dataesr/scientific-paragraphs-categorization huggingface dataset:
Dietary Data Collection and Treatment Women completed mostly validated (except ALSPAC), semi-quantitative (EDEN  [31] , Generation R  [32] , Lifeways  [33, 34] , ROLO  [34, 35] ), or non-quantitative (ALSPAC  [36] , REPRO_PL  [28] , SWS  [37] ) FFQs, which were designed to assess average dietary intake over pre-conception or pregnancy periods (Table  1 ). Women declared food intake on frequency scales ranging from five (in ALSPAC) to nine response categories (in Generation R, Lifeways and ROLO) (Table  S1 ). An item "not ticked" (missing) was considered as "non-consuming" and imputed with zero, assuming that these mothers did not eat it. All food consumption frequencies were converted into daily frequencies (servings per day) to be comparable across cohorts. For cohorts with semi-quantitative FFQs, we also calculated food consumption data in amounts (grams and millilitres per day).
DASH Score Creation Several DASH scores have been developed or adapted in the literature, which differ regarding both the food components included and scoring method  [38]. The DASH diet was initially created to help reduce arterial hypertension but no consensus exists on how to generate DASH scores from FFQs  [39]. To our knowledge, the DASH index proposed by Fung et al. [10]  has been the most widely used. Therefore, we generated DASH scores, using the Fung method, from the data collected within each of the ALPHABET consortium cohorts and adapted to their specificities as described below. Previously published DASH scores were based on whether one meets a recommended minimum number of servings  [40]. In contrast, Fung's DASH index relies on quintile ranking, allowing for a wider, more-discriminating score range  [38] , an approach that we judged more appropriate for ALPHABET considering the diversity of cohorts, time periods, and FFQs used.
Statistical Analyses The different DASH food component consumption (in frequencies and/or amounts) were described for each cohort and time period (pre-pregnancy, early pregnancy and late pregnancy) using the median (interquartile range, IQR) and compared graphically using the quintiles of the distributions. Spearman's correlations were calculated for both DASH scores based on frequencies with those based on amounts (for cohorts with data available for both units). All analyses were carried out with SAS software v9. 4 (SAS Institute Inc, Cary, North Carolina, USA) and RStudio was used to generate the figures.
PET/CT acquisition and reconstruction parameters PET/CT examinations were performed as per the EANM guidelines for PET/CT tumour imaging  [5, 6]. Quantitative data presented in this study are EARLcompliant. Patients fasted at least 6 h before intravenous injection of 18 F-FDG and were scanned on a Biograph TrueV system (Siemens Heathineers) from the skull base to the mid-thighs. A free-breathing CT acquisition (60 mAs, 130 kVp, pitch 1, and 6 × 2 mm collimation) was followed by a PET/CT acquisition with time per bed position of 160 and 220 s for normal weight (BMI ≤ 25 kg/m 2 ) and overweight patients (BMI > 25 kg/m 2 ), respectively. PET/CT studies were planned to be performed 60 ± 5 min post-injection. Extraction of data from the PET/CT DICOM headers showed the following information: mean injected activity ± SD was 4 ± 0.
PET/CT analysis All PET/CT examinations were reviewed on Syngo. via Software (Siemens Medical Solution). A 6. 3-mm Gaussian filter, determined as per the EARL accreditation programme, was applied using the EQ. PET software  [10, 11]. For each PET/ CT examination, maximum standardized uptake values (SUVmax) were measured as follows: -For liver and spleen, an automatic 3-cm-diameter volume of interest (VOI) was placed in the right liver lobe, avoiding liver or spleen lesions in the case of tumour involvement. A manually set VOI fitting the size of the spleen was used in cases where spleen was smaller than the 3 cm VOI. -For bone marrow assessment, a VOI of 2 cm was placed on the sacral promontory, avoiding sacral foramens. Liver mean Hounsfield units (HU) were measured to seek hepatic steatosis  [12].
Furthermore, inflammatory markers such as C-reactive protein (CRP) are higher in dialysis patients and are associated with higher cardiovascular morbidity and mortality  [4] [5] [6] [7]. Kidney transplantation can improve many of these clinical indicators. Previous studies showed an increase in haemoglobin and albumin, a transient increase in cholesterol and a decrease in ferritin, phosphate and PTH levels  [8] [9] [10]. However, these studies reported only on the first year post-transplantation and were performed in a single centre or country. To our knowledge, international studies with long-term follow-up have not yet been performed. In this study, we therefore used data from the European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry to assess whether kidney transplantation is associated with changes in clinical indicators and how each clinical indicator changes over time after kidney transplantation.
Study population National and regional renal registries within Europe provide yearly data on patients treated with RRT to the ERA-EDTA Registry  [11]. The core dataset includes month and year of birth, sex, primary renal disease (PRD), treatment modality at the start of RRT, changes in RRT modality and date and cause of death. Recently the registry has extended its data collection with laboratory measurements. For this study we included the following national and regional renal registries that provided laboratory data for both dialysis and transplanted patients: Denmark (inclusion period ,  Finland (2010-15) ,  Norway (2005-15) , Catalonia  (Spain, 2010-15)  and UK (England, Wales and Northern Ireland, 2005-15). All registries followed their national legislation with regard to ethics committee approval.
Data collection and definitions PRD was classified into six groups: glomerulonephritis (GN), diabetes mellitus, hypertension/renal vascular disease (HT/RVD), polycystic kidney disease, other causes of renal failure and unknown. To study the influence of time, the year of kidney transplantation was divided into the time periods 2006-10 and 2011-15. The clinical indicators studied were categorized into four groups: anaemia (haemoglobin and ferritin), mineral and bone disorders (serum ionized calcium, phosphate and PTH), dyslipidaemia [total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol and triglycerides] and inflammation and malnutrition (serum albumin and CRP). Laboratory variables were determined according to local practice. An overview of the availability of clinical indicators is given in Supplementary data, Table  S1.
Statistical analysis Patient characteristics were summarized using percentages, mean values with standard deviations (SDs) and median values with interquartile ranges (IQRs). For the main analysis, data from all registries were combined. When multiple measurements per year were available, we calculated an average value per patient per calendar year. For the year of kidney transplantation, we only used measurements after transplantation. As this could only be determined for Denmark and the UK (used measurement in the quarter after quarter of transplantation), measurements in the year of transplantation represent only patients from these two countries. To quantify the changes in the clinical indicators, we applied two different linear mixed models with and without adjustment for potential confounders (age at transplantation, sex, PRD, period of transplantation and country) using the SAS software 'proc mixed' procedure (SAS Institute, Cary, NC, USA).
These analyses present the course over time for haemoglobin, ferritin, phosphate, PTH and total cholesterol in 3-month periods between 1 year before and 4 years after transplantation. The linear mixed model was applied to identify at which time point clinical indicators had changed statistically significantly from the last time point pre-transplantation. Additional analyses were performed to examine changes in dyslipidaemia including only those registries with data for all lipid indicators (Denmark, Finland and Norway). Since the UK data comprised 75% of the dataset, sensitivity analyses excluding the UK were used to assess whether the results remained the same. Subgroup analyses were performed for age at transplantation, sex, PRD, kidney donor source (living versus deceased) and time period  (2006-10 versus 2011-15).
Additional subgroup analyses were used to test for the association between eGFR (<30 versus 30-59 versus !60 mL/min/1. 73 m 2 ), albumin (<35 versus 35-50 versus >50 g/L), CRP (<10 versus !10 mg/L) and PTH (<45 versus !45 pmol/L) levels 1 year post-transplantation and changes in the clinical indicators after transplantation. All analyses were performed using SAS software (version 9. 4; SAS Institute). We considered P-value <0. 05 (two-sided) as statistically significant.
ACKNOWLEDGEMENTS The authors would like to thank the patients and the staff of the dialysis and transplant units for contributing the data via their national and regional renal registries. Furthermore, we gratefully acknowledge the following registries and persons for their contribution of data:  Danish Nephrology Registry (DNS) ,  Finnish Registry for Kidney Diseases  ( A. Pylsy  and P. H. Groop),  Norwegian Renal Registry  ( T. Leivestad  and  A. V. Reisaeter ),  UK Renal Registry  (all the staff of the  UK Renal Registry  and of the renal units submitting data), the regional registry of Catalonia (RMRC) ( E. Arcos ,  J. Comas  and  J. Tort ) and the other ERA-EDTA Registry Committee members not mentioned above for their advice in the analysis and the drafting of this article:  C. Zoccali ,  C.
Analysis of Sequencing Data Sequences were aligned using BWA, GATK, Novoalign, Isaac, or LifeScope software. The variants were called using GATK, SAMtools, Annovar, CarpeNovo, Isaac, LifeScope, and in-house pipelines. More details can be found in Tables  S1  and S2. After identification of candidate variants in ACTL6B, their segregation was confirmed by Sanger sequencing using standard protocols.
Blots were washed a further three times in TBST for 5 min, then imaged using a ChemiDoc XRSþ System (Biorad). Blots were imaged and analyzed using ImageLab (Biorad) software, and statistical analysis was preformed using Student's t tests when two samples conditions were present and a one-way ANOVA when more than two sample conditions were present. Blots were normalized to b-actin. Further details on the antibodies used for WB can be found in Table  S5.
Immunofluorescence Adherent cells were washed with PBS, then fixed with 3% paraformaldehyde (Sigma-Aldrich) on slides for 15 min. Samples were permeabilized with 0. 5% TX-100 (Sigma-Aldrich) in 0. 5% PBS-BSA for 15 min, and then blocked in 0. 5% PBS-BSA for an additional 15 min. Primary antibodies were added in appropriate dilutions (Table  S5 ) in 0. 5% PBS-BSA and added to samples for 30 min. Samples were washed in 0. 5% PBS-BSA containing an appropriate dilution of secondary antibody (Table  S5 ) was added to the samples and incubated for 30 min in the dark. Samples were washed with 0. 5% PBS-BSA. Samples were then visualized on an Apotome Florescent microscope (Zeiss). Neurolucida Tracing Software (MBF Bioscience) was used to measure nuclei surface area, soma surface area, and projection length.
Identification of French-Canadian Families with Homozygous Mutations in ACTL6B By exome sequencing of families with neurodevelopmental disorders in Quebec, we identified a family with two children with a homozygous mutation in ACTL6B that eliminates the stop codon (c. 1279del, GenBank: NM_016188. 4) and extends the reading frame by an additional 33 amino acids (p. *427Aspext*33; GenBank: NP_057272. 1; individual R3; Figure  1 , Tables  1  and S1 ). Sequencing both affected children, their unaffected older brother, and both parents from family R3 revealed that c. 1279del was the only mutation identified in the family that was protein altering, followed an autosomalrecessive inheritance model, and was absent from all genomic databases. Both parents and the unaffected brother were carriers, and all are healthy.
De Novo Missense Mutations at Specific Loci in ATCL6B Cause a Different, Severe Neurodevelopmental Disorder Over the course of identifying subjects with mutations in ACTL6B, we found ten individuals with heterozygous de novo missense mutations in ACTL6B with hypotonia, intellectual disability, developmental delay, autism, and Rett-like stereotypies such as handwringing (Figure  1 , Tables  2  and S2 ). Detailed case vignettes of some subjects are presented in the Supplemental Note. This was surprising, given that we observed heterozygous stop/ frameshift mutations in healthy individuals arguing for a recessive inheritance model. Nine of ten of these individuals possess the same well-conserved c. 1027G>A mutation (p. Gly343Arg) (Figure  1C  and Table  2 ). p. Gly343Arg (GenBank: NM_016188. 4; hg19: chr7: 100244258; exon 12) is not seen in the 60,706 ExAC subjects.
The same holds true for the other observed variant, p. Asp77Gly (GenBank: NM_016188. 4; hg19: chr7: 100253082; exon 3), both of which are likely gain-of-function mutations since heterozygous stop mutation carriers have no disease. 3D modeling of the de novo dominant and the recessive biallelic mutations (Figure  1D ) shows no spatial clustering of mutation sites. BAF53B has an actin-related domain, which is subdivided into four subdomains. 20 Subdomains I and III are structural and also contain residues that interact with ATP. 21 Subdomain II is the smallest domain and enables the protein to have polar and non-polar orientations. Previous work has shown that mutations in this subdomain impair dendritic outgrowth. 4 Subdomain IV interacts with subdomain 1  20  and is necessary for the interaction of the protein with actin.
The p. *427Aspext*33 Alteration Phenocopies ACTL6B KO Dendritic Deficits and Is Reversible upon Bi-allelic Genetic Repair From our genotype-phenotype data from affected individuals and their first-degree relatives, we reasoned that recessive mutations cause a loss of function of ACTL6B and thus may mimic the cellular phenotype of the engineered ACTL6B KO cells. To demonstrate this, we reasoned that using these cell lines and comparing them to a clonally repaired version should provide interpretable data. We bi-allelically repaired the ACTL6Bext33 line to a wild-type genotype and simultaneously reprogrammed these edited cell lines, where we had several unsuccessful repairs that could be used as isogenic controls. Homology-directed repair was performed using a wild-type template in p. *427Aspext*33 fibroblasts plated at low density and iPSCs colonies derived from a single fibroblast were isolated, ensuring clonality and purity of repair.
To support the idea that deficits of ACTL6B do not lead cell cultures to become more glial and to provide more specificity than just GFAP, we assessed the transcriptomic data of p. *427Aspext*33 and control cells we had generated in an RNA-seq experiment. We found no consistent pattern in mRNA expression levels of glial markers ALDH1L1, GFAP, and GJA1 and neuronal markers RBFOX3 and TUBB3 to suggest an increase in expression of glialrelated genes in ACTL6Bext33 cells compared to control cells (Figure  S4B ). These data indicate that deficits in ACTL6B do not lead NPCs to become more astrocytic. We therefore ruled out the loss of MAP2 staining being due to cells being pushed toward an astrocytic fate. As a simple measurement of differentiation, we opted to photograph control, ACTL6Bext. 33, ACTL6B KO, and Figure  S5  shows that in contrast to repaired and control cell lines, unrepaired and KO ACTL6B cells are not branched prior to day 20, whereas at day 25 through day 50, all lines show branching.
11 We chose the D0 and D5 time points for proliferating and post-mitotic cells, respectively, and performed ChIP in control and ACTL6Bext33 cells using eight replicates per subject per time period (32 ChIP experiments). We performed several QC experiments with different anti-BRG1 antibodies prior to sequencing to ensure appropriate parameters (not shown) and chose two antibodies to provide overlapping datasets of Brg1-containing BAF complex binding (Figure  6A ). After sequencing and QC, we analyzed differential binding in D5 cells to understand how the genomic targeting of Brg1-containing BAF complexes may be altered by a mutant BAF53B subunit. ChIP peaks were called in at least 2/8 lines, and differential analysis used reads from all replicates within the peak. 10,222 peaks were common across all data points (Figure  6B ), suggesting that BRG1 remains mostly at the same location in the genome, even in mutant ACTL6B cells and irrespective of developmental state.
It should also be noted that our ChIP-seq data that initially highlighted these genes as being dysregulated are based upon only BRG1-containing BAF complexes. As BRG1 and BRM are mutually exclusive components of the BAF complex that are both found in dividing and post-mitotic neural cells,  11  it is possible that the dataset we generated is only a partial picture of the regions of the genome where BAF may bind in these cells. This work brings together extensive clinical samples and human stem cell modeling to demonstrate that mutations in ACTL6B in human cause severe neurological disorders. Substantially more work will need to be done to understand the precise mechanisms of how recessive or dominant mutations in ACTL6B affect incorporation into the BAF complex and how this incorporation can alter differential genomic binding and gene expression patterns.
Acknowledgments We acknowledge funding by  FRQS doctoral  (S. B. );  Indonesian Endowment Fund for Education PhD  award (M. J. );  CONACYT (Mexico ) and MITACS (K. M. V. );  Genome Canada and Genome Quebec  (J. L. M. and E. R. );  Canada Research Chairs program  (G. T. and C. E. ); AMED,  MEXT ,  JST ,  MHLW , and  Takeda Science Foundation  (N. Matsumoto); and  CIHR  grant (C. E. and P. M. C. ). We are grateful to  Gerald Crabtree  who provided a custom antibody to BAF53B. C. E. is grateful to family  R3  who collaborated with his lab from mutation detection to human neuron modeling.
Web Resources Bioconductor, http://bioconductor. org/packages/release/bioc/ html/DiffBind. htmlBurrows-Wheeler Aligner, http://bio-bwa. sourceforge. net/GATK, https://software. broadinstitute. org/gatk/GenBank, https://www. ncbi. nlm. nih. gov/genbank/HOMER, http://homer. ucsd. edu/homer/Imagelab, http://www. bio-rad. com/en-us/product/image-labsoftware? ID¼KRE6P5E8Z MACS2 , https://github. com/taoliu/MACS/wiki/Install-macs2Neurolucida, https://www. mbfbioscience. com/neurolucidaOnline Mendelian Inheritance in Man, http://www. omim. orgTrim Galore, http://www. bioinformatics. babraham. ac. uk/projects/ trim_galore/ VarSome, https://varsome. com/.
In the lungs, some detectable hGAA mRNA expression observed using the LiMP could possibly derive from promoter activity in smooth muscle cells (Figure  S3B ). As expected,  63, 64, 66  vector genome copy numbers (VGCNs) were higher in liver compared with other tissues (Figures  S3D  and S3E ). Overall, the hGAA mRNA expression data show that the tandem promoters LiMP and LiNeuP drive efficient and specific transgene expression in target tissues (Figures  2A-2F ; Figures  S3B  and S3C ). Next we quantified hGAA protein in the plasma of C57BL/6 mice injected with the AAV9-hGAA vectors. One month after treatment, western blot analyses of mouse plasma showed that the amount of circulating hGAA was higher using LiMP compared with C5. 12 and Enh. C5. 12, and LiNeuP compared with hSYN (Figures  2G  and 2H ).
Our data indicate that systemic AAV gene therapy with the tandem LiMP promoter in neonate GaaÀ/À mice provided superior and persistent therapeutic efficacy compared with single tissue-specific promoters. Complete clearance of whole-body pathological glycogen accumulation and significant rescue of cardiac hypertrophy and muscle strength were indeed measured only when using LiMP. As expected,  [14] [15] [16] [17]  partial loss of liver expression because of hepatocyte proliferation was observed in neonate animals treated with the LiMP vector. However, this was not associated with lack of therapeutic efficacy because of the persistence of transgene expression in muscle driven by the dual tissue specificity of the promoter. Previous studies of AAV gene therapy in neonate GaaÀ/À mice showed only partial rescue of the disease phenotype.
However, one limitation of the current work is the lack of studies in large animal models aimed at defining transgene persistence in neonate animals and therapeutic vector doses. Future work will have to address this point, particularly given the generally reduced transduction efficiency of AAV vectors in nonhuman primates (NHPs) compared with mice. 89 Of note, recent studies in fetal rhesus monkeys treated with AAV vectors showed persistent expression in muscle but not liver,  17  supporting the overall model of persistent therapeutic expression mediated by LiMP presented here. In our study, therapeutic efficacy in neonate mice was observed with AAV serotypes currently in the clinic for the treatment of pediatric neuromuscular disorders (Clinicaltrials. gov: NCT02122952 and NCT03199469),  19, 27  but other naturally occurring or engineered AAVs with a similar transduction pattern could be tested.
RNA Extraction and Expression Analysis Snap-frozen tissues were homogenized in Trizol reagent (Thermo Fisher Scientific). Total RNA was extracted from tissue homogenates using the PureLink RNA mini kit with the PureLink DNase set (Thermo Fisher Scientific). RNA was quantified, and 2-5 mg was retro-transcribed to cDNA using the Maxima First Strand cDNA Synthesis Kit for qRT-PCR with dsDNase (Thermo Scientific); RT-minus reactions were performed as a negative control. For hGAA mRNA expression analyses, the qPCR on cDNA was performed using Sybergreen and primers annealing specifically on the codon-optimized hGAA transgene sequence (hGAA forward, 5 0 -agatacgccggacattg gactg-3 0 ; hGAA reverse, 5 0 -agatacgccggacattggactg-3 0 ). Primers annealing on the mouse Actin gene were used to normalize hGAA expression (Actin forward, 5 0 -ggctgtattcccctccatcg-3 0 ; Actin reverse, 5 0 -ccagttggtaacaatgccatgt-3 0 ) for the data depicted in Figure  2.
Statistical Analysis All data shown in the present manuscript are reported as mean ± SD. The number of sampled units, n, upon which we reported statistics, is the single mouse for the in vivo experiments (one mouse is n = 1) and the single independent experiment for the in vitro studies using cell lines (one independent experiments, n = 1). GraphPad Prism 7. 0 software was used for statistical analyses. We assessed the normal distribution of the data obtained from the different measurements (mRNA expression in tissues, anti-hGAA IgG amounts in plasma, hGAA protein expression in plasma, cell lines and tissues, GAA enzyme activity in tissues, glycogen content in tissues, heart weight, and muscle functional analyses) using the Shapiro-Wilk test. The statistical tests used were unpaired Student's t test for two-group comparisons, one-way ANOVA with Tukey post hoc for comparisons of more than two groups, and two-way ANOVA with Tukey or Sidak post hoc for multiple variable comparisons.
For all datasets analyzed by parametric tests, alpha = 0. 05. All statistical tests were performed two-sided. p < 0. 05 was considered significant. The statistical analysis performed for each dataset is indicated in the figure legends. For all figures, *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001, #p < 0. 05, ##p < 0. 01, ###p < 0. 001, ####p < 0. 0001.