dataesr/scientific-paragraphs-categorization huggingface dataset:
Group independent component analysis (ICA) The obtained tw-dFC volumes were analyzed using a spatial group ICA framework as implemented in the Group ICA of FMRI Toolbox (GIFT)  75, 76. Group analysis was performed separately for the primary dataset (HCP) and the validation dataset (LEMON). Briefly, the pipeline for group ICA analysis involves a first step, in which a subject-level principal component analysis (PCA) is performed for dimensionality reduction purposes, and a second step in which dimensionality-reduced data are temporally concatenated and undergo a secondary PCA dimensionality reduction along directions of maximal group variability. Finally, the group PCA-reduced matrix is decomposed into a given number of independent components (ICs) using the Infomax algorithm  77. For the first data reduction step, 120 subject-specific PCA components were chosen, as in previous works  76.
The second data reduction step and subsequent ICA decomposition were performed at three different dimensionality levels (ICA10, ICA20 and ICA100). For each run, the ICA algorithm was repeated 20 times in ICASSO and the n most reliable components were identified as the final grouplevel components, to ensure stability of estimation. ICASSO returns a stability (quality) index (Iq) for each estimate-cluster. This provides a rank for the corresponding ICA estimate. In the ideal case of m one-dimensional independent components, the estimates are concentrated in m compact and close-to-orthogonal clusters. In this case, the index to all estimate-clusters is very close to one, while it drops when the clusters grow wider and less homogeneous  27. Finally, the resulting components for each run were visually inspected to ensure that: 1) the peak activation of each network was localized in the white matter; 2) there was only minimal overlap to vascular, meningeal, ventricular sources of artifacts; 3) the mean power spectra of each network showed prominence of low frequency spectral power.
Reproducibility analysis The reproducibility of results was evaluated at three different levels on the primary dataset: intrasubject reproducibility (test-retest), inter-subject reproducibility (split-half) and inter-cohort reproducibility (comparison with the validation dataset). For test-retest reproducibility analysis, the LR-and RL-phase encoding acquisitions for each subject were employed as the test and retest data respectively; in the split-half reproducibility analysis, the HCP sample was split in random halves (105 subject each) and the ICA was run separately on each of them; finally, in the external reproducibility analysis the results from ICA on the primary dataset were directly compared to the results obtained on the validation dataset. In all cases, the comparison metrics were: i) the pairwise Pearson's correlation coefficient between each spatial component and its corresponding component (i.
Task-based functional network annotation To provide insights on the functional relevance of the identified white matter components, i. e. to give a measure of how large-scale white matter networks may be involved in the execution of complex tasks, we compared the resting-state spatial maps resulting from the main dataset to the task-based white matter activation maps derived with a recently developed method, namely "Functionnectome"  28. To compare these results to the ICs derived from distinct ICA runs, pairwise Pearson's correlation between the z-weighted functionnectome maps and each component's z-map were computed after a thresholding of z > 0. We considered correlations significant if the proportion of shared variance between tw-dFC and functionnectome maps was above 5% (e. g. , a spatial correlation of r > 0.
Connectivity-based component classification Functional network connectivity (FNC), defined as the pairwise correlation between each pair of IC time courses, was measured on the primary dataset for each ICA dimensionality level. For each run of ICA, we sought to classify components in an unsupervised way based on the similarity of their activity profiles, by performing k-means clustering on the group-average FNC matrix. The optimal number of clusters (k) was determined by plotting the ratio of between-group variance to total variance for increasing values of k and identifying the elbow point (elbow method). To obtain robust cluster centroids, k-means clustering was performed on bootstrap resamples, by iterating clustering 100 times on randomly drawn samples of 168 subjects (80% of the total subjects); the resulting centroids were then employed to perform clustering on the whole dataset.
FNC-based cognitive performance prediction tasks To evaluate the role of tw-dFC-derived FNC in predicting individual cognitive performance, we applied a linear regression model with leave-one-out cross validation (LOOCV) to behavioral measures of cognitive performance available in the HCP database. Behavioral measures of fluid intelligence (Penn Progressive Matrices, HCP_ID: PMAT24_A_CR), cognitive flexibility (Dimensional Change Card Sort, HCP_ID: CardSort_AgeAdj) and sustained attention (Short Penn Continuous Performance Test, HCP_ID: SPCPT_TP) were selected as independent variables. FNC measures from ICA100 were used as features to predict, independently and separately, each behavioral variable. More specifically, FNC upper triangular matrices were vectorized to obtain a single feature vector per subject, and underwent a first feature selection step, in which only features with the highest correlation coefficient (p < 0.
Acknowledgments The primary dataset (HCP) was provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ), funded by the 16  NIH  institutes and centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. We also gratefully acknowledge the  Mind-Body-Emotion group  at the  Max Planck Institute for Human Cognitive and Brain Sciences  for the data of the " Leipzig Study for Mind-Body-Emotion Interactions " (LEMON) that have been used as validation dataset.
COVID-19 chain of risks and effects of policy options: illustrative analyses Here, we consider the effects of inputs that sequentially and cumulatively influence the balance of risks for infants living in low-income and middle-income countries (LMICs). We estimated the effects of separation and non-breastfeeding by mothers with confirmed SARS-CoV-2 (with or without symptoms), identified either through universal testing of mothers of newborn babies or through testing of mothers presenting with COVID-19 symptoms. We estimated the potential number of infant deaths in 119 LMICs (appendix pp 4-6) that annually might be attributable to COVID-19 as well as the additional deaths due to separation and nonbreastfeeding among infants who might be affected by such a policy approach. We also present data that are specific for 14 countries that show regional differences in neonatal and child mortality.
GLPG1972/S201086 is a disintegrin and metalloproteinase with thrombospondin motif-5 (ADAMTS-5) inhibitor in development as an osteoarthritis disease-modifying therapy. We report the safety, tolerability, pharmacokinetics, and pharmacodynamics (turnover of plasma/serum ARGS-aggrecan neoepitope fragments [ARGS]) of GLPG1972 in 3 randomized, double-blind, placebo-controlled phase 1 trials. Study A, a first-in-human trial of single (≤2100 mg [fasted] and 300 mg [fed]) and multiple (≤1050 mg once daily [fed]; 14 days) ascending oral (solution) doses, investigated GLPG1972 in healthy men (N = 41; NCT02612246). Study B investigated multiple ascending oral (tablet) doses of GLPG1972 (≤300 mg once daily [fed]; 4 weeks) in male and female participants with osteoarthritis (N = 30; NCT03311009). Study C investigated single (Japanese: ≤1500 mg; White: 300 mg [fasted]) and multiple (Japanese, ≤1050 mg once daily; White, 300 mg once daily [fed]; 14 days) ascending oral (tablet) doses of GLPG1972 in healthy Japanese and White men (N = 88).
Study Designs Three randomized, double-blind, placebo-controlled, single-center, phase 1 studies were conducted to investigate the pharmacokinetics, pharmacodynamics (measuring ARGS levels in plasma or serum), and safety of oral doses of GLPG1972. Study A was a first-inhuman study in healthy male participants conducted in Belgium from October 26, 2015, to April 18, 2016  (NCT02612246). The main objectives were to evaluate the safety and tolerability of oral (solution) GLPG1972 compared with placebo when administered as single ascending doses (SADs) and multiple ascending doses (MADs) for 14 days. Objectives also included characterization of the pharmacokinetics and pharmacodynamics of GLPG1972 after single and multiple administrations, evaluation of potential interactions with CYP3A4 after repeated GLPG1972 dosing, and evaluation of the effect of food on pharmacokinetic parameters.
Study B was a MAD study in male and female participants with knee and/or hip OA conducted in the United States from May 15, 2017, to October 25, 2017 (NCT03311009). The objectives were to evaluate the safety, tolerability, and pharmacokinetics and pharmacodynamics of increasing oral (tablet) daily doses of GLPG1972 for 29 days. Study C evaluated SADs and MADs of oral (tablet) GLPG1972 in healthy Japanese and White men for 14 days and was conducted in Japan between December 8, 2017, and July 23, 2018. The objectives were to evaluate the safety and tolerability of GLPG1972 vs placebo, and the pharmacokinetics and pharmacodynamics of GLPG1972 after single and repeated administration. The studies were each conducted at 1 site (study A: Antwerp, Belgium; study B: Daytona Beach, Florida; study C: Osaka, Japan) in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines.
The MAD phase consisted of 3 consecutive cohorts of 8 Japanese participants who received repeated doses of oral GLPG1972 (300, 600, or 1050 mg) or placebo (in a 3:1 ratio) and 1 cohort of 8 White participants who received repeated oral doses of GLPG1972 300 mg or placebo (in a 3:1 ratio), all under fed conditions for up to 14 days. GLPG1972 was administered as 50-and 100-mg wet granulation tablets immediately following completion of the meal (within 10-15 minutes). Following an overnight fasting period (no food) of at least 10 hours, a standardized breakfast was ingested within 20 minutes; identical composition of every meal taken by each participant during inpatient stay and exact time of meals and quantities consumed were recorded. Dose escalation was based on safety and pharmacokinetic data of the previous dose group.
1, S2 ). DA can be considered a machine learning (ML) method. Compared to interpolation, Kriging, and most other ML approaches, DA has the advantage that it makes estimates based on our collective scientific understanding of Earth's carbon cycle, as encapsulated within GEOS, rather than relying on functional relationships that rarely hold in nature. The value of relying on forecasted fields instead of functional relationships in data analysis has been understood in the numerical weather prediction community since at least 1954  (62) , even before E. Lorenz's seminal work  (63) , yet receives less attention in other disciplines. Fig. 1  demonstrates the impact of data assimilation on OCO-2 coverage for April 2020. Before assimilation (top panel), there are notable patches of missing data where either clouds (e.
g. , the Amazon), aerosols (China), and high solar zenith angles (the poles) prevent reliable measurements. Assimilation produces three-dimensional CO2 fields with global coverage that are updated every three hours (middle panels and Fig. S1 ). Values in missing areas are inferred from nearby observational data and model relationships. The 16-day running means (bottom panel) and monthly means analyzed in this paper follow from simple averaging of the assimilated CO2 fields. GEOS/OCO-2 uses the GEOS Constituent Data Assimilation System (CoDAS), a highperformance computing implementation of Gridpoint Statistical Interpolation (GSI; 64), a technique for finding the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the differences between observed and simulated values.
GEOS/OCO-2 uses a homogeneous, horizontally isotropic background error covariance B whose diagonal is 0. 15 ppm everywhere with a nominal horizontal error correlation length of 500 km and vertical error correlation length proportional to the vertical correlation length of the given tracer. The observation error covariance R uses the reported retrieval error variances scaled by a factor of 0. 85 2 as its diagonal and has no cross-sounding correlations. While these crude error models could be improved, a posteriori diagnostics and evaluation against independent data indicate that the system is sufficiently well tuned. As an additional level of quality control, we do not assimilate retrievals that are over snow and ice, have a glint angle greater than 80°, or are in a swath with less than 4 footprints.
Soundings with a reported uncertainty less than 0. 001 ppm are also flagged and not assimilated. Cross-track variability of XCO2, accounting for the retrieval mode and surface type, is included in the retrieval errors by geometrically averaging it with the reported values. The final step for each interval is to rerun the 6-hour forecast with the optimal increment  * - ! , where  * minimizes the cost function J, applied as a forcing in the same manner as for the meteorological variables  (65). Data processing is divided into six separate streams covering 2015-2020. Each stream begins on October 31 of the previous year to allow some equilibration of the analysis prior to the period of interest beginning on January 1. Differences between overlapping streams are less than 10% of the magnitude of the anomalies analyzed in this paper, and thus can be safely ignored.
The results presented here use no CO2 data other than OCO-2 observations in the present year, here 2020. In previous years, it uses a single number, the atmospheric growth rate, to set the global flux budget as described below. A unique feature of GEOS/OCO-2 is its ability to process data in NRT, as retrievals become available to assimilate. This is accomplished primarily through the use of a surface flux collection, the Low-order Flux Inversion (LoFI; 34) with distinct modes for retrospective and NRT simulations. In retrospective simulations, the system uses surface fluxes informed by several remote sensing datasets that include fire radiative power, nighttime lights, and vegetation properties like leaf area index  (30)  and atmospheric growth rate estimates derived from surface observations.
In near real time, before many of these datasets become available, LoFI uses fluxes and a projected atmospheric growth rate based on data from previous years and the current ENSO phase  (41, 42). This dual capability ensures a strong, multi-platform data constraint on XCO2 on previous years for computing anomalies while the products for the current year highlight areas where land, ocean, and fossil fuel fluxes deviate from expectations. For fossil fuel emissions, we use the 2018 version of the Open-source Data Inventory for Anthropogenic CO2 (ODIAC; 70), which estimates emissions by tracking fossil fuel consumption (i. e. , barrels of oil, tons of coal, etc. ) and cement production  (9)  and ends in 2018. For 2019, we rescale the 2018 monthly gridded maps to match the Global Carbon Project (GCP) 2019 global emissions estimate  (37) , and for 2020 we simply repeat 2019 emissions.
Anomalies and pandemic-free baseline atmospheric CO2 fields Even after constructing gap-filled XCO2 maps, defining 2020 anomalies for CO2 is more challenging than for most other species. For NO2, which is short-lived, simply subtracting a multiyear climatological mean from the 2020 values is often sufficient for highlighting recent emissions changes  (5) , although recent research suggests meteorological variations can play a significant role in the interpretation of NO2 changes  (7, 71). For CO2 and other long-lived species, anomalies calculated against a climatological baseline reveal a strong imprint of circulation anomalies, which can have a greater impact than, and obscure the spatial signature of emissions changes. To minimize the circulation influence, at the beginning of each year we start a companion GEOS simulation that is identical to the analyzed product, except that OCO-2 data are not assimilated.
XCO2 anomaly (ppm) Table  1. GEOS/OCO-2 February-May regional reductions in XCO2 growth and associated uncertainties. Reductions and uncertainties are calculated from the data depicted in Fig. 3  as the peak 2020 reduction (solid boxes) and peak 1 2017-2019 uncertainty (gray shading) during February-May (see Materials and Methods). Start dates and end dates are taken from activity data (3). The average reduction over all four regions is 0. 38 ppm and average uncertainty is 0. 24 ppm, giving a 1 range of 0. 14-0. 62 ppm for the reduction over the Northern Hemisphere. Peak  In contrast to Fig. 2 , the dominant signal shown here is from the carbon-climate teleconnection between the 2019-2020 Indian Ocean Dipole (IOD), the strongest on record, and terrestrial biospheric exchange. In January (A-C) and February (D-F) 2020, there was increased biospheric uptake over India and Africa (blue colors) due to greater than average precipitation in the preceding months, while there was increased respiration and biomass burning over Australia and Southeast Asia (red colors) due to greater than average temperatures.
Perfect discrimination was obtained with all of the MSP created for the Vibrio species and even for very closely related species as V. europaeus and V. bivalvicida. The new EnviBase library was validated through a blind test on 100 Vibrio strains performed by our three collaborators who used the direct transfer and protein extraction methods. The majority of the Vibrio strains were successfully identified with the newly created EnviBase by the three laboratories for both protocol methods. This study documents the first development of a freely accessible database exclusively devoted to Vibrio found in marine environments, taking into account the high diversity of this genus.
This proteomic method has proven to be reliable and safe for the identification of bacteria, yeasts, filamentous fungi, and dermatophytes  (Carbonnelle et al. 2011 (Carbonnelle et al. , 2012;; Gregory et al. 2018; Piamsomboon et al. 2020; Sloan et al. 2017; Tracz et al. 2017; Wang et al. 2002). MALDI-TOF-MS is a rapid, precise, and cost-effective identification method compared to conventional phenotypic techniques or molecular biology. Its ability to analyze whole microorganisms with little sample preparation has greatly reduced the time to identification (30 min). Compared with Vibrio species found in marine environments, pathogenic Vibrio species found in humans are widely represented in Bruker Daltonics library and other free MALDI-TOF databases  (Erler et al. 2015 , Ruvira et al. 2013).
The aim of this study was to develop a MALDI-TOF Vibrio database, called the EnviBase, built on environmental, type strains and clinical strains of mollusk pathogens. By combining different culture conditions and sample preparation protocols, we generated 120 specific main spectra projections (MSP) for the Vibrio genus corresponding to 25 different species listed in a free database; we also validated this newly created database through a blind test. This work is the first study on the development of a database specifically intended to describe a high diversity of Vibrio species affecting marine organisms and which will enable an accurate rapid identification of a large number of Vibrio spp. in a very short time.
, Bremen). Each spot was analyzed three times, resulting in 24 single spectra per medium for each strain. A total of 72 spectra were generated for each strain. As Bruker recommended, a Bacterial Test Standard (BTS 8255343) was used to calibrate the instrument, before each acquisition session. A BTS was also used on each acquisition plate, to check the quality of the acquisition. As the most distinct, clear, and significant spectra lie in a mass range of 5000 to 10,000 Da, the protein mass spectra of samples were acquired in a mass range of 2000-20,000 Da by the flexControl 3. 4 program (Bruker Daltonics). The laser frequency for every run was 60 Hz with a linear positive mode. The default settings of the MALDI-TOF MS instrument were as follows: lens, 8. 5 kV; ion source 1, 20 kV; and ion source 2, 18.
Vibrio database development-software-based analyses To create a main spectrum projection (MSP) corresponding to a reference spectrum affiliated to each strain, the raw spectra were analyzed one by one to identify nonspecific spectra. First, using the flexAnalysis software (version 3. 4, Bruker Daltonics, Inc. ), the main spectral features and shape were analyzed according to the recommendations of Bruker Daltonics (MALDI Biotyper R V1. 1), and low quality spectra (spectra with outlier peaks and flatlines, shift spectra) were excluded. The results of the visual inspection method were further confirmed by two software-based analyses: cluster analysis (PCA) and composite correlation index (CCI) matrix analysis (MALDI Biotyper Compass Explorer module v4. 1). The dataset consisted of mass spectra obtained from the analyzed strains for which the intensities of the mass to charge ratios (m/z) constituted the variables.
The PCA clustering statistical analysis allowed a visualization of the relationships between the spectra highlighting the different groups and outliersby reducing thedataset (MALDI Biotyper Compass User). A CCI matrix analysis was also performed to study the relationships between the spectra, indicating the statistical distance between them. The acquired spectra were subsequently analyzed by the tool in the MALDI Biotyper Compass Explorer software to determine the CCI, a parameter to estimate the distance between spectra. A CCI match value of 1 represents complete correlation, whereas a CCI match value of 0 represents an absenceof correlation. The numerical CCIvalues were automatically visualized as a "heatmap" in the CCI matrix window. The closeness of the spectra was indicated by the color of the squares on the heatmap.
Red color indicates closely related spectra; blue color indicates that they are not closely related. Then, for each strain, the spectra selected by the previously described methods were merged using the MALDI Biotyper Compass Explorer software (version 4. 1. 90) to generate a main reference spectrum (MSP). Hence, an MSP was created from the compilation ofthe spectra generated inthree different media for each strain (by three different laboratories) to provide diversity of conditions, which demonstrate the robustness of the results obtained. The new EnviBase was compiled based on 120 created MSP. Finally, these newly created MSP were tested by comparing matching scoresobtained with Bruker's and Erler's databases  (Erler et al. 2015) , which contain7854 and 997 MSP, respectively (Fig  1a ).
MALDI-TOF MS Vibrio database validation To validate the EnviBase, a blind test was performed by testing 100 strains by the three collaborating laboratories (Fig. 1b ). Table  S3  presents a description of the strains used for this test. Two different methods were used in order to assess their performance in routine use on the results: the whole protein extraction technique (protocol above) and the direct transfer technique. For the latter technique, one loop of a fresh bacteria colony was directly smeared onto a MALDI target plate (96 MSP target polished steel #8280800) and covered with saturated HCCA matrix solution (alphacyano-4hydroxycinnamic acid dissolved in 50% acetonitrile, 47. 5% LC-MS water and 2. 5% trifluoroacetic). These strains were analyzed by MALDI-TOF MS, and the raw spectra generated were first compared with the newly created Vibrio database alone, and then with the three grouped databases (Bruker, Erler and our EnviBase) (Fig.