dataesr/scientific-paragraphs-categorization huggingface dataset:
The whole echocardiography evaluation was assessed by the same operator for each patient. The strain analysis was conducted off-line on a decentralized computer, via the EchoPac™ clinical workstation software version 202 (GE Healthcare, Chicago, Illinois, USA) by level 2 (CR) and level 3 (LZ) operators  [38]  trained in 2D-strain echocardiography. Each anonymized TTE loop was analyzed by an operator blinded to the patient's group (PLD+ or PLD-). The operator traced the LV myocardial contour using the semi-automatized method of speckle tracking after identification of the baso-septal, baso-lateral and apical points  [39]  and adjusted the contouring manually if necessary after the visualization of the tracking on dynamic loops. LV-LS was calculated three times and averaged for each time-point loop.
We must also acknowledge a lack of statistical power to ascertain a correlation between LV-LS variations and preload even in the "hypovolemia" subgroup, due to a small number of patients. We basically calculated the number of required subjects according to our primary goal, which was to determine the sensitivity and specificity of LV-LS variations to assess preload-dependency with an area under the curve of at least 0. 85. We could not anticipate how LV-LS would vary in our cohort and in what proportion. The a posteriori calculated required number of subject to ascertain a statistical difference in LV-LS variation between the groups explains a lack of power in our study. Data for RV-LS was only available for 27 patients (16 PLD+ and 11 PLD-) at both T0 and T1. Regarding the RV, our results suggest a tendency for an increased RV-LS in the PLD + group whereas RV-LS decreased in the PLD-group after PLR.
Statistical analysis Data analysis was performed using SPSS Statistics (version 20. 0, IBM, USA). Patients' demographic and clinical characteristics, including age, gender, course and classi cation of stroke, involved side (left/right) and the Brunnstrom stage, are given by the number of cases (%) for categorical data and mean (SD) for continuous variables (Table  1 ). Tinetti Gait Scale was described as the number of cases (%) and the plantar pressure test, Lower Extremity Fugl-Meyer assessment (FMA-LE) and Berg Balance Scale (BBS), which did not conform to a normal distribution, as well as discontinuous variables such as the Barthel Index (BI) assessment, are presented as the mean (95% con dence interval (CI)). Continuous variables such as the 6-min walking test (6MWT) with a normal distribution are reported as the mean (SD).
As measurements using the fluorescence technique are only possible during moonless nights it is used to collect data for an in-depth understanding of the air showers  [3] [4] [5] [6]  with reduced statistics. On the other hand, secondary particles of the air showers are detected by 1600 water-Cherenkov Detectors (WCDs) located on the ground, which form the surface detector (SD). The detectors are located at a distance of 1. 5 km from each other in a hexagonal configuration (Fig. 1a ) and measure the time-dependent density of secondary shower particles. The measurement is based on Cherenkov light emitted by the relativistic particles as they pass through each water-filled detector. This light is recorded by three photomultiplier tubes (PMTs) attached to the top of the tank and processed using flash analog-to-digital converters with a sampling rate of 40 MHz, translating to bins with a width of 25 ns.
Simulation libraries For generating training and validation data we use CORSIKA 7. 6400  [15]  for the simulation of extensive air showers. The Pierre Auger Offline software  [16]  is used for the simulation of the detector responses, which is based on GEANT4  [17] , and the reconstruction of the air shower parameters  [2]. The CORSIKA showers are reused 5-30 times at other locations of the observatory along with a re-simulation of the detector responses. The simulation library consists of events for primary hydrogen, helium, oxygen and iron, uniformly distributed in azimuth angle and following a zenith angle distribution flat in cos 2  with 0 • ≤  < 65 •. The energy range covers 1 -160 EeV (1 EeV = 10 18 eV) and follows a spectrum of  -1. For the training of the network we use exclusively simulations with the EPOS-LHC hadronic interaction model  [18].
Hybrid dataset The accuracy of the deep-learning based reconstruction of  max is examined using a high-quality set of hybrid data where nearly unbiased  max measurements are performed using the fluorescence technique. A complete description of the hybrid reconstruction and the high-quality event selection can be found in  [3]. Here, events are only kept if  max falls into the FD field of view and the fraction of the shower profile inside the field of view is large enough to allow an unbiased measurement of  max. Additionally, we discard events below the full trigger efficiency of the surface detector at roughly 3. 16 EeV to prevent biased reconstructions. Further, we remove events (less than < 0. 5%) with area over peak (A/P) values  [21] , given in units of 25 ns, which are very low (< 2.
Network prediction During the training of the network the prediction (output) of the neural network is compared to the  labels of the dataset in each iteration. As the input values of the network lie approximately in the range of [-1; 1] and the weights of the model are properly initialized, the expected output values of the network follow approximately a Gaussian with standard deviation  = 1 and mean  = 0. To speed-up the learning process we also normalize the label values to lie in the same range using standard normalization ( = 1,  = 0). Instead of normalizing the label values directly, the normalization is implemented as the final layer in our network for the  max output allowing us to monitor the learning process in physics quantities.
Training For training of the network we use 400,000 simulated air shower events with a mixed composition of hydrogen, helium, oxygen and iron (in equal parts). During the training we use the data augmentation as described in section 2. 4 to increase the amount of data and to improve the generalization capacities of the network. To penalize composition biases we use an element-wise mean squared error (MSE) as objective function: L = ∑︁ 𝑍 =H,He,O,Fe MSE( Xmax,𝑍 ) = ∑︁ 𝑍 =H,He,O,Fe ( Xmax,𝑍 -𝑋 max,𝑍 ) 2 (3. 1) = ∑︁ 𝑍 =H,He,O,Fe Var( Xmax,𝑍 -𝑋 max,𝑍 ) + Xmax,𝑍 -𝑋 max,𝑍 2. (3. 2) Here Xmax, is the predicted shower maximum and  max, the label, for the respective element. Var( • ) represents the batch-wise variance and • the batch-wise expectation value. The network and the training is implemented using Keras  [30]  and the TensorFlow  [31]  backend.
We train our network using an Nvidia Geforce 1080 GTX GPU for 150 epochs with a batch size of 48, which takes about 60 h. We further stop the training if the objective function applied to validation data does not decrease after 10 epochs. We use the ADAM optimizer  [32]  with an initial learning rate of 0. 001 and a decay of 2. 5 • 10 -6. Furthermore, we reduce the learning rate by multiplying by a factor of 0. 7 if the objective function evaluated on the validation data did not decrease in the recent 4 epochs. As part of the work, a wide variety of network architectures was tested. These tests included various changes of each of the three network parts. In particular, different types of recurrent layers and various convolutional approaches were tested. The final model which utilizes LSTMs for the processing of signals and hexagonal convolutions for analyzing the spatial relations showed the best performance and stability.
Acknowledgments ( MDM-2016-0692 );  USA -Department of Energy , Contracts No. DE-AC02-07CH11359 , No. DE-FR02-04ER41300 , No. DE-FG02-99ER41107  and No. DE-SC0011689 ;  National Science Foundation , Grant No. 0450696 ; The  Grainger Foundation ;  Marie Curie-IRSES/EPLANET ;  European Particle Physics Latin American Network ; and  UNESCO.
Climatic time windows selection We used the function 'slidingwin' from the R package 'climwin'  (Bailey & van de Pol 2016; van de Pol et al. 2016)  to identify for each fate the most influential weather time window and measure its reliability. This approach uses two separate data sets: one containing climate data (on a daily scale) and one containing the response variable (i. e. probability of a certain fate) and covariates (i. e. baseline model). Climwin fits all possible time windows, each time varying start and end, within a chosen range and compares the resulting models' output using information criteria, AIC  (Akaike 1998) , corrected for small sample sizes, AICc. Since sociable weavers can breed throughout the year, we tested windows relative to each fate date, with a maximum of 100 days before the fate (i.
The interactive effects of climate, predation and helpers To test the effect of group size on reproductive success during the most relevant climatic window for each fate, we ran binomial linear mixed models using the package 'lme4'  (Bates et al. 2015)  with a logit link function. We ran one model for each fate (i. e. 'predated', 'fully fledged' and 'ejection') and each experimental condition (i. e. natural and protected-the 'predated' broods of the latter were excluded) and weather variable (i. e. temperature and rain) for a total of 10 models (Tables  S12-S21 ). We had five explanatory variables, again all scaled and centred, the same three included in the baseline climatic models (i. e. colony size, brood size and activity ratio), the weather variable for the time windows determined during the climatic analysis and group size.
To test for helpers' mitigation of harsh weather conditions, we added the interaction between weather and group size. We also added an interaction between colony size and group size because it was previously found that larger colonies are poorer reproductive environments  (Covas et al. 2008; Spottiswoode 2009). Colony ID and season ID were included as random factors. The breeders' ID was not included as it would markedly decrease sample sizes, but follow-up analysis including them showed that these random factors did not explain any or negligible variance (data not shown). As indicated above, we repeated the same analyses with helpers' presence/absence instead of group size, which yielded qualitatively similar results (Figures S4, S5, Table  S22 -S31). Finally, to test if group size was associated with decreased nestling mortality within broods, we ran a finer analysis for 'ejection' in protected colonies.
For help with data collection, the authors thank Margaux Rat, Maxime Loubon, Elise Blatti, Maxime Passerault, Thomas Pagnon, Rita Leal, Ryan Olinger, Samuel Perret, Annick Lucas and all the other students, assistants and volunteers. De Beers Mining Corporation provided access to Benfontein Nature Reserve and logistical assistance. PBDA was supported by the South African Claude Leon Postdoctoral Fellowship 2019-20. Data collection was supported by funding from the DST-NRF Centre of Excellence at the FitzPatrick Institute of African Ornithology (South Africa), FCT (Portugal) through grants IF/01411/2014/CP1256/ CT0007 and PTDC/BIA-EVF/5249/2014 to RC and the French ANR (Projects ANR-15-CE32-0012-02 and ANR 19-CE02-0014-02) to CD. R. F. and A. C. F. were funded by FCT (SFRH/BD/130134/2017 and SFRH/ BD/122106/2016 respectively).
INTRODUCTION During the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) broadcasted  20  an open call to forecast COVID-19 cases and deaths at a state level. All data scientists and research teams with models that predicted the course of the COVID-19 pandemic were welcomed to submit their models to the CDC COVID-19 Forecast Hub. CDC COVID-19 Forecast Hub pipeline teams were invited to submit predictions of the numbers of new cases, hospitalizations, and deaths in future days, weeks, and months, for the county, state, and national levels in the U. S. Groups across the U. S. assembled organically and a community emerged that consisted of individuals with different backgrounds and expertise but with a shared vision of forecasting to inform policy. Despite the existence prior to COVID-19 of troves of data which could be analyzed for real-time prediction in the event of a disaster, the pandemic exposed the fault lines in our data-verse.
A. Data Sources The MIT-LCP model is implemented using a gradient boosted regressor to forecast COVID-19 deaths at the state and national levels. It uses novel digital data sources including prior COVID-19 cases and deaths, demographic, socioeconomic, mobility, and healthcare-related county-level covariates for feature generation. The details and characteristics of the data sources are described in Table  I. Table I lists the description, scope, time frame, and the number of features used for each dataset. project's state-level testing data also served as a useful feature for forecasting deaths. The test positivity rate in a specific state was a good indicator of the severity of the pandemic at that time  [4]. The county-level socioeconomic dataset was the only dataset used that was not in a time-series format.
Although it was static, the variables in the dataset, specifically the demographics and education variables were valuable because certain groups of people are more vulnerable to COVID-19  [5] , and the model can leverage these variables to 100 predict mortality. Additionally, the healthcare variables from this dataset served as a strong indicator of each county's healthcare system's relative strength  [6]. Previous studies have established that an increase in mobility metrics leads to an increase in COVID-19 cases and deaths  [7] , so PlaceIQ exposure indices were used in the feature set for training as well. State-level hospitalization data as correlates for pandemic severity were the final variable added to the feature set.
B. Forecasted Data Targets In accordance with the structure of the CDC Forecast Hub, we made probabilistic forecasts for 1-4 weeks ahead of incident and cumulative deaths for each county in the US, which were then aggregated to the state and national level. The primary modeled quantity of the model was weekly incident deaths, which were added to 110 previous forecasts for incident deaths as well as previous cumulative deaths to create the cumulative death forecast. The forecasts evaluated in this paper are state and national-level real-time weekly incident forecasts only. This means that they submitted forecasts weekly during the period of evaluation as new data arrived.
C. Model Pipeline Architecture Figure  1  shows the various stages of the MIT-LCP model pipeline. The first step in the forecasting pipeline was conducting data source retrieval for data sources that refreshed on a daily basis. Upon retrieval, the data were parsed and preprocessed, and used to generate specific features for the model. The datasets were then combined to generate additional features. The regression model was trained on the features and the strength of each feature was determined using SHAP (SHapley Additive exPlanations) values (Figure  2 ) 120  [10]. The features with higher relative strength were then separated from the rest, and the model was then trained again specifically on these features. Finally, the model predicted incident and cumulative deaths for 1-4 weeks ahead at the county-level for all the counties in the US, and these predictions were then aggregated to the state and national level.
D. Software and Algorithms The real-time forecasting model was implemented using Python 3. 6 in Jupyter notebooks. Python packages used include NumPy  [16] , Pandas  [17] , scikit-learn  [18] , and XGBoost  [9]. The model consisted of an ensemble of extreme gradient boosted trees of linear regressors trained with the mean squared error loss function. Gradient boosting involves iterative combinations of ensembles of weak prediction models into one strong learner. The package XGBoost uses second-order Taylor series to approximate the value of the loss function and reduces the likelihood of overfitting  [9]. The model utilized hyper parameter optimization, the approach being a cross-validated grid search. The optimized hyper parameters were the learning rate, number of trees, and maximum tree depth.
For the COVID-19 pandemic, many mechanistic models underperformed compared to a nonmechanistic baseline. Mechanistic models that implemented the SEIR framework typically underperformed relative to other models. However, some mechanistic models utilized the SEIR framework with a more Bayesian approach --such as the UMass-MechBayes model. This particular model performed quite well compared to both the mechanistic SEIR models and MIT-LCP's nonmechanistic model. One major limitation of many non-mechanistic models is the lack of causal inference. Integration of causal inference methods with machine learning in non-mechanistic models could improve the overall 220 performance of the model. A second limitation is the lack of policy data integration with forecasting models. Datasets of policies and public health guidelines issued at the state and local-level could aid in the creation and advancement of forecasting models.
This community spanned multiple institutions, disciplines, and expertise levels, drawing from academia, industry, and government, and has attracted contributors from senior investigators all the way to high 240 school students. This paper's group of authors reflects that diverse, cross-disciplinary community. Prior to COVID-19, the authors had formed a global consortium called MIT Critical Data to advance health data science, which consisted of healthcare practitioners, computer scientists, engineers and social scientists from around the world, who believed that data and learning can be the best medicine for population health. During the pandemic, they came together to contribute their respective skills and interdisciplinary perspectives to assist in the public health fight against COVID-19 in real time.
Construction of metagenomic species and taxonomic annotation. The gene count matrix was used to cluster metagenomic species (MGS) based on co-occurrence by use of the canopy clustering software developed by Nielsen et. al  19. We assembled 3,463 co-abundant Cluster Genes (CAG) having 50 genes or more. In the present study, 1,437 CAGs were composed of more than 500 genes and were considered as MGS. Taxonomic annotation of MGS was based on the aggregation of its gene annotation applying 'BLASTn' as previously reported  19. We computed MGS abundance using the 'momr' R package (https:// cran. r-proje ct. org/ web/ packa ges/ momr/ index. html). MGS abundance was defined as the mean abundance of the 50 MGS genes that correlated strongest to the median gene abundance. We considered the MGS abundance in a sample to be null, if fewer than 10% of its genes were present.
We constructed logistic ridge regressions with the python 'scikit-learn' library: (http:// scikitlearn. org/ stable/ modul es/ linear_ model. html# ridge-regre ssion). Serum levels of BAs were scaled prior to model fitting, so the coefficients would be comparable to one another. The target variables were BMI, HOMA-IR and fasting serum levels of leptin. The latter variables were binarized around their median values. The BMI target was binarized around 27 kg/m 2. Models were fitted separately for women and men. We drew 10,000 partitions of the study sample by randomly splitting the 80% of study samples in a training set and 20% in a test set. For more detail, please see (ESM). Genome-scale metabolic modeling of bile acid biosynthesis. We applied genome-scale metabolic modeling (GSMM) of gut microbiota to achieve insights into the BA biosynthetic and biotransformation potentials / pathway(s).
MGS with more than 500 genes were listed and mapped to the AGORA (version 1. 02) compendium, comprising 773 gut bacteria (including 205 genera and 605 species)  20. AGORA is a comprehensive resource of semi-automatically reconstructed GEMs for the human gut microbiota. A detailed methodology of GSMM applied to BA metabolism can be found in the (ESM). We extended the GSMM framework by adding a common compartment simulating the gut epithelial cells (Supplementary Dataset 1), and another compartment which represented the host body fluids, and finally a common gut lumen compartment that enabled the gut bacteria and the host epithelial cells to exchange dietary metabolites and BAs. In the intestinal epithelial cells, some BA transporters (for e. g. ABC transporter) have been previously identified  21.
Transporters for HCA, particularly THCA (THCA-ABC BA transporter, bicarbonate counter transporter and sodium cotransporter) have been identified and reported in Recon3D  22  , a generic human metabolic reconstruction. The BA transporters obtained from Recon3D and several other pieces of bibliographic evidence were added to the gut epithelial cell model developed in this study (Supplementary Dataset 1)  8, 12, 23, 24. A report on the AGORA approach deployed in the study can be found in the (ESM).
Results Bile acids in fasting human serum. Serum BAs present in at least 50% of the study participants were considered for the data analysis. 20 BAs with known chemical structure and eight with unresolved stereochemistry structures were included in the final dataset (Fig. 2a ). While exact stereochemistry is unknown, hydroxylation and conjugation can be inferred. Serum concentrations of BAs were detected in the range of 0. 125-332. 0 ng/ ml (Fig. 2B ). Of the 28 BAs, eight were C-6 hydroxylated BAs. Five of these with known stereochemistry: HCA, GHCA, THCA, TαMCA, GHDCA, and three of unknown stereochemistry (Supplementary Fig. S1 ).
Higher levels of serum THCA were linked with a 17% lower prevalence of obesity for women, and 14% for men (Supplementary Fig. S3 ). Intriguingly, CDCA and UDCA were associated with an increase in odds of having high HOMA-IR for women, but not for men (Supplementary Fig. S4 ). Additionally, higher levels of serum DCA was associated with a 22% higher prevalence of obesity in men and 9. 4% in women (Supplementary Fig. S3  and S4 ). The findings may suggest sex-specific effects of BAs. Communities of gut bacterial species were linked with serum bile acids. One hundred and eighty-nine of the most abundant (1463) MGSs were organized into 17 bacterial communities (see Methods, ESM and Supplementary Dataset 2). The combined abundance of the species in these communities repre-Figure  1. Framework for analyses of three complementary data sets: adiposity/metabolic traits, fasting serum bile acids and gut metagenomics species including search for bacterial enzymes or pathways involved in biotransformation of bile acids.
S5  and Supplementary Dataset 1). Thus, the applied GSMM in the current version of the AGORA compendium failed to unravel gut bacterial pathways related to biosynthesis of C-6 hydroxylated BAs. Bioinformatics search for gut bacterial genes involved in biosynthesis of C-6 hydroxylated bile acids. We attempted to identify Clusters of Orthologous Groups (COGs) that correlated to a ratio of two closely related sets of BAs, ignoring conjugation, termed 'BAGs' , as described in (Methods). Ratios of closely related BAGs were correlated to MGSs by Spearman's correlation and differences in correlation strength were evaluated by Wilcoxon test. One thousand four hundred and twenty three COGs were associated (FDR corrected P < 0. 05) with one or more BAG ratios (Supplementary Table  S4 ). When investigating the degree of interconnectedness between COGs that significantly correlated with a given BAG ratio, we failed to identify any interconnected and functionally related subclusters of COGs.
Hence, due to a number of shortcomings for annotations in available gene and protein databases, we were unable to identify gut bacterial enzymes that can synthesize human C-6 hydroxylated BAs. When employing a χ 2 -test to investigate the dependency between direction of correlation with a given BAG ratio as evaluated by Spearman's correlation and the presence or absence of a COG for all MGSs; no COG was found to have significant (FDR corrected P < 0. 05) dependencies. Additional information on the applied approach is given in (ESM).