dataesr/scientific-paragraphs-categorization huggingface dataset:
The clinical significance of donor-specific antibodies, specifically whether or not they contribute to antibody-mediated rejection, is not fully elucidated and is currently a topic of considerable interest to transplant physicians. Prospectively collected data on donor-specific antibody status in the TRANSFORM substudy will permit a thorough characterization of donor-specific antibodies in antibody-mediated rejection and a comparison of the effect of the two treatment regimens on de novo donor-specific antibody development post-transplant.
statistical analysis For the primary endpoint (treated BPAR or eGFR [MDRD4] ,50 mL/min/1. 73 m 2 at month 12), treatment by CNI interaction will be assessed using a logistic regression model. If the interaction is not significant at the level of 0. 10 (ie, the effect of treatment is similar for tacrolimus and cyclosporine), then the primary analysis will be based on pooled CNI data in each treatment group. If this test shows significantly different event rates for tacrolimus versus cyclosporine, then testing will be performed for the two CNI therapies separately, with the tacrolimus subgroup being considered primary and the cyclosporine subgroup being considered exploratory due to the expected smaller sample size. Event rates will be compared between groups using a hierarchical testing strategy: noninferiority of the everolimus group versus the mycophenolic acid group for the primary endpoint using a 10% noninferiority margin; noninferiority of the everolimus group versus the mycophenolic acid group for the key secondary endpoint (the composite of treated BPAR, graft loss, or death) with a 10% noninferiority margin; and superiority of the everolimus group to the mycophenolic acid group based on the primary endpoint.
Missing eGFR values will be handled as follows: subjects who lose their grafts will be assigned a value of zero for eGFR at month 12; for other subjects with missing eGFR data, including those who die with a functioning graft, eGFR will be imputed using a multiple imputation method under missing at random assumption based on the longitudinal eGFR (MDRD4) data at all available time points and covariates of randomization strata (donor type and CNI), HLA mismatches (#3 versus. 3), and induction. Based on a sample size of 1,020 patients in each treatment arm (2,040 in total), the study will have at least 95% power to demonstrate noninferiority (α=0. 025, one-sided) for the primary endpoint in the overall study population, and in the tacrolimus subgroup, at month 12. Supportive analyses for the primary endpoint will include an analysis based on the per-protocol population, and different imputations for missing eGFR values under missing not at random assumption.
up (time post-transplant) eGFR at month 12 (mL/min/1. 73 m 2 ) a Death-censored graft loss Mortality HR (95% CI) P-value HR (95% CI) P-value Association between cut-off points for egFr at month 12 and subsequent death-censored kidney graft loss or mortality on multivariate analysis eveRoliMus-based regimen [ClinicalTrials. gov identifier NCT01950819]) trial is the first study in kidney transplanta- tion to evaluate both immunosuppressive efficacy and graft function in a single combined primary endpoint. The rationale and design of the trial are considered here. Study Follow-Kasiske et al 15 n 13,671 10 years $90 1. 04 (0. 73, 1. 48) 0. 8 1. 61 (1. 22, 21. 4) ,0. 001 60-89 reference reference reference reference 45-59 1. 35 (1. 08, 1. 44) 0. 003 0. 98 (0. 84, 1. 13) 0. 8 30-44 2. 01 (1. 75, 2.
An ABC-TonB-ExbBD system represents a ferric iron transport system  (7). MSR-1 has a complete TonB and ExbBD system. Four TonBdependent receptors, two TonB proteins, two TonB C-terminal domains containing proteins, and dozens of ABC transporters, including a ferric iron ABC transporter, are encoded in the genome. A ferrous iron transport system (feo) is related to the ferrous transport system of other bacteria, and two feo operons are present in MSR-1  (8). One Fe(III) reductase-coding gene (MGM-SRv2_0005) was identified, and five ferric uptake regulator-like genes belong to the fur family. Nucleotide sequence accession number. This whole-genome project has been deposited in DDBJ/EMBL/GenBank under the accession no. HG794546. The assembled sequences and annotations are available in the MicroScope Microbial Genome Annotation & Analysis Platform at https://www.
5f , right panel), peroxisomal MAVS induced the release of functional amounts of IFNs that blocked VSV replication. Collectively, these data establish peroxisomes as an organelle that can induce the selective expression of Type III IFNs upon RLR-MAVS pathway activation. To determine if the pathway leading from peroxisomal MAVS was similar to that observed in WT cells, the effect of MAPK inhibitors on virus-induced gene expression was assessed. Whereas WT MAVS signaling was inhibited by the JNK (SP600125), MEK and ERK (PD98059), and p38 (SB202190) inhibitors, VIPERIN mRNA production in the Huh7 cells expressing MAVS-pex was unaffected (Fig. 5g ). siRNA-based knock-down of ERK in MEFs also abolished the ability of SeV to induce STAT1 phosphorylation upon signaling by WT MAVS, but not MAVS-pex (Fig.
The cells were then examined 9 and 16 hours post infection by microarray analysis. Reovirus infection of both genotypes induces a gene expression profile typical for an antiviral immune response, as apparent from GO-term analysis (Supplementary Fig. S6a ). Global transcriptome analysis revealed that Pex19 deficient cells displayed a higher antiviral response at all time points examined (Supplementary Fig. S6b-d ). Of note, of the 20 genes that are most highly upregulated in Pex19 deficient cells compared to reconstituted cells, most are ISGs (Supplementary Fig. S6d ). These data suggest that the presence of mitochondria containing peroxisomal proteins promotes greater RLR-dependent responses. These studies underscore the important role of peroxisomes in regulating RLR signaling, and provide the first insight in immunological consequences of Pex19 deficiency in human disease.
mRNA detection, nCounter analysis and gene arrays Cells were lysed in RLT buffer (Qiagen) supplemented with β-Mercaptoethanol, and passed through Qiashredder columns (Qiagen). RNA was then isolated using RNeasy columns (Qiagen) following the manufacturer's recommendations. RT-qPCR was carried out with a Bio-Rad iQ5 or CFX384 real time cycler with Taqman probes as directed by the manufacturer. nCounter CodeSets were constructed to detect genes selected by the Gene-Selector algorithm and additional controls as described  11. Purified RNA was hybridized for 16 hr with the CodeSet and loaded onto the nCounter prep station, followed by quantification with the nCounter Digital Analyzer. To allow for side-by-side comparisons of nCounter experiments, we normalized the nCounter data in two steps. We first controlled for small variations in the efficiency of processing by normalizing measurements from all samples analyzed on a given run to the levels of chosen positive controls provided by the nCounter instrument.
Second, we normalized the data obtained for each sample to the expression of a control gene (GAPDH). These genes were described to be unchanged in cells exposed to a variety of infectious conditions  11. For every sample, we computed the weighted average of the mRNA counts of the control transcript and normalized the sample's values by multiplying each transcript count by the weighted average of the controls. Microarrays were performed by the Molecular Genetics Core Facility at Boston Children's Hospital supported by NIH-P50-NS40828 and NIH-P30-HD18655. Quantile normalization was used for signal extraction and normalization. For comparison of gene expression profiles between genotypes and time points log-transformed data was plotted and a weighted linear fit calculated. For gene ontology analysis p-values were calculated using the hypergeometric distribution and adjusted for multiple hypotheses testing by Holm's method.
Acknowledgments We would like to thank the members of the  Kagan lab  for helpful discussions. We would like to thank  Marc Fransen  for providing cell tines and reagents and  Bradlee Nelms  for bioinformatic assistance. J. C. K. is supported by  NIH  grants  AI093589 ,  AI072955  and  P30 DK34854 , and an unrestricted gift from  Mead Johnson & Company. Dr. Kagan  holds an Investigators in the  Pathogenesis of Infectious Disease Award  from the  Burroughs Wellcome Fund. CO is supported by a fellowship from the  Crohn's and Colitis Foundation of America. E. D. is supported by an  Erwin Schrödinger Fellowship  of the  Austrian Science Fund (FWF). K. M. F. is supported through the  Herchel Smith Graduate Fellowship Program. L. G. and J. C. K are supported by a  MIT-BCH  Collaborative grant, and L.
Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R 2 = 0. 86 *** at the national scale to R 2 = 0. 26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases.
However, few studies have focused on cropland classes, and they reveal discrepancies in the extent and spatial distribution of cultivated areas among various products. Wu et al. [8]  performed a pixel-by-pixel comparison in China and found complete agreement between four datasets across major agricultural plains with extensive homogeneous croplands, while they found discrepancies in more heterogeneous agricultural landscapes. Fritz et al. [7]  and Hannerz et al. [11]  also found large disagreements in Africa, particularly in the Sahelian belt where the cropping density is lower. In Sub-Saharan African landscapes, crops are particularly difficult to discriminate due to the parcel sizes, which are often smaller than the pixel size  [10, 11] , and landscape fragmentation  [7, 28]. In addition, depending on the environmental (e.
Moreover, the global data, training data and classification algorithms are regularly revised (tentatively every 6 months)  [15, 16]. Finally, Vintrou et al. [32]  found that for crop area mapping, the MODIS LCP performed better than the other existing products. In this study, our objective is to assess the quality and reliability of the MODIS LCP in estimating and locating crop areas in Sub-Saharan Africa in terms of (i) cropland acreage and dynamics, where the consistency between the MODIS LCP and agricultural statistics databases is analyzed at regional, national and subnational scales; and (ii) cropland spatial distribution, where the MODIS LCP cropland map is compared to a sample of cropland maps derived from high-resolution images and analyzed using landscape fragmentation metrics.
Agricultural Statistics Data • FAOSTAT: This database [37] is accessible online. In this study, cropped cultivated areas and cultivated area fractions at the national scale were derived from "arable land" data. The FAO database defines arable land as "the land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category"  [37]. • AGRHYMET: Surface-harvested data from ground surveys of major staple crops in Burkina Faso were used. These ground surveys are conducted every year by AGRHYMET at the provincial scale (two levels below the national scale). The number of surveyed villages per province is proportional to the size of the province.
High-Resolution Images Based on a recent study by Tsendbazar et al. [38] , which assessed global land cover reference datasets, none of the numerous datasets that currently exist were considered appropriate in our study. Specifically, these datasets inherently have the same uncertain accuracy problems as the previous global land cover maps. A specific validation dataset was therefore created. It is composed of 55 cropland maps that each covers an area of 5 × 5 km (100 MODIS pixels). The 55 sample locations were chosen to represent the study area in terms of agricultural landscapes based on the Farming Systems Maps for Sub-Saharan Africa from the FAO (Figure  1b ). At least three sample locations for each farming system were used. In addition, the number of sample locations per farming system was consistent with the size of the farming system within the area.
Forty-nine cropland maps were obtained by photo-interpretation of high-resolution images from GoogleEarth © and were classified as either crop or non-crop. GoogleEarth © images across Sub-Saharan Africa are mainly from Digital Globe (<10 m resolution) and were acquired between 2007 and 2013. This dataset was completed with six available crop maps that were also obtained from high-resolution images: • Four cropland maps from multispectral SPOT images at a 2. 5 m resolution for 2007 in South Mali. The images were classified using an object-based supervised classification method and validated with ground data  [32]. • One cropland map from multispectral SPOT images at a 10 m resolution for 2010 in West Niger. A pixel-based supervised classification method was applied  [39]. • One cropland map from Landsat images at a 30 m resolution for 2006 in North Cameroun.
Assessing Quantification of Crop Areas The MODIS LCP cropped areas were extracted for administrative units (at the country level for all countries and at the regional and provincial levels for Burkina Faso), and the mean between 2001 and 2011 was computed. The same procedure was also applied to the agricultural statistics data. The MODIS LCP cropped areas were compared with FAOSTAT data at regional and national scales and with AGRHYMET Burkina Faso data at regional (level 1 below national-N1) and provincial (level 2 below national-N2) levels. For areal calculations of the MODIS dataset, the numbers of pixels classified as crops were multiplied by the area of each pixel. As in Vintrou et al. [32] , a weight of 0. 5 was applied to the mixed class to take into account the fraction of crops according to the FAO mixed class definition  [41].
Each index was calculated based on the MODIS LCP product for 2011 and within a grid size of 5 km. (2) Analysis of the landscape metric. A normalized PCA (principal component analysis) was computed using the ADE-4 package  [48]  within R software. PCA reduces the size of the dataset and statistically verifies links between the three spatial accuracy indicators (omission, commission and Fscore) and the six fragmentation indices. Thus, PCA permits an analysis of the similarities between the 55 sites. In addition, according to their landscape fragmentation and farming system similarities, the sites were partitioned into homogeneous classes by K-Means clustering. Then, multiple linear regression was applied to the accuracy indicators (dependent variables) and the six fragmentation indicators (explanatory variables).
A multiple linear regression model was fitted at the reference dataset scale. The model selection was conducted using the Bayesian Information Criterion (BIC)  [49]. The region under the Pareto Boundary (in orange) is the unattainable region due to the low-resolution product. The region above the Pareto Boundary (in light green) is the attainable region. The distance between 0 and B is the "optimal" accuracy linked to the spatial resolution of the maps. The distance between the "optimal" accuracy B and the accuracy of the product A is an indicator of the performance of the classification algorithm (adapted from  [43]  with authors' permission).
Spatial Heterogeneity and Fragmentation vs. Mapping Uncertainties The previous section showed that agricultural landscape fragmentation and spatial heterogeneity affect the accuracy, particularly the omission errors and FScores, of the MODIS LCP for crop areas. Figure  7  presents the results of multiple linear regressions computed for validation sites between the six indicators of landscape metric fragmentation (explanatory variables) and the accuracy indicators. Figure  7a  shows a strong relationship between the omission errors and the Crop Fraction in which 62% of the omission error variability of the MODIS LCP is explained by the Crop Fraction. The relationships between the landscape metric fragmentation and the FScore are less clear (R 2 = 0. 41; Figure  7b ). To illustrate how the MODIS LCP accuracy is spatially distributed, a map of user accuracy estimates for cropland classes was produced by applying the linear regression model previously developed by the BIC to the entire MODIS LCP dataset (Figure  8 ).
The maximum user accuracy that could be attained given the agricultural landscape patterns is ~70%, with an omission error of at least 30%. However, most pixels have a user accuracy of 0%-28%, indicating a high degree of uncertainty in the localization of the cropland domain. The user accuracy is better in the West than in the East and is mainly related to Agro-Pastoral Millet/Sorghum and Cereal-Root Crop Mixed farming systems (see Figure  1  and Table  3 ). Finally, an assessment of the "optimal" accuracy that could be attained with higher spatial resolution datasets (300 m, 30 m and 10 m) was performed (Figure  9 ). Regarding the omission and commission errors, an improvement of the accuracy values was observed; the accuracy variability decreased between sites. The relative standard deviations of the omissions and commissions were equal to 7% and 19% at 500 m, respectively, 11% and 9% at 300 m, respectively, 6% and 3% at 30 m, respectively, and less than 1% at 10 m.
Comparison of the MODIS LCP and Statistical Data Regarding crop areal estimations and their dynamics, the results show that the correlation between statistical data and the MODIS LCP decreases when the spatial scale is localized. The differences observed at the national scale could be due to fallows. FAOSTAT data include fallows of less than five years, whereas such information is not given in the MODIS product. Thus, it is likely that older fallows were included in our estimates. Moreover, because of uncertainties in their accuracy, timelessness, and consistency over time across countries and crop types, official national FAO statistics are certainly subject to errors that can limit their ability to quantify the spatial extent of crop areas  [7, 9, 11, 50]. However, as mentioned recently by Vancutsem et al.
[28] , this dataset remains a helpful source of information for assessing the quality of Global Land Cover products, such as the MODIS LCP. At subnational scales, the differences could partly be explained by the fact that some discrepancies in crop areal estimations at fine spatial scales (e. g. , the province level) are masked when aggregating to coarser scales. This has already been highlighted by Hannerz and Lotsch  [11]  for statistical data and by Mayaux and Lambin  [45]  in a study where they analyzed the effects of spatial aggregation of remote sensing data on the estimation of tropical forest areas; these studies concluded that information was lost in the process of scaling up. Another reason for these differences may be that AGRHYMET does not include fallows in their statistics, while fallows are most likely included in the MODIS LCP estimates.
"Optimal" Accuracy of Land Cover Products When assessing the accuracy of a coarse-resolution map, the resolution of the high-resolution validation dataset is often aggregated to match the resolution of the coarse-resolution map, and the dominant class is assigned to the new pixel  [2, 55]. The use of the Pareto Boundary allowed us to compare the MODIS LCP with high-resolution data without degrading the fine resolution of our validation datasets. This is a useful tool for estimating the "optimal" user and producer accuracies that could be attained by the MODIS LCP given the low-resolution bias and fragmentation. However, the Pareto Boundary is not based on the actual low-resolution map but rather on the low-resolution grid; consequently, uncertainty in the positioning of the low-resolution grid may affect the omission and commission errors.
For most of our sites, the classification algorithm performed poorly, i. e. , the observed/optimal Euclidean distance was larger than 0. 50. This may be due to the validation procedure of the MODIS LCP, as it is not based on statistically robust sampling and local experts do not perform the validation  [1, 20, 21]. Finally, the "optimal" accuracy computed for the simulated data with a higher spatial resolution permitted us to evaluate the expected accuracies for other datasets in the case of a perfect algorithm. This highlights the need to strengthen initiatives for the creation of finer resolution land cover maps, such as the Chinese project that intends to map the global land cover based on Landsat imagery  [56].
Validation Databases Given the lack of datasets at high spatial resolutions in Africa to validate Global Land Cover Products, the use of GoogleEarth © data as a reference dataset proved valuable in assessing the quality and accuracy of the MODIS LCP. Photo-interpretation of high-resolution images is more time-consuming than automatic classifications but much less time-consuming than ground surveys. Consequently, the approach represents a good trade-off for accuracy assessments. GoogleEarth © was already used for the validation of the GlobCover product  [17]  and in the recent study of Vancutsem et al. [27]. In this study, 55 validation sites were used to assess the quality of the MODIS LCP, and given the extent of the area considered, we are aware that this is a small sample. However, we are foremost presenting a methodological approach, which should be reinforced with more validation sites.
Conclusions In this paper, we analyzed the reliability and ability of the MODIS Land Cover Product (LCP) to map crop areas in Sub-Saharan Africa. First, we compared the MODIS LCP and agricultural statistics to estimate the accuracy of the MODIS LCP for crop area estimations. We found the ability of the MODIS LCP to estimate crop areas and its dynamics to be satisfactory at regional and national scales (R 2 = 0. 86 ***), but precautions must be taken for crop acreage estimations at finer scales. Then, we assessed the spatial distribution accuracy of the MODIS LCP. Specifically, a high-resolution dataset was employed, and the confusion matrix and Pareto Boundary methods were applied. We found a strong relationship between the user accuracy and the fragmentation of the agricultural landscape with a R 2 equal to 0.