dataesr/scientific-paragraphs-categorization huggingface dataset:
Health administrative database The health administrative database is a linkage of health insurance database (SNIIRAM database), hospital database (PMSI database) and medical causes of death (CépiDC database)  [6]. It records every drug delivered in the community, as well as primary care physicians and pharmacist visits. The data extracted were put into the Observational medical outcomes partnership (OMOP) format  [7]. We extracted, for every person having at least one hospital stay in the study hospital during the study period, year of birth, sex, date of hospital admission and discharge, the list of drugs delivered within the 90 days prior to hospital admission, visits billed related to the intervention by GP and pharmacists until the January 1st, 2020. GP and pharmacists were asked to bill their visits within the 3 months of patient hospital discharge.
Analysis We described the time to billing by GP and pharmacists in the health administrative database. Indeed, according to the intervention implementation file every person received the complete community follow-up. Sensitivity and Predictive Positive Value could not be computed (false positives and true negatives equal to 0). Then we compared medications identified in the medication reconciliation list and the health administrative database. The medication reconciliation was considered to be the gold-standard  [5]. Sensitivity and Predictive Positive Value were computed. It was not possible to determine the true negatives as they would correspond to all drugs that could be prescribed in France.
Results Between 1st January 2015 and 31st December 2018, 328 transitional care interventions were performed at the study hospital according to the intervention implementation file. They were probabilistically matched with 291 (88. 7%) hospital stays identified in the health administrative database. Thirty-seven hospital stays were excluded because of i) absence of medication delivery recorded in the 90 days prior to hospital admission in the health administrative database or ii) lack of consistent criteria between the intervention implementation file and the health administrative database to perform the matching. The 273 persons enrolled in these 291 interventions had a median age of 83. 1 ± 4. 6 years, 65. 3% were women, and the median length of hospital stay was 9 days  [6]. All of the 291 interventions had a completed community follow-up, according to the intervention implementation file.
Only 134 (46. 0%) were timely billed within 3 months after hospital discharge, 50 (17. 2%) had no billing between 2015 and 2020 (Figure  1 ). The median billing time after hospital discharge was 2. 4 months  [1, 3, 6]. The sensitivity of the health administrative database to identify medications delivered in the 90 days prior to hospital admission was 90. 0%, i. e. 90. 0% of unique ATC codes present in the intervention implementation file were found in the health administrative database. The positive predictive value was 50. 1%, one out of two ATC codes identified in the health administrative database was actually present in the medication reconciliations (Table  1 ).
Discussion This study reveals that the data from the French health administrative database were poorly reliable to identify both the community follow-up of the transitional care intervention and chronic medications identified through an in-hospital medication reconciliation. This is in contrast with the known accuracy of health administrative databases found in other countries with universal public health insurance. Less than half of the community follow-up were timely billed, with close to 20% which were never been billed. Using the health administrative database to assess the implementation of this transitional care intervention is not to be recommended. One of the possible explanations of the lack of billing or timely billing by community clinicians could be the complicated process to be followed to bill each visit (outside of their usual billing scheme), making it non-financially appealing to clinicians.
The health administrative database had a high sensitivity, but a poor predictive positive value to identify chronic medications. Several hypotheses could explain these results. First, few treatments were not identified in the administrative database, meaning that they were not delivered to the person in the 90 days prior to hospital admission. Patients might be prescribed treatments, that they do not get from the pharmacy, either because they do not take them, or because they have already them at home. In France, pharmacists are forbidden to unpack drugs, leading some time to huge accumulations of drugs at patients' home  [9]. The examination of the ATC codes that were not identified in the administrative database favors the second hypothesis (data not shown). Second, some treatment delivered and identified in the administrative database were not identified in the in-patient medication reconciliation.
This may be explained by the fact that medication reconciliation only considered chronic and active treatments whereas in the health administrative database, all drugs appeared including short-term (e. g. antibiotics). We did not perform a sensitivity analysis considering only chronic treatment in the health administrative database, as many ATC codes can be both used as chronic or short-term treatment, for example corticosteroids in COPD. In order to improve the predictive positive value, it would be interesting to develop an algorithm to determine chronic treatments from the administrative health database. This concerns in particular medications that are dispensed at least 3 times a year, or medications that have a 3-month conditioning period. In conclusion, our study revealed that the health administrative database should be cautiously used to measure the implementation of a transitional care intervention and to identify medications in older people.
security, yet the historical risks and causes of such shocks are poorly understood. Here, we compile a dataset of subnational maize and wheat yield anomalies in 25 countries dating back to 1900 to better characterize the past, present, and future risk of multiple breadbasket shocks. We find that years in which at least half of all maize or wheat breadbaskets fall 10% (5%) below expected yields has occurred in ∼2-3% (∼14-16%) of years over the last century. Importantly, multiple breadbasket shocks have been decreasing in frequency from 1930 to 2017. The El Ni ño Southern Oscillation (ENSO) most strongly affects the probability of multiple maize breadbasket shocks, while the North Atlantic Oscillation (NAO) most strongly affects the probability of multiple wheat breadbasket shocks, each influencing the probability by up to 40%.
Here, we construct a new century-long maize and wheat yield dataset and use a comprehensive set of climate stress variables to provide the most complete picture to date of how climate has affected multiple breadbasket shocks in the past, present, and may affect them in the future. We have compiled a dataset of subnational crop yield anomalies for maize and wheat consisting of over 34,000 observations from 132 subnational units in 25 countries dating back to 1900 (Fig. 1 ), which we use to robustly estimate the probability of multiple breadbasket shocks, changes to that probability over the last 100 years, and to identify what has caused such shocks in the historical record. We furthermore use measures of growing season climate stress that account for both moisture supply and demand, as well as extreme maximum and minimum temperatures, to assess present and future climate risks in major wheat and maize breadbaskets.
Climate-drivers of multiple breadbasket shocks Figures Using our century-long yield dataset, we find that modes of climate variability have had a large effect on the probability of joint crop shocks in pairs of breadbasket regions. The probability of joint crop shocks differ by up to 40% in opposite phases of major climate modes (Fig. 4 ). For both wheat and maize, the Indian Ocean Dipole (IOD) and, to a lesser extent, ENSO, affect crop yields in a balanced manner, increasing the probability of joint crop shocks in some breadbaskets and reducing the probability of joint crop yield shocks elsewhere. The North Atlantic Oscillation (NAO), however, has a unidirectional influence on the probability of joint wheat or maize breadbasket shocks, although its influence on maize is limited (Fig. 4 ).
Climate change and water stress in breadbasket regions We use our historical crop yield dataset in combination with measures of water and temperature stress to characterize how climate stresses have and will affect breadbasket regions. Following from the results of Figures  4  and 5 , we focus on ENSO as the primary mode of variability affecting multiple maize shocks and the NAO as the primary mode affecting multiple wheat shocks. We identify damaging conditions by constructing breadbasket-specific thresholds of damaging temperature or moisture stress (excess or deficit) during the growing season using the ∼100 years of subnational data available. We characterize possible future climate conditions using CMIP5 model projections of mean-state changes to surface temperature, soil moisture (agricultural moisture stress), or runoff (hydrological moisture stress), averaged over the growing season, added to the full distribution of observed climate variability in each region (see Methods for details).
Agricultural moisture stress will increase only in the high-forcing scenario, but then it will increase by over 100%. Discussion Using our century-long dataset of national and subnational crop yields, we find that the likelihood of multiple maize and wheat yield shocks is affected by climate variability and has not yet increased over the last century, but may yet as a result of climate change. The probability of multiple breadbasket yield shocks actually decreased for both maize and wheat from 1930 to 2017, likely the result of improved cropping systems technology and management that led to maize and wheat yield increases relative to the magnitude of yield shortfalls in poor production years. Climate variability affects both the spatial pattern and overall level of risk for multiple breadbasket yield shocks.
Methods Data Crop yield data are taken from the Twentieth Century Crop Statistics Data Set version 1, which is an aggregation of existing, publicly available crop statistics from national statistics and agricultural agencies for 27 countries. The data set consists of national or subnational maize and wheat production, yield, and harvested area data for all available years from 1900-2017, including digitizing new statistics in Italy, Spain, Indonesia, China, Mexico, Uruguay, Chile, Sweden, and Morocco. Full documentation, including the original sources of data for each country can be found in Anderson et al. , submitted. All data is at the national or administrative level one unit, with the exception of Italy, for which data was only available for seven agriculturally-relevant administrative level two units.
These seven administrative level two units collectively accounted for 52% and 68% of total wheat and maize production, respectively, in Italy in 2010. To understand how potentially damaging hydroclimate conditions have varied with time over the past century we use a collection of hydroclimate variables that account for both supply and demand of moisture, including land surface model-based estimates of surface-level soil moisture and reconstructed estimates of runoff. For our land surface model-based estimates of soil moisture we use Noah model output from the Global Land Data Assimilation System (GLDAS) 2. 0 over 1950-2009  28, 29. For runoff data from 1902-2014, we use global monthly reconstructed estimates of runoff from the GRUN dataset, which is based on a statistical model incorporating reanalysis precipitation and temperature information to predict runoff  30.
Methods The Twentieth Century Crop Statistics Data Set includes absolute yields and yield anomalies calculated using a variety of methods to remove crop yield trends to test for sensitivity, including a low-frequency Gaussian filter with a kernel density of five years and eight years, a low frequency Lowess filter, and a simple nine and fifteen-year running mean. In this analysis we use the low-frequency Gaussian filter with a kernel density of five years, which is similar to the fifteen year running mean without the undesirable property of requiring that we drop the first and last seven years of the time series. Following the calculation of the absolute yield anomalies, we convert yield anomalies at the subnational scale to percent anomalies by dividing the absolute yield anomalies by the expected yield.
Excluded data is shown in light blue and hatched in Figure  2. From the breadbasket percent yield anomaly time series we calculate the number of breadbasket yield shocks or surpluses using thresholds of both 5% and 10%, and count either the number or percent of reporting regions with shocks and surpluses. This data is used to calculate the return period curves. To calculate the relative probability of a certain number of crop shocks occurring in a given phase of a particular climate mode, we compare the probability of at least that many shocks occurring during years that experience the climate mode compared to the overall probability of that many crop shocks occurring based on all available years. To calculate the relative probability of joint yield shocks in pairs of regions and of multiple yield shocks globally during specific modes of climate variability, we calculate the probability of an event occurring in each phase of the climate mode (e.
We restrict the complexity of each GAM model to include a maximum of six spline terms. From the GAM model fits we estimate potentially damaging climate conditions as the x-axis crossing point for each GAM model term. See the supplementary information and SI Figures 4-11 for further details. To project envelopes of possible future climate stress we overlay observed variability onto multimodel mean estimates of projected changes in stress. For temperature stress, we add a constant monthly mean to daily maximum or minimum temperature data before re-calculating the killing degree days or chilling degree days for each crop and each season. For hydroclimate stress, we first smooth the multimodel mean estiamtes of projected hydroclimate stress using a 20 year running window. We then construct an envelope of plausible hydroclimate stress by adding the full historical record of z-scores of hydroclimate stress to each timestep, yielding an ensemble of with a number of values equal to the number of years in the observational record for each projected year and location.
Acknowledgements This work was funded by  ACToday , the first of  Columbia University's World Projects. 1. Porkka ,  M. ,  Kummu ,  M. ,  Siebert ,  S. & Varis , O. From food insufficiency towards trade dependency: a historical analysis of global food availability. PloS one 8, e82714 (2013). 2. Puma, M. J. , Bose, S. , Chon, S. Y. & Cook, B. I. Assessing the  evolving fragility  of the global food system. Environmental Research Letters 10,  024007  (2015). 3. d'Amour, C. B. & Anderson, W. International trade and the stability of food supplies in the global south. Environmental Research Letters 15,  074005  (2020).
The genome of gut microbes encodes a collection of enzymes whose metabolic functions contribute to the bioavailability and bioactivity of unabsorbed (poly)phenols. Datasets from high throughput sequencing, metabolome measurements, and other omics have expanded the understanding of the different modes of actions by which (poly)phenols modulate the microbiome conferring health benefits to the host. Progress have been made to identify direct prebiotic effects of (poly)phenols; albeit up to date, these compounds are not recognized as prebiotics sensu stricto. Interestingly, certain probiotics strains have an enzymatic repertoire, such as tannase, α-L-rhamnosidase, and phenolic acid reductase, involved in the transformation of different (poly)phenols into bioactive phenolic metabolites. In vivo studies have demonstrated that these (poly)phenol-transforming bacteria thrive when provided with phenolic substrates.
In analogy to CAZymes, PAZymes have noteworthy similarities, as they are diverse and target different compounds, having relevance in nutrition and health. From this perspective, an initiative to group PAZymes in sequence databases, as done for CAZymes (i. e. , CAZypedia, CAZyDB)  (265, 266) , would be of great interest. This would facilitate the identification of novel PAZymes, as well PAZymesharboring microorganisms by coupling omics outputs, including metagenomics. Insights on PAZymes-producing species have been obtained from members of Coribacteriaceae, Eggerthellaceae, and Lactobacillae, which have the capacity to release phenolic metabolites that can impact both commensal microbiota and host health. Here, we reviewed the role of several enzymes in breaking down different (poly)phenolic classes, including the tannases, quercetinases, feruloyl esterases, gallate decarboxylases, and phenolic acid reductases that support the direct prebiotic effects of (poly)phenols on lactobacilli species and other gut symbionts.
The analyses focused on options to improve plants either by conventional breeding or by applying modern breeding technology. The results of these analyses are stored in a dedicated database [31] which currently holds approximately 900 keystone scientific publications, forming a comprehensive overview of the current options and possibilities to increase crop yield, nutritional quality and sustainability. Complementing the literature survey, a modelling study was performed to assess the impact of increased photosynthetic efficiency on yield for a number of key crops in Europe. This study confirmed the potential of photosynthesis to drive significantly increased agricultural yields at different geographical locations in Europe. The second year of the CropBooster-P project explored the social, environmental and economic impacts that the different identified scientific and technological options to improve crop varieties may hold, taking a stakeholder-led approach.
Three-dimensional staple location We consider a s = 10-sheet stack with the central four sheets stapled together as shown schematically in Fig. 6 (a). The raw (black) reflected THz TOFT signals away from and near staple as well as the corresponding deconvolved signals by FWDD (red) are shown in Fig. 6 (b) and (c), respectively. Not surprisingly, near the staple, relatively large air gaps just above and below the four stapled sheets are clearly seen, and the four stapled sheets themselves are compressed together, as evident in Fig. 6(c). To better visualize the structure, we present the FWDD reconstructed data in a cross section through the paper stack, known as a B-scan in Fig. 7(a). A B-scan is a cross section in space through the three-dimensional data set of the reflected THz signal, i.
We next turn to an alternative approach to ascertain s, namely THz TOFT. Here the idea is to reconstruct the stratigraphy of the paper stack presented based on the raw THz TOFT data in conjunction with FWDD. The error in s is less than one sheet when s ≲ 20; however, the error increases proportional to s and exceeds unity when s ≳ 20. For thicker stacks, other noise-mitigation strategies, either in the physical measurement or in the signal processing, may increase the number of sheets above 20 for which the error in s remains less than one. In addition, we have not accounted in our analysis for dispersion in n(ν) or α(ν), which eventually limit the effectiveness of FWDD. Stratigraphic reconstruction approaches enabling the inclusion of dispersion have been developed  [7, 25]. Still, it is noteworthy that this approach is not constrained by paper type and can deal with mixed paper.
Technicians collect these data approximately every six weeks using a combination of current meters and hydroacoustic devices. Instream and near-bank vegetation changes and sediment deposition and scour, which can become enhanced during floods, can change the shape of a previously surveyed cross-section and the associated rating curve  (Mason and Weiger, 1995). Changes in the hydraulics and channel cross-section require regular visits and instream measurements. Remote sensing has increasingly become a more desired alternative to manual surveying and conventional, in-situ measurement techniques because remote-sensing instruments do not need to be in contact with the water  (Moramarco et al. , 2019). Remote-sensing instruments provide increased safety to the technicians and are less likely to be lost during major floods than fixed, traditional streamgauging equipment, in addition to providing the potential for less expensive operations and maintenance  (Fulton et al.
2c, d ), on an instrumented flume in the desert (Fig. 2e ,f), on a bridge above a river with frequent morphological changes (Fig. 2g, h ), above a small creek upstream from a popular summer camp (Fig. 2i, j ), on a box culvert over a spring-fed creek (Fig. 2k, l ), above an engineered urban channel (Fig. 2m ,n), and above a stream on a railway bridge (Fig. 2o, p ). In general, it took two to three people a few hours to install the sensors. The basin areas for these streams range from 2 to 3676 km 2 and are characterized by different land-use settings including forested, urban, and desert (Table  1 ). The 5-or 10-minute logging rate is user-defined. Cherry Creek is a tributary of the South Platte River that runs near Denver, Colorado (Fig. 2a ). The area of the basin is 1059 km 2. According to the National Land Cover Database 2011 (NLCD 2011)  (Homer et al.
, 2015) , land use in the upper part of the basin is mostly vegetation (e. g. , herbaceous and shrub/scrub), whereas the lower part is a developed area where the site is located. The relative proportions of sand, silt, and clay are 53. 9%, 29. 7%, and 16. 4%, respectively as per CONUS soil datasets  (Miller and White, 1998). The area has a mean basin slope of  7. 9%. The mean annual precipitation from 1981 to 2010 at the radar location is 404 mm  (PRISM, 2004). The stream radar is mounted on a bridge here (Fig. 2b ) and situated approximately 60 m upstream from a USGS streamgauge (ID -06713500). Mill Creek runs through rural Johnston County, Oklahoma. The basin area is 124 km 2 (Fig. 2c ). The NLCD 2011 land use of the area is  The stream radar at Walnut Gulch is part of the dense instrumentation comprising the U.
These thermal expansions/ contractions may cause the radar to move from its original position leading to a deviation from the actual stage measurements. To evaluate this effect, stage data are selected during periods with steady base flow, and two such periods are chosen to confirm the consistency of the behavior throughout the whole timeseries. For each station, air temperature data are collected from the "Local Climatological Data (LCD)" database of National Oceanic and Atmospheric Administration (NOAA) (NOAA, 2019), which typically records temperature at airports (Table  2 ). It is assumed in this study that air temperature measured at the closest airport is representative of the temperature experienced at the stream radar location, while more accurate measurements could be made at the site or obtained from a nearby personal weather station.