dataesr/scientific-paragraphs-categorization huggingface dataset:
Our finding that the Duke FACE study required unique self-thinning parameters to reduce bias in the simulated stem biomass suggests that when using DA to optimize parameters that are shared across plots, careful examination of prediction bias in key sites that provide a unique constraint on certain parameters (like the Duke FACE) is critical. Based on this example, we suggest that DA efforts using multiple studies and multiple experiment types identify whether particular experiments at a limited number of sites have the potential to uniquely constrain specific parameters. In this case, additional weight or site-specific parameters may be needed to avoid having the signal of the unique experiment over-whelmed by the large amount of data from the other sites and experiments. Additionally, the finding suggests that multisite DA should consider using hierarchical approaches to predicting mortality, particularly because mortality is often not simulated as mechanistically as growth.
This highlights the importance of whole-ecosystem manipulation CO 2 experiments for helping to parameterize and evaluate ecosystem models. Finally, we present an approach for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region. Data availability. Observations used in the DA can be found in the following: the Duke FACE study can be found in  McCarthy et al. (2010) , the PINEMAP studies are available through the TerraC database (http://terrac. ifas. ufl. edu), the US-DK3 eddy-flux tower data are available through the AmeriFlux database (http://ameriflux. lbl. gov), the Waycross data can be found in  Bryars et al. (2013) , the US-NC2 data are available upon request from Asko Noormets, and the FMRC and FPC are available through membership with the cooperatives.
The parameter chains and 3-PG model code are available upon request from R. Quinn Thomas (rqthomas@vt. edu). SSURGO soils database can be found at https://sdmdataaccess. sc. egov. usda. gov. The Supplement related to this article is available online at https://doi. org/10. 5194/bg-14-3525-2017-supplement. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. Funding support came from  USDA-NIFA  Project  2015-67003-23485  and the  Pine Integrated Network: Education, Mitigation , and  Adaptation  project (PINEMAP), a  Coordinated Agricultural Project  funded by the  USDA National Institute of Food and Agriculture , Award no. 2011-68002-30185. Additional funding support came from  USDA-NIFA McIntire-Stennis Program. The  Virginia Space Grant Consortium Graduate STEM   Research Fellowship Program  provided partial support for  Annika L. Jersild. Computational support was provided by  Virginia Tech Advanced Research Computing. This research was also supported by grants from the  French Research Agency  ( MACACC ANR-13-AGRO-0005  and  MARIS ANR-14-CE03-0007 ). We thank  Luke Smallman  and  Mat Williams  for helpful discussions about data assimilation, the corporate and government agency members of the FPC and FMRC research cooperatives for supporting the extensive long-term experimental and observational plots in those datasets, and two anonymous reviewers for helpful feedback on the paper.
These particular primers were chosen among a variety of candidate primers after an in silico analysis, using Primer Search  (Rice, Longden & Bleasby, 2000). The chosen set could amplify 94% of the available fungal sequences in silico, with the exception of Microsporidia, and only 1. 3% of Viridiplantae in the Silva database 115  (Quast et al. , 2013). PCR amplifications were performed with fusion primers containing sequencing adaptors, multiplex identifiers (MID) and PCR primers (Table  S1 ). The DNA was amplified by performing direct PCRs on the total nucleic acid extracts using illustra puReTaq Ready-To-Go PCR Beads (GE Healthcare). Two microliters of DNA template (at ∼1 ng µL -1 ) were used in a final volume of 25 µL with 0. 2 µM concentrations of each primer. The PCR cycling protocol consisted of 35 cycles of denaturation (95 • C for 30 s), annealing (54 • C for 30 s) and elongation (72 • C for 1 min) with an initial denaturation step (95 • C for 4 min) and a final elongation step (72 • C for 7 min).
Sequence analysis using a dedicated automated pipeline Quality trimming and filtering of amplicons, OTU identification, and taxonomic assignments were carried out with a combination of publicly available sequencing data analysis tools (Cutadapt, Mothur, Dnaclust) and in-house python scripts within a Galaxy instance at the Genouest platform (http://www. genouest. org/), as described elsewhere  (Ciobanu et al. , 2014; Nunes et al. , 2015; Ben Maamar et al. , 2015). Briefly, sequences shorter than 200 bp in length, with homopolymers longer than 8 bp or with ambiguous nucleotides, were removed from the dataset. Sequences containing errors in the MID or primer sequences were discarded. Chimeric sequences were eliminated using the chimera. uchime command of the Mothur tool suite. It is well known that both PCR and pyrosequencing can induce erroneous sequences  (Shakya et al.
, 2013b)  leading to poor diversity estimates. To improve sequence quality, two independent PCR reactions were performed for each sample and replicates were sequenced. Only sequences displaying 100% identity among these technical replicates were retained for subsequent analysis. Sequences were grouped into OTUs with a sequence identity threshold of 97%. Consequently one OTU was defined by at least two identical sequences originating from technical replicates. The taxonomic affiliations of the sequences and OTUs were determined by comparison with the SILVA database 115  (Quast et al. , 2013). Two OTUs, representing 1,132 sequences, assigned to the Chloroplastida, were removed. The study accession number in the European Nucleotide Archive is PRJEB12655.
Statistical and diversity analyses The samples were normalised to 1,288 sequences for analyses of alpha and beta diversity whereas the full dataset was used to analyze the core microbiome in co-occurring plants within a single population. All statistical analyses were performed in R (R Core Team, 2013) using the VEGAN package  (Oksanen et al. , 2011). To check the sequencing depth, rarefaction curves were computed using the rarefaction function. Alpha diversity and richness were estimated for each sample using Hill diversity numbers  (Hill, 1973)  and the Chao 1 index  (Chao, 1984). Hill diversity numbers allow accurate comparison of species/OTU diversity across samples. The significance of the Hill diversity numbers depends on the value of the q parameter in the Hill formula. This parameter allows species to be weighted more or less equally.
Data were transformed using the square root and the Wisconsin double standardization implemented in the metaMDS function. Procrustes analysis was conducted on the NMDS scores to assess the concordance between the communities in the DNA and RNA fractions. Significance of the concordance was tested by permutation (10,000) using the protest function  (Peres-Neto & Jackson, 2001). We defined the core microbiome as the proportion of OTUs shared by the studied co-occurring A. stolonifera plants. To determine whether the number of shared OTUs was dependent on the sampling effort, we performed random re-sampling and increased the sampling size from one to the total number of samples for each fraction. We defined the 'DNA core' and the 'RNA core' as the proportion of OTUs present in the DNA and the RNA fraction respectively of all samples.
Phylogenetic tree construction Representative sequences of OTUs were aligned using SINA aligner v1. 2. 11  (Pruesse, Peplies & Glockner, 2012) , imported from the non-redundant SILVA SSU Ref ARB database (release 115). Alignments of the reference sequences and of representative OTU sequences were exported from ARB  (Ludwig et al. , 2004). Gaps and ambiguously aligned positions were excluded. One alignment was obtained for each main fungal phylum. The model of sequence evolution that best fitted the aligned sequences was selected using jModelTest v2. 1. 4 (Darriba et al. , 2012). Phylogenetic trees were constructed by maximum likelihood using TREEFINDER and by bayesian inferences using MrBayes v. 3. 2. 2  (Ronquist et al. , 2012). Maximum likelihood bootstrap values were calculated from 1,000 replicates and Bayesian posterior probabilities were calculated using 100,000 generations sampled every 100 generations.
Phylogenetic structure The community phylogenetic structure was studied by applying the Picante package  (Kembel et al. , 2010)  to the global phylogenetic tree including all the fungal OTUs. Comparison of the phylogenetic relatedness of the OTUs in the DNA and RNA fractions of each sample with the local pool of OTUs (OTUs found in all samples) was based on two indices: the mean pairwise distance (MDP) and the mean nearest taxon distance (MNTD) described by  Webb et al. (2002). The phylogenetic distances measured in the observed communities were compared with those in the null communities generated by randomization (1,000 permutations) by calculating standardized effect sizes (SES). Tip labels were randomly shuffled across the tips of the phylogeny for the null model. In this model, the community data matrix is not randomized in order to fix most of the patterns (species richness, observed occupancy rates and OTU abundance) and to allow the pattern of interest to vary (phylogenetic distance)  (Swenson, 2014).
Figure1Alpha diversity and taxonomic composition of fungal communities in DNA and RNA fractions. (A) Distribution of Hill diversity numbers in DNA and RNA sequence data analyses. Different commonly used diversity indexes are special cases of Hill numbers (e. g. , q = 0 corresponds to the species richness S, q = 1 corresponds to the exponential of the Shannon-Wiener diversity index, q = 2 corresponds to the inverse Simpson index). Asterisks indicate significantly different means between DNA and RNA fractions (paired t -tests). 'ns': p > 0. 05, '*': p < 0. 05, '**': p < 0. 01. (B) Taxonomic composition of fungal communities in DNA and RNA fractions. Assignation of sequence taxonomy using SILVA database 115. Average relative sequence abundance (% ± se) of each phylum in the fungal kingdom for 19 samples.
Statistical analysis Data on study and patient characteristics, efficacy, QoL and safety parameters were extracted. For accurate calculation of absolute changes from baseline, studies were included in the meta-analysis only if sufficient data using relevant statistical elements were reported, i. e. means and measures of precision (e. g. SDs or CIs for continuous outcomes; frequencies for binary outcomes). For studies not reporting results at study level, or when the study only reported mean changes in score but not the precision, reported individual patient data were used to derive aggregated results. As we aimed to show corresponding percentage changes for each study included, missing data on percentage score were imputed. Mean percentage changes in scores were calculated by dividing the mean absolute change in score by the mean baseline score and multiplying by 100.
The missing precision could not be approximated using available data; therefore, the median precision observed in other studies of the dataset for the same outcome was used as a proxy (a commonly used approach). Individual study results for each outcome were pooled at 6 and 12 months and at the last mean follow-up. Fixed-effect and random-effect models were applied, using the inverse variance approach. The most appropriate model in each meta-analysis was chosen based on the presence of heterogeneity (i. e. 'statistical heterogeneity' is present when observed intervention effects are more different from each other than one would expect due to random error/chance alone  [13] ), assessed by Cochran's Q test and I 2 statistics. The latter measure can be regarded as the percentage of the total variability in a set of effect sizes due to true heterogeneity and interpreted according to the classification of Higgins and Thompson in 2002 (25%, low level of heterogeneity; 50%, moderate; 75%, high)  [14].
In the presence of significant heterogeneity, the random-effects analysis is the preferred approach. The effect of study characteristics on heterogeneity in the observed effects was explored using meta-regression. Based on previous literature findings and clinician input, as well as data available in identified studies, the characteristics included in the meta-regression were: duration of follow-up, baseline scores, study location, publication year, gender split, mean age at onset of disease and surgery, duration of disease, proportion of life lived with dystonia (PLD) (defined as duration of disease divided by age at baseline, multiplied by 100), and proportion of dystonia 1 protein (DYT1)-positive patients. Due to lack of reporting in the identified studies, it was not possible to include type of dystonia, comorbidities (e.
However, our systematic review is more extensive than those previously published (a wider search and larger selection of databases). A novel feature of our review is that it also includes both individual patient data and results aggregated at study level; therefore, meta-analysis was the most appropriate statistical tool. Furthermore, it analyzed absolute and percentage changes. The main limitation of our analysis is linked to the quality of data reported in available publications, which led to 38% of identified studies being excluded at the outset. In particular, no data on QoL effects could be included due to the lack of quality reporting. In addition, some efficacy data were excluded from our meta-analysis as no precision (i. e. SD, standard error or CI) was provided around the mean estimates, or results were reported as medians and ranges.
Low-Dose Imaging Conditions Virus particles were imaged in a JEOL JEM-2200FS TEM operating at 200 keV, using low-dose conditions controlled by SerialEM (v3. 5. 0_beta)  [32]  with the use of an in-column Omega Energy Filter, operating at a slit width of 35 eV. Micrographs were recorded using a Direct Electron DE-20 camera (Direct Electron, LP, San Diego, CA, USA), cooled to -40 • C. Movie correction was performed on whole frames using the Direct Electron software package, v2. 7. 1  [33]. Micrographs used for single particle analysis were recorded on the DE-20 using a capture rate of 25 frames per second for a total exposure ranging from 75 to 300 frames (~35 e -/Å 2 total dose recorded at the DE-20 sensor). Cryo-EM images were acquired between 4000 and 20,000× nominal magnifications (14. 7-2.
Fluorescence Microscopy APMV and SMBV particles were stained with 1 µg/mL 4',6-diamino-phenylindole (DAPI, DNA) and 0. 1 µg/mL fluorescein isothiocyanate (FITC, protein) overnight. Virus particles were then imaged using a Zeiss Axio Observer A1 microscope (100×, 1. 45 NA) outfitted with an Axiocam ICc5 camera. DAPI fluorescence was imaged with Zeiss filter set 49 and FITC fluorescence was imaged with Zeiss filter set 38 HE. Micrographs were then processed using Zeiss Zen software.
Cryo-Electron Microscopy (Cryo-EM) Revealed the Size and Morphologies of SMBV Particles SMBV particles, like those of all members of Mimiviridae, are very large, requiring a thick layer of vitreous ice (> 1 µm) to preserve the specimen in a near-native state for cryo-EM imaging. The thickness of the ice layer detracted from the contrast of SMBV cryo-EM images, especially while using a 200-keV TEM. With the use of an in-column Omega Energy Filter (JEOL 2200-FS) and a DE-20 direct detection device (Direct Electron, LP, San Diego, CA, USA), contrast in the cryo-electron micrographs was improved. SMBV particles were also imaged using a 300-keV TEM (JEOL 3200, data not shown), but these images displayed no appreciable difference in quality from the micrographs collected at 200 keV using the Omega Energy Filter.
We generated 20 tomograms displaying 34 fibered SMBV particles. Four representative volumes are displayed in Figure  3  (Supplemental Video 1). A representative tomogram is accessible through the Electron Microscopy Data Bank (EMDB) with the following accession number: EMD-8599. These tomograms displayed the structural features of the SMBV virion in greater detail than the single particle cryo-electron micrographs (Figure  1 ). fracturing  [22] , cryo-sectioning  [43] , or other techniques which are used to reduce sample thickness  [44, 45]. As the most abundant particle morphology, and with the fibers thought to be important for attachment, we decided to focus our cryo-ET efforts on fibered particles. We generated 20 tomograms displaying 34 fibered SMBV particles. Four representative volumes are displayed in Figure  3  (Supplemental Video 1).
A representative tomogram is accessible through the Electron Microscopy Data Bank (EMDB) with the following accession number: EMD-8599. These tomograms displayed the structural features of the SMBV virion in greater detail than the single particle cryo-electron micrographs (Figure  1 ). Figure  3. Cryo-electron tomograms of SMBV particles. These micrographs depict two-dimensional projections of three-dimensional data from four representative SMBV tomograms. Projections represent 10 slices (14. 7-nm thick for (A,B) and 6. 9-nm thick for (C,D) computationally combined using the slicer functionality in IMOD (3dmod). Capsid layers (black), nucleocapsid (cyan), and membrane sac (green) within the SMBV virions are highlighted in the right-hand panels. Tilt series were acquired along a tilt range of ±55° with tilt increments of 1-2°.
Results from the tomograms show that the fibers are rather flexible and it proved difficult to extract individual fibers as 3D volumes since the fibers were very closely packed. Therefore, performing sub-tomogram averaging on extracted density from fibers was not possible with our current data set. Like APMV, the SMBV genome is contained within an internal nucleocapsid. Sitting in the center of the virion, and containing relatively electron dense DNA, the SMBV nucleocapsid was visible within the two dimensional cryo-electron micrographs (Figure  1 ). However, the SMBV nucleocapsids were much easier to resolve in the cryo-electron tomograms (Figure  3 , highlighted in cyan in the right-hand panels). Within the 34 SMBV particles analyzed via cryo-ET, 31 of the particles displayed clear nucleocapsid boundaries.
However, making structural predictions regarding the capsid protein based solely on the genetic material is difficult at best. For example, introns in the mimivirus capsid protein gene have been shown to complicate genomic predictions, and mass spectrometry and recombinant expression systems were required to fully characterize this gene product  [52]. The SMBV capsid protein gene has up to three introns (Genbank AHJ40114. 2). We can conclude that there are sufficient differences in the global architecture of SMBV and APMV. Therefore, it follows logically that there will likely be some differences in the structural protein building blocks that form the native virions. Further detailed biochemical and structural experiments of the SMBV capsid proteins are needed to dissect these differences at the molecular level.
Acknowledgments: We would like to thank  Xudong Fan  and  Carol Flegler  at the  Michigan State University Center for Advanced Microscopy  and  Daniel Ducat  at the  Michigan State University Department of Biochemistry and Molecular Biology  for their guidance and support for the TEM, SEM, and fluorescence light microscopy, respectively. We would like to thank  Kaillathe "Pappan" Padmanabhan  for his assistance in the setup and maintenance of our computational resources. We would like to thank  Kit Pogliano  for suggesting the idea of trying fluorescence microscopy. We would like to thank  Direct Electron , especially  Michael Spilman , for their support and assistance with the DE-20 camera and image processing. We would like to thank  Centro de Microscopia da Universidade Federal de Minas Gerais.
Sensitivity to stress corrosion cracking As illustrated in Figure  3  for P265 BM, the stress-strain curves obtained at 25°C in Bure representative water (pH = 7) at various imposed corrosion potentials show an important decrease of the elongation when compared to the curve obtained in air (5. 10 -7 s -1 ), meaning a susceptibility of the P265 to stress corrosion cracking. The imposed potential of The composition of the water is the result of a thermodynamic calculation based on the mineral composition of the rock and the thermodynamic database of the BRGM. b The composition of the water was given as a medium composition from solutions sampled in different sites where failures has been reported. -750 mV/SCE (Saturated calomel electrode) corresponds to the free corrosion potential. Similar results have also been obtained for the P265 at 90°C and for P235 at 25°C.
Wunch et al. : OCO-2 validation able online through the Goddard Data Center (GES-DISC, 2016) and the CO 2 portal  (JPL-Caltech, 2016). The bias correction procedure for the current B7r dataset is described in  Mandrake et al. (2015). There are three key types of biases addressed by the OCO-2 bias correction procedure: footprint-dependent biases; spurious correlations of the retrieved X CO 2 with other retrieval parameters (a "parameter-dependent" bias); and a multiplicative factor to scale to the World Meteorological Organization (WMO) trace-gas standard scale  (Zhao and Tans, 2006) , which we will refer to as a "scaling" bias. The parameterdependent bias can depend on retrieval parameters such as the surface pressure retrieval error, signal level, airmass, surface albedo, or spurious variability in the retrieved CO 2 profile.
99694 ± 0. 00102. This scaling bias difference results in a 0. 3 ppm offset between OCO-2 and TCCON X CO 2 at 400 ppm; the standard bias-corrected OCO-2 measurements appear to be 0. 3 ppm too high. Panel (c) of Fig. 4  shows the relationship between the OCO-2 X CO 2 after applying the bias correction, scaling, and the residual footprint correction (m = 1. 0007±0. 04, R 2 = 0. 86). The residual footprint correction does not impact the slope or R 2 value of the relationship. Zhang et al. (2017)  have shown that the uncertainties computed on this slope are likely to be significantly overestimated. The long-term time dependence of the difference between the OCO-2 target-mode data and the coincident TCCON data ( X CO 2 ), after the scaling bias is removed, is plotted in Fig. 5. The algorithm, calibration, and instrument cause no apparent time-dependent drift in X CO 2 or their errors.
Thus, the bias correction is successful at reducing both the parameter-dependent and scaling biases with respect to TC-CON and our other bias correction datasets described earlier in this section. However, the target-mode measurements are sensitive enough to point to some residual biases (i. e. , those not corrected by the Mandrake et al. , 2015, bias correction process) that are currently under investigation by the OCO-2 algorithm, calibration, and validation teams. These residual biases are more geographically localized in nature and appear to be related to surface properties or instrument pointing errors and as such might not be expected to be captured by the standard bias correction, which is designed to minimize biases that dominate on a more global scale.