dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010)  and MITERRA-EUROPE (M6;  Velthof et al. , 2009). In test M5 (LR ¼ a þ b,P/ (C,D), where P, C, and D represent the precipitation, clay content and sampling depth, respectively), parameters (i. e. , a and b) were re-calculated based on China's dataset; Test M6, LR ¼ LR max ,f lu ,min(f P ,f SOC ,f D ,f T ), was applied, where LR max stands for maximum LRs under different soil types, and f lu , f P , f SOC , f D , and f T stand for reduction factors for land use, SOC, precipitation minus evapotranspiration, air temperature and root depth, respectively  (Velthof et al. , 2009). The R 2 , RMSE, and BIC were applied to assess their predictive accuracy performance. We finally applied a relative importance analysis  (Zhou et al. , 2015)  to understand the extent to which factors control the difference in LR-N rate relationships and the spatial variation of LR.
Yet it did not reflect the degree of nonlinearity in their model. Our analysis explicitly tests the changes in LR for different crops with multiple N input levels including zero-N control, arriving at the same general conclusion of the a faster than linear L N increase but with a quantitative and higher confidence outcome. NPCP provided a collection of 25 regionally-averaged LR based on nationwide observations in 96 upland soils before 2007  (MOA, 2007). This dataset reflects the responses of regionally-averaged LR to the diverse environmental conditions (i. e. , TN, SOC and clay content). Such responses seem to imply that LR is positively related to soil TN but negatively related to clay content and SOC  (MOA, 2007) , which are also in good agreement for same ranges of f k with ours. FAO methodology provided a qualitative response function between LR and f k (i.
Second, PKU-NLEACH model in particular conditions can separately function as an updated tool for other countries or regions that are subject to interpolation. Nonetheless, there is no sufficient evidence in support of using the calibrated PKU-NLEACH model for regions whose f k are not captured by our dataset. Most field experiments of nitrogen leaching in China are within temperate or sub-tropical zones with moderate SOC levels, which are different in Southeast Asia, Mid-U. S. and East Europe. PKU-NLEACH model, therefore, would need to be re-calibrated using more observations from these countries or regions to avoid potential extrapolation. Another significant drawback in this model is the lack of environmental conditions reflecting crop type, fertilizer type and method  (Zhou et al. , 2014; Xue et al.
According to model inference, the estimated LRs in this analysis can only reflect nitrogen leaching induced by inputs of new nitrogen, not residual nitrogen in agricultural soils  (Novoa and Tejeda, 2006). Therefore, base N leaching due to residual nitrogen need to be rationally estimated in future studies. Overall, this study is the first time to comprehensively test and quantify the nonlinearity and variability of leaching response to N rate for China's cropland. The dataset used for model calibration is limited, yet the associated empirical model developed for that purpose better captures the observed nonlinearity and variability, compared to other linear or uniform models that overstate the fertilizer-induced nitrogen leaching in China. The spatial variability of LR strongly depends on local air temperature, followed by water input and clay content.
Acknowledgments Funding for this study was provided by the  National Natural Science Foundation of China  (Grants  41201077 ,  71103186 ,  41561134016 ), the  Research Fund for the Doctoral Program of Higher Education of China  (Grant  20120001120129 ),  111 Project  (No. B14001 ). We appreciated HWSD and CMFD teams for providing soil and climate datasets, and authors who measured, analyzed, and published data of nitrogen leaching used here.
Acknowledgments The authors wish to express their thanks to the  Third World Academy of Sciences (RG/PHYS/LA Nos. 99-050 ,  02-225  and  05-043 ) and to  ICTP, Trieste, Italy , for the financial support of  Latin-American Network of Ferroelectric Materials  ( NET-43 ). Thanks to CNPq and FAPEMIG agencies from Brazil. APB acknowledges to the  Conseil R egional Languedoc-Roussillon  for her invitation to the  University of Nîmes, France. Thanks to the  Embassy of France in Havana, Cuba , for financial support for the scientific cooperation between the  University of Nîmes  and the  Havana University.
Acknowledgements We gratefully acknowledge interesting discussions with  Federico Bonetti ,  Volker Braun ,  Andrés Collinucci ,  Thomas Hahn ,  Denis Klevers ,  Ruben Minasian ,  Eran Palti ,  Tom Pugh , and  Stephan Stieberger. This research was supported by a grant of the  Max Planck Society. The work of R. S. was also supported by the  ERC Starting Independent Researcher  Grant  259133-ObservableString.
05). A total of 4,636 sequences belonged to known groups of EcM fungi, representing 2. 9% of the subsampled sequences. 11 genera were identified belonging to possible EcM fungi from our soil samples, with Inocybe as the most dominant genus (36. 9% of total EcM sequences), followed by Cortinarius (28. 8%). Six OTUs had an abundance of more than 2% of the total sequences, and four of these (OTU00026, OTU00034, OTU00037, and OTU00046, all classified as Ascomycota) showed a significant or marginally significant difference between the Early Stage and the Late Stage (p ≤ 0. 05). A total of 77 OTUs (18. 5% of all generated reads) were potential indicator OTUs. Though the database cannot classify all of the sequences at a high taxonomic resolution, at a broad level most of these OTUs (63. 6% of OTUs) were classified as Ascomycota.
Accumulating extra EcM species as they arrive in a relatively resource poor environment may improve the flexibility of nutrient capture by the host plant  27. In contrast, in a more nutrient-rich alpine glacier foreland, the same plant species may be more selective about its fungal community, eliminating some species which are no longer helpful in resource capture  9. Additionally, from the perspective of fungal-fungal interaction, the competition between fungal species might be intense in a more nutrient rich environment (such as an alpine glacier foreland), which may also play a role in eliminating some species. In this study, Ascomycota indicator OTUs occurred most frequently in the Early Stage of succession, while Basidiomycota indicator OTUs were more frequent in the Late Stage. Four of the six most abundant OTUs in the whole dataset were classified as Ascomycota and each showed a decreasing trend with deglaciated age.
We were interested in finding what specific fungal subgroups of the Ascomycota and Basidiomycota gave rise to the turnover during succession, but unfortunately low resolution of our ITS1 sequences in the available databases results prevented interpretation at levels below phylum, in many cases. Lack of taxonomic resolution also hampered our identification of EcM fungi. However, the relative abundance of EcM sequences showed a linear regression with the deglaciated age (R = 0. 43, p < 0. 05, Supplementary Fig. 1 ) and was significantly lower in the early stage than middle stage and late stage (P < 0. 05). TOC, TN and pH, which showed high correlation to the deglaciated age in this study, have been considered important soil properties in shaping fungal communities  24, 31. However, only TOC, besides the deglaciated age, was selected as a significant influencing variable by the forward selection in CCA analysis.
Data analysis. Eight samples from the glacial foreland area were excluded from further analysis owing to either insufficient DNA for sequencing, or low read numbers (less than 2000 reads) generated by sequencing. Six of these eight excluded samples were collected from soils near the snout of the glacier (which had a deglaciated age less than 20 years). The remaining 31 samples were divided into three groups in relation to the isochrones of three main moraines  41. The samples collected from areas deglaciated between 20 years and 50 years ago were categorized as the Early Stage. Those more than 50 years and less than 80 years old were assigned to the Mid Stage, while those more than 80 years old were assigned to the Late Stage. The sequence data obtained was processed in Mothur  42. Sequences with any ambiguous bases, sequences with more than 8 homopolymers and sequences with lengths less than 200 bp were removed using the screen.
seqs command in Mothur. Putative chimeric sequences were detected and removed via the Chimera Uchime algorithm contained within Mothur 43 in de novo mode, which first splits sequences into groups and then checks each sequence within a group using the more abundant groups as reference. Rare sequences (≤ 10 reads) were removed to avoid the risk of including spurious reads generated by sequencing errors  44. A final even subsampling depth of 5,200 reads was applied to each sample. Operational taxonomic units (OTUs) were assigned using the QIIME implementation of UCLUST  45  , with a threshold of 97% pairwise identity. Taxonomic classification of each quality sequence was done against the UNITE version 6. 0 database 46 using the classify command in Mothur at an 80% cutoff with 1000 iterations.
Statistical analysis. To assess the relationship between fungal richness/diversity and environmental variables, as well as with age since deglaciation, the richness of OTUs and other diversity indices were calculated using the Mothur platform  42  for samples standardized to 5,200 reads (size decided by default, set to the size of smallest sample size), as diversity is directly correlated with the number of sequences collected. Bray-Curtis distance was calculated to analyze fungal community similarity. To reduce the contribution of highly abundant OTUs in relation to less abundant ones in the calculation of Bray-Curtis measure, abundance data of OTUs were square root transformed. The pairwise differences in community composition of the four groups were calculated by analysis of similarity (ANOSIM) in relation to Bray-Curtis distance.
To assess the best fitting model of correlations between age since deglaciation and diversity/environmental variables, linear, polynomial (quadratic) and power (cubic) models were tried out using SigmaPlot v 10. 0 (Systat Software, San Jose, CA). R version 2. 15. 2 and R package 'labdsv' was used to calculate the indicator value and the significance of OTUs  49. Only OTUs with greater than 10% frequency, an indicator value > 0. 3  50  and a p value < 0. 05 were selected as indicator OTUs. Cluster analysis of all measured environmental variables was carried out using the Varclus procedure in the Hmisc package in the R platform, version 2. 15. 2.
Statistical analysis Statistics were performed as described in figure legends. For normally distributed data, Student's t test or one or two way ANOVA analysis with Tukey's post-test were used. For non-parametric data, Kruskal-Wallis with Dunn's post-test was applied. ANOVA and Kruskal-Wallis were done using Prism software (Graphpad Software). p < 0. 05 was considered significant. treated in a humane manner according to guidelines of the University of Maine IACUC as detailed in protocol number A2014-02-01. The UMaine IACUC/Ethics Committee approved this protocol. Animals were euthanized by carbon dioxide inhalation. Infected animals were monitored twice daily for signs of infection and morbid animals were euthanized. Images were analyzed by scoring viable cells for the phenotype indicated and data is presented as the percent of total cells with the mean ± SEM for three independent experiments.
(H) The average MFI in the far red channel at sites of neutrophil attachment. Data represents the mean ± SEM from three pooled experiments where n represents the number of neutrophil attachment sites where MFI was measured. (I) The average MFI for each group, including a no neutrophil control, from a single experiment. (J-K) Representative images of serial kidney sections from either C57BL/6J (J) or gp91 phox-/-mice (K) after staining with hematoxylin and eosin or periodic acid-Schiff. The scale bar in the first column of each panel represents 200 μm and in the second column represents 100 μm. (K) Images were examined by a pathologist and example areas containing (A-B) WT-FarRed670 was incubated with neutrophils in an imaging dish with CFW and Sytox Green. Timelapses were binned 2x2 and frames were taken at ten minute intervals.
Acknowledgments We would like to thank  Christine Pham  ( Washington University ) for bone marrow from DPPI and NECGPR3-deficient mice,  Judith Berman ,  Megan Lenardon ,  James B. Konopka  and  Janet Quinn  for fungal strains,  Gordon Brown  for the sDectin-1-Fc construct, and  Rebekah Stetson  for technical contributions to this project. We gratefully acknowledge the expert pathology assistance of  James E. Wheeler, MD. We would also like to thank  Brenda Kennedy-Wade  for general mouse care and  Dawna Beane  for her assistance with processing organs for histological analysis.
(I) Relative intensity at the 2 minute post-attachment (T+2) time point is shown for the pooled data as box plots to illustrate the full dataset. Box plot whiskers represent the 1. 5 interquartile range either below or above the lower or upper quartile ** p 0. 01 and *** p 0. 001 (one-way ANOVA with Tukey's post-test). doi:10. 1371/journal. ppat. 1005644. g001.
Data is presented as the mean ± SEM for three experiments. All groups had viability greater than 99%. Ã p-value 0. 05. ÃÃÃ p-value 0. 001. n. s. means non-significant as determined by one way ANOVA with Tukey's post-test. (EPS) S1 Movie. Rapid streptavidin fluorescence loss after neutrophil attachment in live C. albicans. Streptavidin-Alexa 647-labeled SC5314-GFP was incubated with neutrophils. Timelapses were binned 2x2 and frames were taken at one minute intervals. One representative of 13 timelapses is shown with green, far red and DIC channels. (AVI) S2 Movie. Rapid streptavidin fluorescence loss after neutrophil attachment in live C. albicans (far red channel only). Streptavidin-Alexa 647-labeled SC5314-GFP was incubated with neutrophils. Timelapses were binned 2x2 and frames were taken at one minute intervals.
The feedback system also ensures that a constant probe tip position is set each time the probe is switched into flexible mode, which is important for ensuring accuracy and repeatability each time the probe is switched between the two modes. To operate the flexible probing system in the two modes requires the use of two PID control loops. The two loops were implemented in LabVIEW software from National Instruments; Table  1  shows the specifications of the control system for tuning the frequency in both modes. In each case, the piezoelectric actuators were used to apply a superposition of a large static load combined with a small sinusoidal load: 0. 3 V and 0. 1 V for the stiff and flexible modes, respectively. The static load is applied to provide a constant level of compressive force to the suspension structure, which is used to control the probe stiffness.
Experimental Setup for the Probing System Characterisation To provide a stable environment for testing the capability of the variable stiffness probing system, the experimental work was completed in a temperature controlled laboratory (20 ± 0. 5 °C). An overview of the experimental setup is shown in Figure  6. The probing system was mounted on a three-legged platform placed on a pneumatic vibration isolation table; this minimised any disturbance due to vibration. To operate the flexible probing system in the two modes requires the use of two PID control loops. The two loops were implemented in LabVIEW software from National Instruments; Table  1  shows the specifications of the control system for tuning the frequency in both modes. In each case, the piezoelectric actuators were used to apply a superposition of a large static load combined with a small sinusoidal load: 0.
Repeatability in the Stiff Mode It is important for accurate probe measurements that the stylus tip has a well-known and repeatable position before and after being displaced by contact. Therefore, the repeatability of the stylus tip initial position was evaluated for both lateral (y) and vertical (z) displacements (x direction data is omitted for brevity as it is fair to assume that it is equal to that in the y direction based on structural symmetry). For each displacement direction, the repeatable return to a datum position, henceforth called the "zero offset condition" was recorded for three nominal displacements of 4 µm, 8 µm and 14 µm. For each of the nominal displacements, the probe was displaced and then returned to the zero offset condition, with ten repeats being made at each displacement.
However, if there is some error, then the degree to which this causes the stylus tip to be deflected will depend of the sensitivity of stylus tip deflection to frequency change; a low sensitivity will result in a low positional error and vice versa. To overcome this issue, the frequency-based probe stiffness control system (Section 2), was used. To determine the capability of the frequency-based probe stiffness control system to maintain stylus tip position, the relationship between stylus tip position (displacement in the z direction) and the natural frequency of the structure was determined; the results can be seen in Figure  16. This data was collected by setting the frequency control system to target 600 Hz, and then switching off the control system so only a steady state voltage is applied to the actuators.
Using frequency-based control, therefore, provides an effective method of controlling the flexible mode stiffness. The displacement that occurs between the "at rest" unloaded positions when making repeated switches between stiff to flexible control modes, henceforth referred to as switching repeatability, was determined. To measure the switching repeatability, the control mode was switched every ten seconds for a total of nine cycles, and the tip displacement monitored throughout. For this experiment the stylus tip was not constrained, ensuring true "at rest" position were measured. The results are shown in Figure  17. To obtain the data plotted in Figure  17 , the DC voltage from the three capacitive sensors was recorded, in stiff and flexible mode, and then flexible and stiff calibrations were used to calculate the stylus tip positions.
It is clear that the probe was deflected by a total magnitude of approximately 18 µm when transitioning to flexible mode. switches between stiff to flexible control modes, henceforth referred to as switching repeatability, was determined. To measure the switching repeatability, the control mode was switched every ten seconds for a total of nine cycles, and the tip displacement monitored throughout. For this experiment the stylus tip was not constrained, ensuring true "at rest" position were measured. The results are shown in Figure  17. To obtain the data plotted in Figure  17 , the DC voltage from the three capacitive sensors was recorded, in stiff and flexible mode, and then flexible and stiff calibrations were used to calculate the stylus tip positions. It is clear that the probe was deflected by a total magnitude of approximately 18 µm when transitioning to flexible mode.
Outlook and Future Work In this paper, the performance of the first variable stiffness micro-CMM probing system has been characterised. The evaluation was based on a large body of experimental data, that the interested reader is able to access via an online database  [8]. Using this innovative probing technique allows the stiffness of the suspension structure to be modulated during probing; avoiding practical issues caused by the antagonism between the stiffness requirements of the probing system at different phases of a measurement. A control system, based on measuring resonant frequency, has been developed and its ability to tune the stiffness in both stiff and flexible modes was demonstrated. When actuated from one mode to another, the suspension geometry is altered, and the stylus tip moves, but the rest position for both modes is shown to be predictable and repeatable to within 12 nm between the stiff and flexible mode in the z direction.
Acknowledgments: The first author wishes to thank the  Saudi Arabian Cultural Bureau in London  for the scholarship provided to allow this work. The second author would like to acknowledge the funding provided by  EPSRC  to support their contributions as part of grant number  EP/L01498X/1. Additionally, the authors would like to thank  Jagpal Singh  and  Jon Petzing  at  Loughborough University  for providing metrology facilities to complete the experiments. The authors would also like to thank  James Claverley  ( National Physical Laboratory ) for providing access to the nano-positioning stage.
out: analyzed concentrations in the liver vein; sim. out: simulated concentrations in the liver vein. Data are mean values and SD of three mice per time point and dose of CCl 4. ⁄⁄⁄ p <0. 001 and ⁄⁄ p <0. 01 compared to the measured ammonia output. , plasma activity of GDH transiently increases. This is accompanied by a decrease in alpha-ketoglutarate (a-KG) and an increase in glutamate. ⁄ p <0. 05 when compared to the corresponding control (0 h). Similar results were observed in liver tissue (Supplementary Tables  1  and 2 ). (B) Validation of the reverse GDH reaction using an inhibitor of GDH (PDAC). Plasma of mice 24 h after CCl 4 injection was analyzed. a-KG was added alone or in combination with AOA, NADPH, GDH and PDAC. ⁄⁄⁄ p <0. 001, ⁄⁄ p <0. 01 and ⁄ p <0. 05 when compared to controls (0).
10  The 24-hour perihematomal edema growth model was adjusted for time from onset to CT scan, categorized baseline ICH volume, intraventricular extension of ICH, and 24-hour hematoma growth. 11 Data are reported as odds ratios (ORs) and 95% confidence intervals (CIs). A 2-tailed P , 0. 05 was regarded as indicating statistical significance. All analyses were performed using SAS software (version 9. 3; SAS Institute Inc).
Due the limited number of hemodialysis patients in the study, we were also unable to assess its contribution to the prognosis of ICH. Furthermore, the present study includes patients from around the world and thus the CKD-EPI equation, which was developed using a sample of North American and European populations, may have led to overestimation of baseline kidney function measurements (especially in the Asian population) and biased results toward the null. Finally, because the data are derived from a clinical trial population in which patients with a poor prognosis and large hematoma were excluded, there may be concerns of the generalizability of the findings. In summary, these analyses of the INTERACT2 database highlight the adverse prognostic significance of decreased admission eGFRs in patients with ICH, which appears independent of an effect of hematoma or perihematomal edema growth.
ACKNOWLEDGEMENTS The INTERACT2 Investigators are as follows. Executive Committee:  C. S. Anderson  (principal investigator),  J. Chalmers  (chair),  H. Arima ,  S. Davis ,  E. Heeley ,  Y. Huang ,  P. Lavados ,  B. Neal ,  M. W. Parsons ,  R. Lindley ,  L. Morgenstern ,  T. Robinson ,  C. Stapf ,  C. Tzourio ,  J. G. Wang. National Leaders: China2Steering Committee:  Y. Huang  (chair),  S. Chen ,  X. Y. Chen ,  L. Cui ,  Z. Liu ,  C. Lu ,  J. Wang ,  S. Wu ,  E. Xu ,  Q. Yang ,  C. Zhang ,  J. Zhang. Europe2Austria:  R. Beer ,  E. Schmutzhard ; Belgium: P. Redondo; Finland:  M. Kaste ,  L. Soinne ,  T. Tatlisumak ; France:  C. Stapf ; Germany:  K. Wartenberg ; Italy:  S. Ricci ; the Netherlands:  K. Klijn ; Portugal:  E. Azevedo ; Spain:  A. Chamorro ; Switzerland:  M. Arnold ,  U. Fischer ; India:  S.