexts  (Deiner et al. , 2017; Pollock et al. , 2018; Xu, 2016)  and holds potential for surveying parasite communities. Metabarcoding involves amplifying a small (up to a few hundred bases long) genetic marker ("the barcode") using a pair of primers that will anneal in all target taxa. Next-generation sequencing technology is then used to sequence the diversity of barcode variants obtained. These sequences are searched against a reference database (e. g. , the Silva or the BOLD databases) to identify the taxa amplified from the sample whose sequence is also present in the database.
| Data analysis Sequences were demultiplexed using bcl2fastq, and adapters were trimmed with the Illumina software. For each primer set, paired-end reads were merged accepting a 10% mismatch rate using vsearch. Only merged reads of a length comprised between 50 and 500 bp and containing both forward and reverse PCR primers (allowing a 10% mismatch) were kept, and primers were then removed from these sequences using cutadapt. Reads containing ambiguous nucleotides were also removed. Reads were clustered using swarm following a two-step procedure, first with aggregation distance = 1 and then with aggregation distance = 3. vsearch was then used to identify and remove clusters whose seed sequence was deemed chimeric. The remaining clusters were affiliated using BLAST and the Silva 138 or 138. 1 database (https://www.
arb-silva. de/), resulting in an affiliated OTU (Operational Taxonomic Unit) table for each primer set. These analyses were carried out using the FROGS suite in Galaxy  (Escudié et al. , 2018). Subsequent analyses on the affiliated OTU tables were carried out using R. The OTU table taxonomy information was manually curated, and the datasets were trimmed to the twenty most abundant OTUs for each primer set × sample type (blood, fecal, or mix) combination for analyses.
| Sequencing and filtering metrics We carried out one sequencing run that included all primer sets. A total of 2,157,180 reads were obtained, of which 2,038,148 (94. 48%) passed the Illumina "chastity filter". Error rate was 1. 08% and, average proportion of ≥Q30 bases was 84. 90%. These sequencing reads, in fastq format, are available from the  Bourret et al. (2021)  dataset. Between 114,461 and 179,008 read pairs were obtained for each primer set (for the 16 samples combined). These were kept if they could be paired-end assembled, contained both the forward and reverse primer sequences, were 50-500 nucleotides long, had no ambiguous base calls, and were deemed nonchimeric. The proportion of reads passing these filters varied between 66% and 92% for four of the primer sets, while it was 37% for primer set dc2.
This was due to a substantial number of reads being eliminated because they were less than 50 nucleotides long. The remaining sequence clusters (hereafter referred to as Operational Taxonomic Units, OTU) were subsequently affiliated using the SILVA database  (Quast et al. , 2013). While 97%-99% of reads were affiliated for four of the primer sets, only 1% of reads were affiliated in the case of dc2; this marker was subsequently abandoned. Table  3  provides a summary of the number of reads kept and affiliated for each primer set.
| D ISCUSS I ON We tested five different primer sets for avian eukaryotic endoparasite metabarcoding in order to shed light on the usefulness of the data that can be obtained from the different sets and help researchers choose the most appropriate for their aims. For that purpose, eight blood samples, six fecal samples, and two controlled "bird+nematode" mixes were analyzed, and the outcome (in terms of parasite detection) was compared, revealing the pros and cons of each primer set. To apply those criteria, the notions of "parasite clade" and "parasite reads" need to be defined, in the context of a metabarcoding analysis that is contingent upon the phylogenetic resolution of the markers and the completeness of the reference database. In this study, "parasite clade" is meant as any taxonomic family which includes known avian eukaryotic endoparasites; and a "parasite read" is a sequencing read affiliated to a parasite clade.
This is an opportune moment to review some of the extant data on the current status of women in ocean science for several reasons. First, the United Nations (UN) Decade of Ocean Science for Sustainable Development (Ocean Decade) began in 2021, the preparatory process of which had been led by UNESCO's Intergovernmental Oceanographic Commission (IOC). Second, the IOC has published the second edition of the Global Ocean Science Report, entitled Global Ocean Science Report 2020 -Charting capacity for ocean sustainability (GOSR2020)  [1]. Finally, work continues on the 2030 Agenda for Sustainable Development with its 17 interconnected Sustainable Development Goals (SDGs). The Ocean Decade's Implementation Plan 2. 0, submitted to the UN's 75 th General Assembly in 2020, considers it a priority to actively improve gender disparities as ocean science capacity develops and promises that "The Decade will systematically identify and dismantle barriers to achieving gender, geographic and generational balance so that no one is left behind.
Based on the responses to the GOSR2020 questionnaire completed by one focal point per IOC member state submitting data (see the GOSR portal: https://gosr. ioc-unesco. org), it reports on the proportion of women in the workforce, including scientific researchers, for 40 countries with ocean science institutions; and on numbers of women in different age classes of the workforce. Further, it reports on the participation of female and male scientists in, and featured speakers at, selected relevant international conferences/symposia, including percentages of women in specific categories of ocean science. Section 4. 5 "Capacity development" reports on the number of women students at conferences and on continuous professional development participation. Much of this article is directly taken from chapter 4 of the GOSR2020, in italics throughout this article  [8].
These seven SAGA STI GO are further broken down into less than 100 sub-objectives, covering every aspect of women's participation in STEM, in SAGA Working Paper 1  [9] , while Working Paper 2  [10]  suggests ways to collect data to measure the STI GO, Working Paper 3  [11]  provides a survey to measure gender equality in STI polices, Working Paper 4  [12]  provides a survey to obtain data from individuals within an institution on all seven of them, and Working Paper 5  [13]  includes examples of good practices and measures that support each STI GO. Additional good practices can be found on the online SAGA database (https://en. unesco. org/saga). To the extent possible and as pertinent, these gender objectives will be addressed in this article on ocean science, considering that GOSR2020's concluding chapter calls for "… continuous and expanded collection of sexdisaggregated data.
" noting that "An expanded database will allow the development of targeted strategies to increase female participation in ocean science"  [14]. As defined in the GOSR2020, ocean science includes all research disciplines related to the study of the ocean, including physical, chemical and biological, geological, hydrographic, health and social sciences, as well as engineering, the humanities, and multidisciplinary research on the relationship between humans and the ocean  [15]. The GOSR2020 considers the following broad categories of ocean science: marine ecosystem functions and processes; ocean and climate; ocean health; human health and well-being; blue growth; ocean crust and marine geohazards; ocean technology; and ocean observations and marine data. Women do not participate to the same extent in all these categories.
Conference speakers and organizers In addition to looking at conference participation, the GOSR2020 looked at the sex of those who were featured speakers at conferences for which such data was available: One way of illustrating the 'glass ceiling'a perceived barrier to advancement in a profession particularly affecting womenis through the participation of female scientists as featured speakers in international conferences and symposia. The gender of invited and other speakers in plenary sessions was identified for a total of 414 speakers participating in 12 international conferences. […] only 29 % of the total featured speakers were women. The GOSR2020's data demonstrated that when more women are involved in conference organization, women are more likely to be selected as speakers, panel members and keynote speakers, as had been noted elsewhere  [36].
Continuous professional development The GOSR2020 provides information on one regional example of the OceanTeacher Global Academy, run by IOC-UNESCO, for continuous training. In the Latin America and Caribbean region, half of the participants are women by design, following gender policies of UNESCO and INVEMAR (Colombia's Marine and Coastal Research Institute). Unfortunately, there was no data presented on other regions or other training centers. The United Nations-Nippon Foundation Fellowship strongly encourages applications from women, and recent years' awardee lists demonstrate parity between the sexes in awards. On-line training increases access to women if provisions for family travel and childcare are not included in awards  [41].
Leadership positions According to GenderInSITE (Gender in science, innovation, technology and engineering), women's participation in global scientific challenges-and many such challenges require data from ocean science-needs to happen at five different levels: i) the individual scientist and their day-to-day work, to ii) the production of science by institutions and universities, iii) professionalization of science at the national level, iv) global science coordination and v) multilateral organizations relevant to the scientific enterprise, so that enabling research networks can be formed and funding organized. It is particularly important for the scientific leadership addressing global challenges to be gender inclusive so as to ensure greater participation by women at all five levels, which will enable society to best address these challenges  [48].
This need is reiterated by O'Connell  [29] , citing women as chief editors of science magazines, and as directors, managers and administrators of major funding and research programs or department chairs. Half of the top six US oceanographic institutions have recently had women executives. She Figures 2018 indicates that in Europe for 2017 women head 22 % of higher education institutions, represent 27 % of members of boards of research institutions, and 20 % of leaders of such boards. Friends of Ocean Action is an informal group created under the aegis of the World Economic Forum in 2018 to stimulate progress on SDG 14. One of its 10 action tracks is "gender parity in the ocean", with a focus on ocean and gender data. Interestingly, there are no targets with a gender indicator under SDG 14, and the SDG Gender Index of 2018 sought to redress this, so efforts by the Friends of Ocean Action to "fast-track" an ocean data platform that incorporates gender data by country is welcome.
Conclusion Although the numbers and percentages of women in ocean science have increased dramatically over recent decades, more sex-disaggregated data is needed for ocean science, in order to best inform institutions and countries about the educational and career progression of men and women in these fields, elucidate the gender L. A. Brooks and I. Déniz-González: Current status of women in ocean science inclusivity of relevant professional organizations and lead to more informed, inclusive and efficient policies, whether to support ocean research and management, or to attain the targets of SDG 14, taking into consideration regional, cultural and societal conditions  [52]. Several tools already exist to assist with the collection and assessment of such data, including the GOSR, SAGA working papers, the Gender Gap in Science project and the Baltic Consortium.
Acknowledgment: Special thanks to the  IOC of UNESCO  for access to the text of the GOSR2020 before its publication and permission to quote from the text and reproduce table 4. 3. At the IOC thanks in particular to  Kirsten Isensee  for her comments on the text and  Salvatore Aricò  for his collegial support. Thanks to  Mark Cesa  and  Mei-Hung Chiu  of  IUPAC  who invited and supported the idea of this paper, and to an anonymous reviewer for useful suggestions.
Methods Plasmids and strains. Genetic parts of constructs used in this study are provided in Supporting Information. All constructs were cloned into plasmid J64100_p15A with p15a origin of replication and chloramphenicol resistance gene by isothermal Gibson assembly. All experiments were performed using E. coli strain NEB10β (New England Biolabs). Plasmids and materials will be made available through Addgene. Sequences will be deposited into GENBANK. Functional characterization of synthetic bile salt receptors. Plasmids encoding different constructs were transformed into chemically competent E. coli NEB10β (New England Biolabs), plated on LB agar plates supplemented with 25 µg/mL chloramphenicol and incubated at 37ºC overnight. For each measurement, three fresh colonies were picked and inoculated into 5 mL of LB/chloramphenicol and grown at 37℃ with vigorous shaking for 16-18 hours.
coli strain NEB10β containing a reference construct and grown in parallel for each experiment. We used the constitutive promoter J23101 and RBS_B0032 as our in vivo reference standard and placed superfolder GFP as a reporter gene in plasmid pSB4K5. We quanti ed the geometric mean of uorescence intensity (MFI) of the ow cytometry data and calculated RPUs according to the following equation: RPU = (MFI sample ) / (MFI reference promoter ) Flow cytometry analysis. Flow cytometry was performed using an Attune NxT cytometer coupled with high-throughput autosampler (Thermo Fisher Scienti c) and Attune NxT™ Version 2. 7 Software and BD LSR Fortessa (Becton Dickinson), with FACSDiVa software. 30,000 cells were collected for each data point. Flow cytometry data were analyzed using FlowJo (Treestar Inc.
Samples were sequenced on Illumina PE250 platform at Novogen (Hong Kong, China) with paired-end reads of 250 bases. NGS data processing. The datasets generated and analyzed during the current study are available in the NCBI SRA repository (BioProject ID: PRJNA714981 ; https://www. ncbi. nlm. nih. gov/bioproject/714981). The python scripts for NGS data processing are available upon request. Sequence counts were converted into amino acid sequences and listed in supplementary le 1. Sequence logos were prepared by R package Logolas 69. Colorimetric assay of for bile salt detection. Overnight cultures of TcpP-LacZ diluted in 1/250 fold into 100 mL of LB with 25 µg/mL chloramphenicol, incubated at 37 ℃ for 4 hours to reach an OD600 of around 0. 4. The cells were put on ice for 30 mins and then spun down at 4℃ and 2,200 g for 10 mins.
ACKNOWLEDGEMENTS We thank members of the synthetic biology group and the  CBS  for fruitful discussions. We are grateful to the patients for participating in this study and providing their samples, and to the personnel of the Montpellier CHU hospital for collecting and preparing the samples. This work was supported by an  ERC   starting grant  " COMPUCELL " to J. B. J. B. also acknowledges support from the  INSERM Atip-Avenir program  and the  Bettencourt-Schueller Foundation. The CBS acknowledges support from the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01. AUTHOR CONTRIBUTIONS H. J. C. , A. Z. , G. C. and J. B. designed experiments. H. J. C. , A. Z. P. L. V. , E. F. R. performed experiments. H. J. C. , A. Z. , P. L. V. , E. F. R. , G. C. ,and J. B.
Climate data Baseline and future climate data were obtained from CHELSA Version 1. 2 (climatologies at high resolution for the earth's land surface areas 50 ) at a spatial resolution of 30 arc-seconds (∼1 km at the equator). A detailed description of the generation of these data is given elsewhere 50. We selected four climate variables; two of them describing mean conditions: (1) mean annual temperature (MAT) and (2) annual precipitation (AP), and two variables describing extremes of climate: (3) maximum temperature of the warmest month (MTWM) and (4) precipitation of the driest quarter (PDQ) (Table  S2 ). All climate data were projected to the Mollweide projection system (ESRI:54009) at a 1 km resolution using bilinear interpolation. Baseline data represent climate conditions during the period 1979-2013.
Methodological approach First, species realised niches (assessed here) are not equal to their fundamental niche. We used occurrence records to approximate distribution, but biological factors such as competition, abiotic factors (e. g. soil and nutrients) and dispersal limits may mean that our use of occurrence records underestimate species' climate envelopes. Second, climate data based on coarse-grained spatial interpolations from weather stations that are shielded from direct solar radiation used here, fail to identify areas where harsh conditions can be exacerbated due to the urban heat island effect 4 or areas where conditions are more benign due to the vegetation effect on microclimate (e. g. high canopy cover) 5 or presence of wind Supplemental Table  S3. Global Circulation Models (GCMs) from the 10 CMIP5 models considered in this study.
Taxonomy was standardized and verified against GBIF and then against The Plant List (TPL; 46-48 45 www. theplantlist. org) using Taxonstand, taxize, and taxizehelper packages in R. Biodiversity Information Facility (i. e. 181,914,869 records from 3,949 published datasets) (GBIF. org;18 December 2019 GBIF Occurrence Download https://doi. org/10. 15468/dl. cpwlwc). We only retained occurrence records with sufficient information on geographical coordinates.
This study aimed to identify and validate suitable reference genes for transcript abundance studies in Oidiodendron maius, a globally distributed, model ericoid mycorrhizal fungus. Results A shortlist of 251 non-differentially expressed genes was generated from RNA-Seq analyses of O. maius grown on three different carbon sources or in symbiosis with Vaccinium myrtillus. Subsequently, a set of criteria (stable expression, valid annotation and relatively high expression) was applied to select three candidate reference genes. These three genes were validated across a further eleven carbon sources using ddPCR and the application of geNorm and NormFinder stability analysis algorithms. Expression stability analysis of three genes -EfTu, vma, and sar -con rmed their reliability as internal references; the geometric mean of their expression values demonstrated the highest stability as a normalization factor.