dataesr/scientific-paragraphs-categorization huggingface dataset:
DF algorithms include the wavelength-based method  [129] , the Jointly Sparse Fusion of Images (J-SparseFI)  [130]  designed for pan-sharpening fusion, and other algorithms  [131, 132]. The effectiveness of DF has been demonstrated in improving spatio-temporal distributions of total organic carbon (TOC)  [133] , chlorophyll-a concentrations, and water transparency  [134] , and the prediction of harmful algal blooms  [135]. NASA-promoted STAR-FM software (https://www. ars. usda. gov/research/software/ download/?softwareid=432, accessed on July 2021), designed for the fusion of Landsat and MODIS images, is available for direct implementation of fusion techniques at the pixel level  [136]. Spatial and spectral information of the pixel is correlated and combined to better characterize the single pixel.
These same models complement sensor networks to fill data gaps, and provide estimates where sensor networks are not deployed or have missing data. An example of a fully integrated monitoring and modeling platform is ODYSSEA (http://odysseaplatform. eu/, accessed on July 2021) (Figure  8 ). The ODYSSEA platform is connected constantly with existing observational systems, models, and databases in the Mediterranean Sea, building on key initiatives, such as Copernicus, Global Earth Observation System of Systems (GEOSS), Global Ocean Observing System (GOOS), European Marine Observation and Data Network (EMODnet), among others. Further, the platform is connected to nine regional pilot observatories where novel monitoring systems are installed (to increase spatial and temporal resolution). Copernicus data and products are automatically and regularly fetched, organized, harmonized, and combined with in situ, and meteorological information from National Oceanic and Atmospheric Administration (NOAA) and distributed to enhance monitoring capabilities of water quality issues.
The integrated framework described here for global and regional scale is also implemented by other platforms at local scales. An example is the HiSea platform (http: //hiseaproject. com, accessed on July 2021) that delivers high resolution information obtained through the integration of in situ, remote sensing, and modeled data and the downscaling of Copernicus and NOAA products. Copernicus provides information at a regional scale that cannot be used to properly describe the system dynamics at the local scale. This is even more true for estuaries, coastal lagoons, and inland waters. HiSea aims to remove the barriers by making information easily accessible to a broader public through access to a single platform. Projects requiring full integration need to be chosen selectively because of limited resources in operationalizing fully integrated monitoring networks.
Moving to larger scale requires replication, where integrative components can be articulated and standardized  [154]. The framework proposed in this paper includes those components. A growing list of integrated data to information systems specific to water quality management includes NOAA's tides and currents  [155] ; harmful algae bloom forecasting systems  [156, 157] ; Spain's Portus (https://portus. puertos. es, accessed on July 2021); EAWAG's Swiss lake information portal (https://simstrat. eawag. ch/ for 1-D and http://meteolakes. ch/ for 3-D models, both links accessed on July 2021); the UK Lakes Portal (https://eip. ceh. ac. uk/apps/lakes, accessed on July 2021) the HAB Satellite Analysis tool from San Francisco Estuary Institute (https://fhab. sfei. org/, accessed on July 2021); Datalakes online platform for Swiss lakes (https://live.
Prior requirements, such as calibration, validation, and data quality control and assurance, were outside the scope of consideration here. The sustainability of various observing systems remains a major concern. However, through improved communication between scientists and stakeholders, there is an improved likelihood that scientifically sound information can be transferred and understood by stakeholders for management decisions. Stakeholder obstacles include correct and easily accessible interpretation of data into information, particularly when programming skills may be required to derive additional information, and expert knowledge is required to understand technology limitations and capabilities. The GEO AquaWatch Initiative will continue to improve the transfer of data to information for water-quality-related decisions through integrated efforts, as conceptualized here.
Acknowledgments: Contributions were made from members of the Group on Earth Observations AquaWatch Initiative Working Group 3, focused on products and information. This group is responsible for identifying, generating, and evaluating products derived from complementary remote sensing and in situ data sets, as well as supporting modeling and data assimilation activities for regional and global water quality nowcasts, forecasts, and predictions. This group works to facilitate routine and sustained production of fit for purpose products for use in the scientific community, as well as information products for policy makers, decision makers, and the general public. El Serafy, Schaeffer, Neely, and Spinosa conceptualized and led the writing effort; Odermatt, and Weathers contributed to the integration of the manuscript sections with editing; and all other co-authors are otherwise listed alphabetically with equal contributions of writing.
PET-MR data acquisition 18 F-flutemetamol brain PET-MR imaging was performed using a hybrid 3T mMR Biograph system (Siemens Medical Solutions, Erlangen, Germany). Dynamic PET acquisition started at the time of intravenous injection of a target dose of 185 MBq (184. 2 ± 6. 2 MBq) 18 Fflutemetamol and lasted for 30 min in 3D list mode (phase 1). A second period of 30 min PET data acquisition was repeated at 90 min post 18 Fflutemetamol injection to 120 min (phase 2). Before the start of each PET phase (1 and 2), a separate MR attenuation correction map was acquired using the manufacturer's ultrashort echo time sequence. The following MRI sequences were performed during PET phase 1: 3D T 1 -weighted, T 2 -weighted, fluid-attenuated inversion recovery (FLAIR) and SWI sequences; PET phase 2: 3D T 1 -weighted, T 2 -weighted, FLAIR, T 2 * -weighted gradient recalled echo and diffusion tensor imaging.
Patient diagnosis A neuroradiologist assessed the SWI MR data using the Syngo Via software (Siemens Medical Solutions, Erlangen, Germany) to categorise participants as probable CAA or no CAA as defined by the modified Boston criteria  ( Linn et al. , 2010 ) , masked to the clinical and 18 Fflutemetamol PET data. The size of ICH was measured using the Syngo Via software. According to these criteria, patients with two or more haemorrhagic foci (macrohaemorrhage, microbleed or cortical superficial siderosis) restricted to lobar, cortical or cortical-subcortical regions, were classified as probable CAA. Patients with deep ICH (non-lobar haemorrhage in the modified Boston criteria), were classified as neither probable or possible CAA (no CAA)  ( Linn et al. , 2010 ). This assessment was used as the reference standard technique to evaluate quantitative 18 F-flutemetamol data analysis.
Visual assessments from 18 F-flutemetamol PET-MR data Two neuroradiologists (MR and GT) who had completed the Vizamyl TM ( 18 F-flutemetamol) Electronic Training Programme independently, rated the 18 F-flutemetamol uptake on the decay corrected PET images of phase 2 (derived from the 90 to 120 min post-injection interval) fused to the participant's 3D T 1 -weighted images, using the Syngo Via software. Scans were classified as 18 F-flutemetamol positive overall, if there was at least one positive region. In the absence of positive regions, scans were classified as negative. In the event of disagreement, the images were reviewed together and a consensus was reached. The consensus decision for overall positive/negative classification was used to determine 18 F-flutemetamol PET positivity/negativity.
Image analysis To derive time-activity curves (TAC) and SUVR from the PET data across all brain regions, PET-MR co-registration and segmentation was performed using dedicated software  (PMOD 3. 8, Switzerland). Firstly, the 3D T 1 -weighted MR data acquired in phases 1 and 2 were each spatially registered to a T 1 -weighted Montreal Neurological Institute (MNI) brain template (via non-linear registration). The transformation matrix from the previous non-linear registration step was saved and then used to register (the dynamic and static) PET data to the registered (to the MNI template) 3D T 1 -weighted data. Tissue probability maps were generated on the co-registered 3D T 1 -weighted data based on the tissue probability map operation of SPM8 incorporated into PMOD  ( Ashburner and Friston, 2005 ) , which were then used to extract TAC and SUVR from the cortical areas.
Based on the MNI template, standard volumes of interest (VOIs) were defined on the co-registered PET images using automated anatomical labelling in PMOD for 9 brain regions: the left and right frontal, parietal, temporal, occipital lobes, and the cerebellar cortex. Tissue quantification (perfusion-dependant and tracer uptake) comparisons were performed on TAC and SUVR extracted from the 8 cortical VOIs (target tissue) across all subjects. The cerebellar cortex TAC (reference tissue) extracted from each participant was used for the estimation of the mIF (in Eq. (  5 ) ) and as the reference tissue for the SRTM and FRTM analysis. Co-registration and VOI segmentation were performed separately in phases 1 and 2. To interpolate regional brain uptake between phases 1 and 2, a biexponential function was fitted to all TAC in Matlab (MathWorks Inc.
, Natick, MA) as described by  Nelissen et al. (2009 ) : 𝐸𝑥𝑝𝑜𝑛𝑒𝑛𝑡𝑖𝑎𝑙 𝑓 𝑢𝑛𝑐𝑡𝑖𝑜𝑛 = 𝜑 1 𝑒 -𝜃 1 𝑡 + 𝜑 2 𝑒 -𝜃 2 𝑡 (1) Note that the second term of Eq. (  1 ) was allowed to tend to 0 (using the standard fittype Matlab function) when interpolation was approaching mono-exponential decays.
Quantitative analysis Kinetic model analysis was performed using customised in-house software developed in Matlab. The code will be made available at ( https://github. com/Georgerun/NeuroKinModel ). The following steps were followed to measure mIF: 1 1-TC modelling was initially performed in the cerebellar cortex (used as the reference tissue) of all participants using a metabolitecorrected pIF derived from a small cohort of six patients: three healthy controls and three AD patients from a previous 18 Fflutemetamol PET study  ( Nelissen et al. , 2009 ; Heurling et al. , 2015 ). This step allowed to extract K 1 ′and k 2 ′ estimates across all subjects. 2 To reduce any bias that can be introduced by using a small cohortderived pIF, K 1 ′and k 2 ′ estimates across all sixteen subjects were averaged and mean K 1 ′and mean k 2 ′ were estimated.
Statistical analysis Dedicated software was used for statistical analysis (R Foundation for statistical computing, Vienna, Austria; MedCalc Software, Ostend, Belgium). Pearson correlation tests were performed to assess correlations and Bland Altman plot analysis was used to investigate systematic bias in kinetic model estimates, derived from reduced (30 min) against full interpolated and concatenated (0-30, 90-120 min) PET acquisition time frames. Box and whisker plots investigated differences in K 1 and V T between cases with probable CAA and deep ICH patients. Statistical differences in perfusion-dependant (K 1 , R 1 ), k 2 and tracer uptake (V T , BP ND and SUVR) estimates between probable CAA and deep ICH patients were assessed on a per cortical VOI region, using a two-sample unpaired t -test.
Quantitative analysis from full PET-MR acquisition In total, 128 target tissue TACs were analysed (64 TACs from cases with probable CAA against 64 TACs from patients with deep ICH). Initially, 1-TC and 2-TC modelling was performed using the pIF from the full interpolated (0-30, 90-120 min) PET-MR data (mean values are presented in the Supplementary file 1). The whole blood and metabolite corrected pIF are illustrated in the Supplementary file 2. Significant differences between cases with probable CAA and patients with deep ICH were only observed for the K 1 estimate for both models ( P < 0. 01). No significant differences were observed in K 1 ′and k 2 ′ estimates between 1-TC and 2-TC modelling, and between cases with probable CAA and patients with deep ICH for both models. The mean pIF 1-TC modelderived K 1 ′and k 2 ′ estimates used to calculate the mIF are shown in the Supplementary file 1.
88 (2. 72, 3. 08) and 2. 74 (2. 63, 2. 88); 3. 10 (2. 87, 3. 48) and 3. 12 (2. 87, 3. 43), respectively ( Fig. 4  ). SRTM and FRTM models were also investigated using the full interpolated (0-30, 90-120 min) data. Significant differences between cases with probable CAA and patients with deep ICH were only observed for the R 1 and for the SRTM-derived k 2 estimates ( Table  2  ). To investigate whether the interpolated data can affect model estimates, kinetic modelling was repeated using full concatenated (eliminating interpolation) PET-MR data. Although significant differences between cases with probable CAA versus patients with deep ICH were maintained in the full concatenated analysis, these were lower compared to the full interpolated analysis across all models ( Table  2  ). SUVR analysis extracted from phase 2 was significantly higher in cases with probable CAA versus patients with deep ICH (mean SUVR = 1.
, 2014). The transcription machinery works as an interplay between DNA-binding proteins called transcription factors (TFs) and cis-regulatory elements (CREs). TFs bind short sequences known as TF-binding sites (TFBSs) located at CREs (e. g. promoters, enhancers, silencers). The different sites recognized by a TF are usually summarized as motifs or matrices. TFs may act as either activators or repressors of gene expression, leading to dynamic changes of the cellular pathways. For peach, annotation of TFs is available in resources such as the database plantTFDB  (Tian et al. , 2019). As of November 2020, plantTFDB v5. 0 stored 2,780 peach TFs classified into 58 families (http://planttfdb. cbi. pku. edu. cn). However, while much is known about TF families, most TFBSs and motifs are yet to be characterized.
Deciphering the cis-regulatory network has become a prerequisite toward scoping out the foundations of transcriptional regulation in peach and other plants. The computational exploration of DNA motifs has been greatly stimulated by the availability of genomic data and the release of whole genome sequence assemblies  (Verde et al. , 2013 (Verde et al. , , 2017)). In this context, a variety of plant motif finders has emerged. Notwithstanding their value, they are hampered by certain limitations such as a restricted range of species (Promzea for maize [Zea mays] (Liseron-  Monfils et al. , 2013)  and AthaMap for Arabidopsis [Arabidopsis thaliana]  (Steffens et al. , 2005) ]; out-of-date databases (PlantCare, last updated in 2002;  Lescot et al. , 2002)  or platforms allowing only the recovery of experimentally defined motifs (PlantPAN;  Chang et al.
All together, we demonstrated the utility of our strategy in analyzing genome-wide data to provide insights on gene regulation dynamics across tissues and specific conditions. In addition, the motifs predicted in this study can be browsed at https://eead-csic-compbio. github. io/coexpression_motif_ discovery, where we provide readers with direct links to the results, a source code repository and a Docker software container to reproduce the analysis on any other plant species. A step-by-step tutorial for Web users is also available at https://github. com/RSAT-doc/motif_discovery_clusters.
Identification of differentially expressed transcripts and module definition using weighted co-expression networks After quality assessment and pseudo-alignment, an expression matrix was generated from eight published peach transcriptomes, including treated and control samples with their corresponding biological replicates. Differential analysis yielded 11,335 altered transcripts using Q 5 0. 01 and jbj 4 1 thresholds. The number of differentially expressed transcripts (DETs) identified in each RNA-seq experiment is given in Table  1. Detailed information about quality control, pseudo-alignment and differential expression analyses is available on Table  S1. Expression values of 11,335 stress-related transcripts and 64 samples were used to construct the co-expression modules using the weighted gene co-expression network analysis (WGCNA) package.
TFs annotation and prediction of their TFBS using footprintDB To verify whether modules with predicted motifs might contain their potential binding TFs, gene-encoding TFs were   S2. An interactive report with source code is accessible at https://eead-csic-compbio. github. io/coexpression_motif_discovery/ peach/. annotated and shortlisted in Figure  4. Subsequently, they were individually examined for their potential DNA motifs using footprintDB, and the results were compared with those obtained with RSAT. For consistency, control subsets of 50 random TFs selected from outside each module were additionally assessed. Motif-to-motif similarities between footprintDB and RSAT predicted matrices were computed using the Ncor score. Note this is an independent analysis, which does not use the clustered sequences; instead, it uses protein sequence of TFs.
Our results revealed that consensus sequences predicted from the module corresponding TFs showed higher similarity to consensus sequences enriched in modules than those predicted from random TFs (Table  3  and Supplemental Figure  S4 ). For instance, the binding motif tTTGGCGGGAAA enriched in module M41 is almost identical to E2FE-predicted site TTTTGGCGGGAAAA for the E2FE Table  2  Gene ontology enrichment in co-expression modules using PlantRegMap/PlantTFDB portal v5. 0 and the adjusted P-value (FDR 5 0. 05)    3 ). See Supplemental Table  S6  for the abbreviations. Table  3  Similarity comparison between RSAT and footprintDB DNA-binding motif predictions (matrix similarities were computed using cor 5 0. 7 and Ncor 5 0. 5) The best predictions in footprintDB were selected in Arabidopsis.
Detailed scanning results can be accessed at https://eead-csic-compbio. github. io/coex pression_motif_discovery/peach. Repetitive (AT) elements were also scanned to check their relevance, for example, whether they correspond to the TATA box. The underlying data included in Supplemental Figure  S5  showed that TFBSs of these motifs were remarkably distant to the TSS and were distributed across the complete proximal region.
The performance of motif discovery using hierarchical clustering To demonstrate the efficiency of our methodology, we reproduced the analyses described above with conventional clustering approaches within the clValid R package. After testing nine algorithms, hierarchical clustering (HC) with k = 26 gave the best score and was thus selected (see Supplemental Figure  S6 ). Of these, we could match 14 clusters to previously discussed network-based modules having similar expression profile and sharing at least 15% of genes. In Figure  6 , we present as an example the comparison of the motifs discovered in module M4 and hierarchical cluster C2, sharing 57. 9% of genes. As expected, in this example and in 11/14 of the cases (see Supplemental Figure  S7  and Supplemental Table  S4 ), we were able to predict the same motif family in both network and HC strategies, which supports the robustness of the protocol proposed.
The performance of motif discovery using ChIPseq-based clusters As a positive control, we tested whether our motif discovery protocol was able to identify the cognate regulatory sequences of genes tagged in ChIPseq experiments. Thus, in this section, we deal with superior quality clusters, as all genes in each considered study are known to be physically bound by a TF. As this type of data is not available in peach, we used public Arabidopsis datasets from 10 different TF families. Using curated data from the JASPAR database, we observed that the experimental motifs were successfully discovered by at least one algorithm in all datasets. The results are summarized in Figure  7A  and Supplemental Table  S5 , where normalized similarity scores (Ncor) were used to compute the similarity between JASPAR and de novo motifs.
The performance of motif discovery on monocots The goal of this section was to check the efficiency of our protocol on larger genome species. Four clusters of maize co-expressed genes with experimentally confirmed TFBSs were extracted from  (Yu et al. , 2015)  and used to reproduce the analysis (ABI4, E2F1, Myb59, and WRI). Motifs among the first three modules were successfully detected "de novo. " As shown in Figure  7B , the motifs discovered by RSAT showed higher significance within the Up 2 upstream region. Moreover, their logos were similar to those reported by  Yu et al. (2015)  with better matching scores within Up 2 windows (Ncor). Note that the WRI1 motif was also discovered but was not significant in the context of the negative controls.
Discussion In this study, transcriptional profiling of eight independent data sets was conducted to decipher the intricate process of gene regulation in peach and to reveal meaningful biological signatures. DETs were grouped into 45 co-expression modules undergoing similar changes in their expression patterns. Unlike conventional clustering methods (such as k-means and HC), which are based on geometric distances, WGCNA is a graph-based approach relying on network topology as inferred from the correlation among expression values  (Li et al. , 2018). In our hands, the WGCNA algorithm robustly and accurately defined modules within a complex multi-condition dataset. Discerning regulatory signals from blocks of co-expressed genes is a common presumption used to identify functional genomic elements.
, 2015). Interestingly, when tuning the promoter upstream length to a tract of [-500 bp, + 200 bp] relative to the TSS, these low complexity motifs were limited to module M2. It would seem that long upstream promoter regions unbalance the signal-to-noise ratio, exacerbating the identification of such AT motifs. Along the same lines, we observed a dependence of (AT)-rich sites on the dataset size. Indeed, AT-low-complexity motifs were only detected in the first six modules, which contained upstream regions from 560 to 1,795 genes. In light of these considerations, we believe that in our study case, they may result in part due to the properties of DNA sequences (upstream region length, cluster size) rather than the performance of the chosen algorithm. In fact, our results (Supplemental Table  S3 ) revealed that AT-rich occurrence in random clusters increases in parallel with the module size.
Concerning the sensitivity, we were able to detect 18 significant elements in modules while only 15 were revealed in HC clusters (Supplemental Table  S4 ). Moreover, for 11,335 DETs, the clValid function required a longer execution time (nearly 48 h) to reveal the best clustering algorithm and the optimum number of clusters. In contrast, only a few minutes were needed to generate 45 modules using the WGCNA R package. A second validation experiment was a positive control in which we analyzed ideal gene clusters that group genes tagged in 10 different Arabidopsis ChIPseq experiments. Comparing the de novo predicted motifs to the corresponding curated motifs in JASPAR we observed a high similarity in terms of Ncor scores (Figure  7A  and Supplemental Table  S5 ). Note, however, that the motifs were not identical, as instead of symmetrical chromatin peaks we analyzed proximal promoters of variable length, a setup that is more challenging than standard ChIPseq analysis.