dataesr/scientific-paragraphs-categorization huggingface dataset:
Their approach yields networks akin to Fig GA, which are not relevant from a cognitive perspective. A template-extracting approach that clusters the low-dimensional brain map representations is proposed; yet it remains exogenous to the model and does not perform convincingly. The consensus post-hoc transformation method we propose in this work addresses the issue of interpretability and finds cognitive directions that efficiently capture mental state information. As Fig B shows, these meaningful networks can be used as a cognitive atlas for improving decoding on newly acquired datasets, without joint training. Consensus through matrix factorization of the model weights also increases model performance (Fig B).
On the other hand, computational predictions of snoRNA targets such as realized by SNOTARGET  (Bazeley et al. 2008)  or PLEXY  (Kehr et al. 2011 ) have proposed that Snord116 can hybridize with multiple cellular RNAs. Of note, SNOTARGET predictions included the Ankrd11 mRNA that encodes a chromatin regulator essential for neural development  (Gallagher et al. 2015)  and a recent study suggested that its expression correlates with the number of Snord116 gene copies in mouse  (Keshavarz et al. 2021). Another study computing snoRNA targets has predicted that Snord116 methylate human 18S at position U1162  (Kehr et al. 2014) , but an experimental validation is pending. In the last years, several molecular studies have interrogated RNA-RNA interactions using high-throughput methods. A comprehensive collection of these data has been recently released, called the RISE database  (Gong et al.
Microevolution of the SNORD116 genes in Human To gain more insights into the history of the SNORD116 genes, we analyzed the occurrence of single nucleotide polymorphisms (SNPs) in human populations. We collected data from the high-quality 1000 Genome dataset that contains 121 single polymorphic sites concerning SNORD116 copies. Of these, only four SNPs are shared by African, American, East Asian, European, and South Asian populations, whereas there was a high prevalence of rare variants, as 93 SNPs (77%) have a minor allele frequency (MAF) <0. 001 (for more details, see supplementary Information Table  3 ). Accordingly, numerous SNPs were found to be singletons or specific to a population (Fig. 4A ). If not due to sequencing errors and according to evolutionary considerations, the presence of numerous SNPs that are infrequent could have several causes, e.
Conservation of ASE1-RNA hybridization potentials The conservation of primary structures in non-coding RNAs often underlies their ability to hybridize with complementary RNAs, and not only regular C/D snoRNAs but also orphan ones may use this strategy to affect RNA targets in various ways. As C/D snoRNA-rRNA interactions largely involve hybridization between perfect or close complementary sequences, we used a simple BLAST approach to test the potential of Snord116 ASE sequences to hybridize with cellular RNAs. We used two highquality RNA datasets, the Ensembl human and mouse transcript collections. Indeed, we hypothesized that some SNORD116 copies have similar molecular functions in the two species, as the phenotypic consequences of SNORD116/Snord116 deficiencies in patients and mouse models largely overlap.
Identification of SNORD116 gene sequences We selected 16 eutherians species: Human (Homo sapiens, Hsa), chimpanzee (Pan troglodytes, Ptr), rhesus macaque (Macaca mulatta, Mml), marmoset (Callithrix jacchus, Cja), mouse lemur (Microcebus murinus, MiM), greater galago (Otolemur garnettii, Oga), northern treeshrew (Tupaia belangeri, Tbe), mouse (Mus musculus, Mmu), rat (rattus norvegicus, Rno), kangaroo rat (Dipodomys ordii, Dor), squirrel (Spermophilus tridecemlineatus, Str), guinea pig (Cavia porcellus, Cpo), Rabbit (Oryctolagus cuniculus, Ocu), pig (Sus scrofa, Ssc), brown bat (Myotis lucifugus, Mlu) and hedgehog (Erinaceus europeus, Eeu). To obtain SNORD116 gene sequences, we combined data from whole genome annotations using the UCSC (genome. ucsc. edu) and Ensembl (www. ensembl. org/index.
html) genome browsers and from the snoRNA databases snoRNA-LBME-db  (Lestrade et al. 2006 ) and snOPY  (Yoshihama et al. 2013). We only collected sequences from Snrpn/Ube3a loci. The genome assemblies used were GRCh38. p13 for Human, Pan_tro_3. 0 for chimpanzee, Mmul_10 for rhesus macaque, ASM275486v1 for marmoset, Mmur_2. 0 for mouse lemur, OtoGar3 for greater galago, TupBel1 for northern treeshrew, GRCm38 for mouse, Rnor_6. 0 for rat, DipOrd1 for kangaroo rat, SpeTri2. 0 for squirrel, Cavpor3 for guinea pig, OryCun2. 0 for Rabbit, Sscrofa11. 1 for pig, Myoluc2. 0 for brown bat and EriEur2 for hedgehog. For each species, we checked the accuracy of the annotations and identified gene copies that could have been omitted in datasets using the BLAT/BLAST option of the UCSC and Ensembl browsers.
Phylogenetic analyses The sequences were aligned using the MUSCLE application in EMBL-EBI  (Madeira et al. 2019 ) with default parameters. The number of base substitutions per site was estimated using the MEGA X software by averaging between sequence groups or overall sequence pairs in each group  (Kumar et al. 2018 ) using the Kimura 2-parameter model. The p-distance corresponds to the proportion (p) of nucleotide sites at which two sequences being compared differ and is obtained by dividing the number of nucleotide differences by the total number of nucleotides being compared. All ambiguous positions were removed for each sequence pair (pairwise deletion option). Variance was estimated using the bootstrap method and 1 000 replicates. The phylogenetic network was constructed with the SIMPLE application in EMBL-EBI using a neighbour-joining clustering method and the phylogenetic trees were generated using the iTOL tool  (Letunic et al.
Identification of exon splicing enhancers/silencers and circular RNA splicing sites The presence of exon splicing enhancers (ESE) and silencers (ESS) was estimated using the ACESCAN2 web server  (Yeo et al. 2005 ) and human Splicing Finder software  (Desmet et al. 2009 ). The sequence of human circular RNAs was found using circBase  (Glažar et al. 2014).
We also used Raji-spike cells and Jurkat-CD16-NFAT-rLuc reporter cells as a simple assay to evaluate ADCC of COVID-19 sera. This system measures the capacity of a serum to activate NFAT through CD16 (the pathway initiating ADCC in NK cells) in the presence of antigen-expressing target cells (Figure  2F ). The convalescent sera activated CD16 above the threshold calculated with the pre-pandemic samples (p < 0. 0001; Mann-Whitney test) (Figure  2G ). The extent of CD16 activation correlated to ADCC activity against S-Fuse-infected cells, suggesting that anti-spike antibodies contribute to the killing of infected cells (r = 0. 77, p < 0. 0001; Spearman correlation) (Figure  2H ). Overall, these data show that sera from SARS-CoV-2-infected individuals harbor polyfunctional antibodies triggering complement deposition and ADCC.
001; Mann-Whitney test) (Figures S3D and S3E). Altogether, these data show that the intensity of the antibody response specific to SARS-CoV-2 is slightly lower in asymptomatic than it is in mildly symptomatic individuals. (G) Raji-spike cells were co-cultured with Jurkat-CD16-NFAT-rLuc in presence of pre-pandemic (n = 13) or COVID-19 (n = 11) sera for 18 h. The ADCC score was measured as the fold increase of luciferase expression over the ''no serum'' condition. (H) Correlation of the ADCC and the NFAT test score ED 50 with sera from pre-pandemic individuals (gray, n = 13) and COVID-19 patients (blue or red, n = 11). Correlation r and p values were calculated using a Spearman correlation test. Blue: asymptomatic individuals; red: symptomatic patients. Dotted line: positivity threshold calculated with pre-pandemic sera.
METHODS DETAILS Serological analysis of antibodies S-Flow The S-Flow assay was performed as previously described. 19 Briefly, 293T-S were incubated at 4 C for 30 min with sera (1:300 dilution, unless otherwise specified) in PBS containing 0. 5% BSA and 2 mM EDTA, washed with PBS, and stained using either anti-IgG Alexa Fluor 647 (dilution 1:600; Thermo Fisher Scientific), or anti-IgM Alexa Fluor 488 (dilution 1:600; Thermo Fisher Scientific), or anti-IgA Alexa Fluor 647 (dilution 1:800; Jackson ImmunoResearch). Cells were washed with PBS and fixed for 10 min using 4% paraformaldehyde (PFA). Data were acquired on an Attune NxT instrument (Life Technologies). Specific binding was calculated with the following formula: 100 3 (% binding on 293T-Spike À % binding on control cells)/(100 À % binding on control cells).
The GFP area was quantified with the Harmony software (PerkinElmer). ADCC was measured using the following formula: 100 x (GFP area in ''no serum'' -GFP area in ''tested serum'')/(GFP area in ''no serum''). CD16 activation reporter assay ADCC was quantified using the ADCC Reporter Bioassay (Promega) according to manufacturer's instructions. Briefly, 5x10  4  Raji-Spike cells were co-cultured with 5x10 4 Jurkat-CD16-NFAT-rLuc cells in presence or absence of pre-pandemic or COVID-19 sera at the indicated dilution. Luciferase was measured after 18 hours of incubation using an EnSpire plate reader (PerkinElmer). ADCC was measured as the fold induction of Luciferase activity compared to the ''no serum'' condition.
5%/Saponin 0. 05%, Invitrogen). The anti-Spike antibody was a kind gift of Hugo Mouquet (Institut Pasteur, Paris). Data were acquired on an Attune NxT instrument (Life Technologies). For each serum, complement-dependent cytotoxicity (CDC) of infected cells was calculated using the following formula: 100 3 (% of infected cells with HIHS À % of infected cells with NHS)/(% of infected cells with HIHS). Complement activation assay on Raji-Spike cells Complement-dependent cytotoxicity (CDC) of Raji cells was measured as previously described. 30 Briefly Raji-Spike cells (5x10 4 ) were cultivated in the presence of 50% normal (NHS) or heat-inactivated (HIHS) human serum and with or without pre-pandemic or COVID-19 sera (diluted 1:33 unless otherwise stated). After 24h, cells were washed with PBS and the live/dead fixable aqua dead cell marker (1:1,000 in PBS; Life Technologies) was added for 30 min at 4 C before fixation.
1). 18 hours later, cells were fixed with 2% PFA, washed, stained with Hoechst (dilution 1:1,000, Invitrogen) and acquired with an Opera Phenix high content confocal microscope (PerkinElmer). For each well, the GFP area and the number of nuclei were quantified using the Harmony software (PerkinElmer). The percentage of neutralization was calculated using the nuclei count or the GFP area using the following formula, setting the ''no serum'' condition at 0% and the non-infected condition at 100%: 100 x (1 -(value with serum -value in ''non-infected'')/(value in ''no serum'' -value in ''non-infected'')). Live-imaging 1x10 4 U2OS-ACE2-GFP-1-10 and 1x10 4 U2OS-ACE2-GFP-11 cells were plated overnight in each compartment of a m-Dish35 mm Quad (Ibidi). The next day, cells were infected at a MOI of 0.
QUANTIFICATION AND STATISTICAL ANALYSIS Calculations, figures and statistics were performed using Excel 365 (Microsoft), Prism 9 (GraphPad Software) or RStudio Desktop 1. 3. 1093 (R Studio, PBC). For R analysis we used the following packages: corrplot (https://github. com/taiyun/corrplot), pheatmap (https://cran. r-project. org/web/packages/pheatmap/index. html), factoextra and FactoMineR (https://cran. r-project. org/web/ packages/factoextra/index. html) and readxl (https://cran. r-project. org/web/packages/readxl/index. html). All information about sample sizes and statistical tests performed can be found in the figure legends.
ACKNOWLEDGMENTS We thank the individuals who donated their blood. We thank  M. M. Rajah  for critical reading of the manuscript;  J. Toubiana  and  S. Brisse  ( Institut Pasteur ) for providing pre-pandemic serum samples;  R. Lemahieu  for organizing the epidemiological survey; and the  ICAReB team  for management and distribution of serum samples. We thank  F. Mentre ,  S. Tubiana , the  French  COVID-19 Cohort, and REACTing for cohort management. We thank  S. Ferandes Pellerin  and  N. Jolly  for their coordination of the CORSER studies. This work was supported by the  Urgence COVID-19 Fundraising Campaign of Institut Pasteur. O. S. is funded by  Institut Pasteur ,  ANRS ,  Sidaction , the  Vaccine Research Institute  ( ANR-10-LABX-77 ),  Labex IBEID  ( ANR-10-LABX-62-IBEID ), the '' TIMTAMDEN '' project (a collaboration of  Inserm, Institut Pasteur , and  IRD ;  ANR-14-CE14-0029 ), the  ANR  '' CHIKV-Viro-Immuno ' '  project ( ANR-14-CE14-0015-01 ), and the  Gilead HIV cure program.
J. D. and L. G. are supported by the  French Ministry of Higher Education, Research and Innovation. H. M. is funded by the  Institut Pasteur , the  Milieu Interieur Program  ( ANR-10-LABX-69-01 ),  Inserm , and  REACTing  and  EU RECOVER  grants. C. P. is supported by a fellowship from the  Agence Nationale de Recherches sur le Sida et les Hepatites Virales (ANRS).
Raw data pre-processing Raw NGS data in fastq format was quality filtered by using the software QAP  [17] , with the following criteria: read length ≥250 bp and base quality ≥25 to remove adaptors and filter out low-quality or short reads. Totally 16,010,732 clean reads from 17,309,648 raw reads were left after filtration and used for subsequent analysis. Next, clean reads were mapped to the reference genome (genotype B, Gen-Bank accession D00329; genotype C, GenBank accession X04615), and read pairs were assembled to amplicon sequences based on their mapping positions. Amplicon sequences were subsequently processed to correct sequencing errors, and finally, the viral haplotypes in fastq format were generated. All QS data were analyzed using QAP software  [17].
Diagnostic model construction using machine learning algorithms Based on pathological classification (IT vs. CHB), three machine learning methods were applied to construct diagnostic models based on viral OTU quantification of patients in the training group, including Knearest neighbour (KNN), support vector machine (SVM), and the random forest (RF) algorithm. These three machine-learning methods were implemented using R package KKNN, e1071, and randomForest, respectively. All R packages can be downloaded from CRAN or Bioconductor. All three models were performed with five-fold cross-validation to avoid overfitting. And then the diagnostic models were validated in the validation group. The workflow of the experiment and data analysis is shown in Figure  1B.
In addition, realizing the full application potential of emerging technologies requires solutions for merging various measurement techniques and platforms into useful information for actionable management decisions, requiring effective communication between data providers and water resource managers  [3]. The Group on Earth Observations (GEO) AquaWatch is a community of practice that aims to develop and build the global capacity and utility of water quality data, products, and information to support water resources management and decision making. GEO AquaWatch targets a wide range of stakeholders, from economists to scientists, to industry representatives, resource managers, nongovernmental groups, and policy makers at the local, regional and national levels (https://www. geoaquawatch. org/, accessed on July 2021).
Integration of inland and coastal water quality data products are required for the successful implementation of the United Nations 2030 Sustainable Development Goals (SDG) and the Sendai Framework for Disaster Risk Reduction. Water quality monitoring is relevant toward SDG 6. 3 to improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials; SDG 6. 6 to protect and restore water-related ecosystems; SDG 14. 1 to prevent and significantly reduce marine pollution of all kinds; and for disaster risk reduction water quality monitoring and forecasting is critical for the development and assessment of safe water and sanitation systems. Here, we provide a conceptual approach developed through GEO AquaWatch for integrating data products to create water quality nowcasts (very short time forecast) and forecasts.
This requires long term data streams that are consistent both spatially and temporally, which can be a challenge to find among government agencies. Data continuity and fidelity can also be hindered by short project funding cycles that have long term consequences. Developing countries and remote locations often have minimal technical or institutional capacity to monitor water quality, manage the data, and report results  [12]. This compounds the challenges of moving toward integrated water quality measures. Although international standards for water quality assessment (e. g. , ISO-International Organization for Standardization) and data sharing, including metadata (e. g. , FGDC-Federal Geographic Data Committee, ODC-Open Data Cube), are in place, no single standard is widely adopted and followed, thereby contributing to this challenge All this can lead to large observational gaps of robust water quality data, needed to underpin water management decisions.
One solution is to rely on innovative monitoring approaches, such as via the integration of satellite and drone remote sensing, citizen science, and models. A demonstration project in Bangladesh recently highlighted the impact of the use of satellite data by decision makers, where coordinated or centralized capabilities ensured users were trained for the use of satellite information and understood the limitations of different satellite products  [13]. Global provision of water quality data often requires the integration of different types of data from different sources for use by different communities. Scattered initiatives provide databases and platforms but lack concrete sustainable support to maintain and widely use those infrastructures. A global platform will enable the community to access information globally, on which analysis can be carried out, decisions can be made, and policy questions can be answered.
25 ms -1. The Saildrone can perform autonomous long-range data collection (up to a 12-month mission, depending on biofouling). There are numerous commercially-available ASV and small autonomous underwater vehicles (AUV) for water water quality monitoring that are becoming available to all stakeholders. They have the potential to play a significant role in coastal and inland water quality monitoring in years to come. However, listing them here is beyond the scope of this paper. Disadvantages of drones include frequent changes to codes, restrictions, and controlling legislation at the state and national levels  [99]. Local and national laws now commonly require specialized training, certifications, and aircraft registration. Drone pilots must be aware of restricted airspaces. Obtaining permissions can sometimes be lengthy and complicated, although a new USA FAA flight approval process (LAANC) can provide dependable and rapid permission for flights in restricted airspaces using computer interactions between a certified operator's flight planning software (for example B4UFLY) and local control tower operations.
Volunteer Monitoring Volunteer monitoring often referred to as citizen science, is the collection of scientific samples and data by members of the general public  [100]. It is often carried out in collaboration with professional scientists to record biodiversity or monitor the environment. It can provide scientifically robust data at large spatial coverage and high frequency (e. g. , CoCo-RAHs, https://www. cocorahs. org/ accessed on July 2021 Weather Observation Website [WOW] https://wow. metoffice. gov. uk/, accessed on July 2021). Volunteer monitoring is also an effective approach for engaging the public in science and environmental monitoring, empowering individuals and communities to understand and manage the quality of their local environment  [101]. Volunteer monitoring data can be complementary to remote sensing data, as they can be used together to provide independent cross-validation  [34] , although data quality of specific citizen science applications are not quite on par with scientific spectrometers  [102].
Models A major benefit to the integration of modeling with monitoring datasets is the greater explanatory power over single observations alone  [107]. Although remote sensing and most citizen science programs provide surface measures, and discrete and continuous sampling provide measures at depth, they all are limited to observation times. One-and three-dimensional integrated models provide time-stepped processes and distribution of water quality parameters laterally, as well in depth. Models provide tools quantifying rate processes and managing the ecosystem response to anthropogenic and natural conditions. Deterministic water quality models rely on a first correct description of hydrodynamic processes and conditions (e. g. , stratification, shear, etc. )  [108]. Compared with three-dimensional process-based models, one-dimensional (1-D) models have the advantage of being computationally inexpensive and can provide good estimates of the seasonal dynamics  [109].
Data Integration GEO AquaWatch, long-recognized as the Global Water Quality Initiative with an extensive global network, coordinates an integrated approach to community stakeholder involvement, and its Knowledge Hub is recognized as a trusted global water quality data and information center. Ongoing efforts by GEO AquaWatch working groups and short-term, cross-cutting focus group activities have yeilded metadata compilations, best practices for data providers, aquatic analysis ready data recommendations, and expanded community-wide information-sharing. Transformational water quality knowledge is delivered to GEO AquaWatch stakeholders via our website and social media accounts, a successful and sustaining webinar series, topical workshops, and Initiative meetings. Printed and electronic work products, such as whitepapers, publications, and data products, services, and tools compiled by our diverse subject matter experts, are easy-access, communitywide resources serving the gamut of water quality users.
Leveraging GEO AquaWatch's association with other national and international water quality organizations amplifies best-practice messaging and enables broader user input. Stakeholder engagement in the delivery of fit-for-purpose, open source products, meeting user needs, is foundational to GEO AquaWatch's portfolio of projects. A highlight is it's namesake Google Earth Engine project for inland and coastal waters whichcompiles a reference database of biogeochemical, inherent, and apparent optical properties that is essential for calibration and validation of satellite products. AquaWatch features a selfguided user interface with a nimble API that delivers chunk-sized data streams to the Google Earth Engine Servers. To effectively achieve integrated inland coastal water quality management, users ideally inform their decision-making through combination of evaluating acute conditions and general trends in satellite-derived and in situ data, and utilizing iterative models; but we recognize many users lack one or more of these "three pillars" of data assimiliation and must otherwise rely upon the best available information.
The complexity of environmental systems, and the need for meaningful information to be provided to stakeholders, requires the integration of data collected from different and heterogeneous sources. GEO AquaWatch's Real Earth Portal, expected to debut in 2022, synthesizes multi-platform water quality data from disparate sources and over broad temporal-spatial scales accessible through the GEO AquaWatch website. Many hydrodynamic and ecological models have evolved as relevant predictive tools, and it is essential to accurately calibrate and validate them with available observations to validly represent the reality. Data fusion (DF) techniques have been used to maximally extract consistent spatial and temporal description of variables within hydrodynamic-biological models, cohesively and systematically, to produce more robust and informative datasets.