dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions For a decade, the Caribbean countries, particularly the LA, have suffered from the impacts induced by the massive and regular arrival of Sargassum on their coastal areas. This study presents the application of a clustering approach to determine the types of surface current circulations integrating the additional wind drift and their possible links to the Sargassum beachings observed on the LA coasts. The Guadeloupe archipelago was chosen as a beaching observational site for the period 2019-2020. This analysis was performed using the most recent versions of ocean current analysis, Mercator and HYCOM. The surface wind speed data from the ERA-5 model were also used. The clustering of the spatiotemporal surface current fields including windage was produced using the K-means algorithm combined with the expert distance metric.
The silhouette index was used to determine the optimal number of clusters. For this region (8-17 • N, 66-55 • W) divided into three subregions, we identify four coherent patterns from datasets. They contain the current structures related to the Guiana currents, the branches of the subtropical Atlantic gyre, and the front and the retroflection rings related to the NBC. The finer resolution of HYCOM analysis provided more detailed information on surface current velocities near the islands than Mercator fields (i. e. , mean local velocity difference of 0. 3 m s -1 ). Offshore, these differences remain very small. Links between clusters and observed beachings in Guadeloupe were studied considering windage, paragon velocity distributions, and monthly abundance maps. The surface current circulations characterizing the MC3-HC3 and MC4-HC2 cluster pairs seemed the most favorable for the transport and the beaching of Sargassum on the Lesser Antilles coasts.
The performance score of this predictive model showed that HYCOM seems more suitable to reproduce small-scale current patterns inducing (or not) beachings in the Lesser Antilles. For 1 year of tests (i. e. , 2021), the decision tree accuracy respectively reached 70. 1 % and 58. 2 % for HYCOM and Mercator with a temporal uncertainty range ± 3 d around the forecast date. This accuracy could be improved by weighting and prioritizing the different modules. The daily probability of Sargassum offshore abundance used in the decision tree would also be improved with better-quality Sargassum remote sensing observations, particularly the FA density data gap within 30 km of the coasts. Due to the very recent availability of the selected HY-COM new-generation version, the present study was conducted only for 2 years  (i.
Subsequently, the lists of patients were analyzed according to the relevance of the diagnoses and inclusion criteria and to eliminate duplicates. Data collection was carried out by consulting follow-up and hospitalization reports available in patients' medical les. The duration of the follow-up was determined between the date of diagnosis and the date of the last contact. The patients were divided according to their department of origin and not according to the place where they were treated. The variables collected are shown in Table  1  of the supplementary data. The diagnostic criteria for patients followed for systemic lupus were de ned according to the SLICC and the EULAR/ACR2019 classi cation 8,9. Anti-nuclear antibodies (ANA) were considered positive if greater than or equal to 1/320 for both girls and boys.
A clinical are in childhood was described as a clinical and laboratory manifestation requiring a change in background therapy or steroid pulse (> 1 mg/kg/d). Neurolupus was de ned according to international criteria  10, 11. Lupus-related renal impairment was de ned according to the international classi cation 12. Antiphospholipid syndrome was de ned according to international guidelines  13. Patients present for a short stay (vacation) in one of the territories and followed in another center were excluded from the analysis. Qualitative data are described as numbers and percentages. The differences in percentages were tested with the χ2 test and Fisher's exact test for sparse data. The statistical analyses were carried out with Stata software. A P-value of 0. 05 was considered signi cant. The Institutional Review Board of the University Hospital of Martinique approved the study under number 2021/116.
Statements And Declarations Arthur FELIX contributed to the design and implementation of the research, performed the data extraction, to the analysis of the results and to the writing and correction of the manuscript. Orange: clinical signs at onset, Green: immunological pro le, Blue: evolutive pro le and proportion of organ involvement during childhood. ANA: anti-nuclear antibody. Figure 3 Evolution during follow up according age at onset Steroid in mg per day. A clinical are in childhood was described as a clinical and laboratory manifestation requiring a change in background therapy or steroids pulse (> 1mg/kg/d). The number of are per year during childhood was calculated with the total number of are and the duration of the follow up before the age of 18 years old.
| DNA methylation alterations in progeroid laminopathies We used the Infinium MethylationEPIC BeadChip to compare genome-wide DNA methylation signatures in whole blood DNA of progeroid laminopathy patients with LMNA mutations versus ageand gender-matched controls. Differentially methylated sites and regions (genes, promoters, CpG islands, and tiling regions) between samples were analyzed following adjustment for age and gender and cell type composition via the RefFreeEWAS package  (Houseman et al. , 2014). An initial differential methylation analysis comparing 8 classical HGPS vs age-and gender-matched controls and 7 progeroid laminopathy patients (non-classical mutation) vs matched controls revealed no differentially methylated sites/regions with a false discovery rate (FDR)-adjusted p value < 0.
035), whereas classic HGPS showed no differences (p = 0. 88) (Figure  S2 ). We additionally performed an analysis focused on samples <10 years old across all groups, which similarly revealed no age acceleration, IEAA, or EEAA differences in patients vs controls (Figure  S3 ). We further investigated overlap between the 61 significant CpGs in progeroid laminopathies and differentially methylated CpG sites in the adult progeroid syndrome, Werner syndrome, in the GSE131752 dataset  (Maierhofer et al. , 2019). This analysis revealed a single common CpG site (cg06216080) with significantly altered DNA methylation in blood DNA of typical WS (Figure  3a ). We additionally investigated nearby CpG sites that showed no significant methylation difference following FDR adjustment. Here, we could observe a similar pattern of DNA methylation changes when comparing progeroid laminopathies vs controls across several nearby CpG sites (Figure  S4 ).
| Differential expression analysis of epigenetically altered and interacting genes Further leveraging publicly available RNA sequencing (RNA-seq) and expression array datasets, we examined the expression profile of the ENSG00000223989 long non-coding RNA (lncRNA) gene in iPSC-derived smooth muscle cells and primary skin fibroblasts of the HGPS patients as well as patients suffering from LMNA-related dilated cardiomyopathy (DCM) (Table  S3 ). In addition, we measured the expression of CTNNBIP1 since antisense lncRNAs are known to control the sense gene expression of neighboring proteincoding genes  (Villegas & Zaphiropoulos, 2015). In these cell lines, ENSG00000223989 lncRNA levels were below the detection limit of our RNA-seq analysis. Using expression array, we observed a trend of differential CTNNBIP1 expression between the HGPS SMCs and control SMCs under static conditions (adj.
| DNA methylation quantification Genomic DNA was bisulfite converted via the EZ DNA Methylation™ Kit (Zymo Research, Irvine, CA, USA). Following whole-genome amplification and enzymatic fragmentation, DNA methylation levels were quantified using the Infinium MethylationEPIC BeadChips kit (Illumina) according to the manufacturer's instructions. All samples were processed simultaneously to avoid batch effects. Furthermore, cases and controls were randomly hybridized on the array to reduce positional effect bias. BeadChips were scanned using an Illumina iScan, and raw intensity data files (IDAT) were exported to R software package. Analysis of the IDAT files was performed in R using the RnBeads package  (Muller et al. , 2019). Quality control and preprocessing steps involved the following: (i) Filtering out probes overlapping SNPs (n = 17,371), (ii) Filtering out probes and/or samples with the highest fraction of unreliable measurements using greedycut (n = 2379); in total, 19750 probes were removed and all samples were retained, and (iii) Subsequently, data normalization was performed using Dasen, followed by an additional filtering step to remove probes located on sex chromosomes (n = 18,986).
| Differential DNA methylation analysis of cg06216080 A total of 1026 blood DNA samples, out of which 425 are T2D patients, were collected from Qatar BioBank (QBB) and profiled for DNA methylation using Illumina EPIC arrays in 3 batches. A linear regression model was used to compute the association between cg06216080 and age in each of controls and T2D cases, separately. Controls and cases were divided each into 3 groups based on BMI (lean/normal, overweight, and obese) that were tested separately for association with cg06216080. Controls and cases were also tested for association with cg06216080 above specific age ranges  (50, 55, 60, and 65). Covariates that were added to the model are BMI, gender, batch effect, well position, plate number, measured cell counts (neutrophils, basophils, eosinophils, lymphocytes, monocytes), smoking surrogate (AHRR), and four principal components from genomic data to correct for population stratification.
a) Chronological age (x-axis) vs DNA methylation age (y-axis) and (b) age acceleration measured using the Horvath clock as well as (c-d) DNAmAgeSkinBloodClock F I G U R E 3 (a) Venn diagram showing overlap of a single genome-wide significant CpG site (cg06216080) in blood DNA of Hutchinson-Gilford Progeria syndrome (HGPS), Werner syndrome (WS), and Down syndrome (DS) (b) DNA methylation (β values) distribution for cg06216080 in all cases and control samples in blood DNA of HGPS, WS, and DS. Median is indicated by a solid black line 9,908,001-9,909,000) encompassing cg06216080 as well as 3 additional probes with nominal significance following FDR adjustment (comb. p. adj. fdr = 0. 115, control β values mean of means = 0. 3713, patient β values mean of means = 0. 3917). Next, we investigated whether cg06216080 is epigenetically altered during biological aging in an unpublished dataset of 601 healthy controls and 425 individuals with Type 2 diabetes (T2D).
pathways have been shown to be associated with aging and age-related diseases (Jimenez-Moreno et al. , 2008; Liu & Ikegami, 2020; Veeranna et al. , 2011) , which might indicate a function for those pathways in the observed phenotypes of progeroid laminopathies. We could not observe DNA methylation differences when comparing probes located in lamin A LADs as well as redistributed lamin A LAD genomic regions. This is in contrast to a recent study byKöhler et al. where DNA methylation alterations were reported in lamin A LADs of primary fibroblasts obtained from classic HGPS patients(Kohler et al. , 2020). Köhler et al. mainly  focused on LADs specific to the cell type in which they performed differential methylation analysis (fibroblasts). Unfortunately, this was not possible in our analysis due to lack of published lamin A LAD datasets for blood cells.
Optimal Channel Networks and Hydrological Model We simulated the spatial-temporal dynamics of POM over Optimal Channel Networks (OCNs)  (Rinaldo and others 1992)  obtained using the Rpackage OCNet (cran. r-project. org/package=OCNe t). This mathematical framework generates synthetic yet realistic stream networks with the same universal fractal properties and metrics shared by all real networks (Rodrı ´guez-Iturbe and Rinaldo 2001; Rinaldo and others 2014), and it is frequently used to generate stream network analogues for biological and ecological applications (for a review, see  Carraro and others 2020). The use of OCNs gave us direct control over the size of the catchment and its drainage density (Figure  1A ). Starting from a pixel description of the catchment (Carraro and others 2020), we discretized the network into stream reaches (that is, the channel segments between two consecutive confluences or between the head of a first-order stream and the next confluence) and we termed l i [L] the length of the generic reach i (Figure  1A ).
We assumed an annual leaf litter input per unit length of 2. 4 kg m À1 y À1 , according to values for POM mass reported in previous literature (Acun ˜a and others 2007). However, note that this value does not affect model results that are presented as normalized values with respect to the total leaf litter input (for example, the fraction exported or degraded). To capture the seasonality of leaf litter input with most input flux concentrated in autumn, we assume that its annual time series, / LF ðtÞ, follows a Gaussian distribution with the mean being the Julian day 300 and a standard deviation of 20 d. Equations (  7 ) and (  8 ) are integrated numerically using a forward Euler scheme with an adaptive timestep (longer allowed timestep: Dt ¼ 1 h). The model was implemented in MATLAB R2019b.
Two types of experiments were represented in the selected database: i) sequential: when the bags were exposed first to wet and then to dry conditions, or vice versa; and ii) those in which bags with the same material were exposed to wet and dry conditions simultaneously. Here, as in the reviewed studies, we considered AFDM as a good proxy for POM in the model. We extracted literature information on time (days) of bag collection, corresponding AFDM loss, habitat and dry or wet conditions from a total of n=19 time series (Supplementary Information). We assumed the initial distribution of leaf litter reactivity (say, in wet conditions: p W ðK W Þ) to follow a Gamma distribution which proved to be a flexible and effective descriptor of OM reactivity  (Koehler and Tranvik 2015; Catalan and others 2017).
ACKNOWLEDGEMENTS This study is based on work from  COST Action CA15113 (SMIRES, Science and Management of Intermittent Rivers and Ephemeral Streams , www. smires. eu),supported by  COST (European Cooperation in Science and Technology ). NC received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No. 839709. RdC and MT were funded by project  FLUFLUX  ( ERC-STG 716196 ). CML was supported by  Flow-ReSet (German Science Foundation , DFG:  ME 5498/2-1 ). SB was funded by the  CANTERA and EVASIONA projects  ( RTI2018-094521-B-100 ,  PID2021-122817NB-100 ) and a  Ramon y Cajal fellowship  ( RYC-2017-22643 ) from the  Spanish Government and AEI/FEDER UE. DvS is a Serra-Hunter Fellow and was funded by project  ALTER-C (PID2020-114024GB-C31 ) from  MCIN/AEI/ 10.
Theory The DFT calculations are carried out by Vienna ab-initio simulation (VASP) package  46, 47  with the projected augmented wave (PAW) method  48. Exchange-correlation functional with non-local correction via van-der Waals interaction developed by Hamada  49  were used. VASPKIT package  50  was used to analyze the DOS calculation data. The molecular structures, charge densities, and spin distributions are visualized by VESTA  51. The Pb substrate is modeled by 4-layer thickness slab with the supercell size of (8 × 8). To stabilize the ligand spin, we employ U = 4. 5 eV and J = 1. 0 eV on p-orbitals of N and C atoms. We used pseudopotential of Tb(III) ion in VASP with a fixed f-electron state, which is a standard method for assessing the ligand electronic state of the phthalocyanine complex  52.
The AFM coupling between the two orbitals in Tb 2 Pc 3 on Pb(111) could be seen from the spin density for the majority and minority spin components in Supplementary Fig. S11. The minority spin component is orthogonal to, and smaller than, the majority spin component. Considering that the two LUMO orbitals are orthogonal, this suggests that the first (second) LUMO orbital is occupied by majority (minority) spin, and consequently there is AFM coupling between the two orbitals. We note that the Pb(111) substrate plays an important role to form the AFM coupling, and the magnetic coupling in an isolated Tb 2 Pc 3 may be different. The NRG calculations were performed with the NRG Ljubljana package (https://github. com/rokzitko/nrgljubljana). The discretization parameter was Λ = 4, we kept up to 5000 multiplets (the sole conserved quantum number was the total spin S), or cutoff 9 in units of the current NRG scale.
Acknowledgements We thank  Prof. Michal Otepka ,  Prof. Pavel Jelinek , and  Prof. Guoying Gao  for helpful discussions. This work was funded by the  National Key Research and Development Program of China  (Grant Nos. 2017YFA0403501 ,  2018YFA0307000 ), the  National Natural Science Foundation of China  (Grant Nos. U20A6002 ,  11874161 , and  11774105 ). R. Ž. acknowledges the support of the  Slovenian Research Agency (ARRS)  under  P1-0044  and  P1-0416. DFT calculations were performed using a computer facility at the  Research Center for Computational Science (Okazaki, Japan ).
In the deep-learning-based measurement, on the other hand, a pipeline-based framework is no longer necessary. Consequently, deep-learningbased methods are less prone to error propagation in their pipeline. However, recent work has focused on heart rate measurement performance rather than understanding  [25]. Subsequently, the limitations of the system are not always clear. Besides, it is well known that the training dataset used is critical. Usually, the rPPG signal estimated from any of these methods is noisy due to the estimation technique, illumination variations, internal noise of the digital camera, and motion. Consequently, once the rPPG signals are acquired, unnecessary information such as frequencies out of the normal physiological range of interest are removed using a filtering process.
However, these networks show the well-known vanishing/exploding gradients problem during backpropagation, and because of this, RNNs cannot store information for a very long time. The long shortterm memory (LSTM), on the other hand, is a network with memory blocks in its recurrent connections, saving information for more extended periods, avoiding the problem present in RNNs networks  [23, 39]. The LSTM networks have been used successfully in the literature to process medical signals such as ECG, proving the potential of this type of network  [40] [41] [42]. In  [43] , the authors propose a method of PPG denoising based on a bidirectional recurrent denoising auto-encoder (BRDAE) to retain the recurrent information in the PPG signal. The network training and testing are performed on an artificial noise-augmented PPG database, along with additional PPG signals acquired from subjects during their daily routine.
To the best of our knowledge, the only approach used in the literature to filter rPPG signals directly with an LSTM-based filter is the one proposed in  [45] , where the authors proposed a two-layer LSTM model to filter the rPPG signals in the MMSE-HR database. The authors affirm that a deep neural network requires thousands of training data, and their way of facing this problem is to train the network in synthetic signals based on sine signals with random Gaussian noise and then fine-tune the network in the database. However, although the proposed synthetic signals manage to simulate the rPPG signals with frequencies corresponding to the heart rate, they do not contain the characteristic shape of the signals in a real acquisition scenario, preventing the network from learning the subtle details of the signal such as saving the dicrotic notch shape or suppressing double peaks as described in  [46].
There is no in-depth study on the specific aspect of neural-network-based filtering, namely the influence of amount of training data, influence of input signal quality and influence of dataset used for training and testing. To our knowledge, our paper is the first to present such a study on biomedical signals and, in particular, on rPPG signals. Indeed, in deep-learning-based applications, the amount of data used during the training of a network plays a fundamental role in its performance. For example, in computer vision applications, generalization tends to improve with the size of the training sets  [48]. However, there is no definitive answer to whether the amount of data during training improves all deep-learning applications. For this reason, it is critical to define in each deep-learning-based application if there is a dependency on the amount of data used during the training.
In this way, it is possible to better understand the limitations and sensitivities of these networks. In  [47] , we proposed the LSTM-DF filter to be used on rPPG signals estimated by the PVM method in the MMSE database  [49] , where we considered the filtering of these signals as a sequence-to-sequence regression problem, so we entered multiple inputs (samples) to the LSTM-DF filter to generate multiple outputs. This sequence-to-sequence approach is known as many-to-many (LSTM-MTM). The other sequence-to-sequence approach is the many-to-one (LSTM-MTO), where multiple inputs give a single output, like the one used in  [44]. In this article, we analyze the performance of LSTM recurrent networks in rPPG signal filtering. Using the LSTM deep-filter, we develop an in-depth study of the network performance, adding experiments and approaches to determine the limitations and sensitivities of using these networks, allowing us to understand the best configuration to train an LSTM-based model to filter rPPG signals.
To the best of our knowledge, this is the first work where the performance of LSTM recurrent networks for rPPG signal filtering is studied. The main contributions of this work include (1) using three public-domain databases in intra-dataset and cross-dataset scenarios, we experimentally demonstrate the advantages of using LSTM networks in rPPG signal filtering. (2)  We present a comparison between two sequence-to-sequence LSTM-based filters, namely MTO and MTM approaches, where MTM proved to be the most successful method. (3) We compare the use of the LSTM-based filter with six stateof-the-art rPPG signal estimation methods: PVM, POS, PbV, G-R, Chrom, and Green, where we observed high stability of the LSTM-based filter concerning different input data obtained with different methods. (4) Interestingly, we also show that a relatively small dataset can be enough to train the network and that there is a significant dependence on the signal-to-noise-ratio average on the training signals.
The remainder of the paper is organized as follows: in "Results" and "Discussion" sections, we present the outcome of the proposed experiments and a discussion. After "Conclusions" section, in "Methods" section, we explain the three public databases used, the protocols proposed to understand the limitations and sensitivities of the LSTMbased network, and the two approaches of the LSTM-based filter, namely MTO and MTM. Finally, we show the evaluation metrics and the implementation details of the network used.