dataesr/scientific-paragraphs-categorization huggingface dataset:
APOE genotyping For the entire EMIF-AD MBD cohort, APOE genotyping data from the local genetic analyses were available for 1121 (91%) individuals. Central genetic analyses were performed at Lübeck University, Germany for 805 DNA and 148 whole blood samples. From the blood samples, DNA was extracted using the QIAamp® DNA Blood Mini Kit (QIA-GEN GmbH, Hilden, Germany) resulting in 953 DNA samples, of which 926 passed quality control. Genome-wide SNP genotyping was performed using the Infinium Global Screening Array (GSA) with Shared Custom Content (Illumina Inc. ). APOE genotypes were determined either directly (rs7212) or by imputation (rs429358). For 80 samples for which no local APOE genotype was available, and for 45 mismatches between local and GSA-derived genotypes, the APOE genotype was determined using TaqMan assays (ThermoFisher Scientific, Foster City, CA, USA) on a QuantStudio-12 K-Flex system.
MRI quantitative analysis Good quality 3D T1 images (n = 850) were uploaded on the N4U platform (https://neugrid4you. eu/) for automated quantitative processing. Subcortical volumes, cortical thickness and surface area measures were estimated from 3D T1 MRI using Freesurfer (version 5. 3. 0, https://surfer. nmr. mgh. harvard. edu) as previously described  [30]. All segmentations were visually inspected. We excluded data from 20 subjects for subcortical volumes (five due to complete failure of the algorithm and 15 due to segmentation errors) and from 75 subjects for cortical thickness and surface area (five due to complete failure of the algorithm, 66 due to segmentations errors of the cortical ribbon and four for other failures). Subcortical volumes were normalized by total intracranial volume (TIV).
Univariate analysis Univariate statistical analyses were performed in R (version 3. 3. 1). Comparisons of clinical characteristics between amyloid positive and negative subjects within each diagnostic group were performed using independent t tests or Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables. Baseline comparisons in quantitative MRI measures between groups were performed with linear mixed models (continuous outcome measures) (lme4 package, version 1. 1-12; lmerTest package 2. 0-36), mixed effects ordered logistic regressions (ordinal outcome measures) (ordinal package, version 2015. 6-28) and mixed effects logistic regressions (dichotomous outcome measures) (lme4 package). In each model, we entered amyloid status (negative, positive) and diagnosis (CN, MCI and AD) and their interaction as fixed effects.
That means we adjusted weights of each SVM feature inversely proportional to amyloid positive versus negative frequencies. Machine-learning approach We used the python Scikitlearn library (version 0. 19. 1) to perform SVM classification  [34]. To prevent overfitting (i. e. the classifier works perfectly on the training data, but is poorly generalizable to new data), we performed feature relevance evaluation and dimensionality reduction using a tree-based feature selection approach with a nested 10-fold cross-validation design  [35, 36]. This was performed separately within each subgroup (CN, MCI and whole sample). The nested cross-validation consists of an inner loop for model building and parameter estimation, and an outer loop for model testing. Consequently, the dataset was divided into two parts: a training plus validation subset and a test subset.
In the inner loop, SVM models were trained with varying SVM hyper-parameters (i. e. cost parameters C and kernel function) based on a grid search, and a feature selection was performed using classification trees. The validation set was used to determine the SVM hyper-parameters over the grid of possible values. The performance of the resulting model, with optimized SVM hyper-parameters and features, was subsequently evaluated on the test set in the outer loop. For this outer loop, we used a 10-fold cross-validation scheme so that the data were divided into 10 equally sized parts. Nine of these were used as the training/validation set and one as the test set, and the 10 parts were permuted in each iteration of the outer loop so that each one was used for testing once. Finally, the SVM results were averaged over the 10 folds to estimate the predictive power of the proposed model on the whole dataset.
Feature selection As the input for the classifier, we used demographic information, neuropsychological information, APOE ε4 genotype and MRI measures (visual ratings, subcortical volumes, regional cortical thickness and regional surface area measures). To combine information measured on different scales, continuous demographic and MRI measures were normalized to z-scores. In the adopted treebased feature selection strategy, the Gini index was used to measure the relevance of each feature  [37]. Features with a Gini index above the mean were kept, others were discarded. The complete list of features considered and selected, in the whole dataset and for CN and MCI separately, is reported in Additional file 1: Table  S8. Performance evaluation To assess the performance of the classifier, we computed the averaged receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity and accuracy for the testing datasets.
Demographic and cognitive comparisons We included 810 subjects divided over three diagnostic groups: CN (n = 337), MCI (n = 375) and AD dementia (n = 98). Within the CN group, 92 (27%) subjects were amyloid positive, in the MCI group 235 (63%) and in the AD-dementia group 95 (97%). Demographic and clinical data according to diagnosis and amyloid status are presented in Table  1. The amyloid positive MCI subjects were older and had lower cognitive scores compared to the amyloid negative MCI subjects. In CN, there were no differences in age or cognition between amyloid positive and amyloid negative subjects. Amyloid positive subjects were more often APOE ε4 carriers in both the MCI and CN groups.
Classifier results from demographic information combined with only neuropsychology (green), or MRI measures (blue) or APOE ε4 genotype (yellow). ROC significant differences assessed with DeLong's test. *p < 0. 05, **p < 0. 001, ns not significant. APOE, apolipoprotein E, MRI magnetic resonance imaging group, and in line with a previous study in MCI  [40]. In that study, a SVM classifier to predict amyloid pathology in subjects with MCI was also developed. Using cognitive data, hippocampal volume, APOE ε4 genotype and peripheral blood protein markers from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, they obtained AUC = 0. 80 for predicting amyloid pathology in subjects with MCI. In contrast to a previous study  [12] , we did not find that combining MRI markers with APOE ε4 genotype improved prediction of amyloid pathology in MCI over only including APOE ε4.
However, as already discussed, this also increased generalizability. Second, not everyone had the same measure of amyloid pathology. When possible, we used centralized analysis of the CSF Aβ 42/40 ratio to identify amyloid positivity, which has been shown to correlate highly with PET measures of amyloid pathology  [46, 47]. For data from one cohort, we only had amyloid PET data available. Although CSF and PET measures are usually in good agreement, some studies have suggested that CSF values might become abnormal earlier than PET  [48, 49]. Finally, the same dataset was used to train and test the SVM classifier. Although nested k-fold cross-validation grants good generalizability of the SVM model  [36] , studies in independent datasets are needed to further validate our results.
Conclusions Amyloid pathology is associated with structural MRI changes in AD typical regions in CN subjects and in subjects with MCI. We developed a classifier that can predict amyloid pathology at a single subject level using a combination of easily obtainable, non-invasive measures. Our results are of interest for trial designers who intend to recruit a large number of amyloid positive subjects. Implementing pre-screening procedures consisting of simple, non-invasive tests could substantially reduce screening failure rates. In future studies, the classifier might be improved by adding data from other minimally invasive tests, such as blood proteins and genetic markers  [40]. In the EMIF-AD MBD study, plasma proteomics and metabolomics, and genomics and epigenomics, will also be analysed.
Funding The present study was conducted as part of the  EMIF-AD  project which has received support from the  Innovative Medicines Initiative Joint Undertaking  under EMIF grant agreement n°115372, resources of which are composed of a financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and an  EFPIA  companies' in-kind contribution. The DESCRIPA study was funded by the  European Commission  within the  Fifth Framework Programme  ( QLRT-2001-2455 ). The EDAR study was funded by the  European Commission  within the  Fifth Framework Programme  (contract #  37670 ). The VUmc Alzheimer Center is supported by  Stichting Alzheimer Nederland  and  Stichting VUmc  fonds, and the clinical database structure was developed with funding from  Stichting Dioraphte.
The Leuven cohort was funded by the  Stichting voor Alzheimer Onderzoek  (grant numbers # 11020 , # 13007  and # 15005 ). The GAP study is supported by grants from the  Department of Economic Promotion, Rural Areas and Territorial Balance of the Provincial Government of Gipuzkoa  ( 124/16 ), the  Department of Health of the Basque Government  ( 2016111096 ), the  Carlos III Institute of Health  ( PI15/00919 ,  PN de I + D + I 2013-2016 ),  Obra Social Kutxa-Fundazioa  and anonymous private donors. The  Gothenburg MCI study  was supported by the  Sahlgrenska University Hospital, Gothenburg, Sweden. The Lausanne cohort study was supported by a grant from the  Swiss National Research Foundation  to JP ( SNF 320030_141179 ). The research at VIB-CMN is funded in part by the  University of Antwerp Research Fund.
RV is a senior clinical investigator of the  Flemish Research Foundation (FWO). CVB is partly supported by the  Flemish government-initiated Flanders Impulse Program on Networks for Dementia Research (VIND)  and the  Methusalem Excellence Program , the  Research Foundation Flanders (FWO)  and the  University of Antwerp Research Fund, Belgium. FB is supported by the  NIHR UCLH Biomedical Research Centre. HZ is supported by the  Dementia Research Institute at UCL  and is a Wallenberg Academy Fellow. SJV receives research support from  ZonMw. VW has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement no. 666992.
Trichostatin A downregulates Notch3 protein levels in T-ALL cells Previous studies demonstrated that acetylation regulates Notch3-ICD stability in Notch3 transgenic mice and in one human T-ALL cell line  [16]. To validate and broaden these findings, we initially investigated the effects of HDACi on Notch3 in T-ALL cells treated in vitro with the pan-HDAC inhibitor Trichostatin A (TSA) at 0. 5 μM, a concentration selected on the basis of published data  [16, 17]. After 16 h of treatment, whole-cell lysates were extracted and analyzed by western blot. Accumulation of acetylated α-tubulin and reduction of c-Myb, two known targets of HDACi  [17] , were used as read-out of TSA activity in these experiments. Interestingly, TSA decreased Notch3 FL levels in all the three cell lines tested (Fig. 1a ); Notch3-ICD levels were also reduced (not shown).
Immunofluorescence analysis The protocol for immunofluorescence analysis has been reported elsewhere  [35]. Primary antibodies used included anti-Notch3 mAb (1:100; Abcam) and anti-LAMP2 mAb (1:150; Novus Biologicals, Littleton, CO, USA), both incubated overnight at 4 °C. Confocal microscopy was carried out on a Zeiss LSM 510 microscope (Zeiss, Jena, Germany) using a ×63 oil immersion objective (NA = 1. 4). n = 21 DMSO-treated cells and n = 24 TSA-treated cells were analyzed by scanning, with an optical slice of 1 μm and average of 15 optical slices/cell. Images were acquired with a resolution of 1024 × 1024 pixels. The colocalization coefficient (which indicates the extent of localization of Notch3 protein in LAMP2-positive vesicles) was calculated, using the Zeiss Histogram software tool, as the ratio between the Notch3/LAMP2 double-positive pixels and total number of Notch3-positive pixels.
Since then researchers have witnessed the rising in APCs; circumstance that creates more difficulty for a Latin-American author to publish. Besides, as it was said in  (Katie, 2016) : the number of article processing charges paid doubled between 2013 and 2014. The average APC increased by 6% in those years, a rise well above the cost of inflation. Publishers' APC costs are converging to a more uniform price range, although they still vary widely. Journals with low APCs are raising their prices, perhaps to avoid being perceived as low quality following expectations set by the Finch report  (Finch, 2012). As the asymmetries between the "commercial" and the "open" are extended, the corporations impose rules that restrict access to knowledge in multiple ways. The paper in  Elizabeth & Denise (2016)  demonstrated it by analyzing the Sherpa/ROMEO database: "Just as there is an upward trend among these 107 publishers in the number of publishers allowing some form of self-archiving, there is also a year-on-year rise in the number of restrictions and conditions constraining the right to self-archive and the offer of paid open access options… Restrictions around when a paper may be self-archived grew 1000%.
Redalyc architecture On the base of the structure shown there are peer-reviewed journals along with its publisher institutions who sign an agreement with Redalyc in order to allow the electronic distribution of metadata and the publication online of the full-text articles. For a journal to be indexed in Redalyc must pass a rigorous selection process based on quality criteria  (Redalyc, 2018)  including among other mandatory requirements to carry a peer review process and to publish at least 75% of original content, joint with criteria like exogeneity of referees and editorial board, efficiency of editorial processes, editorial, visibility and technological practices, periodicity compliance, etc. After getting an internal result the International Advisory Board, which is composed by experts from diverse fields and different countries, is asked for a qualitative assessment.
What is more, Redalyc provides homepages not only for journals but also for countries, authors, institutions and areas of knowledge with their journals collection, search engines, advanced filters, different data visualizations and metrics. Regarding visibility, Redalyc works with search engines, libraries, directories and social media to disseminate broadly the scientific content and to improve its discoverability. As well as with repositories and aggregators through interoperability protocols like OAI-PMH. Besides, the signed agreements between Redalyc and journals allow Redalyc to populate institutional repositories too, helping repositories to automatically integrate metadata of articles corresponding to the institutional output; for example, in the case of the National Autonomous University of Mexico UNAM, Mexico Redalyc is able to send more than 10 thousand articles at metadata level to the institutional repository at UNAM.
In different dimensions that is the case of more than 10 thousand institutions which have research output published in the journals indexed by Redalyc that can take advantage of Redalyc's database to populate their repositories. The same happens with the scientific output at a country-level as shown below, data which can be used to strengthen national repositories. Redalyc has also developed a full integration with ORCID. In the ORCID Registry, authors can use a wizard created by Redalyc to connect their ORCID ID to works published in journals indexed by Redalyc. As well as the platform called RedalycAuthors enables researchers to create a scholar homepage, monitor article metrics and export works to an ORCID profile. Redalyc is one of the thirteen databases that have done a full integration with ORCID, including ResearcherID from Clarivate Analytics and Scopus from Elsevier.
This effort contributes to the insertion of Latin American authors in the global scientific conversation. Finally, Redalyc offers an alternative set of metrics, who are intended to answer issues, such as, institutional strengths, influential journals in a knowledge area, use of the knowledge generated by an institution, cohesion among institutions in terms of collaboration, trends in the internationalization of scientific output, among others. The Study of the Use, Assessment and Impact of the National Consortium of Scientific and Technological Information Resources of Mexico  (Scimago, 2017)  shows that Redalyc website is one of the most used sources by students to search for information in Mexico, preceded by CONRICyT (National Consortium of Scientific and Technological Information Resources)-which groups all subscription databases the country of Mexico is paying access for-, Scielo Mexico and the journals websites, despite the fact that Redalyc doesn't receive any national financial support while CONRICyT and Scielo Mexico are sustained by the National Council of Science and Technology CONACyT.
THE ASSOCIATION BETWEEN LIFE SPACE MOBILITY AND COGNITION IN OLDER ADULTS N. De Silva, S. Venkateshan, A. Kuspinar, McMaster University As people age, both mobility and cognitive function may decline at varying rates. This systematic review evaluated the relationship between cognition and life space mobility in older adults. Electronic databases PsycINFO, Embase, Ovid and CINAHL were searched using key terms including 'life space' AND 'cognition', yielding 163 abstracts for screening. Data were extracted from 31 peer-reviewed articles in English that included subjects over the age of 60, as well as quantitative measures describing the association between life space mobility and cognition. The range of correlation coefficients between life space mobility and the Mini-Mental State Examination were r= -0.
P yx and P zx reflect the helicity domains ratio, and P yy ( = -P zz ) and P yz ( = P zy ) reflect the magnetic structure shape projected to the plane perpendicular to the scattering vector. We observed magnetic reflections at (0, -q, -1/2) with q ≃ 0. 4 for 11 K ≤ T ≤ 14 K in the ICM1 phase, and at (0, -1/2, -1/2) below 11 K in the CM1 phase at P = 0. 2 GPa, as is evident from the temperature dependence of the intensity and the diffraction profiles shown in Fig. 2a, b. We used the polarization channel +x on incident and -x on scattered neutrons, which represents the total magnetic scattering, for measuring the temperature dependence of integrated intensity of each magnetic Bragg reflection in all measured pressures. The quality of these data with the signal-to-noise ratio of ∼1 was sufficient to allow the determination of P αβ on a reasonable time scale.
The cut samples were mounted in the HAC with the monoclinic a-axis (hexagonal [1 10]) vertical, in order to provide access to the monoclinic (0, K, L) (hexagonal (H, H, L)) reflections. The incident neutrons are polarized and monochromatized at the Heusler monochrometer. The incident wavelength 0. 85 Å was employed. The final neutron spin was analyzed with  3  He filter cells. The data has been corrected for the exponential decay of the 3 He polarization. The polarization matrices were calculated with the Mag2Pol program  69. Hybrid anvil cell. A sapphire anvil with culet sizes, 4. 2, 2. 7, or 2. 4 mm diameter, supported by CuBe or MP35N alloys were used as an anvil on one side. For the opposite side, a nonmagnetic diamond composite (with a SiC binder) and a WC with a nonmagnetic Ni binder were exployed up to 2.
The injection volume was 5 μL, and samples were stored at 7 • C in the autosampler before the injection. The duration of one analysis was 14 min. The detection was performed with a Q-EXACTIVE-Orbitrap mass spectrometer with a Heated Electrospray Ionization probe operated in the positive ionization mode. Full scans were acquired with a scan speed of 3. 7 scan/sec and a mass range from 50 to 700 u. m. a. (unified atomic mass unit) with a high resolution of 70 000. Data were identified and quantified (ng/g wet weight) using TraceFinder software according to the calibration solution (Calibration table: see Calibration.
Aminotransferase-coding genes expression RNA extraction, DNase treatment and reverse transcription: The -80 • C cell pellets were melted with 1 mL TRIzol reagent (Invitrogen, Cergy Pontoise, France) and broken with 0. 5-mm diameter zirconium beads (Biospec products, Bartlesville, OK) in a BeadBeater (Precellys, Saint Quentin en Yvelynes, France) using two 20-s mixing sequences at a speed of 6500 rpm. RNA extraction, DNase treatment and reverse transcription were done following protocols previously described  (Monnet, Back and Irlinger 2012). Real-time PCR and data analyses: Oligonucleotide primers were designed using LightCycler probe design software (v1. 0; Roche Applied Science, Mannheim, Germany) and synthesised by Eurogentec (Seraing, Belgium) (primers list, see Primers Table, Supporting Information).
Production of Intracellular metabolites and volatile compounds Cells were collected after 3 days of culture during the stationary phase to simulate what could be the condition at the end of cheese ripening when the availability of nutrients is lowered on the cheese surface. The growth parameters and biomass production of both strains were similar in our conditions (data not shown). Volatile compounds (underlined in the next sections) and intracellular metabolites produced by Gc918 and Gc203 after 3 days in minimal medium enriched or not with methionine were separated and analysed (boxplots representation for each molecule presented, see Boxplots, Supporting Information). Both strains had the same behaviour with an increase in metabolites and volatile compounds when methionine was added to the medium, with the highest increase in volatile compounds for Gc203.
V. DISCRETE SETS OF DATA In experiments or in Monte Carlo simulations of BM, the particle position is recorded at some discrete time moments such that one stores a given trajectory as a finite set of data. Here we analyze how the features unveiled in the previous Sections will change, if instead of continuous-time BM we rather use a picture based on a discrete-time random walk. Suppose that the time interval (0, T ) is divided into M equally-sized subintervals ∆ = T /M , such that the particle position is recorded at time moments t m = ∆m, m = 1, 2,. , M. We use the convention that at t = 0 the particle starts at the origin and focus on the behavior of a single-component PSD-the extension of our analysis over the general case of k components is straightforward but results in rather cumbersome expressions.
All the positive-ion mass spectra were acquired in the linear mode at an acceleration voltage of 20 kV in the delayed extraction mode. A signal-to-noise of 3. 0 was selected to define peaks, with a maximum of 100 peaks per spectrum. Spectra were automatically acquired with a mass/charge (m/z) ranging from 2000 to 20,000 Da using FlexControl and FlexAnalysis 2. 4 software (Bruker Daltonics). The x-axis of spectra represented the m/z ratio (in daltons) of ionized molecules, and the y-axis indicated the intensity (relative abundance) of these ions.