dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Using a population-based cohort of young adults with data collected 2 years before the pandemic and 4 months after the onset of the first wave of the pandemic (Summer 2020) in the province of Quebec, this study examined changes in symptoms of depression and anxiety and investigated whether these changes are linked to pandemic-related stressors and/or pre-existing vulnerabilities. On average, young adults did not report change in depression and anxiety levels across the full spectrum of symptom severity. However, the prevalence of severe depressive symptoms increased by 1. 9% during the initial months of the pandemic, while there was no change in severe anxiety symptoms. Most COVID-19-related stressors (with the exception of living alone) and variables pertaining to pre-existing vulnerabilities (e.
In our study, symptoms of youth with severe pre-existing symptoms did not deteriorate but rather improved, possibly reflecting a natural improvement of mental illness over time, an effect of seasonality (2018 data were collected in the spring, while 2020 data were collected in the summer), or a reduction in social stressors during periods of confinement. Conversely, young adults with low levels of depression and anxiety symptoms experienced a significantly greater deterioration in mental health from before to during the pandemic. These results correspond with findings from a longitudinal study by Hamza and colleagues conducted with students at a Canadian university (N = 773), in which participants with pre-existing mental health problems showed decreasing depressive and anxiety symptoms from before (May 2019) to during (May 2020) the pandemic, whereas their peers without pre-existing mental health problems showed increasing depressive and anxiety symptoms during the pandemic  [17].
Methodological considerations This study has a number of strengths, including its longitudinal design; the use of standardized measures of depression and anxiety; and data collected before and during the pandemic from the same participants. We acknowledge the following limitations. As in all longitudinal surveys, attrition occurred over the years and the most vulnerable individuals were underrepresented. Although all analyses were weighted, such differential attrition could potentially result in underestimation of the rates of anxiety and depression and consequently of the mental impact of COVID-19. Mental health outcomes were measured by self-report questionnaires, which do not provide clinical diagnoses. While we were able to use longitudinal data with mental health assessments before and during the COVID-19 pandemic, it is difficult to differentiate between mental health changes attributable to the pandemic versus developmental changes that are typical for this age group or changes due to seasonal effects, since pre-pandemic measures were obtained in the spring, while intra-pandemic measures were collected during the summer.
Acknowledgements The  larger Quebec Longitudinal Study of Child Development  was supported by funding from the  Quebec provincial government's Ministry of Health and Ministry of Family Affairs , the  Lucie and André Chagnon Foundation , the  Fonds de Recherche du Québec en Société et Culture, Canada's Social Sciences and Humanities Research Council , the  Canadian Institutes of Health Research , the  Centre Hospitalier Universitaire de Sainte-Justine , and the  Institut de la Statistique du Québec. The authors thank  Alain Girard  and  Elise Chartrand  for their contributions to the statistical analysis and all participants of the  Quebec Longitudinal Study of Child Development.
Funding This project was supported by l' Observatoire sur l'Éducation et la Santé des Enfants , funded by  FRQ-SC. Drs Pennestri ,  Chadi  and  Castellanos-Ryan  hold a Junior 1 salary award from the  Fonds de recherche du Québec en santé (FRQS). Drs Gouin ,  Ouellet-Morin ,  Boivin, Tremblay , and Geoffroy hold a Canada Research Chair.
Pre-testing Participants underwent an initial screening session to assess height, weight, blood pressure and body composition (by dual-energy X-ray absorptiometry; Hologic Inc. , DXA; Discovery A, QDR series). Whole-body and appendicular (sum of lean mass of both arms and legs) lean mass and body fat was determined using the software package Apex (en-CORE 2005, version 4. 0. 2. Hologic) and reference values from the National Health and Nutrition Examination Survey (NHANES) population-based dataset  (24). Participants were deemed healthy based on their responses to a medical questionnaire and screening results. All participants were instructed to refrain from strenuous physical activity and alcohol consumption for 3 d before the experimental trial. On the evening before the experimental trial, all participants consumed a pre-packaged standardised meal (Maaltijdpannetje, Aviko) containing 55 % energy as carbohydrate, 30 % energy as fat and 15 % energy as protein before 20:00, after which they remained fasted.
Statistical analysis All data are expressed as mean and standard deviation. Subjects' characteristics and baseline data (including basal FSR and anabolic signalling) were analysed using unpaired, two-tailed Student's t tests. The primary outcome of the study was mixed muscle FSR (change from the basal to postprandial period), secondary outcomes included plasma glucose, insulin, amino acid concentrations (changes over time, total AUC and time to peak) and anabolic signalling responses (changes over time). Two-factor repeated-measures ANOVA with time as withinsubject factor and intervention as between-treatment factor was used to compare differences over time in plasma glucose, insulin, amino acid concentrations and enrichments, anabolic signalling, and FSR (basal to the 0-120 min and 120-300 min postprandial period, and basal to the cumulative 0-300 min postprandial period).
Acknowledgements We greatly appreciate the assistance of  Ino van der Heijden ,  Linda Mans ,  Michelle E. G. Weijzen ,  Lisanne H. P. Houben  and  Joey S. J. Smeets  in the execution of the experiments. Technical expertise from  Hasibe Aydeniz ,  Stefan H. M. Gorissen ,  Joy P. B. Goessens ,  Annemarie P. Gijssen ,  Henk Schierbeek  and  Wendy E. Sluijsmans  during the sample analyses was greatly appreciated. Financial support and food items for this research were obtained from  Tereos (Marckolsheim, France ). The project is organised by and executed under the auspices of TiFN (Wageningen, the Netherlands), a public-private partnership on precompetitive research in food and nutrition. The researchers are responsible for the study design, data collection and analysis, decision to publish, and preparation of the manuscript.
8] [19]  This has consequences when distances within IDPs are measured by different techniques. 4] [25]  In this context, fluorophores have been attached in silico to describe measured E FRET of individual distances (one distance per protein). 19, 26 While these approaches are promising, the study of IDPs demands a systematic analysis integrating distance information between different regions of the protein, its global extension, but also local structural information to accommodate heterogeneity in compaction, as well as population of transiently structured elements. Here, we propose an approach for the systematic integration of various solution state structural data of IDPs based on the implementation of FRET efficiencies into the algorithm ASTEROIDS that derives representative structural ensembles of IDPs from NMR and SAXS data describing both local conformational propensities and long-range distance information.
■ RESULTS FRET Distance Networks in Conformational Ensembles. 2] [33] [34]  A large ensemble of conformers (e. g. , 10 000) is calculated based on a statistical distribution of Φ and Ψ angles of the protein backbone using the software flexible-meccano. 29 From this large ensemble, smaller subensembles that describe the experimental data are selected using the genetic algorithm ASTEROIDS. 35 istance measurements through FRET rely on the attachment of a donor and an acceptor fluorophore to specific sites within the protein chain. Our goal being to describe the experimental FRET efficiencies directly, the fluorescent dyes thus have to be accounted for in the conformational ensemble. We calculated accessible volumes for the fluorophores Alexa488 and Alexa594 attached to cysteines via maleimide chemistry and comprising a C 5 linker connecting the Cys side chain and the fluorophore, as previously described.
Reproduction further improves with increasing numbers of conformers (Figure  4C  and SI Figure  6 ). We thus conclude that, overall, an ensemble size of 200 conformers, as proposed earlier for ensembles selected based on PREs and residual dipolar couplings,  39  is a good size to reconcile reproducibility, statistics, and computation speed. Description of Experimental FRET, PREs, and Chemical Shift Data. While our comprehensive in silico data set demonstrates how to accurately describe long-range distances within intrinsically disordered proteins, we aimed to test the validity of this approach on experimental data. For this, we used a 110 residue long protein from the disordered Nterminus of the measles virus phosphoprotein (P 1-100 ). This protein has been extensively characterized by NMR spectroscopy  1, 40  and harbors two transient α-helices, as can be inferred from backbone chemical shifts (Figure  5D ).
17 n order to test whether the distance distributions of our conformational selection are in agreement with our experimental fluorescence lifetimes, we first extracted the fluorescence intensity decays of the FRET population from our single-molecule data (SI Figure  14A ). The IRF was measured independently under the same experimental conditions, described with a double Gaussian function, and convoluted with the multiexponential decays expected for our conformational ensemble. The resulting decay curves described the experimental intensity decays remarkably well (Figure  5H , SI Figure  14B ), indicating that our conformational ensemble correctly reproduces another set of independent long-range data that was not used in the ASTEROIDS selection process, thus confirming the validity of the selected ensemble as well as the time scales applied for motional sampling of both dyes and proteins within the ensemble.
The following dye parametrization was used in the calculation: Scripts provided by Walczewska-Szewc et al. 36  have been adapted to contain the changes above. Attachment points were read from the respective PDB files of the conformational ensemble in an automated way using in-house software and were then used for the calculation of AVs and distance histograms. Parametrization for Alexa647 was adapted from Peter et al. 67  PDB files containing full side chains were used for AV calculation. Conformer-wise FRET files were then generated as an input for ASTEROIDS  27, 28  selection, containing the different FRET distances used in the selection. Incorporation of FRET Efficiencies into Multiconformational Models. AVs were calculated per conformer as described. Pairwise distances between the sampled volumes of the two fluorophores are calculated and converted into FRET efficiencies according to ε = + ( ) r ( ) 1 1 r R 6 0 (5) with the Forster distance R 0 and the distance r between the sampled points in the AV.
Fluorescence anisotropies measured on the different P 1-100 samples suggested that this assumption was valid (SI Table  2 ). R 0 was then calculated according to  46 Ä Ç Å Å Å Å Å Å Å Å Å Å Å É Ö Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ π λ κ ϕ = R N J n 9 ln(10) 128 ( ) 0 5 A 2 D 4 1/6 (10)   with the Avogadro number N A , the overlap integral J(λ), the orientation factor κ 2 , the quantum yield of the donor in the absence of acceptor Φ D , and the refractive index n. n = 1. 3 was used for P 1-100 in its measurement buffer. An R 0 of 56 Å was obtained for the dye pair Alexa488/Alexa594 in 50 mM Na-phosphate pH 6, 150 mM NaCl, and 2 mM dithiothreitol (DTT). The same R 0 was used to compute the in silico data set. Generation of in Silico Ensemble. Flexible-meccano  29  was used to generate a large conformational ensemble (10 000 conformers) of a 155 amino acid long IDP.
Photons were counted using a Hydraharp (Picoquant). smFRET experiments were performed at room temperature. FRET histograms were calculated using custom code written in Python. Lists of photon arrival times were first extracted using a code written in C, adapted from a demo-code provided by Picoquant. 78 hoton streams were then binned with a 1 ms bin width and subjected to a Lee filter before bust integration and thresholding. 79 A threshold of at least 50 photons was used. Fluorescence intensities were corrected for background contribution, spectral crosstalk, differences in quantum yield (determined as described previously  70  ), and differences in detection efficiencies between the green and the orange channel. Microtimes were extracted for bursts corresponding to the FRET peak and to the 0-FRET peak separately, and population averaged lifetime histograms were built.
No tillage (Nt) is often presented as a means to grow crops with positive environmental externalities, such as enhanced carbon sequestration, improved soil quality, reduced soil erosion, and increased biodiversity. However, whether Nt systems are as productive as those relying on conventional tillage (Ct) is a controversial issue, fraught by a high variability over time and space. Here, we expand existing datasets to include the results of the most recent field experiments, and we produce a global dataset comparing the crop yields obtained under Ct and Nt systems. In addition to crop yield, our dataset also reports information on crop growing season, management practices, soil characteristics and key climate parameters throughout the experimental year. The final dataset contains 4403 paired yield observations between 1980 and 2017 for eight major staple crops in 50 countries.
Several studies  1, 12, [20] [21] [22]  have been conducted to synthetize the current evidence on the productivity in NT systems. Some of these studies relied on global datasets including results of field experiments comparing NT and CT cropping systems over a large range of soil and climate conditions. However, these datasets do not include the most recent published experiments, and provide no or limited information on soil characteristics, climate variables, and management practices. In particular, information on fertilizer inputs, weed and pest control, and intraand inter-annual climatic variability are frequently missing. Other studies comparing NT and CT rely on a limited number of experiments, are only conducted at a regional scale, or did not make their data fully available  [23] [24] [25].
Thus, a global dataset reporting findings from the most recent field experiments and including information about a wide range of climatic parameters, soil characteristics and agricultural management practices is still lacking. To address this gap, we present an updated and extended dataset comparing CT and NT productivity including the most recently published experimental studies, and a detailed description of their environmental characteristics and management practices. Our dataset contains 4403 paired (NT vs. CT) yield observations collected between 1980 and 2017 for eight major staple crops in 50 countries. For each experiment, we provide information on soil texture, pH, the year and month of crop planting and harvesting, the location of the experiment, fertilization, weed and pest control practices, crop type, crop rotation, crop residue management, and crop irrigation.
Methods Data collection. The literature search was done in February 2020 using the following keywords 'Conservation agriculture/No-till/No tillage/Zero tillage' & 'Yield/Yield change' in the websites 'ScienceDirect' , 'Science Citation Index (web of science)'. A total of 1012 potentially relevant papers were identified by reviewing the title and abstract, and these papers were then screened according to the procedure summarized in Fig. 1. Papers not reporting yield data for CT and NT systems were excluded, as well as papers reporting experiments on reduced tillage (RT) systems. Papers reporting only mean yield data across different years or sites were also excluded. We then checked whether information on fertilization, weed and pest control, crop irrigation, crop rotation and crop residue management were reported for both CT and NT practices.
After these screening and selection steps, all relevant data were manually extracted from the selected papers, including general information about the paper, location and year of the experiment, the number of years under NT when the crop was sown, soil characteristics, crop growing season, crop type, crop management practices and crop yield of CT and NT. However, due to a large number of missing data, the crop growing season, climatic variables and soil characteristics were finally collected through several external databases (Online-only Table  1 ). The growing season information was generated from a crop calendar database  26, 27  based on the crop type and the locations of the experiments reported in the papers. The precipitation, average temperature in the growing season were extracted from the UDel_AirT_Precip data provided by NOAA/OAR/ESRL PSL 28.
The maximum and minimum air temperature during the growing season were generated from CPC Global Temperature data provided by NOAA/OAR/ESRL PSL  29  and the potential evapotranspiration data over the growing season were extracted from GLEAM database  30, 31. Soil textures were collected from the HWSD database  32  using the locations of the experimental sites reported in the selected papers. The column "Yield increase with NT" reports whether the differences between Yield CT and Yield NT are positive or negative ("Yes" indicates that crop yield is increased with NT practice, while "No" indicates that yield is not increased). Category VI includes data extracted from the external databases, including crop growing season, climate variables (including precipitation, potential evapotranspiration, minimum/average/maximum temperature) during the growing season, and soil texture.
Crop growing season is defined by a start month and end month, which were extracted from the external crop calendar databases  26  of spring barley, winter barley, cotton, maize, rice, sorghum, soybean, sunflower, spring wheat and winter wheat based on the crop type and study sites. Data on the crop calendar corresponds to averaged data and does not change intra-annually, thus the growing season extracted may be different from the actual growing season. The climatic variables from the external databases are: • Accumulated precipitation (P) during the growing season (sum of monthly precipitations during the growing season), • Accumulated potential evapotranspiration (E) (sum of monthly evapotranspiration rates during the growing season), • Precipitation balance (PB), defined as PB = P -E  12.
technical Validation To ensure the reliability of the information collected from the papers, we carefully checked and compared all the collected data with the original paper several times. Quality control of the database was conducted based on outlier detection. For each crop, the outliers of crop yield in CT system and NT system were identified based on the Interquartile Rule 35 outlier detection method. For each crop species, an interquartile range (IQR) is defined as the difference between the first and third quartile of crop yield, and a threshold is calculated by adding 1. 5 IQR to the third quartile. Any yield data beyond this threshold is flagged as an outlier for the crop species considered. The ratio of crop yield in NT and CT systems ( ) Yield Yield NT CT were also calculated. All outliers and all the observations with a ratio higher than 2 were checked and compared with the values reported in the original papers one more time.
The crop yield values reported in our dataset are consistent with results of previous published studies. Comparing crop yield data of NT and CT, the adoption of NT practice overall leads to a yield decrease (Fig. 4a ). A similar trend of crop production decrease with NT was reported in previous studies  1, 12, 20, 36. We also find that the combination of NT with crop rotation and soil cover (known as CA) trends to increase crop yield compared to NT practice without rotation and soil cover (Fig. 4a ), which is also in line with previous studies  1, 16, 37. Further analysis conducted on each crop confirms that NT tends to decrease the yield of maize 1 , rice  21  , and wheat 1 (Fig. 4b ). The productivity of NT is found higher under dry conditions compared with wetter conditions (Fig. 4c ), and similar trends were reported in previous studies  1, 12.
Usage notes. Our dataset can be used to analyze the factors influencing the productivity of NT (or CA) vs. CT. It is possible to train machine learning models to predict the probability of yield increase with NT (or CA) system (e. g. Supplementary Figs. 1  and 2 ) or the range of yield changes resulting based on the soil type, climate and agronomic inputs provided by this dataset. Global maps of probability of yield increase with NT (or CA) or the range of yield changes can be generated based on the outputs of machine learning models trained with our dataset, and enable policy-makers or agricultural advisors to identify the most promising regions for CA implementation. Details of how to train machine leaning models with our dataset are provided in Supplementary Materials. The crop yield data for 2018 and later can be extracted from the identified papers, but since some key climatic variables are missing in the external database for this time period (in particular, evapotranspiration), those data are not listed in the dataset provided.
, • Average air temperature (Tave) during the growing season, • Maximum air temperature (Tmax): the maximum value among the daily temperatures in the growing season, • Minimum air temperature (Tmin): the minimum value among the daily temperatures in the growing season. Soil texture was extracted from an external database based 32  on the experiments' locations. In total seven texture classes were included: sandy loam, loam, silt loam, sandy clay loam, clay loam, sandy clay and clay.
LAMP assays LAMP primers LAMP primers for P. mirabilis were designed by using the online Primer Explorer V4 software  [22]  and according to the general criteria described by  Notomi et al. (2000). The P. mirabilis target, walR, encodes an enzyme of the glycosyl transferase family involved in the biosynthesis of lipopolysaccharides  [23]. The walR sequences were obtained from GenBank NCBI (www. ncbi. nlm. nih. gov/) and were tested in silico through BLAST searches. Alignment analysis of available analogue nucleotide sequences (Accession number ADK56074. 1, HM146786. 1, HM146785. 1, HQ25931. 1, HQ25930. 1, KQ960958. 1) were performed to design the following P.
Analytical performance of the LAMP assays The BLAST analysis of the designed primers amplifying the walR gene from P. mirabilis did not show any mismatches. In addition, the P. mirabilis LAMP assay amplified only the DNA of the reference U2A 1878 and all 38 clinical strains of P. mirabilis (Table  1 ). No cross amplification was observed with the non-P. mirabilis reference (N = 16) strains (Fig. 1 ) (Additional files 1, 2, 3, 4; Fig. S1-S4. The captions correspond to that of the Fig. 1 ) nor with other clinical isolates causing UTIs (N = 20). The specificity of the LAMP assays targeting E. coli, K. pneumoniae and E. faecalis was further confirmed by successful amplification of only the corresponding reference strains (data not shown). Positive SYBR Green I LAMP products or expected bands on agarose gels were only observed in the presence of the target DNA from reference strains.