dataesr/scientific-paragraphs-categorization huggingface dataset:
Assets significantly predict children's educational outcomes, reduce the size of the income effect and, in some cases, even render it insignificant. The indirect effect of family income on child development includes parental behaviour toward the child, family relationships, the home environment, stimulating material at home, and activities. Washbrook et al. (2014)  use the same ALSPAC data as we do here and find both direct and indirect effects of family income on the cognitive outcomes of children aged between 7 and 9, but not on their non-cognitive outcomes. Yeung et al. (2002)  uncover both direct and indirect income effects on child cognitive outcomes at ages 3 through 5 in PSID data, with the direct effect being reduced by the introduction of the indirect effects. Yeung et al. also look at economic instability, measured by a year-on-year fall in income of at least 30 percent.
However, there is no relationship between family income and child psychiatric disorder in the British Child and Adolescent Mental Health Survey  (Ford et al. , 2004). We will here add to this existing literature by providing systematic evidence from a largescale long-run birth cohort survey. We consider not only cognitive, but also health, behaviour and subjective well-being outcomes. These latter are reported not only by the carer, but also by the children themselves and sometimes by the child's teacher (the cognitive outcomes are matched in from the national exam results database). We relate these outcomes to family income, as in most of the existing literature, and, more originally, to household financial problems as reported by the child's mother over an eleven-year period. Our broad conclusion is that income on its own is an insufficient statistic for family economic resources and the demands that are made on them: conditional on income and home ownership, the incidence of financial problems is a significant predictor of almost all of our adolescent-outcome measures, and with an effect size that is typically much larger than that of income.
Explanatory Variables We wish to relate the above dependent variables to the financial resources that were available to the household when the child was growing up. Household income is measured in ALSPAC when the child is aged 3, 4, 7, 8, and 11. The question "On average, about how much is the take home family income each week (include social benefits etc. )?"is answered using a scale of five income bands at ages 3, 4, 7 and 8, and ten income bands at age 11. We convert these ALSPAC band values at each wave to income figures using data from the Family Resources Survey (FRS) on the distribution of net household income in the South West region, deflated to 2008 prices. We are careful to match this distribution by year of birth (for 1991 births at age 3, we use the 1994 income distribution, but the 1995 income distribution for 1992 births, and so on).
The summary statistics of the control variables after imputation, as they appear in the regression analysis, are presented in Appendix Table  A3. Cohort data suffers from attrition, which increases with child age to reach about 40 percent after child age 16. Attrition is more concentrated in lower-income and less-educated families, 9 We have checked that all of our results also apply when using a dummy for having had at least one MFP during the first eleven years of childhood. 10 These investments are measured as the sum of the frequency with which each parent carries out a certain list of activities with the child, such as bathing her, making things with her, singing to her, reading, playing and active play and preparing food for her. We calculate the average score for the father and the mother.
It is commonplace in the existing literature to find that low income, debt and financial insecurity among adults reduce their subjective well-being. Some examples are  Clark et al. (2016)  regarding poverty,  Brown et al. (2005)  and Gathergood (2012) for debt,  Kopasker et al. (2018)  with respect to insecurity, and  Deaton (2012)  and  Wahlbeck and McDaid (2012)  for financial crises. We do indeed find a correlation in ALSPAC data between mother's mental health and both MFP and income. When we add mother's mental health to the regressions described in Section 4. 1. 1 above, we find that this plays a significant mediating role for most non-cognitive outcomes, as summarised in Table  1. The first two columns show our baseline estimated coefficients (those in Figure  2 ) for income and financial problems; columns 3 and 4 then present these same coefficients controlling for mother's mental health, with the last two columns showing the percentage change in the two estimated coefficients.
This paper was produced as part of the  Centre's Wellbeing Programme. The Centre for Economic Performance is financed by the  Economic and Social Research Council. laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The  UK Medical Research Council  and the  Wellcome Trust  (Grant ref:  102215/2/13/2 ) and the  University of Bristol  provide core support for ALSPAC. This work was also supported by the  French National Research Agency , through the  program Investissements d'Avenir ,  ANR-10-LABX-93-01. Andrew Clark  is grateful for support from CEPREMAP, the  US National Institute on Aging  (Grant  R01AG040640 ), the  John Templeton Foundation  and the  What Works Centre for Wellbeing. Marta Barazzetta  and  Conchita D'Ambrosio  also thank the  Fonds National de la Recherche Luxembourg  for financial support.
This genetic marker was previously tested on a collection of 1211 European field samples predominantly of animal origin, referenced on a publicly available database. In this study, we investigated a panel of 66 metacestode samples (between 1981 and 2019) recovered surgically from 63 patients diagnosed with alveolar echinococcosis originating from four European countries (France, Switzerland, Germany, Belgium). In this study, we identified nine EmsB profiles, five of which were found in patients located in the same areas of France and Switzerland. One profile was detected on both sides of the French-Swiss border, whereas most patients from non-endemic regions clustered together in another profile. EmsB profiles appeared to remain stable over time because similar profiles were detected in patients who underwent surgery recently and patients who underwent surgery some time ago.
Discussion This study included only data on human AE that had passed a prior quality control process. This preselection was a prerequisite for data analysis, given the complexity of the EmsB marker and the nature of the FSA. We added DMSO to the PCR mixture to improve the stability of the EmsB results as suggested by  Baskaran et al. (1996)  and  Jensen et al. (2010)   [33, 34]. Moreover, the use of an internal calibrator appeared to be essential, given that different sequencing machines were used. For a robust quality control process, we recommend these conditions: (i) the use of two PCR conditions for each sample tested; (ii) calculation of the Euclidean distance between the two FSA obtained from the two PCRs; and (iii) the use of an internal calibrator. For validation of this process, we investigated DNA from samples from the same patient conserved in different ways (frozen and FFPE tissue specimens).
More EmsB data should also be collected, particularly given that some of the profiles seem to be rare or did not match other samples in the dendrogram approach. The genetic diversity of EmsB in Europe is undoubtedly greater than currently thought using sequencing of few mitochondrial genes  [13] [14] [15]. This study brings the number of genotyped parasite samples in the EmsB data collection to more than 1300. Based on this large amount of data, we can now speculate, to some extent, on the conditions in which contamination occurs when hosts encounter parasites. Patients living in endemic areas repeatedly come into contact with locally circulating E. multilocularis isolates, but not necessarily with the numerically dominant profiles. The P8 profile, for example, was described in five patients living outside of areas of high endemicity.
granulosus sensu stricto sample (originating from an Algerian sheep) from the Chrono-environment Laboratory  [21]. For the NCR-E collection, information about patients, such as place of residence and endemicity at this site, as well as change in residence and/or trips to other endemic areas, were obtained from the FrancEchino database (NRC-E). Biological material was obtained for standard diagnosis on the basis of the physicians' prescriptions. Data were rendered anonymous for analysis. For the ethic statement in this study, according to French Public Health Law  [39] , protocols of this type do not require approval from an ethics committee and are exempt from the requirement for formal informed consent. At the IPA, DNA from AE lesions was purified with the QIAamp DNA Mini kit (Qiagen, Hilden, Germany).
Hierarchical Clustering Analysis With the FSA technique, the electrophoregrams for the EmsB target are presented as a series of peaks corresponding to alleles  [18, 20]. The presence or absence of peaks and the heights of the associated fluorescence intensity peaks were recorded as previously described  [32]. These data were used to assess the genetic diversity of the parasite and to establish profiles by hierarchical clustering analysis using Euclidean distance and the unweighted pair group method with arithmetic mean (UPGMA)  [41]. A multiscale bootstrap resampling (B = 1000) was performed to assess the stability of the clusters, resulting in approximately unbiased p-values  [42, 43]. Clustering analyses were performed with RStudio software (R version 3. 5. 1) [44] and the pvclust package  [45].
EWET Collection of Reference and Individual Research of Similarity The EmsB genotyping results obtained for these AE patient lesions were compared to data referenced in database of the EmsB Website for the Echinococcus typing project (EWET project) [32], for 1211 genotyped EmsB samples from 13 European countries, isolated from adult worms (fox, cat, and raccoon dog hosts) and metacestodes (human, monkey, and rodent hosts) (Figure  6a  and Table  6 )  [18, [21] [22] [23] 26, 27, 29, 46, 47]. Patients and EWET reference data were mapped with Quantum GIS software version 3. 6. 0 (QGIS, Open Source Geospatial Foundation Project. http://qgis. osgeo. org), and Eurostat map bases (https://ec. europa. eu/eurostat/fr/web/gisco/geodata/reference-data/administrativeunits-statistical-units/countries).
, 2013). None of the 11 models reproduces the spatial distribution of SOC stocks presented in the Harmonized World Soil Database (HWSD)  (Luo et al. , 2015). In the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), an ensemble of 10 terrestrial biosphere models showed a wide range in estimated global SOC storage in 2010, from 425 to 2,111 Pg C (1 Pg = 10 15 g)  (Tian et al. , 2015). It has been suggested that uncertainty in simulating SOC dynamics in ESMs may result from variations in parameterization in addition to model structure and external forcings  (Luo et al. , 2009 (Luo et al. , , 2016;; Brovkin et al. , 2013; Lovenduski and Bonan, 2017; Bonan and Doney, 2018). Different parameter values can strongly influence the projection of SOC dynamics among different models or within the same model  (Luo et al.
, 2016). Parameter values in the current generation of ESMs are mostly determined on an ad hoc basis and may be derived from the results of field experiments, other models, or informed from scientific or gray literature  (Luo et al. , 2001). It is largely untested whether or not the ad hoc parameterization is representative of the system properties to be simulated. Data assimilation techniques have been used to estimate parameter values from observations  (Luo et al. , 2003 (Luo et al. , , 2016)). Parameter values constrained by data assimilation can improve ESM simulation of SOC compared to default parameter values. For instance, the global representation of SOC distribution in the Community Land Model version 3. 5 (CLM3. 5) compared to the HWSD database was improved (explained variation increased from 27 to 41%) by constraining model parameters with a Bayesian Markov Chain Monte Carlo (MCMC) data assimilation method  (Hararuk et al.
, 2014). With the ever-increasing stream of high quality geospatial data and developing data-processing approaches, more comprehensive field-measured data and innovative methods are called in improving the representation of carbon dynamics in ESMs  (Reichstein et al. , 2019). Although the HWSD has been used to constrain models  (Hararuk et al. , 2014) , it is a data product generated through harmonization of data points from soil survey (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012). The harmonization itself potentially introduces significant errors to the data product. Wu et al. (2019)  showed that there is a limited capacity to further improve ecosystem model skill for carbon cycle assessment when using upscaled observational datasets. This finding suggests that methods that can directly utilize point observations rather than upscaled data products are preferable.
This study presents an innovative method, via combined data assimilation and deep learning, that can be used to optimally represent SOC in a complex land biogeochemical model (CLM5) with an extensive dataset of vertical soil profiles across the conterminous United States. We first applied two data assimilation schemes (one-batch and site-by-site) to 25,444 vertical soil profiles with the matrix form of CLM5. In the one-batch data assimilation, we used all the 25,444 profiles as one batch to estimate parameter values of CLM5 (assuming parameters to be spatially constant). In the site-by-site data assimilation, one set of parameter values of CLM5 was estimated from one vertical SOC profile at each site. Three methods were then conducted to represent the spatial and vertical distribution of SOC by CLM5 across the conterminous United States.
Data Sources This study used all the SOC profiles in the conterminousUnited States from the World Soil Information Service (WoSIS) dataset. WoSIS (www. isric. org) is a worldwide, quality-assessed, georeferenced soil dataset  (Batjes et al. , 2016 (Batjes et al. , , 2017)). For SOC, the depth of vertical profiles varies from the soil surface (at the top of organic or mineral soil material) of the land to a depth of more than 3 meters across sites. The geographical locations of all the soil profiles and the number of layers of each profile are as shown in Figure  S2. A total of 26,509 soil profiles with a total of 240,148 layers at different depths in the conterminous United States were available for our study. In addition, we used the mean annual net primary productivity (NPP), soil temperature, and soil water potential to drive the data assimilation and SOC distribution representation.
The NPP data is from the MODIS NPP dataset  (DAAC, 2018). We took the mean value from 2000 to 2014 of the record. Mean annual soil temperature and soil water potential were obtained from the output of CLM5 model. After running the model to a steady state by the pre-industrial climate forcing (I1850CRUCLM45BGC), we collected 10-year records of mean annual soil temperature and soil water potential of the conterminous United States. The original CLM5 model was run at the resolution of 1. 2 • (latitude) × 2. 5 • (longitude). The final outputs were interpolate into the resolution of 0. 5 • × 0. 5 •. We used two sets of global SOC data, WISE30sec  (Batjes, 2016)  and SoilGrids250m  (Hengl et al. , 2017) , as references to compare with spatial and vertical distributions of SOC obtained from our study over the conterminous United States.
WISE30sec is an updated version of the dataset HWSD (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012), generated by using traditional mapping methods at a resolution of 30 by 30 arc sec. SoilGrids250m is a global gridded soil information dataset generated by using machine learning techniques at 250 m resolution. We took data of SOC content over three depth intervals from these two datasets, 0 to 30, 0 to 100, and 0 to 200 cm. All the original data with high resolution was resampled to a resolution of 0. 5 by 0. 5 degrees.
1  (Sambridge and Mosegaard, 2002) : f (x|θ ) = Aexp (-Bφ (m)) (5. 1) φ (m) = k i=1 (z i -x i ) 2 2σ 2 i (5. 2) where A and B are constants, φ is a misfit function (cost function), and m represents the model used in this study. The misfit function can be further expressed as Equation  5. 2  (Hararuk et al. , 2014) , where z i denotes the modeled results, x i represents the observations, σ i is the standard deviation of the observations, and k is the number of observations. Considering the quantitive uncertainty assessment of SOC content is absent in WoSIS dataset, we assumed a standard deviation of 30% of the reported value at each site  (Harmon and Challenor, 1997). We applied an adaptive Metropolis algorithm to generate posterior distributions of parameters  (Haario et al. , 2001). A parameter chain was generated by a proposal distribution (prior distribution).
Weights at different depth in calculating cost function and acceptance control were the same as those in the one-batch method. After the MCMC process, the first half of the accepted parameter values in the formal run were discarded as burn-in. The G-R statistics of each parameter was then calculated for each soil profile. Meanwhile, we randomly selected one chain after eliminating burn-in period to generate the posterior distributions of parameters. For each soil site, we randomly sampled parameter values from the posterior distributions of parameters for 500 times and applied them into CLM5 matrix model to estimate the vertical SOC content distribution. The final estimations were the average of all sampling results. In the site-by-site data assimilation, to evaluate the effectiveness of data assimilation, the coefficient of determination (R 2 ) in linear regression of modeled against observed SOC (i.
e. , SOC mod vs. SOC obs ) was calculated for each soil profile after MCMC. Coefficients of the linear model were fixed, where the slope is 1 and the intercept is 0. In this context, negative R 2 values indicate the effect of estimations on SOC content after data assimilation is even worse than taking the average observed value as predictions. In this study, we took profiles having negative R 2 values as invalid and hereby discarded them from the whole dataset. Moreover, at those sites where only one observational data was available along the depth of the soil, we did not apply the data assimilation to the data point. After those data sets were excluded, 25,444 out of 26,905 soil profiles were used in our study, which accounted for 94. 57% of the entire dataset.
One-Batch Method After the one-batch data assimilation, we randomly sampled parameter values from the posterior distributions 1,000 times and applying the sampled parameter values to CLM5 matrix model. We estimated SOC content distributions at different sites by calculating the average of the results. Meanwhile, we provided the uncertainty of simulation by showing the 5 and 95% quantiles of the 1,000 simulations. The same sampled parameter values were further assigned in CLM5 to estimate SOC content distributions at each grid on the map of the conterminous United States at a resolution of 0. 5 degrees.
Random-Sampling Method After site-by-site data assimilation, the Maximum Likelihood Estimates (MLE) of parameters' posterior distributions were identified at each site, assuming the posterior probability distribution is generalized extreme distributed  (Xu et al. , 2006; Hararuk et al. , 2014). We assembled MLE values from the 25,444 profiles to build the probability density functions of parameters at the continental scale (i. e. , continentally homogeneous distribution), from which we randomly sampled parameter values for 1,000 times to simulate the SOC content distribution in CLM5 matrix model at each soil profile. The average value of sampling outcomes was taken as the final simulation result. Same with the one-batch method, the uncertainty of the simulation results was given by the 5 and 95% quantiles of the 1,000 simulations.
Using the one-batch method, the mismatch between observed and modeled SOC content in the original CLM5 model was moderately reduced in the north and east parts of the conterminous United States (Figure  2C ). However, geographical biases in model representation of SOC were not eliminated. The one-batch method underestimated SOC storage in the Intermontane Plateaus and southern Great Plains (see Figure  S3  for physiographical regions of the conterminous United States). Meanwhile, overestimation still existed in the Great Lakes areas and the northeast conterminous United States. Overall, the one-batch method explained 43% variation in the observed SOC content with RMSE = 11. 4 kg C m -3 (Figure  2D  and Table  1 ). In the site-by-site data assimilation, the optimized parameters at individual sites can fit modeled SOC storage with observations very well.
Meanwhile, SOC content in the east and northwest conterminous United States estimated by the original CLM5 was significantly higher than that indicated by the references. Using data assimilation, both the one-batch method (Figures  3M-O ) and random-sampling method (Figures  3P-R ) reproduced the continental SOC content gradient from the northeast to the southwest with reasonable values. However, high SOC content in the Great Plains was still not properly represented by FIGURE 3 | Modeled spatial and vertical SOC distribution across the Conterminous United States by different methods and datasets. Three depth intervals were presented, which were 0-30, 0-100, and 0-200 cm respectively. Because the site-by-site method obtained an almost linear agreement between observed and modeled SOC content, the estimations of SOC content distribution at each site by the site-by-site method was taken as observations for comparison.
The results of WISE30sec and SoilGrids250m were also presented as references. The uncertainties of the simulation by the random-sampling method and one-batch method were shown in Figure  S4. and SoilGrids250m ) were presented by three depth intervals, which are 0-30, 30-100, 100-200 cm, respectively. The error bars for the random-sampling method and the one-batch method present the intervals between the 5 and 95% quantiles in the 1,000 simulations. these two methods. Significantly, the neural networking method hybridized with data assimilation helped CLM5 mapped the most comprehensive spatial SOC distribution in this study (Figures  3S-U ). In addition to capturing the continental SOC distribution pattern, the neural networking method presented relatively accurate sub-regional SOC distribution patterns in the Great Plains, similar to the references.
DISCUSSION Toward More Realistic Representations of SOC Distribution To the best of our knowledge, this study is the first to systematically explore how to best represent SOC distribution in Earth system models, either by conventional data assimilation methods alone or in combination with a deep learning technique. We have demonstrated that massive observational datasets can be assimilated into ESMs. It has long been recognized that large gaps exist between the representation of SOC distribution by ESMs and observations. Although Bayesian data assimilation has been applied to integration of observations with process-oriented models to improve simulation performance at individual sites  (Xu et al. , 2006; Li et al. , 2016) , the complexity of ESMs and the computational cost in the MCMC process hindered the progress of using big data to inform models at a continental or global scale.