dataesr/scientific-paragraphs-categorization huggingface dataset:
The cells were incubated for 30 min at 37 °C with 0. 1 μM green fluorescent calcein-AM to detect live cells and with 0. 5 μM red fluorescent ethidium homodimer-1 (EthD-1) to detect dead cells. The samples were imaged immediately with an Olympus IX51 microscope equipped with an Olympus DP30BW camera. For all staining experiments, four images per well were taken and used for image analysis. CellProfiler  [34]  software was used to perform image analysis as previously described  [39] , where the areas of calcein-AM-and EthD-1-positive cells in the images were determined from. 2. 10. Statistics. An independent t-test was used for normally distributed data. A p value less than 0. 05 was considered significant. If Bonferroni correction was applied, a p value less than 0. 025 was considered significant.
The relative expression of GAP43 was decreased after OGD and 24 h of reperfusion compared to after control culture, whereas no effect of ASCs on its expression levels was detected (Figure  6 (c)). In hiPSC-neurons, the relative gene expression of TUBB3 was not different between the OGD group and the   (c-g ) Area of dead SH-SY5Y-neurons (c) and (g) hiPSC-neurons. For quantification of live and dead cells, images were analyzed from two to three wells/group, and four images were taken from each well (one experiment for SH-SY5Y-neurons and representative data of two separate experiments for hiPSC-neurons). (d) Analysis (CyQuant) of the number of SH-SY5Y-neurons (n = 3/group, data from four independent experiments) and (h) hiPSC-neurons (n = 3/group, representative data of two separate experiments).
Acknowledgments We thank the iPS  Cell Core Facility ,  Tampere University , for providing undifferentiated hiPSCs and  Hanna Mäkelä ,  Eija Hannuksela ,  Sari Kalliokoski , and  Anna-Maija Honkala  for their technical assistance with cell maintenance and molecular biology analyses. The authors acknowledge the  Biocenter Finland (BF)  and  Tampere Imaging Facility (TIF)  for their services and  Outi Paloheimo  for technical assistance. We appreciate the technical help received from  Tanja Hyvärinen ,  Laura Keto , and  Sonja Nousiainen  ( Tampere University ). This work was supported by  Business Finland , the  Academy of Finland  (grant numbers  326588  to SM,  312414  to SN, and  330707  to SH), the  Competitive State Research Financing of the Expert Responsibility area of Tampereen Yliopisto Hospital , the  Finnish Cultural Foundation  (grant number  50191927  to SH and grant number  00190409  to MJ),  Päivikki ja Sakari Sohlbergin Säätiö (SH) , the  Tampereen Yliopisto Graduate School for Medicine and Life Sciences (MJ) , and the  European Union 's  H2020  programme (grant No  681044 ;  RESSTORE (Regenerative Stem Cell Therapy for Stroke in Europe) ).
Data extraction and analysis Titles and abstracts of the identified records were independently screened by a core team of reviewers according to the inclusion and exclusion criteria. Data were extracted by the same reviewers using a standardised template as direct quotations as reported in the articles (i. e. first-order constructs), pooled, refined and synthesised accordingly. A qualitative (thematic) analysis approach  [37]  using NVivo was performed to analyse the data; extracted data were coded, from which main and sub-themes emerged relating to the definition, concepts and components of intermediate care.
Data collection The Delphi surveys were open between the 1-15 July 2019 (first round) and 22 July-12 August 2019 (second round). Weekly reminders were sent to the participants. Using a fiveitem Likert scale (1, completely disagree to 5, completely agree), participants were asked to rate the statements generated in the scoping review. They were also given the opportunity to add comments. In Round 2, participants rated the revised statements. The virtual consensus meeting was conducted as two sessions (morning and afternoon) due to time zone differences between the participant countries. Meetings were moderated by a member of the core research group. Outcomes of the first session were shared with the participants of the second meeting prior to commencing the discussion. All statements and their agreement levels (expressed as percentage) were read out by another member of the core team, which started the group discussions.
Data acquisition, integration and handling were performed using a PC with the soware package EAGER-200 (Carlo Erba Instruments). Syntheses [3-CO-3,3-{k 2 -4,4 0 -(CH 3 ) 2 -2,2 0 -bipy}-closo-3,1,2-RuC 2 B 9 H 11 ] (RuCB1). [3,3,3-(CO) 3 -closo-3,1,2-RuC 2 B 9 H 11 ] (0. 20 g, 0. 63 mmol) was combined with 1 equivalent of 4,4 0 -dimethyl-2,2 0bipyridine (0. 10 g, 0. 63 mmol) and 2 equivalents of Me 3 NO (0. 10 g, 1. 26 mmol) in a Schlenk and MeCN (40 mL) was added to the reactants. The reaction mixture was stirred for 24 hours at room temperature. Aer that, the solvent was removed in vacuum and the residue obtained was chromatographed in silica gel using as eluent a mixture of dichloromethane and nhexane (4 : 1). Extraction yielded a bright canary yellow band that was removed from the column with an increased proportion of the n-hexane phase.
X-ray structure analysis The measurements were carried out on a BRUKER SMART APEX CCD diffractometer using graphite-monochromated Mo Ka radiation (l ¼ 0. 71073 Å) from an X-ray Tube. For RuCB1 the measurement was made in the range 1. 836 to 28. 363 for q. Hemi-sphere data collection was carried out with u and 4 scans. A total of 13 262 reections were collected of which 4534 [R(int) ¼ 0. 0272] were unique. For RuCB2 the measurement was made in the range 2. 237 to 28. 386 for q. Full-sphere data collection was carried out with u and 4 scans. A total of 35 439 reections were collected of which 5582 [R(int) ¼ 0. 0317] were unique. The structures were solved by the dual-space algorithm and rened by full-matrix least-squares methods on F 2. The nonhydrogen atoms were rened anisotropically. The H-atoms were placed in geometrically optimized positions and forced to ride on the atom to which they are attached, except the carborane B-H and C-H, which were located in the difference Fourier map and rened freely.
Acknowledgements Centro de Química Estrutural and Centro de Ciências e Tecnologias Nucleares acknowledge Fundação para a Ciência e Tecnologia (FCT)  for the Projects  UIDB/00100/2020  and  UID/ MULTI/04349/2013 , respectively. This work was also funded in the scope of the project  PTDC/QUI-QIN/28662/2017  (FCT). R. G. Teixeira  thanks  FCT  for his Ph. D. Grant ( SFRH/BD/135830/ 2018 ). A. Valente acknowledges the  CEECIND 2017 Initiative  ( CEECIND/01974/2017 ) and the  COST Actions 17104 STRAT-AGEM  and  CM1302  (SIPs) ( European Cooperation in Science and Technology). C. Viñas  thanks  MINECO  ( CTQ2016-75150-R ) and  Generalitat de Catalunya  ( 2017 SGR 1720 ) for the nancial support.
A second group consisted of individuals with no apparent clinical symptoms of cognitive decline or dementia (control group-CTL). The final two groups consisted of individuals diagnosed with Mild Cognitive Impairment (MCI) who either remained cognitively stable throughout the follow-up term of the study (sMCI) or converted to a clinical AD diagnosis at a later study visit (cMCI). All samples that underwent metabolic phenotyping were collected at a single time point (baseline assessment). Metabolic phenotyping was completed using a complementary dual-platform approach consisting of both proton nuclear magnetic resonance spectroscopy ( 1 H-NMR) and ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) to ensure comprehensive metabolite coverage. Critically, in order to address the challenge of rich metabolic datasets, where analytical variables far exceed the numbers of samples available to study, we applied a novel approach consisting of metabolomic dataset normalisation followed by a feature reduction process that only selected those metabolites which associated with a metabolic regulatory genetic element-a technique known as Quantitative Trait Loci for metabolites (mQTL).
Also, to account for the imbalance between the diagnostic classes, with the prevalence of AD and CTL classes over sMCI and cMCI classes, we applied re-sampling. First, we analysed all data in relation to the four original diagnostic categories (AD/CTL/cMCI/sMCI). Then, hypothesising that cMCI would be most similar to AD and sMCI most similar to control, we performed binary over-sampling by creating AD + cMCI and CTL + sMCI groups. Finally, we applied the under-sampling by using AD and CTL groups only. As the model trained on 1542 metabolic features together with sample covariates and only two diagnostic classes (AD and CTL) gave the lowest prediction error, it was selected as the final model (Fig. 2 ). To further validate the performance of the final model, MCI samples were used. The model proved effective in separating MCI samples from individuals who subsequently converted to dementia (cMCI) from those who remained stable (sMCI), finding that 82.
Annotated metabolites. Note (*) signifies isomers that cannot be differentiated using mass spectrometry fragmentation data. Column headers: Assay-metabolomic assay; #-a number of metabolic features in the dataset; PIC-Permutation Importance Score from Random Forest algorithm showing the importance of metabolite for classification purpose; F (3, 471)-ANOVA results (MANOVA in case of multiple metabolic features) presented as F-statistic; p-value-ANOVA (MANOVA in case of multiple metabolic features) results presented as adjusted p-value; CTL vs AD, CTL vs cMCI and CTL vs sMCI-post hoc tests results presented as adjusted p-value. In the last four columns, Scientific Notation is used due to the presence of very small numbers. nucleoside with adenosyl moiety". We were not able to identify its structure definitively in this analysis but the mass spectrometric data provided the molecular formula C10H11N5O3, and fragmentation data indicated the presence of adenosyl moiety in the molecule (some details regarding structure elucidation effort are presented in the "Supplementary Document" "Metabolite_Annotation_details.
Discussion Previous studies have reported data suggesting that a panel of metabolites circulating in blood was able to predict incipient AD with very high degrees of accuracy  51, 52  , raising considerable hopes for finding pre-clinical AD biomarkers in blood. Subsequent studies using a similar, if not identical approach, in multiple, larger cohorts failed to replicate these findings  [53] [54] [55]. Other studies have reported metabolic and lipidomic differences in blood from people with disease compared to age-matched controls with various degrees of power, success and outcome  [56] [57] [58] [59]. However, none of these studies have been unequivocally replicated. One of the limitations of studies with high dimensionality datasets and relatively small numbers of samples is susceptibility to over-fitting.
With increased throughput and lower cost of metabolic assays, such larger studies will become possible in future. In the absence of studies with large sample size, one approach to combat the limitation of high dimensionality in molecular studies is to reduce the dimensionality of the data. To achieve this, here we used the mQTL approach. In doing so, we provide for the possibility of a degree of validation in other, much larger dataset derived from genetic associations studies, also enabling the inference of a degree of causality when an association is discovered. While this work requires replication, this finding holds promise for biomarkers in urine-arguably the most readily available biomarker fluid. Using this mQTL targeting approach, we show a highly significant association of a relatively small set of 32 metabolic features with AD.
While the strengths of the study lie in the mQTL approach and the acquisition of a large amount of metabolic phenotyping data, there are undoubtedly limitations to consider. First, although large compared to previously reported urine studies, the analysis remains vulnerable to over-fitting and bias as the number of features is three times that of the number of samples, even following the mQTL based feature reduction. Clearly, larger and independent datasets are necessary to replicate the findings we report here. This could be achieved using a targeted quantitative mass spectrometry assay, specifically designed to quantitate the metabolic pathways of the key metabolites identified here. This would both help validate the findings and provide greater insight into pathway mechanisms and the networks involved.
Secondly, the cohort used here, lacks specific diagnostic biomarkers (such as PET or CSF measures) indicative of pathological load and the diagnostic categorisation rests on experienced clinician assessment together with systematic assessment by a research worker, albeit using a widely tested and proven methodology. Ideally, data would also be collected on diet and life-style factors such as exercise and other environmental exposures together with detailed assessments of co-morbid and prior medical history and medication use. Such exploration of factors beyond diagnostic category that might influence the metabolic profile will be important topics for future studies. Lastly, by focusing on the metabolites with an associated genetic variant, we may exclude metabolites that have a significant association with AD but are largely influenced by the environment, lifestyle and not as strongly by genetics.
Study participants. The data and biospecimens were collected (AddNeuroMed/Dementia Case Registry (ANM/DCR) cohort) in European, multi-site study, public-private partnership  [21] [22] [23]. The cohort includes participants with established AD, MCI and normal cognition who were evaluated using well-established systematic interviews for diagnosis. Formally, the diagnoses are not pathologically confirmed or supported by specific biomarkers of pathology, but it has been shown that this diagnostic method is highly predictive using MRI scans  21  and subsequent post-mortem classification  75. In addition, all disease categories, and conversion to dementia from MCI, were diagnosed by an experienced clinician according to criteria as described below. Briefly, the inclusion and exclusion criteria for study groups were as follows.
All samples were collected under human participant research protocols, including informed consent containing the clause necessary for allowing bio-specimens and data sharing abroad. Blood and urine were collected at baseline and stored at -80 • C. Genetic data from blood samples were obtained using the Illumina 610-Quad chip in two different batches as previously described  24  , while the third batch was obtained using HumanOm-niExpress 24 v1. 1. For this study morning (non-first void) urine samples were collected at baseline assessment and were subjected to metabolic profiling analysis via UHLC-MS and by 1 H NMR spectroscopy (561 and 575 samples respectively) (Table  1 ). 1 H NMR metabolic phenotyping. Samples were analyzed by  1  H-NMR spectroscopy in-line with previously published standard protocols for the study of human urine samples  76.
UHPLC-MS metabolic phenotyping. UHPLC-MS analysis of urine samples was performed as previously described  78  utilizing a combination of reversed-phase chromatography (RPC) and hydrophilic interaction chromatography (HILIC). UHPLC was performed using Waters Acquity UPLC systems (Waters Corp. , Milford, MA, USA), coupled to Waters Xevo G2-S QTOF mass spectrometers (Waters Corp. , Wilmslow, UK) via Z-spray electrospray ionization (ESI) sources. RPC separations were paired with both positive and negative ion mode detection (generating URPOS and URNEG datasets respectively), while the HILIC separation was paired with positive ion mode detection only (UHPOS). All UHPLC-MS datasets underwent feature extraction using Progenesis QI 2. 1 software (Nonlinear Dynamics, Newcastle, UK) as previously described, and feature filtering was performed using previously described quality control materials  78.
The R package MatrixEQTL  80  was used for QTL mapping by modelling the effect of genotype as additive linear including covariates to account for: age, sex, data collection centre and cohort. The null-hypothesis, "there is no QTL effect for the metabolic feature concentration", was tested against the alternative, "there is QTL effect between SNP and metabolic feature concentration", for each pair of meta-bolic feature and SNP. We corrected results for multiple comparisons by calculating q-value using the Benjamini-Hochberg procedure  81. The number of metabolic features and SNPs found to be associated using stringent q-value threshold 0. 01 is presented in Table  3. Data preparation For mQTL analysis. UHPLC-MS metabolic datasets UHPOS, URPOS and URNEG were normalised with EigenMS method 82 that removes bias of unknown complexity from this type of metabolomics experiment at the same time preserving known bias, diagnoses in the case of this study.
Subsequently, both EigenMS processed UHPLC-MS data, and raw one-dimensional 1 H NMR metabolic data were normalised using the quantile normalisation  83  method to make metabolomics data suitable for the QTL mapping. Imputation and quality control of genomics data. After quality control using PLINK  84  , genomics data from separate batches were assembled and remapped to "hg19-build37" reference genome. The imputation was performed with IMPUTE2 software  85. Since SNPs with low minor allele frequency are non-informative and have a potential of creating spurious findings, the imputation results were converted into matrix form and filtered using minor allele frequency threshold 10%. The final genomics matrix includes 12,105,785 SNPs. The results of population stratification indicated biases by cohort and data collection centres (Supplementary materials Figures  S1  and S2 ).
As the comparison criteria, we used the Out-Of-Bag (OOB) errors which is a standard approach for Random Forests. OOB error is a method of measuring the prediction error utilising bootstrap aggregation to subsample data used for training. It helps to avoid the need for an independent validation dataset  89. Results in the form of OOB errors of the RF models built for the different combinations of feature set and diagnostic groups are shown in Fig. 2. Each model was repeated ten times, using an increasing number of trees per run. The binary classification AD/CTL gives the best OOB errors for all four feature sets: the feature set A-0. 0325, B-0. 0404, C-0. 0363 and D-0. 0292, correspondingly. The feature set D, metabolic features and covariates, and binary classification AD/CTL, Alzheimer's disease and healthy control samples only, after the tuning of RF parameters, gave the OOB predicted error 0.
Metabolite annotation. For metabolite identification features of interest derived from the UHPLC-MS datasets first underwent correlation analysis using an R script developed in-house. This enabled the observation of co-eluting adducts and in-source fragments that are characteristic of metabolites. Further structural data were obtained via the use of high-resolution accurate mass to charge ratio (m/z) values and collision-induced dissociation (CID) fragmentation patterns. CID experiments were completed at a range of collision voltages at 5 V step intervals (5-45 V). The front quadrupole of the QTOF MS system was engaged to select for the feature of interest prior to CID. Chromatographic retention time matching to in-house standards was also completed where the standard was available for purchase.
Acknowledgements The work was supported by the  Innovative Medicines Initiative  ( IMI) Joint Undertaking under EMIF Grant  agreement No. 115372 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution, and by the  Medical Research Council and National Institute for Health Research  [Grant number  MC_PC_12025 ], and infrastructure support was provided by the  National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). AddNeuroMed was a pilot project for the  Innovative Medicines Initiative  funded through the  EU   FP6 programme. The  UK Dementia Research Institute (DRI)  is an initiative funded by the  Medical Council, Alzheimer's Society and Alzheimer's Research UK.
Statistical analyses All analyses were performed on the safety population, defined as all patients who received at least one dose of study drug. Patients were analyzed according to the study drug consumed (i. e. as treated). TEAEs were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 4. 02. TEAEs must have occurred after the start of study treatment and within 30 days following the final dose of study treatment. Descriptive statistics (number and percentage) were used to summarize the safety data by treatment arm. Percentages were based on the number of subjects followed for TEAEs at a given month or period. Months were defined as 28-day intervals. No inferential statistics were performed. Statistical analyses were performed using SAS software, version 9.
Trial results The first patient was enrolled in August 2013. A total of 553 patients were enrolled-203 patients in the gBRCAmut cohort and 350 patients in the non-gBRCAmut cohort (Fig. 1 ; Table  1 )  [12]. In the gBRCAmut cohort, median PFS was 21. 0 months with niraparib versus 5. 5 months with placebo (hazard ratio [HR] 0. 27, P < 0. 0001). In the overall non-gBRCAmut cohort, median PFS was 9. 3 months with niraparib versus 3. 9 months with placebo (HR 0. 45, P < 0. 0001). The database for the current efficacy analysis was locked on June 20, 2016, and follow-up for OS is ongoing. The median duration of follow-up at the time of data cutoff was 16. 9 months for patients in the overall population.
A small percentage of patients (<5% by month or time interval) experienced grade ≥ 3 hypertension that persisted past 6 months of receiving niraparib. This is consistent with the known safety profile of niraparib. Renal and hepatic toxicities were rare, and the incidence of AML/MDS was consistent with that of other PARP inhibitors. The ENGOT-OV16/NOVA trial data support that long-term use of niraparib is safe. Similar long-term safety results were reported for patients receiving olaparib  [14]. In Study 19, 24% of olaparibtreated patients received treatment for ≥2 years. Safety profiles were also similar, with most adverse events occurring within the first 6 months and few reports of TEAEs after 6 months. Direct comparison between trials cannot be made for multiple reasons, including differences in patient population, sample size, and trial design.
We then calculated an age/depth relationship using smooth spline interpolation in the Rcode package "Clam" version 2. 2  (Blaauw, 2010). The first 69 cm of the sediment sequence from ROB15-P5 core covered the last 9. 98 kyr BP (Fig. 2 ). The mean sedimentation rates estimated from this sediment record at 0. 11 mm yr -1 varied between 0. 28 and 0. 03 mm yr -1. The higher sedimentation rate corresponds to the upper part of the core. A major change in the sedimentation rate occurred around 2125±85 BP at 35 cm corresponding to the change in color in Unit II. Above this depth, the mean sedimentation rate was around 0. 19 mm yr -1 and below this depth, around 0. 053 mm yr -1 (Fig. 2 ). In the ROB15-P5 core, sediment accumulation rates varied between 2. 10 -7 g m -2 yr -1 and 1. 310 -8 g m -2 yr -1 and presented the same variations as the sedimentation rates, except in the older part of the core due to higher density of the sediment.