dataesr/scientific-paragraphs-categorization huggingface dataset:
Briefly, 3D datasets were acquired in a single beat during a breath hold lasting a few seconds, ensuring optimal temporal and spatial resolution. The volumetric datasets were immediately evaluated on-board using the DHM software (Philips Healthcare), which automatically identifies LV endo-and epi-cardial borders at end-diastole and LA borders at end-systole, allowing prompt quantification of the volumes of these chambers (Figure  1 ). In our study, 3DE images were analyzed using the default settings of the boundary detection sliders (end-diastolic default position = 60/60; end-systolic default position = 30/30). We categorized DHM data into three main categories depending on image and waveform quality: good, suboptimal, and poor. Qualitative criteria of acquisition/display of volumes and volume curves were: (a) optimal/suboptimal definition of atrial and ventricular shape including the entire LV and LA cavities without artefacts and optimal/suboptimal recognition of the mitral annulus (with simultaneous and opposite volume changes in LA vs.
CMR Protocol CMR imaging was performed using a 1. 5-T scanner (Discovery MR450; GE Healthcare, Milwaukee, WI, USA) within 7 days from the echocardiographic examination to ensure that identical hemodynamic conditions were not made during changes in therapeutic regimens. All studies were carried out using dedicated cardiac software, phased-array surface receiver coils, and electrocardiogram triggering. Breath-hold steady-state free-precession cine imaging was performed in long-and short-axis orientations. A stack of short-axis slices encompassing right and left chambers from base to apex was used for ventricular and atrial volumes and systolic function assessment. CMR datasets were transferred to a dedicated workstation and analyzed with a cardiac software (Report Card version 4. 0, GE Healthcare, Chicago, IL, USA).
Reproducibility To assess the reproducibility of DHM measurements, intraobserver and interobserver variability were evaluated in a subset of 15 randomly selected patients. Physicians were blinded to the original LA, LV, and EF results. To derive interobserver variability, one physician was asked to recalculate DHM acquisition for 15 patient datasets after 4 weeks. Two investigators (G. I and V. M. ) were asked to perform these measurements twice to assess intraobserver variability. The same loops were re-analyzed 2 weeks later by the two investigators who were blinded to all previous measurements. These investigators repeated the analysis with and without contour adjustments.
Statistical Analysis Results are presented as mean ± standard deviation (SD). Volumes and LVEF data obtained with the DHM were compared with 2D and CMR values by using linear regression analysis with Pearson's correlation coefficients. Moreover, Bland-Altman analysis was applied to evaluate the inter-technique agreement by calculating the bias (mean difference) and the 95% limits of agreement (defined as 1. 96 SD around the mean difference). To assess the association between the categorical variables, a chi-square test was applied. Differences were considered significant with p-values < 0. 05. Statistical analysis was performed using SPSS 25 (SPSS Inc. , Chicago, IL, USA).
Results Baseline characteristics of the study patients are summarized in Table  1. DHM analysis of the LV was feasible in 522 patients (87%), while 78 3D datasets were discarded due to incorrect automatic identification of LV/LA borders. Feasibility was high in all categories and pathologies but slightly lower in patients with atrial fibrillation (81%) and cases with coronary artery disease (81%) compared to normal subjects (97%) (Table  2 ). LV quantification using the automated software required major adjustment of LV borders in 38/522 patients (7%), whereas minor adjustments were required in 149/522 patients (29%). We classified DHM datasets depending on the acquisition quality and volume waveform quality as good (n = 327), suboptimal (n = 195), and poor (n = 78). The average time needed for DHM acquisition and analysis was 41 ± 12 seconds using automated software with manual corrections as needed.
Discussion This is the first study to test the feasibility and accuracy of this automated software in a large, unselected population. The main findings of this study are as follows: (1) the dynamic heart model automated measurement of LV and LA volumes and function is highly feasible in a large, unselected population, and analysis duration is very short; (2) independent from pathologies and heart rhythm, the quality of DHM images is good, with only a minority of cases requiring major manual adjustment of boundaries; (3) DHM LV volume values had good agreement with biplane 2D measurements despite an overestimation of both diastolic and systolic LV volumes, and the same is true in terms of the correlation between DHM values vs. CMR with a DHM underestimation of LV volumes; (4) LVEF correlation among the three techniques was very high; (5) measurements of LA volumes by DHM showed excellent agreement with CMR volumes.
With regards to feasibility, quality, and the percentage of major or minor adjustments, in normal subjects, valvular heart disease and coronary artery disease showed similar good DHM quality and need for minor adjustments. On the contrary, in dilated cardiomyopathy and miscellaneous pathologies, manual minor or major adjustments were needed in approximatively 60% of the cases. This observation is in agreement with previous data showing that the accuracy of HM volumes was lower in some pathologies because of incorrect detection of LV endocardium. Specifically, in some cases, the automatic detection of LV and LA borders may lead to the incorrect identification of the atrioventricular junction (i. e. , presence of a sigmoidal shape of the interventricular septum that was interpreted by the software as mitral annulus, or cases with normal LV and very large LA), or very dilated LV in which the apical and antero-lateral endocardium is not perfectly identified.
Previous studies clearly demonstrated that HM was feasible, very rapid, and accurate. Once the 3DE dataset is loaded, the software used in the present study does not require any input to automatically perform the measurements. Moreover, after launching the acquired 3DE dataset, the examiner immediately displays: LA and LV contours on four-, three-, and two-chamber cut-planes extracted from the 3DE datasets; moving casts of the LV and LA with the perception of the ideal recognition of the two cavities; and LV/LA reciprocal volumetric changes throughout the cardiac cycle. The different phases of the cardiac cycle were identified using maximum and minimum values of the volume curves and their time-derivative LV and LA curves. In cases where the operator felt that the automatically detected surface was incorrect, manual corrections were easily performed.
Tsang et al. [1]  first described the heart model method based on LV and LA models built by using automated endocardial surface detection in conjunction with information from a large 3D echocardiographic database. Casts of the LV and LA were derived without geometrical assumptions. Importantly, to avoid problems related to foreshortening, LVand LA-focused apical two-and four-chamber views were identified as those in which the long-axis dimension of the relevant chamber was maximized. This method is not only ideal for the LV but also for the LA since it is not easy to obtain the correct four-and two-chamber views in the standard 2D examinations; therefore, the biplane formula may be suboptimal. LA volume measurements by standard 2D examination should be performed in LA-focused views to avoid foreshortening and, thus, underestimating volumes.
Limitations Machine learning software requires a high-quality 3D dataset, thus restraining its application in patients with inadequate TTE windows. Although the use of the DHM algorithm showed high feasibility of left chamber analysis, automated border detection was difficult in some of the datasets. With 3DE imaging, specifically in single-beat acquisitions, low-frame-rate datasets may miss the true end-systolic frame. Despite the attempt to maximize frame rates, it was not always possible to ensure that it was sufficiently high to avoid this pitfall.
Big Data Stream Processing Systems There are currently several open source Distributed Stream Processing Engines (DSPE), such as Apache Storm  [12] , Apache Samza  [10] , and Heron  [54]. While they support developing ML applications, they do not have dedicated ML libraries. Apache Spark  [11] , originally a batch processing framework, now offers streaming support for micro-batches or continuous streams. Spark ML and MLLib are Spark's ML libraries with only simple linear classifiers and clustering algorithms for streaming, none of which are state of the art. Also, StreamDM  [82]  is an Apache-licensed, open source software for mining big data streams using Spark Streaming, and developed by Huawei. Apache Flink  [6]  is a processing framework that focuses on streaming tasks and arranges them in directed acyclic graphs, similar to Spark's topologies.
Its ML library, FlinkML, supports batch-based ML. Apache Apex [3] and Apache Beam  [4]  are unified stream and batch processing engines containing basic ML libraries. In the Cloud front, Google Cloud Dataflow  [39]  is a streaming data processing system that can emulate batch processing and has some support for ML. MS Azure  [59]  is Microsoft's cloud system performing both batch and stream processing. The batch processing engine has ML algorithms, but the Streaming Analytics tool can only compute aggregation and statistics, without any other streaming ML. AWS Kinesis  [13]  is Amazon's offering for processing data streams in real-time but, similar to MS Azure, it does not provide native support for streaming ML. Massive Online Analysis (MOA)  [20]  consists of well-known online algorithms for streaming classification, clustering, and change detection mechanisms.
PLATFORM DESIGN Our proposed platform, S2CE, is a complete, cloud-edge orchestration platform, based on four key components for input/output transformations, machine and deep learning processing, as well as cloud and edge resource management and tuning. S2CE aims to accelerate the building of tools that facilitate and enhance real-time artificial intelligence over voluminous and heterogeneous streams of data from IoT and other sources, in order to extract meaningful knowledge and perform extreme-scale predictive analytics over cloud and edge computing infrastructures. A key objective of this platform is to provide the big data industry with standardized interconnection methods and a stream mining framework within the Apache big data ecosystem, to enhance and automate decision making processes.
Output Interface: This component will implement standardized, secured interconnections to allow the algorithmic results from the ML module to be outputted for downstream streaming engines, S2CE instantiations, and even end-users to consume on their devices in a seamless, secure way through a pipeline. S2CE will offer new connectors and interfaces to create stream processing pipelines and output its resulting predictions and models for other engines to use downstream. The output data streams will be splittable to different formats and substreams, depending on the utility of the overall pipeline. Computing Infrastructure Interface: This component will allow S2CE to operate on top of several well-established distributed stream processing engines (DSPEs) such as Apache Flink, Storm, and Spark Streaming.
Furthermore, the Transformations and Machine Learning components will consume raw streams or pre-models already built at the edge, to build final, full-blown ML models. Performance metrics will be used to monitor and adjust resource consumption both at the cloud and edge infrastructure. These APIs allow S2CE to have the following key features with respect to big data mining systems: • High extensibility and flexibility to accommodate future heterogeneous data sources and pools, distributed computing platforms, ML algorithms, tuning algorithms, etc. • Scalability to consume data from multiple sources with extreme volumes and velocity. • Federated-ness for connecting and building big data stream pipelines across different clouds and edge. • Standardized interoperability with other stream or batch platforms, for faster and efficient data sharing.
Innovation Exchange with Apache Ecosystem's Open Source  and Spark components  [62]. Other vendors, such as SAS Institute and IBM, developed their own stream-related offerings, such as the SAS Cloud Analytics  [71]  and IBM Streams  [46] , respectively. Some of these systems can or even are advised to be blended with open source ML solutions including Spark MLLib  [47]. In addition, commercial offerings from Cloudera and other companies integrating big data projects into comprehensive platforms have provided support and integration with these dominant streaming engines and analytics. The risks introduced by developing new solutions, and major costs of big data projects arising from hardware infrastructure are commonly observed. Hence, many companies build their big data solutions based on open source platforms yielding no license cost, or costs for core development of the tools.
(3) The adoption of existing industry standards. In particular, the platform can provide integration components with popular systems such as Kafka, making it possible to create efficient and high throughput data pipelines using S2CE. The S2CE should be provided together with thoroughly designed integration patterns for using S2CE with third-party modules such as data storage platforms (e. g. , Apache HBase), data ingestion (e. g. , Apache Flume), and DSPEs (e. g. , Apache Storm, Apache Flink). Finally, S2CE can be coupled with extensive monitoring and management abilities, easing S2CE adoption across companies. In conclusion, we envision S2CE to become the go-to, one-stop-shop platform for mining of big data streams over cloud and edge, and adopted by big data practitioners for its easy pipeline integration and management, and extended by machine learning experts.
| Participants and data extraction We retrospectively collected data from the medical records of all children and adolescents born between 1994 and 2010 and followed-up at the G2 MLille Center. These data were anonymously used in accordance with general data protection regulation European legislation and after collecting written consent from the patients and their parents. Patients referred to the G2 MLille Center are notably assessed using a multidisciplinary work-up that includes neuropediatric and psychiatric assessments and a systematic psychological and neurocognitive exploration. We created a grid for data extraction that listed all the retained variables of interest (see Section 2. 2). For each of these variables, we indicated their occurrence time in relation to the time of diagnosis.
| Variables of interest The first extracted variable of interest was the presence/ absence of psychiatric signs according to the DSM-5 classification during the course of the IMD (DSM criteria were revised within the study timeframe and were retrospectively applied to the medical report descriptions). Psychiatric signs could have been observed during acute or more stabilized stages of the disorders. We also collected descriptive data regarding medical history, clinical examinations, and complementary investigations, as well as the personal-family context. We notably gathered the following data: (a) the age at the onset of the first psychiatric and nonpsychiatric signs (to specify an acute or a progressive onset as well as the primary or secondary nature of the symptoms); (b) the presence of trigger factors; (c) the presence of previously described atypical clinical features  6  (ie, catatonia, unimodal visual hallucinations, delirium, clinical fluctuations and cognitive regression); and finally (d) the therapeutic management and response (including the propensity to experience side effects or worsening under psychotropic medications after 4 weeks of continuous treatment) and follow-up information.
| Statistical analyses Statistical analyses were performed using R software for statistical computing v3. 6. 1, including the jmv, ggplot, and ggridges libraries. The sample was first described based on symptom frequency rates and the visualization of symptom distribution over time. Due to nonnormality, we used nonparametric tests for statistical comparisons. Continuous variables were compared using Welch's t tests or Wilcoxon rank tests (for independent and paired samples, respectively). Dichotomous variables were compared using chi-squared tests. Significance was assigned to a P value below. 05.
Rivers studied Datasets from aquatic invertebrate studies from 14 IRES in Europe (seven rivers), North America (five rivers) and New Zealand (two rivers) were compiled for this study (see locations on the map in the Supporting information). All the selected rivers are naturally intermittent  (Datry et al. 2014b ). The datasets consisted of matrices of taxon abundances and sampling dates at multiple sites within intermittent and perennial river reaches. Invertebrates were collected from riffle habitats using standardized and comparable sampling methods, from at least three sites per river (Table  1 ). For further information on the studies that generated the datasets, refer to published reports and information on sites in the Supporting information and in  Datry et al. (2014b).
Flow intermittence quantification For each river, annual flow intermittence (FI, in %) was calculated for each sampling site, defined as the proportion of the year with no flow. FI has been shown previously to be one of the strongest determinants of IRES biodiversity  (Arscott et al. 2010 , Datry et al. 2014b , Leigh and Datry 2017 , Crabot et al. 2020). FI was calculated based on three different procedures, reflecting the type and quantity of discharge data available for each studied river. In five rivers (Albarine, Asse, Little Stour, Orari, Selwyn), flow gauging stations and point discharge measures were used to run the statistical model ELFMOD, which reconstitutes longitudinal flow patterns along river courses bounded by flow gauges  (Larned et al. 2011). FI was then derived from modeled mean daily discharge.
Invertebrate data processing The raw invertebrate data were processed to ensure homogeneity regarding the identification level applied across sites and rivers. In contrast with  Datry et al. (2014b) , we did not investigate the effects of the taxonomic resolution on diversity measures but sought to optimize the tradeoff between accurate taxonomic resolution and data completeness by using genus and family. The identification levels applied for each invertebrate group and each river are given in the Supporting information. Due to different sampling efforts applied across rivers, raw abundances were averaged across the different sampling dates for each site and each taxon and were expressed as relative abundances of the total number of individuals in the sampled assemblages.
e. for a given taxon, the sum of its affinity scores for all the modalities = 1 for each trait). Practically, information for the twelve traits was already fuzzy coded for most European taxa  (Tachet et al. 2010). For taxa recorded in the United States and New Zealand rivers, local trait databases were compiled, homogenized with the European dataset (in terms of trait modalities) and fuzzy coded. Finally, any missing trait information was completed following a literature review undertaken by the authors (Supporting information). To aggregate taxa into functional groups with similar trait profiles  (Usseglio-Polatera et al. 2000) , a Fuzzy  Correspondence Analysis (FCA -  Chevenet et al. 1994 ) was first run using the trait profiles of 486 taxa. Subsequently, a hierarchical cluster analysis was performed applying the minimum variance criterion  (Ward 1963 ) to a matrix of Euclidean distances calculated based on the coordinates of taxa on the first six axes of the FCA.
These accounted for 41. 91% of the total trait variability among taxa. The final number of functional groups was determined using the shape of the dendrogram, by selecting a partitioning level (a given distance value) corresponding to the best compromise between the number of groups and the homogeneity of group size (maximizing evenness of taxa among groups). These analyses are presented in the Supporting information. Lastly, the relative abundance of functional groups was obtained by summing the relative abundances of the taxa belonging to each group. The final trait database and the functional groups assigned to taxa are available in the Supporting information.
Beta diversity Spatial beta diversity was calculated for taxonomy and functional traits as the pairwise compositional differences between sampling sites for each sampling date at the river scale. For taxonomic beta diversity, the presence-absence Jaccard index was calculated for each river using the beta. div. comp function in the adespatial package  (Dray et al. 2020). For functional analyses, we computed taxon-bytaxon Gower distances from the trait matrix and generated a dendrogram using hierarchical clustering analysis based on these distances with the unweighted pair group method using the arithmetic mean  (Cardoso et al. 2013 ). Functional beta diversity was calculated for each river based on the site-by-taxon matrix and the dendrogram using the R beta function in the BAT package  (Cardoso et al.
Relationships between alpha diversity and flow intermittence Both taxonomic and functional richness decreased when flow intermittence increased (Table  2 , figures in the Supporting information). Richness type (taxonomic or functional) had no significant effects, either alone or in interaction with flow intermittence, indicating no difference in taxonomic and functional richness along the FI gradient (Table  2 ). Across all datasets, an increase of 10% in flow intermittence resulted in a decrease of 2. 30 (± 0. 10) taxa and 0. 57 (± 0. 03) in functional richness. Integrating river and sites as random intercepts in the model greatly improved the model fit (R 2 fixed = 0. 41; R 2 fixed+random = 0. 89). Rivers and sites exhibited differences in richness when no flow intermittence occurred (random intercept standard deviation = 0.
2016 , Aspin et al. 2018 , Crabot et al. 2020). This is partly because trait selection, trait coding and analytic approaches differed greatly. For example,  Schriever et al. (2015)  developed a database with seven traits spanning 30 modalities while  Aspin et al. (2018)  used 10 traits with 49 modalities from  Tachet et al. (2010). In other studies, Vander Vorste et al. (  2016 ) used Rao's quadratic entropy to evaluate functional diversity  (Rao 1982)  while  Crabot et al. (2020)  followed the procedure from  Cardoso et al. (2013)  based on taxon-bytaxon functional Gower distances and hierarchical clustering. To our knowledge, the current study is the first to quantify the functional responses of intermittent river communities to drying across multiple climate zones and contexts. Given that global change is resulting in more frequent extreme events, including droughts  (Döll and Schmied 2012, Spinoni et al.
Acknowledgements -TD was supported by the  H2020 European Research and Innovation action  Grant Agreement no. 869226  (DRYvER). We thank  Brian Smith  for assisting with trait codes for the New Zealand taxa, and  Cyrielle Bonne  for assisting with trait codes for the French taxa. We thank  A. Santos  for providing us with data from one river network included in this study. This manuscript benefited from the meaningful comments from  Brent Johnson  and  Roy Martin. Although  this work was reviewed by the  USEPA  and approved for publication, it might not necessarily reflect official Agency policy. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.