dataesr/scientific-paragraphs-categorization huggingface dataset:
Data are organized by station with information on the provider, the period of availability (date in Coordinated Universal Time -UTC) and the number of data (Fig. 2A ). For particulate contaminant concentration, the name of the time series is related to the sampling method, as illustrated in Fig. 2A  for Hg, with: -CX = discontinuous concentration of particulate contaminant X in SPM samples collected by continuous flow centrifuge, -CX-2 = discontinuous concentration of particulate contaminant X in SPM samples collected by particle trap. The user can visualize each time series online (Fig. 2B ) and download them individually after choosing several parameters (Fig. 2C ) such as: the period of the time series by selecting start and end dates; the file format: BDOH (raw) or Hydro2-QTVAR (specific format used by the French national hydrological services); the type of transformation and time steps: identical (raw data, constant or variable time steps), linear interpolation at constant time steps, mean (1 or 6 hours, daily, monthly, yearly, event scale) or accumulation (1 or 6 hours, daily, monthly, yearly, event scale).
This parameter is only available for calculated and continuous time series; the time zone (by default in the database: UTC +00). Data are sent by e-mail in a compressed folder (export. zip) containing the time series as flat text files (STATION_PARAMETER. txt) and another file (Report. txt) that contains all the necessary metadata (producer, parameter, genealogy , time zone, conversion factors). The file of the time series can be processed by any software (Excel, R, Matlab, etc. ). Each value of the time series is associated with a quality code (Fig. 2D ) according to Table  1. the value (coded "l" or "i") was estimated or modeled following the method described in the Method section. lq limit of quantification the value is lower than the limit of quantification ld limit of detection (used for radionuclides only) the value is lower than the limit of detection 3 Methods.
, 2018). Prior to chemical analysis, SPM collected with the two sampling techniques are transferred to Clean brown glass bottles (250 https://doi. org/10. 5194/essd-2021-350 Open In addition, excess SPM samples are stored in a chamber at -80°C. This will allow, according to the needs and the development of new analytical techniques, to carry out later analysis without aging of the samples. More than 1300 samples are currently stored this way. Meta-data and location of the samples inside the chamber are saved within the software Collec-Science  (Quinton et al. , 2020). Access Earth System Science Data Discussions Preprint.
A refractive index in the range of kaolinite was used for the solid phase (RI=1. 56). Each sample was analyzed 6 times (90 seconds each) with water circulation at 80% and the result finally recorded is an average of the 5 last runs, because some air bubbles sometime alter the first run just after the rinsing phase. Sample size reproducibility based on independent replicate measurements of the same sample did not exceed 2% residual  (Psomiadis et al. , 2014). The database provides information on characteristic diameters D10, D50 and D90 of the particle size distribution. Non negligible differences between these parameters were found during an intercomparison of the two devices  (Lepage et al. ,.
Examples of applications using the dataset With the data acquired by this network, it was possible to improve the calculation of the SPM and associated contaminant fluxes near the outlet of the Rhône River, and also to evaluate the fluxes coming from the upper Rhône River and the tributaries  (Poulier et al. , 2019; Delile et al. , 2020). It was estimated that on average 6. 6 Mt of SPM transited each year in the Rhône at Beaucaire, with strong variation ranging from 1. 4 to 18. 0 Mt y -1. The Durance and Isère tributaries were found to be the main contributors to SPM fluxes. Through this long-term continuous monitoring and its spatial resolution with sampling on the main tributaries, seasonal variations in several contaminant concentrations have recently been highlighted  (Delile et al. , 2020)  as well as the impact of dam flushing operations  (Lepage et al.
Finally, within the OSR and using this database, a 1-D hydrodynamic model of SPM dynamics was developed  (Launay et al. , 2019). This model simulated the concentration of SPM at Arles during a flood event occurring in the Isère and the Durance tributaries. This model was also applied by  Dabrin et al. (2021)  for the fingerprinting approach and the combination of these two approaches demonstrated that a large portion of SPM from the Arve River was old sediment stored behind the Verbois dam and re-suspended during the dam flushing operation.
Data availability All the data are made publicly available in French and English through the BDOH/OSR database  (Thollet et al. , 2021)  at https://doi. org/10. 15454/RJCQZ7 (Lepage et al. , 2021). The BDOH (Base de Données pour les Observatoires en Hydrologie) application is managed by INRAE. The data is freely available for visualization, and for download after registration of a personal account. In order to respect the anonymity of the reviewers, we have created a specific account to access this feature: -login : review. bdoh@gmail. com -password : bdohreview.
Perspectives The database presented in this paper will be continuously updated in the coming years, at least until the end of the current OSR program (2021-2024). Discharge, SSC, and particulate contaminants are continuously measured at the permanent stations and data are regularly updated online. The following improvements are planned: -Uncertainties on the SPM and contaminants fluxes will be calculated and published; -Other TME and new contaminants such as gadolinium will be included in the dataset depending on the authorities's concern and scientific purpose. These contaminants might also be analyzed on the SPM samples already collected and stored in the chamber at -80°C; -Additional parameters to describe the particle size distribution will be added such as the percentage of clay/silt/sand; -Link between the different contaminants measured on the same SPM sample will be added as a common sample name; -The values lower than LQ or LD will be replaced by a more precise statistical method when feasible.
Acknowledgments This study was conducted within the  Rhône Sediment Observatory , a multi-partner research program funded through the Plan Rhône by the  European Regional Development Fund (ERDF) ,  Agence de l'eau RMC ,  CNR , EDF, and three regional councils ( Auvergne-Rhône-Alpes ,  Région Sud -PACA , and Occitanie). The authors would like to thank the  OSR  staff for suspended particulate matter sampling and analytical measurement:  H. Angot ,  J. Panay ,  C. Le Bescond ,  M. Lagouy ,  G. Grisot ,  L. Richard ,  S. Gairoard ,  J. C. Gattacceca ,  G. Dur ,  F. Eyrolle ,  F. Giner ,  D. Mourier ,  P. Paulat ,  J. Faramond ,  C. Antonelli ,  A. De  vismes,  C. Ardois ,  C. Le Corre ,  A. Bonnefoy ,  M. Fornier. We are very grateful to CNR, DREAL ARA, EDF,  Grand Lyon ,  FOEN, SIG, SPC Grand Delta, Véolia and VNF  for cooperating with OSR and sharing their monitoring data.
Both parts separated and differentiated the GM1 subtypes in the data collection and explored concepts that are not included in this report. This study was sponsored and led by the Cure GM1 Foundation, with collaboration from the National Tay-Sachs & Allied Diseases Association (NTSAD). Both organizations are headquartered in the United States but serve families across the globe. Research expertise and analysis was provided by RTI International. The study was informed by a multidisciplinary advisory committee of academic and industry researchers, specialist physicians, and parents. The advisory committee reviewed and refined the study aims and instruments, reviewed and responded to emerging results, and those on the authoring list reviewed and commented on this report.
| Inclusion and recruitment Parents or primary caregivers of children with GM1 were eligible to participate; all participants were biological parents or stepparents; thus, we use the term "parent" throughout. Affected children were diagnosed at least 3 months prior to participation. Bereaved parents were eligible to participate if their child had died within the past 36 months. Only one parent in a parenting dyad (e. g. , the mother and father of one child) was allowed to participate in the same part; participation of two parents in a dyad was allowed across parts (i. e. , one parent could participate in the focus group part and another in the interview part). Parents had to be able to participate in English language data collection (Part 1) or in English or Portuguese (Part 2). As this study sought to better understand the perspective of the caregivers for individuals with a pediatric phenotype of GM1, parents of individuals with adult-onset GM1 were excluded from the study and individuals who themselves have a diagnosis of GM1 were excluded from the study.
Because we anticipated that stage of progression would influence responses, we asked parents to respond to focus group questions based on their child's current status, but to also think back and ahead. Focus groups lasted approximately 90 min. Results from Part 1 informed the Part 2 interview guide and the addition of an object-case best-worst scaling (BWS) prioritization activity to the Part 2 pre-interview REDCap survey (which was also translated into Portuguese). BWS is a stated preference method designed to obtain ranking and relative importance data by asking participants to choose the most and least desirable options across a series of questions  (Flynn, 2010; Najafzadeh et al. , 2018) ; here, in each question we asked parents to indicate the "most important" and "least important" symptoms to treat out of five symptoms/features of GM1 in the context of a non-curative treatment.
| Part 1 The REDCap data were analyzed descriptively by a research analyst. A study investigator created structured debrief reports for each focus group. Each focus group was audio-recorded and transcribed using a computerized transcription program (Temi). Transcripts were coded and analyzed using a standard approach to qualitative content analysis  (Saldaña, 2009). Coding was completed by a trained and experienced qualitative analyst in NVivo using a codebook developed from the focus group moderator guide and study aims. The codebook was reviewed and refined by two investigators prior to implementation. The research team compared the debrief reports with qualitative content analysis from coding for quality assurance. Two investigators and the study PI  (Peay)  determined the final interpretation.
| Part 2 The REDCap data were analyzed descriptively by a research analyst. Individual BWS rankings were calculated using BWS simple scoring  (Peay et al. , 2016)  based on the difference between the number of times the participant selected a symptom as "most important" and the number of times the participant selected the symptom as "least important"; the same approach was used to calculate the aggregate rankings that are reported here. English language interviews were transcribed using a computerized transcription program (Temi); Portuguese language interviews were transcribed by a study team member fluent in Portuguese. Transcripts were analyzed by three experienced qualitative analysts using a rapid analysis process (RAP) in Excel  (Taylor et al. , 2018). Domains, or content categories, were determined from the interview guide and study aims, and were organized into a matrix to allow analysis of domains by participant and across participants.
Each row in the matrix represented one participant, creating a condensed narrative of their experience with GM1. Each column of the matrix represented a domain that correlated to a section or topic in the interview. Coding consisted of assigning summarized content from transcripts to domains within the RAP matrix. The quantitative data from the BWS activity for each participant was also included to allow triangulation of quantitative survey results and qualitative interview data. Two analysts piloted the draft RAP matrix using the same two transcripts to refine the domain coding definitions. The remaining coding was divided among three analysts, and the final interpretation determined by the three analysts and the study PI  (Peay). All qualitative analysts involved in coding and interpretation immersed themselves in the data.
Additional research with slightly larger sample sizes could build on this study to explore the nuanced changes that come with progression in each subtype of GM1. Future research should attempt to include a more demographically representative sample. Due to feasibility limitations, our data collection was conducted in English (focus groups and interviews) and Portuguese (interviews), which excluded perspectives from parents who did not speak these languages. Despite this limitation, interview and focus group participants represented a range of countries. Variation in access to diagnostic, medical, and supportive services may have influenced respondents' responses. Participants were recruited by the Cure GM1 Foundation and NTSAD; thus, the sample may be biased to those who engage with disease-focused foundations.
The Artificial Neural Networks ANN is a powerful and reliable model to predict complex phenomena with multiple variables. ANN is also very flexible in terms of the number of variables, the training algorithm, transfer functions, and the structure. An ANN consists of several layers: an input layer, some hidden layers, and an output layer. In addition, each layer involves some neurons. The number of hidden layers is usually one or, in specific cases, two. Using more than two layers is rarely done and is not recommended  [27]. Two separate multilayer backpropagation feedforward ANNs have been used to predict the nugget size and the maximum tensile residual stress. Theses ANNs have been implemented using Matlab. The Levenberg-Marquardt training algorithm has been utilized to train the ANNs. This algorithm minimizes a combination of squared errors and weights and then determines the correct combination.
Breast Density was determined by Volpara VDM (Volumetric Density Measurement) Software. MBI, radiological, pathological and histological findings were reviewed. Subjects were surveyed to assess patient experience. Results: A total of 25 patients underwent MBI. 24 of these were included in final data analysis (11 Group-1, 8 Group-2 and 5 Group-3). The MBI system detected and localised 12 of 13 benign breast lesions, and 9 out of the 11 breast cancers. This included 1 case of a radiographically occult invasive lobular cancer. No device related adverse events were recorded. 92% (n = 23) of women reported that they would recommend MBI imaging to other women.
Ethical Approval and Trial Registration Ethical approval was granted by the local Clinical Research and Ethics Committee and authorised by the Health Products Regulatory Authority (HPRA), Dublin, Ireland. Ethical standards complied with international guidelines including the Declaration of Helsinki and in accordance with the ethical principles underlying European Union (EU) Directive 93/42/EEC and EN ISO 14155. The study was registered with the U. S. National Library of Medicine (ClinicalTrials. gov NCT03475992).
Radiological and Histological Data Collection All mammographic studies were undertaken with full field digital mammography (FFDM) systems [GE Senographe Essential (Chicago, Illinois, United States) and Hologic Selenia Dimension (Marlborough, Massachusetts, United States)]. Ultrasound was performed with the patient in the supine or decubitus position using a high-resolution 12. 5 MHz linear array transducer (Philips EPIQ 5W; Amsterdam, Netherlands). In Group 1 and Group 3, ultrasound guided biopsies were retrieved using a TruCore II biopsy gun (Argon Medical; Frisco, Texas, USA) or a Achieve automatic biopsy device (Merit Medical; Galway, Ireland) with a fourteengauge automated needle. The radiological database (AGFA IMPAX, Agfa-Gevaert, Mortsel, Belgium) was assessed and all conventional imaging (mammography and ultrasonography) studies reviewed.
Mammographic location, BIRADS category for density and BIRADS category for predication of malignancy were recorded. The fifth edition of the American College of Radiology (ACR) BI-RADS was employed  (20). Mammographic breast density was assessed using Volpara Ò Volumetric Density Measurement (VDM) Software. Sonographic lesion location was recorded. All personal or identifying details were removed from patient data, and a trial number assigned, ensuring anonymity. The anonymised data was kept in a secure database designed and maintained by the Clinical Research Facility of the host institution.
Imaging and Data Analysis As this study was undertaken to address early feasibility, the number of subjects included in the study (n = 25) was limited and not intended to permit a clinically meaningful statistical analysis. MBI image formation and morphological post-processing for persistent breast lesion (ROI) detection and characterization was performed offsite by the MVG group. Given that this was a First-in-Human (FiH) investigation for the Wavelia MBI modality, the MBI data processing chain progressively evolved, throughout patient recruitment, before stabilization for final data analysis at patient group level. 3 interim data reviews were performed to assess and confirm the clinical relevance of the MBI findings based on the available reference clinical data.
Patient Demographics A total of 24 female patients with a palpable breast lump were included in final analysis. 1 patient who underwent MBI was excluded from the final data and imaging analysis. This case was a patient who presented with a palpable lump which was determined to be normal breast tissue. This patient had small scattered cysts identified in a different breast quadrant, and as a result was erroneously enrolled in Group 2 prior to imaging. The mean age (range) was 50. 5 years (35 -83). A further breakdown of age in each patient group is detailed in (Table  3 ). There were 11 cases of invasive carcinoma [6 invasive ductal carcinoma (IDC), and 5 invasive lobular carcinoma (ILC)], 8 patients with an underlying breast cyst, 4 cases of benign breast disease (fibroadenoma) and 1 case of a complicated cyst.
Experimental data All experimental protocols were approved by local and national institutional animal care and ethics committees. Six pigs underwent balloon-occlusion of the left anterior descending (LAD) coronary artery to create an ischemia-reperfusion MI model  [16]. In-vivo late-gadolinium enhanced (LGE) MRI was performed on each pig a median of 53. 5 days post MI. Images were acquired at an isotropic spatial resolution of 1 mm. Image segmentation was performed in Seg3D  [17]  by a single observer. LV myocardium was manually segmented in each image series. Scar was segmented according to the signal intensity range within the segmented myocardium, where the threshold for scar was ≤60% of the maximum intensity within the image, and the threshold for border zone (BZ) was ≤40% and >60%  [18].
Anatomy modelling 2. 2. 1. Image segmentation and post-processing The RV blood pool was semi-automatically segmented in Seg3D  [17]  by a single observer using signal intensity thresholding and a connected component filter, followed by manual correction. The RV wall was created by dilating the RV blood pool uniformly by 4 mm  [19]. The RV segmentation was combined with the LV, scar, and BZ segmentation. To improve segmentation quality and remove any potential 'staircasing' at outer surfaces, the bi-ventricular (BIV) segmentations, including scar and BZ, were up-sampled and smoothed using custom written software  [20]. The scar segmentations were down-sampled in Seg3D in the z-direction from 1 mm to 4 or 10 mm to represent typical clinical LGE-MRI slice thickness. The down-sampled scars were reconstructed in 3D using a statistical shape approach based on the logOdds function  [9] , used in many previous clinical modelling studies  [2, 5, 6].
Briefly, this involved application of Gaussian blurring and 3-dimenstional cubic interpolation, followed by logistic filtering to segment the interpolated and smoothed scar shape. For each dataset, this then created three 'equivalent' models (shown in Fig. 2 ): high-resolution scar (made from original 1 mm imaging data), and low-resolution scar (made from 4 mm or 10 mm down-sampled and reconstructed imaging data).
Mesh and fibre generation BIV meshes were generated from the high-resolution segmentations for each pig using custom written software based on the freely available library C-Gal  [21]. Average mean edge length was approximately 325 μm, in line with other ventricular modelling studies  [2, 7]. BIV fibres were generated using a rule-based method  [22]. An LV version of each BIV model was generated by extracting the LV sub-mesh using MESH-TOOL  [23]. Additional versions of each BIV and LV model were generated by replacing the high-resolution scar (1 mm) with the low-resolution scar (4 mm or 10 mm, Section 4. 2. 1). In total, 4 anatomical models were generated for each pig, namely BIV models for each scar resolution (1, 4, and 10 mm) and an LV model with high-resolution scar (1 mm), as shown in Fig.
Electrophysiology data processing 2. 3. 1. Local EP properties Activation and repolarization times (AT, RT respectively) and ARI were estimated from EGM data exported from Precision™ using bespoke algorithms implemented in OpenEP  [24] , based on conventional approaches applied to similar datasets  [25]. Specifically, AT was defined as  the point of maximum negative derivative (dV/dt) of the EGM (using the AT computed by Precision™ (EAMS, Abbott, Minnesota, MN, USA) to estimate an initial search window). This method of maximum negative gradient proved more consistent than a threshold-based approach for AT. RT was calculated using the Wyatt method as the maximum dV/dt prior to the T-wave peak, with a pacing-dependent search window after the AT used to constrain the EGM T-wave. If no peak of EGM was detected within the search window, a negative T-wave was assumed, and the minimum dV/dt prior to the T-wave peak was used.
This process was repeated until no outliers were left and the final median was used to define the global QT-interval. An example of 12-lead ECG and corresponding QRSd and QT interval is shown in Fig. 3-B ). The mean APD of each pig was estimated from the QTint on the 12lead ECG. Specifically, considering that the end of the T-wave corresponds to the total ventricular RT and that the QRS corresponds to the total ventricular AT, the QTint minus the QRSd provides an estimate of the mean APD within the ventricles. Table  1  shows the QRSd, the QTint, QTint minus QRSd on 12-lead ECG, and the mean ARI on EGM computed for each pig using data obtained during RV pacing at a 500 ms cycle length.
The FEC over the RV side of the septum was kept in the LV-only models. CVs within the FEC in the longitudinal direction was set to 6x the healthy  [3]  longitudinal CV (6 × 0. 67 m/s at baseline), with the transverse CV equal to the healthy value (0. 3 m/s)  [3]. Since part of the FEC resides on top of scar, the CV within this region was set as 6x faster than the BZ CV (6 × 0. 15 m/s at baseline). Such a representation was based on histological data showing a thin layer of surviving endocardial tissue over scar, where fibrosis and fibre disarray have been reported  [29]. A His-Purkinje system (HPS) was generated for one of the models using custom written software  [31]  and the simulated activation sequences were compared against the same model with FEC. Details can be found in Supplemental Section 1.