dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012 1403S-EPS 1403S transconjugant, pEPS6073 + This study Streptococcus thermophilus DGCC7856 Starter strain, 7127-like EPS homolog IFF Collection 7856ΔEPS DGCC7856 EPS mutant This study 7856Rev DGCC7856 EPS revertant This study DGCC12520 Starter strain, 6073-like EPS homolog IFF Collection 12520ΔEPS DGCC12520 EPS mutant This study 12520Rev DGCC12520 EPS revertant This study (accession number OP323076) Bacteriophages D4840 P335 group, host DGCC6073 IFF Collection (accession number OP323077) D6890 P335 group, host DGCC6073 IFF Collection (accession number OP323071) D2544 P335 group, host DGCC7158 IFF Collection (accession number OP323072) D2950 P335 group, host DGCC7158 IFF Collection (accession number OP323073) D3906 P335 group, host DGCC7158 IFF Collection (accession number OP323074) D4044 P335 group, host DGCC7204 IFF Collection (accession number OP323075) D4351 P335 group, host DGCC7127 IFF Collection Plasmids pGhost9::ISS1 Em R , Temperature sensitive vector, insertion sequence ISS1 Maguin et al.
Single-cell RNA-sequencing data analysis For PBMC, original data were generated as recently described  (8). Briefly, blood samples were collected at time of indication biopsy or protocol biopsy at Nantes' hospital, France. They were isolated using density gradient and frozen in their respective serum complemented with 10%DMSO for storage in liquid nitrogen and shipment. Upon arrival in Leuven, Belgium, they were then thawed, centrifuged at 200 g for 5 min at 4 • C before resuspension in PBS containing 0. 04% UltraPure BSA (AM2616, ThermoFisher Scientific) and strained through a 40 µm cell strainer to further remove cell clumps and large fragments. Libraries were generated using the Chromium Single Cell 5 ′ library and Gel Bead & Multiplex Kit from 10x Genomics. We aimed to profile 5,000 cells per library if sufficient cells were retained during dissociation.
All libraries were sequenced on Illumina NextSeq until sufficient saturation was reached. After quality control, raw sequencing reads were aligned to the human reference genome GRCh38 and processed to a matrix representing the UMIs per cell barcode per gene using CellRanger (10x Genomics, v3. 1). For allograft-derived cells, previously published human single-cell RNA-sequencing data from 7 kidney allograft biopsies were used. The associated raw counts or matrices were downloaded from the Gene Expression Omnibus (GEO, GSE145927, https://www. ncbi. nlm. nih. gov/geo) for ABMR samples  (9)  and the Kidney Precision Medicine Project (https://atlas. kpmp. org/repository) for No ABMR samples. Filtered gene expression matrices from online data were merged and analyzed using the Seurat R package, version 4  (10).
Briefly, cell matrices were filtered with the following parameters: cells with <400 and >10,000 detected genes and >25% mitochondrial transcripts were excluded. After filtering, all objects were integrated using 3,000 features. A full dataset UMAP was generated using Seurat's DimPlot function using the top 17 principal components. Clusters were built using the FindNeighbors and FindClusters functions in Seurat (resolution = 0. 6). Cluster identification was performed using the FeaturePlot function by evaluating the expression of specific markers in each cluster as previously described  (11, 12). UMPA, dot plots and violin plots were generated using the DimPlot, DotPlot and VlnPlot functions, respectively, in Seurat, with normalized counts in the RNA assay as input data.
CellChat analysis CellChat uses a mass action-based model for quantifying the communication probability between a given ligand and its cognate receptor and takes into consideration the proportion of cells in each group across all sequenced cells and expressed co-factors (https://github. com/sqjin/CellChat)  (13). Seurat objects encompassing all of the allograft-derived cells or PBMC were used to generate corresponding CellChat objects. Recently reported LILRA1 and LILRB1 ligand-receptor pairs (14) (Table  1 ) were added to the CellChat database. The aggregated cell-cell communication network was calculated by counting the number of links or summarizing the communication probability. The contribution of each ligandreceptor pair to the overall signaling pathway was computed and the extractEnrichedLR function was used to extract all significant interactions (L-R pairs) and related signaling.
LILRs are mainly expressed by non-classical monocytes in the blood after kidney transplantation In order to test the hypothesis that LILRs might contribute to ABMR, we performed a deep immunologic and transcriptional analysis of circulating blood immune cells across a cohort of confirmed ABMR patients (E-MTAB-11450, N = 12, 6 patients with ABMR and 6 patients without ABMR, Figure  1A )  (8). All of the immune cell clusters were identified and annotated using specific markers (Figures  1B, C ), as previously described  (8). Four clusters were composed of myeloid cells, with FCGR3A-CD14+ classical monocytes, FCGR3A+ CD14+ intermediate monocytes, FCGR3A+ CD14non-classical monocytes and CLEC10A+ dendritic cells. Six lymphoid clusters were also identified: a large CD19+B cell cluster, but also CD3D+IL7R+ and CD3D+CD8A+ T cells; NCR1+FCGR3A-and NCR1+FCGR3A+ NK cells, IL3RA+ pDC and RORA+ ILC2.
LILR expression is strictly restricted to myeloid cells within the allograft The infiltration of recipient-derived leucocytes in the allograft in ABMR is well characterized. Two ABMR-specific acute histological lesions are scored based on this cell infiltrate in the microvascular compartment of the allograft: glomerulitis and peritubular capillaritis. We recently showed that monocytes could represent 20-80% of the immune infiltrate in ABMR  (21). In order to map the expression of the LILRs within the allograft, we took advantage of publicly available sc-RNAseq datasets (N = 7, 2 patients with ABMR and 5 patients without ABMR, Figure  2A ) as previously described  (6, 11, 12). Regarding tissue-specific cells, we identified three main epithelial renal cell clusters (LRP2+ proximal tubule cells, TMEM213+ principal cells, SLC26A7+ intercalated cells), three endothelial cells (EC) clusters (CXCL9+ activated EC, PLVAP+ cortical EC, CLDN5+ medular EC), a cluster corresponding to vascular and smooth muscle cells (MYH11+ vSMp).
LILRs expression is strongly impacted during macrophage di erentiation Given that circulating recipient-derived monocytes infiltrate the allograft and differentiate into macrophages  (22, 23) , we assessed the differential expression of LILRs during macrophage differentiation. In this aim, we reanalyzed public RNA sequencing data from purified monocytes and their differentiated counterparts  (15) , as depicted in Figure  4A. These datasets included RNA extracted from monocytes (N = 9 donors) or monocyte-derived macrophages from the same donors differentiated with M-CSF (M0, N = 3), M-CSF and LPS and IFN-γ (M1, N = 2), M-CSF and IFN-γ (Mreg, N = 4) or M-CSF and IL-4 (M2, N = 5) according to a clearlydefined protocol  (24). Interestingly, macrophage differentiation induced strong transcriptional changes.
g. , Luciferase assay, Western) or prediction was reported for any LILRs. In contrast, some of their ligands, such as CD47, SIRPA, S100A8, S100A9, and B2M, were suggested as targets of one or more miRNAs (Figure  5A ). Considering the weak evidence regarding interaction (resulting from crosslinking and immunoprecipitation experiments), LILRB2 was the only LILR regulated by miR-3191-5p and miR-10b-3p. In addition, HLA-A,-B and-C were both regulated by two or more miRNAs such as miR-6854-5p or miR-6810-5p (Figure  5B ). We interrogated the Fantom 5 database  (18)  to explore the cell origins of all these miRNAs. Interestingly, monocytes expressed high levels of miR-6854-5p, miR-106a-5p, miR-4660, miR-20a-5p, and miR-17-5p. In contrast, there was a profound decrease in miR-106a-5p, miR-4660, miR20a-5p, and miR-17-5p in differentiated macrophages concomitantly with an increase in miR-6854-5p and miR-6810-5p (Figure  5C ).
In addition, in contrast to what was observed in the blood compartment, S100A9 expression was decreased within the allograft in patients with ABMR. These results echo our findings based on in vitro data showing that S100A8 and S100A9 are profoundly repressed during macrophage differentiation. We can thus speculate that cis-activation by the LILRB1/S100A8-9 axis at the surface of monocyte cells is repressed during macrophage differentiation and that the LILRB1/S100A8-9 balance is dysregulated in ABMR. Finally, we assessed the expression of potential modulators of LILR expression, such as miRNAs. Indeed, miRNAs have been found to play critical roles in many biological processes by controlling gene expression at the post-transcriptional level. They appear to fine-tune the immune response by targeting key regulatory molecules especially in monocytes  (33).
Surprisingly, we found no prediction of LILR regulation by miRNA. Only weak evidence based on immunoprecipitation experiments suggested that LILRB2 is regulated by miR-10b-3p and miR-3191-5p, but these miRNAs were barely expressed by myeloid cells. Regarding LILR reported ligands and CD47/SIRPA, we found that the let-7 family, miR24-3p, and miR-196a-5p can regulate S100A8 and S100A9 activity through posttranscriptional modulation. Interestingly, miR-17-5p and miR-20a-5p were both experimentally confirmed as modulators of CD47, SIRPA, and B2M. These two miRNAs were elevated in monocytes but strongly decreased in macrophages, suggesting that CD47, SIRPA, and B2M were no longer repressed in macrophages. Our study presents several limitations. We reanalyzed public RNAseq dataset to decipher the impact of monocyteto-macrophage differentiation in LILR expression but these cells were derived from healthy volunteers and we cannot exclude that maintenance immunosuppression influences these findings.
Moreover, single cell RNAseq dataset were used to map LILR transcription in blood and kidney biopsies, but further experimental validation is required for the associated proteins. Notably, the CellChat analyses based on transcripts expression only predict cell communications and these communications and cell-cell contact should be validated by other methods such as immunostaining. In addition, we highlighted nonclassical CD16+ CD14-monocytes as the cell population of interest in kidney transplantation in term of LILRs expression in both blood and allograft, but in vitro differentiation analysis and miRNA analyses were performed in classical CD14+ monocytes. Non-classical CD16+ monocyte differentiation is still ill-defined, but further investigation could provide better insight into the workings of the LILR pathways.
Methods : A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain-predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT).
Yet more information can be extracted from an MRI than meets the eye. By using large datasets of brain images of healthy individuals, a machine learning model can be trained to estimate the age of a given brain. For a new brain image, the model will output the best guess of the age of that person's brain, that is, the 'brain age', which can look older or younger than the actual, chronological age of that person. The elegance of brain age lies in its interpretable nature; it is easily graspable how old a brain appears to be. In several brain disorders  [15] , including MS  [15] [16] [17] , brains typically look older than those of their healthy peers. On top of being an interpretable metric, recent evidence indicates that brain age could explain clinical symptomatology in MS, reflected by statistically significant, albeit weak, correlations.
Data description Data are described by subdividing them into HC_train, HC_test and MS_test, used to train and test the brain age decoding model, as shown in Figure  1. HC_train was constructed from a large sample of 1673 healthy control (HC) subjects from online publicly available repositories (only subjects of 18 years or older were included, consistent with the training dataset of Cole et al. [19] ). Refer to Table  S1  and Figure  S1  for a more detailed description. For the test datasets, two centres contributed retrospective data to this study. First, in Brussels, both HC (n = 50) and MS (n = 97) subjects were assessed as part of a study  [20, 21]   Results: Brain age was significantly related to SDMT scores in the MS_test dataset (r = -0. 46, p < 0. 001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r = -0.
Magnetic resonance imaging preprocessing and brain age pipeline Several preparatory steps were involved in the construction of our brain age model. They are summarized below. 1. Brain MRI segmentation. From the HC_train dataset, 3D T1weighted MRI, sex and chronological age were extracted. Next, the T1-weighted MR images were evaluated by the FDA cleared icobrain software (version 4. 4) of icometrix NV (Leuven, Belgium). This is an end-to-end automatic software that segments and subsequently quantifies distinct regions of the brain, which was originally published in Jain et al. [25]. The pipeline relies on T1-weighted MR images and, when available, also uses FLAIR images to segment white matter lesions in the brain. These lesions are filled in the T1 image with white matter intensities, and the T1 image is subsequently segmented by fitting a probabilistic model for grey matter, white matter and cerebrospinal fluid image intensities.
Brain age correction. According to Le et al. [26] , age-predicting models are prone to 'regression towards the mean', a phenomenon that results in overestimation of the brain age of younger subjects and underestimation of the brain age of older subjects. There appears to be firm consensus throughout the literature that such bias should be corrected for, and several methods exist to do so  [27]. The method described by Cole et al. [17]  was used. First, BrainAge raw on HC_train was estimated by adopting 10-fold cross-validation (CV). Second, a linear regression equation was fitted between the obtained raw brain ages and the respective chronological ages: Here, 0 and 1 represent the intercept and slope of the regression line, respectively, whereas the error term represents the residuals between data points and the regression line.
The brain age pipeline Linear regression between brain age, obtained with 10-fold CV on HC_train (cf. Figure  1a ), and chronological age yielded the following correction weights: 0 = 8. 60, 1 = 0. 79. In the same dataset, the MAE between corrected brain age and chronological age was 7. 91 years. The result of the brain age correction is added to the supplementary material as Figure  S2. In a next step, a final brain age model was fitted by using all available HC_train data for training (cf. Figure  1a ). The model's feature weights can be consulted in.
Model performance and clinical implications Our model achieved an MAE of 7. 91 years on the HC_train dataset (10-fold CV) and 7. 85 years on an independent HC test set. This is relatively large compared to previously published models that adopted a more complex methodology compared to ours. Hence, although the models differ with respect to performance in age decoding, the models are comparable in terms of clinical significance. As one should always strive to make models as simple as possible  [38] , our model is deemed to be more suitable for clinical practice.
serve to correct each brain age predicted by our model using They were first used to correct the raw brain age estimates of HC_train. In summary, after segmentation of the MR images, 10-fold CV on HC_train was first performed to obtain the correction weights, which additionally allows the mean absolute error (MAE) to be calculated on the HC_train dataset, providing an intuition in model performance on HC_train. Secondly, all available training data were brain age = w 0 + w 1 feature 1 + w 2 feature 2 + … + w n feature n feature normalized = featuremean feature HC_train std feature HC_train BrainAge raw = 0 + 1 ChronologicalAge + BrainAge = BrainAge raw -0 ∕ 1 F I G U R E 1 Brain age pipeline. The pipeline is subdivided into (a) a training phase and (b) a testing phase, where 'Train Data' refers to the HC_train data and 'Test Data' represents either the HC_test dataset or the MS_test dataset.
whole brain volume, as shown by Golan et al. [29], reporting a correlation of r = 0. 46 between whole brain volume and global cognitive functioning. This is consistent with findings of other studies with large sample sizes, reporting a higher prevalence of cognitive F I G U R E 3 Scatterplot between brain age and SDMT in the MS_ test dataset. The textbox describes the Pearson r statistic, along with the p value [Colour figure can be viewed at wileyonlinelibrary. com] F I G U R E 4 The relationship between brain age and SDMT, independent of chronological age. Left: Scatterplot between BPAD and SDMT in the MS_test dataset. The textbox describes the Pearson r statistic, along with the p value. Right: Forest plot visualizing the significance of the weights (β n ) in the linear regression equation SDMT = 0 + 1 BrainAge + 2 ChronologicalAge + in the MS_test dataset (note that variables were normalized with respect to mean and standard deviation before input in the regression equation).
Funding information This work was supported by a Baekeland grant appointed to  Stijn Denissen  by  Flanders Innovation and Entrepreneurship  (HBC. 2019. 2579,www. vlaio. be);research grants received by  Guy Nagels  from  Biogen  and  Genzyme ; and a senior clinical research fellow grant appointed to  Guy Nagels  by the  FWO Flanders  (1805620 N, www. fwo. be). Jeroen Van Schependom is a senior research fellow of VUB. Study sponsors were not involved in the content of this manuscript.
0 and 1 1 serve to correct each brain age predicted by our model using They were first used to correct the raw brain age estimates of HC_train. In summary, after segmentation of the MR images, 10-fold CV on HC_train was first performed to obtain the correction weights, which additionally allows the mean absolute error (MAE) to be calculated on the HC_train dataset, providing an intuition in model performance on HC_train. Secondly, all available training data were brain age = w 0 + w 1 feature 1 + w 2 feature 2 + … + w n feature n feature normalized = featuremean feature HC_train std feature HC_train BrainAge raw = 0 + 1 ChronologicalAge + BrainAge = BrainAge raw -0 ∕ 1 F I G U R E 1 Brain age pipeline. The pipeline is subdivided into (a) a training phase and (b) a testing phase, where 'Train Data' refers to the HC_train data and 'Test Data' represents either the HC_test dataset or the MS_test dataset.
on understanding the neural origins of cognitive disturbances in MS. The MS subjects of this study were recruited at the National Multiple Sclerosis Center of Melsbroek. Second, the Universitätsmedizin Greifswald contributed data from 104 MS subjects to this study. Altogether, this resulted in 50 sub- jects for the HC_test and 201 subjects for the MS_test. For all data, T1-weighted MRI, sex and age at image acquisition were available. For the test datasets, fluid attenuated inversion recovery (FLAIR).
Outputs from the brain age pipeline: corrected brain age and BPAD for the HC_train, HC_test and MS_test datasets Dataset HC_train HC_test MS_test Source Public Brussels Brussels Greifswald Total N 1673 50 97 104 201 Brain age (mean ± SD) 41. 9 ± 21. 9  † 46. 1 ± 16. 8 61. 8 ± 16. 6 62. 6 ± 22. 9 62. 2 ± 20. 1 BPAD (mean ± SD) 0 ± 10. 0  † -1. 9 ± 9. 7 13. 7 ± 14. 7 19. 5 ± 16. 0 16. 7 ± 15. 6 Group comparison between HC_test (blue) and MS_test (orange) for brain age, BPAD and chronological age. Left: The raincloud plots show the distribution of brain age, BPAD and chronological age for MS_test and HC_test. A reference line at x = 0 is included as visual aid. Right: The scatterplot shows the relationship between brain age and chronological age for MS_test and HC_test. The dotted line is added as reference, namely where brain age = chronological age [Colour figure can be viewed at wileyonlinelibrary.
12-bit analog to digital converter). This camera was recently implanted at the HERMES beamline  31  of Synchrotron SOLEIL (Saint Aubin, France) and used for ptychographic imaging at the carbon K-edge. The readout time for the version of the camera used for these measurements, along with software overhead issues, meant the fastest sampling our system could achieve was 100 ms. Most STXM-based soft X-ray ptychography experiments to date have been measured with the sample at the focus of the zone plate optic. Ptychographic reconstruction is also possible when the sample is out of the focal plane, as long as the defocus distance is known since this is used as an input parameter in the Python tools for Nano-structures Crystallography (PyNX) reconstruction code  6, 7, 32. Defocus conditions have been used in previous STXM-based ptychography  3, 33  and in ptychography carried out in a full-field transmission X-ray microscope  34.
2 eV (~5 × 10 6 ph/s) focused to ~62 nm, positioned at the edge of an Au bar of a Siemens Star test pattern (see Fig. 1b  for location). The white circle in the center of Fig. 1a  is the outer limit of the projected illumination from the 50 nm Fresnel zone plate (FZP). Its annular shape is seen more clearly in Fig. 2. There is a large amount of scattered X-ray signals outside of the annulus. The intensity of the scattered light drops below the thermal noise of the camera at scattering angles (and thus momentum values, q) smaller than the one corresponding to the edge of the camera. Figure  1b  presents the reconstructed amplitude image of a 2 μm × 2 μm region at the center of the Siemens Star where the smallest lithographed features are located. The ptychographic dataset consists of a 50 × 50 square array of diffraction patterns like the one in Fig.
1a , recorded with the sample positioned in the focal plane of the FZP, and using 500 ms exposure time per pattern. The reconstruction procedure is described in the Methods and Samples section. Details of the acquisition and reconstruction parameters are summarized in Supplementary Table  1  for all ptychography results presented in this paper and the associated supplementary information. Figure  1c  is the corresponding STXM image recorded with the 62 nm spot from the 50 nm FZP. The dramatic improvement in spatial resolution provided by ptychography is evident. CNT imaging. Figure  2a  presents the diffraction image obtained with a 1. 0 μm X-ray beam spot size (sample in defocus position) at 285. 2 eV. Figure  2b  presents the ptychography amplitude image (i. e. , the amplitude image from the reconstruction of a ptychographic dataset) from a 2 μm × 2 μm area of the CNT sample.
Supplementary Table  1  summarizes the details of data acquisition and ptychographic reconstruction, along with an estimate of the radiation dose imparted, using an excel spreadsheet described in Supplementary Note 2. The yellow dotted annulus in Fig. 2b  indicates the area illuminated by the 1. 0 μm defocused probe. X-ray scattering from the CNTs is clearly detectable, although the signal is weaker than that from the Siemens Star sample. Therefore, the maximum momentum transfer at which a usable signal can be detected on the camera is smaller. Figure  2c  is an STXM image of the same area recorded with the 62 nm spot from the 50 nm FZP. Again, ptychography improves the spatial resolution, but to a much lower extent than with the Siemens Star sample, which is as expected, given that the scattering capability of Au is much stronger than that of the carbon nanotubes.