dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample description A total of 94 individuals were included in the study after exclusion of two subjects with APOE ε2/ε4 genotypes based on the observation that those two alleles show differential effects on amyloid deposition  [36, 37]. To consider additional effect of APOE, subjects were also grouped according to their APOEε4 status. Among those, 81 passed the RNAseq quality test and were included in the transcriptomics study (44 amyloid (+) and 37 amyloid (-)). In the case of lipidomics, one individual was excluded for being an outlier and there was no data available for three. We thus had lipidomics data on 45 amyloid (+) and 45 amyloid (-). For metabolomics, all non APOE ε2/ε4 subjects were included (47 amyloid (+) and 47 amyloid (-)). For each of the datasets, all data available were used for the univariate analysis.
Lipidomics PCA of the 373 features from lipidomics data showed no effect of sex, APOE, or amyloid status (Fig. 3 ). The same statistical approach described for metabolomics was performed on lipidomics. Differential analysis of the lipidomics dataset highlighted 129 annotated lipids selected by the WT. However, none passed the threshold for CAT scores and consequently no feature was retained as different in the plasma of amyloid (+) versus amyloid (-) subjects (Supplementary Table  2 ).
Transcriptomics Similarly to the lipids dataset, PCA of the 15,616 transcripts expressed in peripheral blood in the 83 subjects analysed showed no effect of sex, APOE, or amyloid status (Fig. 4 ). Differential gene expression analysis between amyloid (+) and amyloid (-) subjects was performed using three methods: WT, CAT score and DESeq2. This led to the selection of 330 differentially expressed genes by WT, 133 by CAT scores, and 389 by DESeq2 (raw p b 0•05). Finally, a core set of 64 overlapping genes was extracted from these three lists (Fig. 5  and Supplementary Table  3 ). Of these 64 genes, 31 were upregulated and 33 were downregulated in the amyloid (+) group. The main biological processes related to these 64 genes were tested by a GO enrichment analysis, which highlighted genes involved in inflammation and fatty acid metabolism (Fig.
Multi-omics data integration In order to investigate how the omics can complement each other to explain the differences between amyloid (+) and amyloid (-) subjects, we performed an integrative analysis using sPLS-DA for multi-block data whereby 'multi-block' refers to datasets organised in blocks of variables for the same group of individuals, hence preserving the block structure of each dataset. This required the organisation of the omics datasets into three blocks of explanatory variables (metabolomics, lipidomics and transcriptomics) based on the amyloid status added as a dummy block matrix (positive or negative). Parameter tuning performed prior to sPLS-DA suggested the use of three components for each omics dataset. The number of selected variables to be retained on the three components after adding sparsity constraint were respectively set to 4, 3, and 3 for the metabolomics block, to 12, 12, and 3 for the lipidomics block and to 3, 3, and 12 for the transcriptomics block.
Fig. 6  shows the list of features for the two first components for all three omics datasets with their corresponding loading plots. Metabolites could efficiently separate amyloid (+) and amyloid (-) subjects, while transcripts showed trends towards separation, and lipidomics data did not discriminate amyloid (+) and amyloid (-) subjects. The weak performance of the lipidomics block confirmed the results of the previous univariate selection showing no difference in the levels of lipids according to the level of amyloid in the brain of the subjects. Bootstrap procedure was also performed to assess the robustness of the feature selections. This analysis highlighted a short set of three metabolites (X280, X75 and X5), one lipid (V300), and three genes (ACSBG2, RASL11A, RNU12) that were much more frequent over the repeats and could correspond to the most discriminant features.
The corresponding variable factor map also confirmed the relative importance of the three types of omics. Similarly to what was observed with the single blocks, the best omics contributor was the metabolomics block followed by a rather similar influence of both transcriptomics and lipidomics blocks. To assess the potential of multi-omics signature to detect amyloid status we performed a ROC analysis. We retained the top frequent features from each omics dataset as assessed by the bootstrap procedure (Supplementary Table  5 ), which notably included several of the best features identified by the multivariate analysis. With these stable features, we repeated the sPLS-DA with the three omics blocks and three additional blocks that were built as a pairwise combination of the individual omics blocks.
Correlations between the levels of the five most significant metabolites, brain amyloid burden and cerebrospinal fluid biomarkers We collected the available data on 20 subjects from our omics study who underwent lumbar puncture: 9 amyloid (-) and 11 amyloid (+). Supplementary Table  6  indicates that in the amyloid (+) subjects,  Florbetapir SUVr was correlated with cerebrospinal fluid (CSF) phospho-tau (p = 0•00062 and p =. 051) but not with total tau. Such correlations were not found in the amyloid (-) subjects. Finally, we analysed the correlations between the four mediumchain fatty acids and 4-nitrophenol, brain amyloid load and tau, phospho-tau and Aβ42 cerebrospinal fluid (CSF) levels in amyloid (+) and amyloid (-) subjects, separately or altogether. When analyzing the amyloid (+) and (-) subjects together, strong correlations were found between tau and phospho-tau and between metabolites and Florbetapir SUVr, as well as between metabolites themselves.
Discussion We report the successful application of a multi-omics approach to find a peripheral blood biomarker signature of brain amyloid deposition in a cohort of asymptomatic individuals at risk for AD. To our knowledge, this is the first multi-omics study focusing on such a population instead of patients with sporadic late-onset AD or familial early-onset AD  [40]. Our omics approach was based on the analysis of three different datasetsmetabolomics, lipidomics, and transcriptomicsobtained from two groups of individuals amyloid (+) and amyloid (-), either separately or in combination. We used two differential statistical approaches, a univariate method and a multivariate one. The univariate method allows to detect features in each dataset that are differential between groups. The multivariate strategy integrates several datasets to find a multi-omics signature of amyloid deposition.
Similar approaches have previously proven successful in other neurodegenerative diseases such as Parkinson's disease  [41]  or spinocerebellar ataxia  [32]. ROC analysis of the combined omics data applied to the INSIGHT-preAD cohort showed that combining metabolomics and transcriptomics features was slightly more predictive than each omics taken separately. However, it was clear that the contribution of each dataset was not equivalent, the metabolomics dataset providing the best performance. Some specific features such as nonanoic acid and 4-nitrophenol were both highly differential in the univariate analysis and had an important impact on the group separation in the integrative superblock analysis. The transcriptomics dataset showed a moderate discriminatory power, but slightly more than the lipidomics one.
Still, these two datasets should not be discarded as they were part of the final multiblock signature, highlighting the particular power of this multivariate approach in the search of biomarkers. A strength of multi-omics approaches is their ability to analyse the interplay between different datasets. Here, we were particularly interested in the relationship between the levels of metabolites in plasma and gene expression in blood cells. We focused on fatty acids as they proved to differentiate between groups in the metabolomics univariate  analysis and because the GO enrichment analysis of the univariate transcriptomics analysis pointed out to an enrichment of genes involved in fatty acid metabolism (Fig. 5B ). However, we were not able to establish any correlation between gene expression and metabolite levels (data not shown).
For example the AD Neuroimaging Initiative has nearly 500 control subjects with Florbetapir PET scans accessible. However no blood metabolomics data are available yet (http://adni. loni. usc. edu/). Of note, even though amyloid deposition is a risk factor for AD, it does not always lead to the development of dementia, thus our amyloid (+) population is not comparable to an AD population. Regarding the applicability of our findings in the general population, studies showed that 17 to 76•6% subjects have memory complaints depending on tests and age range  [71] [72] [73]. The INSIGHT cohort would then be representative of these highly variable percentages of the general population. Grading subjective cognitive decline with sensitive tests remains a challenge. In addition, other pathologies of the brain are possibly associated with amyloid deposition such as neuroinflammation and vascular changes.
Although the results of our study look very promising, sample size and geographic distribution did not allow for an exposome analysis in order to establish whether the levels of 4-nitrophenol could be related to place of residence. Amyloid positivity was assessed from the SUVr values that were very narrow in the amyloid (-) group, thus limiting correlative studies in this group. Following our results, we will explore changes in fatty acids and 4-nitrophenol in mouse models of Aβ deposition. Similarly, it would be interesting to do a detailed exposome to determine possible environmental risk factors of amyloid deposition. Supplementary data to this article can be found online at https://doi. org/10. 1016/j. ebiom. 2019. 08. 051.
Acknowledgments This study was supported by  INSERM  in collaboration with  Institut du Cerveau et de la Moelle Epiniere (ICM), Institut Hospitalo-Universitaire-A ICM , and  Pfizer , and was supported with funding from  Pfizer  and the  Investissement d'Avenir  ( ANR-10-AIHU-06 ) that was used for the recruitment of clinical research assistants, neuropsychologists, and a study physician. The study was done in collaboration with the  Centre Hospitalier Universitaire de Bordeaux  (study number  CIC EC7 ). Avid provided the 1 8 F-florbetapir ligand for amyloid β PET.
HCD fragmentation was performed at 27 normalized collision energy and MS/MS spectra were analyzed at 60,000 resolution in the Orbitrap. For the parallel reaction monitoring assays, peptides were loaded in buffer A and eluted with a 90 min linear gradient of buffer B at 200 nL/min. MS spectra were acquired in the Orbitrap analyzer with a mass range of 400-1500 m/z and 15,000 resolution followed by dataindependent MS/MS spectra of selected precursor ions. HCD fragmentation was performed at 27 of normalized collision energy and MS/MS spectra were analyzed at 60,000 resolution in the Orbitrap. Data analysis was performed with Xcalibur 2. 2 (Thermo Scientific).
Database search For protein identification, searches were performed with Comet release 2016. 01 using 5 ppm precursor ion tolerance, Met oxidation as a dynamic modification, trypsin digestion with 1 missed cleavage (unless otherwise specified) and fixed Cys carbamidomethylation (57. 021464 Da)  [29, 30]. TMT labeling at N-terminal end and Lys was also considered as a fixed modification (229. 162932 Da). Fragment ion tolerance was 0. 02 bin, 0 mass offset. Precursor tolerance type and isotope error were set to 1. Precursor charge range was 2-4, maximum precursor charge 5 and maximum fragment charge 3. Only y-and bions were used for scoring. Peptide identification from MS/MS data was performed using the probability ratio method  [31]. False discovery rates (FDR) of peptide identifications were calculated using the refined method  [32] ; 1% FDR was used as the default criterion for peptide identification.
Annotation of modifications An in-house developed script was used for the semisupervised annotation of peptide modifications as described  [25]. Briefly, the script matches DMass values against the Unimod database, considering the amino acid modification positions revealed by Comet-PTM as well as the preceding and following residues. For simplicity, artifactual modifications introduced during sample preparation and modifications that could not be annotated in Unimod were not considered in further analysis. Met mono-oxidation (+16 Da) is not considered as a PTM.
Funding This work was partly funded by the  Spanish MINECO  ( SAF2016-80843-R  and  BIO2015-67580-P ),  Fondo de Investigaciones Sanitarias ISCiii-FEDER  ( Biobancos RD09/0076/00101 , and ProteoRed  PRB2 IPT13/0001  and  PRB3 PT17/0019 ),  Instituto de Salud Carlos III  ( PI1600139 ), and  FEDER "Una manera de hacer Europa", CIBERDEM  and  CIBERCV are Instituto de Salud Carlos III  projects. The project leading to these results has also received funding from  "la Caixa" Banking Foundation  under the project code  HR17-00247. The CNIC is supported by the  MINECO  and the  Pro-CNIC Foundation , and is a  Severo Ochoa Center of Excellence (MINECO  award  SEV-2015-0505 ).
Statistical analyses The proportion of ponds containing pumpkinseed was compared between the treatments (one stocking event &two stocking events) and control ponds using a Chi Square test. Based on individuals captured using the same technique (i. e. angling, n = 40), difference in the body length and age of pumpkinseed was compared between control and treated ponds (one stocking event & two stocking events) using non-parametric Wilcoxon rank sum test. All statistical analyses were performed using R 2. 11R (R Development Core Team, 2011). The software FiSAT II was used to perform non-linear estimation of growth parameters (the curvature parameter of the von Bertalanffy Growth Function (K) and the asymptotic length (L∞) from length-at-age data (i. e. total length (± 1 mm) and age (years) determined from scales reading; no pumpkinseeds larger than 70 mm TL were captured).
Acknowledgments We are grateful to the  DREAL (Directions Régionales de l'Environnement, de l'Aménagement et du Logement) Aquitaine -Limousin -Poitou-Charentes , the  SyRVA  and the  Agence de l'Eau Loire-Bretagne  for financial contributions. JC was supported by the  ERADINVA  project (Convention  ONEMA UPS 2017/019 ).
TKD/EBSD datasets were then rotated to have the 0001 Al2Si2Eu plane-normal at the centre of the pole figure, i. e. at the normal direction (ND), to find a further orientation relationship perpendicular to the (0001) Al2Si2Eu lattice plane. Two mutually perpendicular orientation relationships between the crystals define a fixed three-dimensional orientation relationship. Figure  5  shows an example of three Si crystals next to an Al 2 Si 2 Eu phase which fulfil the 011 Si //0001 Al2Si2Eu orientation relationship (Fig. 5(b, c, d, e )). In this condition, pole coincidences depicting parallel plane-normals between the low index {011}, {111} Si plane-families and the {6710} Al 2 Si 2 Eu plane-family were determined (Fig. 5(c, d, e )). The simultaneous orientation relationships 011 Si //0001 Al2Si2Eu and 111 Si //6710 Al2Si2Eu or 011 Si //6710 Al2Si2Eu describe a fixed growth orientation between Si and Al 2 Si 2 Eu which is not present in the case of Si and Al 2 Si 2 Yb.
04 µm) for 2 hours. TEM and APT samples were prepared in a dual-beam focused ion beam/scanning electron microscopy workstation (FIB/SEM) (Helios NanoLab 600 ™ , FEI Company, USA). APT specimen of the eutectic Si phase were prepared by the phase selective sample preparation method described in  60. After lift out and thinning of the samples, a low energy milling at 2 kV was performed to minimize gallium induced damage  61. An EDAX Hikari detector within the FIB/SEM workstation was used to record EBSD and TKD data using an electron beam of 20 kV/22 nA and 30 kV/5. 5 nA, respectively. Al 2 Si 2 Eu and Al 2 Si 2 Yb crystal structures from literature  [32] [33] [34]  were added to OIM Data Collection 7 database by means of the built-in structure creation wizard. Post-processing of the data was performed using the OIM Analysis software (EDAX).
Results are shown in the form of pole figures and inverse pole figure maps. In case of the TKD measurement shown in Fig. 5 , a pseudosymmetry clean-up followed by a grain dilation was performed for visual representation. Pole figures were calculated from uncleaned data. Conventional EBSD scans in reflection mode yielded better pattern quality. Data points for pole figure calculation were extracted from uncleaned raw data using the grain highlight function of OIM Data Analysis software. This allows a point-and-click selection of data points belonging to a continuous grain based on a misorientation threshold (here 5°). Data points from the intermetallic phase (Al 2 Si 2 Eu or Al 2 Si 2 Yb) and the neighbouring Si phase were highlighted and used for the calculation of pole figures. No clean-up was used.
Acknowledgements This work has been co-funded by the  European Regional Development Fund (ERDF). The Atom Probe was financed by the  DFG  and the  Federal State Government of Saarland  ( INST 256/298-1 FUGG ). J. Barrirero  acknowledges the  Erasmus Mundus Programme  of the  European Commission  within the  Doctoral Programme DocMASE  for financial support. J. Li  gratefully acknowledges the financial support from the  Major International (Regional) Joint Research Project  (No. 51420105005 ) and the  Overseas, Hong Kong, Macao Scholars Cooperative Research Fund  (No. 51728101 ) from  China. The authors want to thank the  Australian Centre for Microscopy and Microanalysis  for making the plugin AtomBlend for Blender available.
Method To achieve consensus among a panel of experts in the field, the Delphi method was used  [16]. According to this method, online questionnaires were presented to participants during several rounds. The goal was to obtain consensus by reducing the range of answers after each round. A systematic literature search was conducted using the PubMed database on "prostate" (and synonyms), "cancer" (and synonyms), "focal laser ablation" (and synonyms), "magnetic resonance imaging" (and synonyms), and "focal therapy" (and synonyms), with filters: Full text, to 2016/07/01, Humans, English. This search yielded 20 articles (Fig. 1 ), while only 10 articles were eligible for controversial topic selection. Panelists were selected based on the literature search and peer recommendations. The survey was conducted and presented in four subsequent rounds between 28 November 2016 and 14 June 2017, using online questionnaire software (www.
Neuropsychological examination and recruitment of PD subjects based on published criteria The complex neuropsychological testing evaluated global cognitive functions (Mini-Mental State Examination, MMSE), five cognitive domains  [2]  All five cognitive domains were inspected for cognitive decline. Subjects who scored below -1. 5 SD in two tests in one domain compared to normative data were categorized as having MCI. In addition, we applied the MCI criteria of Litvan et al. [15] , according to which subjects have concerns regarding a change in cognition, impairment in one or more cognitive domains, and preservation of independence in functional abilities. The cognitive domain z-scores were computed as the average z-scores of the tests included in the particular domain  [2]. Exclusion criteria for subjects included alcohol/drug abuse, hallucinations or visual misperceptions, and any diagnosed psychiatric disorder.
Resting-state fMRI data analysis Resting-state fMRI data were analyzed using MATLAB 2014a (MathWorks, Inc. ) and SPM12 software (https://www. fil. ion. ucl. ac. uk/spm/software/spm12/). The pre-processing of the functional data consisted of realignment and unwarping, normalization into standard anatomical space (MNI), and spatial smoothing using gaussian filter kernel with FWHM of 5 mm. We thoroughly checked for motion artefacts using framewise displacement (FD)  [46]. Subjects with more than 10% of scans with FD > 0. 75 mm or at least one scan with FD > 3 mm were excluded (seven HC, two PD-MCI; the mean FD was equal between groups, p > 0. 05, ANOVA). In addition, the functional time series of the remaining subjects were scrubbed  [46]  (scans that showed FD > 0. 75 mm were removed). Volumes preceding or following high-motion volumes were not removed, following the recommendation of Siegel et al.
ACKNOWLEDGMENTS The study was supported by the  EU Joint Programming initiative within Neurodegenerative Diseases , funded by the  Norwegian Strategic Research Council  ( APGeM-Preclinical genotype-phenotype predictors of Alzheimer's disease and other dementias , grant agreement number  3056-00001 ) and by the grant project of the  Agency of Health Research   AZV 15-33854A. The work was supported from  European Regional Development Fund-Project  " National infrastructure for biological and medical imaging " (No. CZ. 02. 1. 01/0. 0/0. 0/16013/0001775 ). We acknowledge the core facility MAFIL of  CEITEC  supported by the  MEYS CR  ( LM2015062   Czech-BioImaging ). Thanks to  Anne Johnson  for English language editing. Authors' disclosures available online (https:// www. j-alz. com/manuscript-disclosures/18-0834r1).
Cell death measurements using annexin V/propidium iodide staining Analysis of dying cells was carried out using annexin V (Annexin V-PE) and propidium iodide (PI) staining (BD Biosciences), according to standard protocols. This was followed by flow cytometry using a MACSQUANT MQ10 (Miltenyi Biotec, Santa Barbara, CA, USA). Results were analyzed using FlowJo software (v. 10, BD Biosciences).
Confocal microscopy Cells were fixed (20 min, 4% PFA) on polylysine-coated slides (0. 01%) and stained with DAPI using ProLong ® Gold Antifade Mountant with DAPI (Thermofisher, Waltham, MA, USA). Analysis by confocal microscopy (LSM-780, Zeiss) was performed in order to observe fluorescent signals in mRFP-EGFP-LC3 KARPAS-299 cells. Image analysis was performed using the ImageJ software (US National Institutes of Health, Bethesda, MD, USA).
Statistical analysis Results are presented as mean values±Standard Error of Mean (SEM) from at least three independent experiments, unless otherwise indicated. Determination of statistical significance was performed using the two-tailed Student t-test for side-by-side comparison of two conditions. Statistical analyses of xenografted tumor growths were performed using the two-way analysis of variance (ANOVA) followed by the Bonferroni test using GraphPad Prism 6 software (GraphPad, San Diego, CA, USA). For all tests, statistical significance is indicated by: *P≤0. 05; **P≤0. 01; ***P≤0. 001; ****P≤0. 0001.
To quantify airway luminal area, image acquisition software (NIS-Elements; Nikon) was used. Images of the airways were acquired every 2 s during the whole course of the experiment, starting 2 min before the addition of the first dose of contracting agent to allow for baseline measurements of the airway caliber. Airway constriction was then expressed as percentage of the L538 initial (baseline) area of the airway lumen. Per slice, one airway was measured. After the cumulative concentration-response curve was established, slices were thoroughly washed in fresh medium. Histochemistry. After a subsequent overnight washout, lung slices were placed in cassettes supported by biopsy foam pads, fixed in 10% formalin for 24 h, and embedded in paraffin. Paraffin sections (4 m thin) were cut from the slices to assess remodeling parameters.