dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioluminescence and statistical data analysis Bioluminescence data were analyzed with the Actimetrics LumiCycle analysis software (Actimetrics LTD, Wilmette, IL). RNA-seq data and qPCR data analysis were performed using RStudio, GraphPad Prism five and Excel 2016. Panther analyses were performed using the PANTHER version 12. 0 released on 10. 07. 2017. Statistical analyses were performed using Student's t-test. Differences were considered significant for (*) p-value <0. 05, (**) p-value <0. 01, and (***) p-value <0. 001. Exact p-values and raw data for Figures 2 and 3 are listed in Supplementary file 2.
Acknowledgements The authors thank  Jacques Philippe  and  Sylvain Sardy  for constructive discussions;  Pamela Pulimeno  for help with conducting the in vitro around-the-clock sample collection, RNA extractions and RT-qPCR experiments;  Svetlana Skarupelova  and  Camille Saini  for help with RNA extractions and  Anne-Marie Makhlouf  for lentivirus preparation;  Luciana Romano  and  Deborah Beilser  for their help with in vitro RNA sequencing ( University of Geneva );  Marc Moniatte  and  Jonathan Paz Montoya  ( PCF, EPFL ) for help with the lipid analyses; and  Ondine Walter  ( NIHS ) for the ethical and logistical management of human samples. This work was funded by the  Sinergia Swiss National Science Foundation  (Grant No. CRSII3-154405  to HR, CD, EL), the  Swiss National Science Foundation  (Grant No.
, each was based on a single free induction decay (FID) of 32,768 complex data points acquired with a repetition time of 1 s. FID acquisition time was 0. 806 s; spectral width was 269. 35 ppm; the carrier frequency was set to 120 ppm; the duration of the radio-frequency pulse was 1 μs, which corresponded to a nutation (flip) angle of 4 o. The data were processed with one degree of zero filling and multiplication with an exponential line-broadening factor of 1 Hz before Fourier transformation. 13 C NMR chemical shifts of each of MGBH and MGMH were assigned as in  33. For the HTA and MG peaks, the assignment was done by adding extra GSH to a hemolysate, which increased one of the two peaks that was then assigned to HTA. NMR spectra were phased and baseline corrected in Bruker TopSpin 3. 5, prior to being imported into Mathematica 34 for post-processing.
Body temperature was maintained with warm air blown though the magnet bore and was recorded with a rectal thermometer. Breathing rate (~80 cycles min -1 ) and temperature were recorded with a purpose-built device from Biotrig (Small Animal Instruments, Stony Brook, New York, USA). In vivo magnetic resonance spectroscopy. Experiments were conducted on a 7 T horizontal-bore magnet (Agilent, Palo Alto, CA, USA) using an actively decoupled double-tuned  13  C-1 H volume transmit coil (Rapid Biomedical, GmbH, Rimpar, Germany; inside diameter 42 mm) and a 20-mm diameter  13  C surface receiver coil (Rapid Biomedical). Tumors were located with transverse 1 H images acquired with a spin-echo pulse sequence using a repetition time of 1. 5 s, echo time of 10 ms, a field of view of 40 × 40 mm, a data matrix of 128 × 128 complex points, a slice thickness of 2 mm, and 15 slices.
Time courses were recorded as single transient spectra from a 6-mm thick slice of the tumor (or brain or liver) acquired using a slice-selective excitation pulse with an estimated flip-angle of 10 o. Using a repetition time of 1 s, 180 spectra were acquired in 3 min. The data were imported into a MatLab (Mathworks, MA, USA) script and peaks were integrated. Further analyses including graphical output were conducted in Mathematica 34. Quantitative analysis of time-course data. Initially, the glyoxalase pathway reactions were modeled using the scheme shown in Supplementary Figure  11a , which included 'unlabeled' (non-hyperpolarized) species. Each kinetic step was accounted for by a first-order rate constant, with k Glo1 and k Glo2 denoting equivalent apparent first-order rate constants that characterize the two enzymes.
Acknowledgements The work was supported by an  ARC (Australian Research Council)  grant  DP140102596  to  P. W. K. Work  in K. M. B. 's laboratory was supported by a  Cancer Research UK   Programme grant  ( 17242 ), and the  Cancer Research UK-Engineering and Physical Sciences Research Council (CRUK/EPSRC ) Imaging Centre in Cambridge and Manchester (16465). Dr Christoph Naumann  and  Mr Joseph S. Mendel  are thanked for previous preparations of labeled methylglyoxal and NMR studies of them. Professor Young-Tae Chang  and  Dr Jason Lee  are thanked for preparations of labeled methylglyoxal made in the initial stages of the project at the  Singapore Bioimaging Consortium. Dr Ann Kwan  is thanked for assistance with and maintenance of the NMR spectrometer at the  University of Sydney.
A correlation between disease severity and gene expression or metabolite levels was found for sphingosine, ceramide(d18:1/26:0), Sgpp2, Sphk1, and Ugt8a. Joint-analysis revealed a significant enrichment of glycosphingolipid metabolism in SOD1 G86R mice, due to the down-regulation of ceramide, glucosylceramide, and lactosylceramide and the overexpression of genes involved in their recycling in the lysosome. A drug-gene interaction database was interrogated to identify potential drugs able to modulate the dysregulated genes from the signaling pathway. Our results suggest that complex lipids are pivotally changed during the first phase of motor symptoms in an animal model of amyotrophic lateral sclerosis.
RNA-Sequencing RNA-sequencing was performed as previously described  (Henriques et al. , 2017). Briefly, total RNA was extracted from frozen samples of spinal cord from 95 days old mice (n = 5/group). Libraries of template molecules suitable for high throughput DNA sequencing were created and reads were mapped onto mm10 assembly of mouse genome using Tophat v2. 0. 14  (Kim et al. , 2013). Quantification of gene expression was performed using HTSeq v0. 6. 1  (Anders and Huber, 2010)  and Ensembl release 81 database. Supervised statistical analysis for differential gene expression has been performed using R (3. 3. 2) and the DESeq2 Bioconductor (v3. 2) library. Multiple testing was adjusted by Benjamini and Hochberg FDR correction  (Benjamini and Hochberg, 1995). After normalization and rlog transformation, hierarchical clustering (single method), and PCA (principal component analysis) was performed with the indicated subset of genes, accordingly to KEGG pathways, using JMP 11.
0. 0. Validation of gene expression was assessed by qPCR with a CFX96 using SYBR green Supermix reagent (BioRad), with the samples used for RNA-sequencing. Relative quantification of each gene was determined using the Biorad software and normalized to reference genes (Pol2, TBP, and 18S). Primers sequences are provided in Supplementary Table  1. Comparison between groups was studied with student's t-test and p-value < 0. 05 were considered significant (PRISM 6. 0b, GraphPad, San Diego, CA). Data are expressed as the mean ± SEM. RNA-sequencing data are available at the Gene Expression Omnibus (GEO) database repository, under the accession number GSE106364.
According to three independent databases, "sphingolipid metabolism" (KEGG database, p adj < 3. 00. 10 -0  ; and Reactom database, p adj < 3. 9. 10 -0  ) as well as "glycosphingolipid metabolism" (EHMN database; p adj < 2. 5. 10 -04 ) were the metabolic pathways showing the most significant enrichment. Indeed, many metabolites showing altered levels were identified as sphingolipids. Sphingomyelin (d18:0/26:1), ceramide-P (d18:1/26:1), and ceramide (d18:1/22:0) were among the most down-regulated lipid metabolites (Table  2 ). Notably, several phospholipid metabolites, such as phosphatidylcholine(o-22:1/20:4), presented with significant down-regulation (Table  3 ). Other subclasses of dysregulated lipids included di-and triglycerides. Saturation and unsaturation of acyl chains directly influence biophysical properties of complex lipids with repercussion on membrane fluidity.
Sphingolipid Metabolism Is Altered at Transcriptomic and Lipidomic Levels in the Spinal Cord of SOD1 G86R Mice To investigate the relationship between transcriptomic and lipidomic changes in SOD1 G86R mice, a joint inter-omics analysis was performed using significantly dysregulated genes and metabolites  (Kamburov et al. , 2011a). The objective was to identify molecular pathways associated with ALS, with a special regard toward lipid metabolism. Three distinct databases have identified "sphingolipid, " "glycosphingolipid metabolism, " and "sphingolipid signaling" pathways as significantly enriched in the spinal cord of SOD1 G86R mice (Table  5 , Supplementary Table  2 ). Overlapping genes are those coding for lysosomal proteins involved in the recycling of glycosphingolipids (e. g. , Hexb, Glb1, Asah1), for ceramide kinase (Cerk), for Sgpl1, an enzyme involved in the degradation of sphingosine-1-phosphate.
Overlapping lipid metabolites were ceramide, mono-hexosylceramide (glucosylceramide or galactosylceramide), lactosylceramide, and sphingomyelin. Pathways related to other lipids, such as phospholipids or triglycerides, were not detected as significantly enriched. Next, hierarchical clustering was performed using all genes, dysregulated or not, listed as part of the sphingolipid metabolism by KEGG. Genotype had a strong influence on the distribution of samples on unsupervised hierarchical clustering plot (Figure  3A ). A cluster of genes, which are associated with the breakdown of glycosphingolipids, showed a clear differential pattern of expression in SOD1 G86R mice compared to wild type controls. Differential expression for genes (i. e. , HexB, Asah1, Cerk, Glb1, and Arsb) involved in the breakdown of  Given are the dysregulated metabolites identified as sphingolipids by the Human Metabolome Database (HMDB), in the spinal cord of symptomatic SOD1 G86R mice, after positive identification.
The table shows the pathways, the source of the database, number and identity of genes and metabolites dysregulated per pathway, and p-value and q-value (false discovery rate) for each pathway. phosphatase 2), and Sphk1 (sphingosine kinase 1) were significantly correlated or anti-correlated to muscle strength (Figures  3C-E ). Ugt8a codes for the galactosylceramide synthase. SGPP2 and SPHK1 are two enzymes involved respectively in the synthesis and degradation of sphingosine-1-phosphate. Interestingly, muscle strength showed a significant correlation between the intensity level of ceramide(d18:1/26:0) and sphingosine, product and/or substrate of Ugt8a, Sgpp2, and Sphk1 (Figures  3F, G ). Collectively, our results suggest that the metabolism of sphingolipids, and particularly the glycosphingolipid and the axis "ceramide, sphingosine, sphingosine-1-phosphate" is impaired at transcriptomic and lipidomic levels, and vary depending on disease progression (Figure  3H ).
Several sphingolipids, such as sphingosine-1-P, ceramide and sphingomyelin, are bioactive molecules able to module cell response to stress, survival, and neuroinflammation. KEGG identified "sphingolipid signaling pathway" as dysregulated, suggesting that altered levels of sphingolipids in ALS could translate into changes in cell signaling. DGIdb database was used to search for potential pharmaceutical modulators of the "sphingolipid signaling pathway" using genes identified as dysregulated in this pathway  (Wagner et al. , 2016). A total of 67 compounds were identified, including 10 negative modulators of TNF-a signaling, and 5 modulators of sphingosine-1-phosphate. Notably, 6 of them are currently investigated or were tested at preclinical and/or clinical level in ALS (Pyrimethamine, fingolimod phosphate, pentoxifylline, celastrol, lenalidomide, thalidomide; Supplementary Table  3 ).
FUNDING This work was supported by the  European Community 's  Health Seventh Framework Programme  under grant agreement No. 259867  ( Euro-MOTOR ),  Target ALS, "Association André combat la SLA " and " Association pour la Recherche et le Développement de Moyens de Lutte contre les Maladies Neurodégénératives " (AREMANE). RNA-sequencing was performed by the  IGBMC GenomEast platform , a member of the " France Génomique "  consortium  ( ANR-10-INBS-0009 ).
63 807. 6 10. 4 ECMDB23726 PG(19:iso/19:iso) 0. 49 889. 6 10. 4 HMDB09814 PI(18:0/20:3) 0. 58 Given are the dysregulated metabolites identified as phospholipids by the Human Metabolome Database (HMDB), in the spinal cord of symptomatic SOD1 G86R mice, after positive identification. m/z, mass to charge ratio; RT, retention time (min).
6 9. 8 ECMDB23708 PG(18:1n-9/19:iso) 0. 53 564. 5 9. 4 HMDB04950 Ceramide (d18:1n-9/18:0) Sphingolipid 0. 52 566. 5 10. 1 HMDB11761 Ceramide (d18:1n-9/18:0) 0. 63 620. 6 10. 7 HMDB04952 Ceramide (d18:1n-9/22:0) 0. 53 562. 5 9. 4 HMDB04948 Ceramide (d18:1n-9/18:1n-9) 0. 52 562. 5 9. 5 HMDB04948 Ceramide (d18:1n-9/18:1n-9) 0. 52 618. 6 10. 7 HMDB11775 Ceramide(d18:1n-9/22:1n-13) 0. 50 672. 5 10. 0 HMDB10702 CerP(d18:1n-9/20:0) 0. 59 782. 6 10. 4 HMDB04974 Glucosylceramide (d18:1n-9/22:0) 0. 51 886. 6 10. 9 HMDB11592 Lactosyceramide (d18:1n-9/18:1n-9) 0. 46 714. 6 10. 5 HMDB29216 SM C16:1n-7 0. 61 Given are the dysregulated metabolites identified by the Human Metabolome Database (HMDB), in the spinal cord of symptomatic SOD1 G86R mice, after negative identification. m/z, mass to charge ratio; RT, retention time (min).
The assessment of peripapillary retinal nerve fiber layer thickness (pRNFLT) with spectral-domain OCT has become of increasing importance in the evaluation of glaucoma and its progression. 1, 2 Although debated, pRNFLT measurements hold promise as a biomarker for neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis. 3, 4 lthough pRNFLT measurements have increased insight into the development of diseases, it has been difficult to evaluate which changes fall within the physiologic range. Most OCT devices compare pRNFLT measurements with reference databases that are built into the machine analysis software. These data are mostly derived from relatively small sample populations. Whether these databases adequately capture normal anatomic variation across a wide age range remains unclear.
Included Studies The European Eye Epidemiology (E3) consortium is a collaborative network of population-based studies across Europe with the overarching aim of developing and analyzing large pooled datasets to increase understanding of eye disease and vision loss. 15 For this study, we analyzed data on pRNFLT from 8 different studies. The included data were cross-sectional, and the right eye was chosen to be the study eye. All studies adhered to the tenets of the Declaration of Helsinki and had local ethical committee approval. All participants gave written informed consent.
Because OCT devices were changed within the course of the Rotterdam Study (from 3D-OCT 1000 to 3D-OCT 2000, Topcon Medical Systems, Oakland, NJ), we controlled for the OCT device in the multivariable regression models of the Rotterdam Study II and III. In the TwinsUK Study, we performed a hierarchical multivariable regression model to control for family dependencies between twins (See Study Descriptions and pRNFLT Distributions, available online at www. aaojournal. org). Subsequently, random-effects meta-analysis was used to combine effect estimates (beta coefficients) of each individual predictor from the multivariable regression models among studies. A random-effects approach was chosen a priori on the basis of the heterogeneity in the data caused by the different OCT devices  17  and the set-up of the studies.
Our analyses were conducted twice, with and without patients with known glaucoma. Not all independent variables of the multivariable regression model were available in every participating study. Therefore, the multivariable regression models in the respective studies were performed without the missing variables, and the study was excluded from the meta-analysis of that respective missing covariate. All analyses were performed with the statistical software RStudio (R version 3. 4. 1, RStudio Inc. , Boston, MA, https:// www. rstudio. com/), and statistical significance was set at P < 0. 05.
Women had a thicker pRNFLT than men did in the meta-analysis. However, when correcting for AL rather than SE in the 5 studies with data on AL, this association disappeared. The SE was positively associated with pRNFLT, even after excluding highly myopic eyes (<À6 diopters) and highly hyperopic eyes (>þ4 diopters), as well as eyes with pseudophakia (Fig S1A  , B , available at www. aaojournal. org). Longer AL was associated with reduced pRNFLT in our sensitivity analyses (b ¼ e3. 48 mm per mm longer AL, 95% confidence interval [CI], e4. 18 to e2. 77) (Fig S1C, available at www. aaojournal. org). Both former and current smoking were associated with thicker pRNFLT, but prevalence and associations differed considerably between studies. To assess the influence of education on smoking, we corrected for education, and the associations persisted.
After excluding data from the LIFE Study, which is the largest study with the highest proportion of smokers (data weighted >60% in the meta-analysis), the association remained significant for current but not for former smoking (Fig S1DeG, available at www. aaojournal. org). For BMI, we found a small but significant association with increased pRNFLT after excluding patients with glaucoma. All associations except for former smoking held true after excluding the 619 patients with known glaucoma (Table  3 ). Furthermore, we detected no relevant changes of associations when performing the multivariable regression analyses stratified by sex or when excluding the LIFE study cohort being the largest single study (results not reported).
Discussion Our study confirms the previously reported associations of age and SE with pRNFLT and identifies several additional factors associated with pRNFLT, namely, IOP (even in individuals without a history of glaucoma), stroke, hypertension, and smoking. Furthermore, we found a trend of reduced pRNFLT in participants with dementia. Our results suggest that a number of ocular and systemic factors need to be considered when assessing pRNFLT. To date, none of this has been implemented as potentially influencing factors in reference databases for OCT devices or any algorithms assessing pRNFLT change. First publications on determinants of OCT-based pRNFLT measurements reported older age and greater AL to be associated with thinner pRNFLT. 18, 19 Budenz et al  19  investigated determinants of pRNFLT in 328 normal subjects aged 18 to 85 years using time-domain OCT and described a decrease of 2.
As described previously, structural decline of pRNFLT occurs before functional loss in perimetry in patients with glaucoma. An earlier study reported the difference in pRNFLT between glaucomatous and healthy eyes 8 years before the onset of visual field impairment to be approximately 5 mm. 33 This is in the range of some associations found in our study and underlines the potential impact on the interpretation of pRNFLT. Our results have 2 main clinical implications. First, the normative databases built into the devices should reflect our results when presenting normal values for pRNFLT. Also, the presence of vascular disease including a history of stroke should be considered when defining normative datasets or when clinically evaluating pRNFLT. As discussed earlier, the magnitude of impact of the respective determinants may have clinical relevance, especially in the presence of more than 1 factor reducing pRNFLT.
In the sensitivity analysis, we excluded participants with an image quality value <45 (as recommended by the manufacturer) in the Rotterdam Studies IeIII. We found no relevant changes of direction in any association, but the CIs became broader because of a reduced sample size (Table  S3 , available at www. aaojournal. org). Thus, although the lack of centralized quality control is a limitation to our analyses, the impact of poor-quality scans seems to be low as indicated by our supplemental sensitivity analyses. Within each study, the number of participants in whom OCT imaging could not be performed or in whom the images were of low quality and thus unusable is only a small proportion (Table  S2 , available at www. aaojournal. org). For example, in the Rotterdam Study IeIII the number of participants with no or insufficient OCT data was 10%, 6%, and 15%, respectively.
In addition, information on diseases was assessed differently. Although glaucoma was defined on the basis of optic disc evaluation and perimetry in the Alienor Study and Rotterdam Study IeIII, it was self-reported in the LIFE Study. Furthermore, we did not distinguish between the various types of dementia, which may have a different impact on pRNFLT. These differences contribute again to larger heterogeneity, and the relation between self-reported diseases and pRNFLT may have been estimated with less precision. Last, our data were cross-sectional only, and causal deductions from the detected associations are limited and further longitudinal studies are needed. In conclusion, the current analyses identified important additional determinants of pRNFLT, which should be considered when assessing pRNFLT both clinically and in epidemiologic research.
Acknowledgments The authors acknowledge the contribution of the following:  Alberta A. H. J. Thiadens  ( Rotterdam) ,  Nomdo M. Jansonius  ( Groningen/Rotterdam ), and  Paulus de Jong (Rotterdam). The authors thank  Dr. Matthias Nuechter  ( LIFE ) for his enthusiastic assistance to this collaborative research,  Dr. Kerstin Wirkner  (LIFE) and her team for data acquisition, and  Dr. Toralf Kirsten  ( LIFE ) for IT structure and overall support. Furthermore, the authors thank  Verena Brendler  (LIFE) and  Yvonne Dietz  ( LIFE ) for data management. Ophthalmology Volume 125, Number  10 , October 2018 The Alienor Study was supported by  Laboratoires Théa (Clermont-Ferrand, France) ;  University of Bordeaux (Bordeaux, France) ;  Fondation Voir et Entendre (Paris, France) ; and  Caisse Nationale de Solidarité pour l'Autonomie CNSA (CNSA).
The Coimbra Study is an Investigator Initiated Study financially supported by  Novartis Pharma AG. This publication is supported by the  Leipzig Research Centre for Civilization Diseases (LIFE) , an organizational unit affiliated to the  Medical Faculty of Leipzig University. LIFE is funded by means of the  European Union , by the  European Regional Development Fund  and by funds of the  Free State of Saxony  within the framework of the  excellence initiative  (project nos:  713-241202 ,  713-241202 ,  14505/2470 ,  14575/2470 ). Montrachet Study: Funding was provided by an  Inter-regional grant  ( PHRC ) and the  Regional Council of Burgundy. Funded by  INRA ,  CNRS ,  Université de Bourgogne, Regional Council of Burgundy France (PARI Agrale 1) ,  FEDER (European Funding for Regional Economic Development) , and  French Government  grant managed by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference  ANR-11-LABX-0021-01-LipSTIC Labex ).