dataesr/scientific-paragraphs-categorization huggingface dataset:
31 once they are identified, as well as with other loci showing much stronger association with AD in particular APOE genotype strata (e. g. , MS4A6A/MS4A4A/ MS4A6E) or through interaction with APOE (e. g. , TMEM106B). Our study provides a firm genetic connection of AD to several other pathologically distinct disorders in which dementia is a cardinal or common characteristic. Research (ALF) between Stockholm County Council and the Karolinska Institutet, the Swedish Brain Foundation and the Swedish Alzheimer Foundation. Map position is based on 1000 Genomes database release GRCh37/hg19 assembly, February 2009. Significance threshold after multiple testing determined as 0. 05/292,000 exon probes = 1. 7x10 -7 ; ExprID: exon-specific probeset ID. AVEALL: average expression levels across 10 regions including cerebellum (CRBL), frontal cortex (FCTX), hippocampus (HIPP), medulla (specifically inferior olivary nucleus, MEDU), occipital cortex (specifically primary visual cortex, OCTX), putamen (PUTM), substantia nigra (SNIG), thalamus (THAL), temporal cortex (TCTX), and intralobular white matter (WHMT).
EADI This work was supported by the  National Foundation  for Alzheimer's disease and related disorders, the  Institut Pasteur de Lille  and the  Centre National de Génotypage. The Three-City Study was performed as part of a collaboration between the  Institut National de la Santé et de la Recherche Médicale (Inserm) , the  Victor Segalen Bordeaux II University and Sanofi-Synthélabo. The  Fondation pour la Recherche Médicale  funded the preparation and initiation of the study. The 3C Study was also funded by the  Caisse Nationale Maladie des Travailleurs Salariés ,  Direction Générale de la Santé ,  MGEN ,  Institut de laLongévité ,  Agence Française de Sécurité Sanitaire des Produits de Santé , the  Aquitaine  and  Bourgogne RegionalCouncils ,  Fondation de France  and the joint  French Ministry of Research/INSERM "Cohortes et collections de données biologiques" programme.
Italian sample collections: the Bologna site (FL) obtained funds from the  Italian Ministry of research and University as well as Carimonte Foundation. The Florence site was supported by a grant from the  Italian ministry of Health  ( RFPS-2006-7-334858 ) and grant  RF-2010-2319722. The Milan site was supported by a grant from the "fondazione Monzino". We thank the expert contribution of  Mr. Carmelo Romano. The Roma site received financial support from  Italian Ministry of Health , Grant  RF07-08  and  RC08-09-10-11-12. The Pisa site is grateful to  Dr. Annalisa LoGerfo  for her technical assistance in the DNA purification studies. Spanish sample collection: the Madrid site (MB) was supported by grants of the  Ministerio de Educación y Ciencia  and the  Ministerio de Sanidad y Consumo (Instituto de Salud Carlos III) , and an institutional grant of the FundaciónRamón Areces to the  CBMSO.
Washington University was funded by  NIH  grants,  Barnes Jewish Foundation  and the  Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the  MRC Prion Unit/UCL Department of Neurodegenerative Disease collection  was supported by the  UCLH/UCL Biomedical Centre and Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by  Lundbeck SA. The Bonn group was supported by the  German Federal Ministry of Education and Research (BMBF) ,  Competence Network Dementia and Competence Network Degenerative Dementia , and by the  Alfried Krupp von Bohlen und Halbach-Stiftung. The  GERAD1  Consortium also used samples ascertained by the  NIMH AD Genetics Initiative.
The  Kathleen Price Bryan Brain Bank at Duke University Medical Center  is funded by  NINDS  grant #  NS39764 ,  NIMH MH60451  and by  Glaxo Smith Kline. Genotyping  of the TGEN2 cohort was supported by  Kronos Science. The TGen series was also funded by  NIA  grant  AG034504  to AJM, The  Banner Alzheimer's Foundation ,  The Johnnie B. Byrd Sr. Alzheimer's Institute , the  Medical Research Council , and the state of  Arizona  and also includes samples from the following sites:  Newcastle Brain Tissue Resource  (funding via the  Medical Research Council , local NHS trusts and  Newcastle University) ,  MRC London Brain Bank for Neurodegenerative Diseases  (funding via the  Medical Research Council) ,  SouthWest Dementia Brain Bank  (funding via numerous sources including the  Higher Education Funding Council for England (HEFCE) ,  Alzheimer's Research Trust (ART) ,  BRACE  as well as  North Bristol NHS Trust Research and Innovation Department and DeNDRoN) ,  The Netherlands Brain Bank  (funding via numerous sources including Stichting  MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek ),  Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona.
We thank  Drs. D. Stephen Snyder  and  Marilyn Miller  from  NIA  who are ex-officio ADGC members. Support was also from the  Alzheimer's Association (LAF ,  IIRG-08-89720 ;  MP-V, IIRG-05-14147 ) and the  US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program. P. S. G. -H. is supported by the  Wellcome Trust ,  Howard Hughes Medical Institute , and  Canadian Institute of Health CHARGE AGES: The  AGES-Reykjavik Study  is funded by  NIH  contract  N01-AG-12100  ( NIA  with contributions from the  NEI ,  NIDCD and NHLBI) , the  NIA   Intramural Research Program ,  Hjartavernd  (the  Icelandic Heart Association) , and the  Althingi (the Icelandic Parliament ). ASPS/PRODEM: The  Austrian Stroke Prevention Study  and The Prospective Dementia Register of the Austrian Alzheimer Society was supported by  The Austrian Science Fond (FWF)  grant number  P20545-P05  (H.
Framingham Heart Study (FHS) : This work was supported by the  National Heart, Lung and Blood Institute's Framingham Heart Study  (Contract No. N01-HC-25195 ) and its contract with  Affymetrix, Inc  for genotyping services (Contract No. N02-HL-6-4278 ). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGAII) funded by the  Robert Dawson Evans Endowment  of the  Department of Medicine at Boston University School of Medicine and Boston Medical Center. This study as also supported by grants from the  National Institute on Aging :  AG08122  and  AG033193  (Seshadri). Drs. Seshadri  and DeStefano were also supported by additional grants from the  National Institute on Aging : ( R01 AG16495 ;  AG031287 ,  AG033040 ), the  National Institute of Neurological Disorders and Stroke  ( R01 NS17950 ), and the  National Heart, Lung and Blood Institute  ( U01 HL096917 ,  HL093029  and  K24HL038444 ,  RC2-HL102419  and  UC2 HL103010.
Acknowledgements This work was supported by  Collaborative Research Based on Industrial Demand  from the  Japan Science and Technology Agency , and  Grants-in-Aid for Scientific Research  No. 26246031  from the  Ministry of Education, Culture, Sports, Science, and Technology of Japan. We also gratefully acknowledge financial support from the  Renovation Centre of Instruments for Science Education and Technology at Osaka University, Japan. The authors are grateful to  Prof. Kaoru Minoshima  of the  University of Electro-Communications  and  Prof. Tetsuo Iwata  of  Tokushima University, Japan , for fruitful discussions. -24-Reference.
23 Although we did not identify any genome-wide significant differentially methylated probes in the blood study, when we focused on epigenetic changes in and around genetic loci already reported to be associated with AMD, we identified subtle but locally significant DNAm differences in the blood of NV patients near ARMS2, a top-ranked GWAS risk locus,  5  which we then replicated in an independent blood cohort from Baltimore. In the independent blood cohort, we found that the methylation level of one of the differentially methylated probes in ARMS2 correlated with the genotype of the ARMS2 risk SNP rs10490924. We then performed genome-wide methylation analyses of retina samples with and without AMD, and, using an integrative analysis of the peripheral blood and retinal tissue datasets, we identified consistent change in DNAm at the promoter of PRSS50, which we again replicated in both sample types.
Illumina 450K profiling of blood from individuals with and without AMD We performed an epigenome-wide association study on peripheral whole blood from 100 AMD (GA and NV) case-control trios (a subset of the Michigan AMD-MMAP cohort; dbGaP phs000182) using the Illumina HumanMethylation450 (450K) platform. Each trio (1 individual with bilateral GA, 1 individual with bilateral NV, 1 control individual) was matched for sex and age (<1 year difference between the 3 individuals in each trio). We included a series of technical control samples (including technical, biological, and spike-in replicates) and performed several established quality control steps (removing 2 samples with low median intensities, across-sample normalization, dropping probes susceptible to genetic variation, and exploring effects of cellular composition)  24  to ensure high-quality genomic data (Fig.
Illumina 450K profiling of retinal tissue The Illumina 450K platform was used to analyze retina from 9 eyes with gross and histological evidence of AMD (average age 88. 3 years) and retina from 9 control eyes (average age 83. 2 years) that did not come from individuals in the discovery blood cohort. We note high genome-wide concordance between mean methylation levels across the blood and eye DNAm data sets -59. 5% and 73. 3% of probes had mean proportion DNAm methylation levels (i. e. , Illumina "Beta" scale) within 0. 05 and 0. 1 comparing blood to eye, respectively, suggesting that many probes overall had similar absolute levels of proportion methylation across these 2 tissue types. Similarly, 76. 4% of probes were in the same general methylation level bin, where the bins were "unmethylated," containing CpGs with methylation proportions between 0-0.
Illumina infinium human methylation450 (450K) bead array DNA samples (1. 3 mg) were processed for the Illumina Infinium HumanMethylation450 Bead Array platform  24  (Illumina 450K) at the Center for Inherited Disease Research (CIDR; Johns Hopkins University) as per the manufacturers' protocols. All matched samples (GA-NV-control) were run within a single BeadChip array. A series of technical controls were incorporated in the experimental design: 1) lymphoblastoid cell lines (LCLs); inputs that were 2) 0%; 3) 50%; and 4) 100% methylated (included in each processing plate); and 5) 8 of the blood samples, which were run in duplicate. Data was deposited to dbGaP, http://www. ncbi. nlm. nih. gov/gap, MMAP-Methylation in AMD (phs000457. v1. p1).
Illumina 450K microarray data preprocessing We processed the Illumina 450K data using the minfi Bioconductor package. 25 We first used the raw methylated (M) and unmethylated (U) channel intensities to perform quality control checks that also used the technical control samples (LCLs and duplicates) described above. We then performed a modified version of quantile normalization (preprocessQuantile in the minfi package) to normalize across samples, which, briefly, forces the distribution of type I and type II probes on the Illumina 450K microarray to be the same across samples. This normalization is performed separately on the M and U channels, and the resulting normalized DNA methylation proportions were derived based on the logit (base 2) transform: y D log 2 (M/U), which was converted to the proportion methylation ("b" scale referred to by Illumina) by: 2 y /(1C2 y ).
We estimated cell composition in the peripheral blood data set using the estimateCellComposition function in the minfi package. 26, 27 Probes annotated to the sex chromosomes (chrX D 11,232 and chrY D 416) and probes containing an annotated SNP (via dbSNP v137) at the target CpG site (N D 16,756) and at the single base extension site (N D 7,880) were removed prior to statistical analyses. The data from retinal tissue and blood were preprocessed separately, given the large imbalance of sample sizes between the 2 datasets.
Statistical analysis We performed differential methylation analysis using 3 different subsets of the blood data: 1) either GA or NV -"all" model; 2) just NV -"NV" model; and 3) just GA -"GA" model, compared to the shared control population. Within the eye data set, we fit a single model comparing patients with AMD to controls. For each of these AMD outcomes in blood and within eye, we fit the following statistical model: Y ij D a i C b i AMD j C z i SV j C e i;j where Y ij is the proportion DNA methylation levels at probe i and subject j, a i is the mean methylation level for the controls, AMD J is the AMD status for subject j, and b i is the effect of each AMD outcome on DNAm levels. We further adjusted for estimated surrogate variables estimated from each model (blood: 47 for all, 32 for NV, and 35 for GA; eye: 5) to control for potential unmeasured technical and/or biological confounding,  28, 29  including cellular composition of blood.
Bisulfite pyrosequencing of ARMS2 and PRSS50 Pyrosequencing assays were designed using algorithms built into the PyroMark Assay Design Software (Version 2. 0. 1, Qiagen, Germantown, Maryland). Briefly, primers designed to target specific CpG sites within the regions of interest containing the differentially methylated probes were chosen from a list generated by the software on the basis of the algorithms' predicted assay quality. Genomic DNA (500 ng) was bisulfite converted using the EZ DNA Methylation Gold kit (Zymo Research, Irvine, CA) according to the manufacturer's instructions. Following bisulfite treatment all previously unmethylated cytosine residues are converted to uracil, whereas methylated cytosine residues remain unconverted. The regions of interests were amplified from the bisulfite-treated DNA using PCR primer sets #1 (ARMS2) or #2 (PRSS50) as shown in Table  S3.
PCR products were bound to Streptavidin Sepharose High Performance beads (GE Healthcare Life Sciences, Rydalmere, Australia) and a single-strand template was isolated using the Pyrosequencing Vacuum Prep Tool (Qiagen, Maryland USA). The beads were transferred to an optically clear, 24 well sequencing plate in 0. 3 mM of the sequencing primer (Table  S3 ). Serial pyrosequencing was performed where multiple sequencing primers were contained within a single PCR amplicon. 36 Pyrosequencing was performed on a PyroMark 24 Pyrosequencing System (Qiagen, Germantown, Maryland) as per the manufacturer's instructions. Data were analyzed on the PyroMark Q24 software (Qiagen, Germantown, Maryland). This software calculates the C peak as a percentage of the T plus C peak at each CpG site taking into account sequence length and signal strength.
The methylation level for each sample was calculated for 6 CpG sites in ARMS2 and 7 CpG sites in PRSS50. An unpaired Student's t-test was used to compare the methylation difference between the disease and control groups. meQTL analysis in Illumina 450K samples Observed high-quality genotypes on 290 subjects with Illumina 450k data (out of 298, 97. 3%) were obtained from dbGaP (accession: phs000182. v2. p1). SNPs on chromosome 10 were phased using ShapeIT,  37  and the 6 megabase area flanking the AMD risk SNP (chr10:121214448-127214448) was imputed up to the latest 1000 Genomes Phase 3 reference panel using Impute2  38  to obtain genotype calls for rs10490924 and rs72631113. Using the previously calculated surrogate variables (SVs, see above), we performed meQTL analyses separately within AMD patients (NV and GA together) and controls, at both SNPs, using the linear model of the form: Meth j D a C bSNP j C zSV j C e j for person j.
Funding This work was supported by the  National Institutes of Health  [ R01EY020406  to SLM,  R01EY023188  to SLM and JQ,  R01EY024580  to JQ]; the  National Eye Institute Core Grant  [ P30EY001765 ]; the  Intramural Research Program  of the  National Eye Institute  (to AS);  Foundation Fighting Blindness  (to KEB); the  generosity of Agnes Nixon (SLM). The collection of the Michigan AMD-MMAP cohort was funded by the  National Eye Institute  [ EY016862  to AS]. Illumina Human Methylation 450K profiling was performed by the  Center for Inherited Disease Research (CIDR)  through the  NEI grant   X01HG006605.
RT-PCR After collection, fractions were analyzed through real-time PCR (RT-PCR) using the Applied Biosystem StepOne ™ RT PCR system. RT-PCR was done with two primers: the forward and the reverse primer (see Table  1  for each system). The primers were drawn using Primer express 3. 0 software (Applied Biosystem) to limit complementarity to each other, in order to decrease non-specific amplification of self-dimerizing primers. The Mfold web server was also used to limit nonspecific interaction between St 1 -Chol and St 2 -Amp and with primers. For amplification, 20 µL of PCR mix was prepared with SYBR R Green PCR core reagents consisting of 2 µL of 10X SYBR Green PCR buffer, 2. 4 µL of 25 mM MgCl 2 solution, 1. 6 µl of dNTP mix, 0. 1 µL of AmpliTaq Gold DNA polymerase (1 U/µL), 0. 2 µL of Amperase (1 µL/µL), 2 µL of 10 µM of each primer (forward and reverse primer), 2 µL of collected fraction as St 2 -Amp template, and 7.
Photopolymerization leads to more stable structures without structural or morphological changes. High polymerization times lead to incomplete polymerization of lipids, with remaining unchanged monomer surfactant. The loading capacity, the intracellular delivery of a hydrophobic fluorescent probe (NR12S) by micelles and their cytotoxicities are strongly influenced by the photopolymerization degree. It thus appears that adjusting micelles polymerization enables fine-tuning of the intracellular delivery/cytotoxicity ratio. The micelles obtained after short irradiation times seem to be a good compromise between efficiency and toxicity for delivery applications. This work was supported by the French government (MESR), the Region Alsace, the LABEX MEDALIS, icFRC, CNRS and the ANR JCJC (ANR-11-JS07-014-01).
Second, it increases the cost in professional and medical applications such as those carried out in hospitals. Third, calibration is an overload that affects the usability of the system in the long term and, therefore, the introduction and acceptance of such systems. Previous works have aimed at minimizing and/or simplifying the calibration phase. For instance, in motor imagery  [1] , the supervised and unsupervised adaptation of classification parameters has shown to avoid inter-session recalibration  [5]  and to increase the BCI performance  [6]. Other approaches have tried to limit the calibration time by using clustering methods in the feature space  [7] ; ensembles of classifiers built from very large datasets  [8] ; intersubject generalization by using a pool of subjects to initialize the classifier  [9] [10] [11] ; or even by generating artificial training datasets via resampling  [12].
All participants were asked to read and sign an informed consent form to participate in the study. The participants were seated on a comfortable chair one meter away from a computer screen that displayed all the information related to the experiment. For the executed experiments we used an unsupervised self-calibration approach (c. f. section 2. 5) to simultaneously train the errorrelated potentials (ErrP)  [21]  classifier and solve the task (i. e. , reach the target position). The data recorded during the experiments is publicly available, and can be downloaded from https://github. com/flowersteam/self_calibration_BCI_plosOne_2015.
Data recording and feature extraction EEG signals were recorded from 16 fronto-central active electrodes using a gTec system, with the ground on AFz and the reference on the left earlobe. EEG data were digitized with a sampling frequency of 256 Hz, power-line notch filtered, and band-pass filtered at  [1, 10]  Hz. The data acquisition and online processing was developed under a self-made BCI platform. In this work, ErrP features were extracted from channels Fz, FCz and Cz within a time window of  [200, 800]  ms downsampled by a factor of 8 (being 0 ms the action onset), forming a vector of 57 features  [17]. Correct actions (i. e. optimal policy) from each state for the goal exemplified in (a). Extracted from  [18]  with permission. doi:10. 1371/journal. pone. 0131491. g001.
Practicalities We now describe some implementation details that were used in the experimental evaluation of the method. The code is available under a GNU GPL license and can be found at https:// github. com/jgrizou/lfui. 2. 6. 1 Power information. One critical difficulty of learning from unlabeled signals is for cases where the signals can be interpreted in a symmetric way. In other words, it is possible to obtain the same likelihood by switching the labels. Such misinterpretation is possible in environments with symmetric properties (see  [20]  for reaching tasks and  [13, 14]  for a speller). For the domain of our experiments the symmetries can be disambiguated using the goal reached action, but, in general, symmetric tasks (e. g. opposite corners) will be hard to discriminate and might produce an inconsistent agent behavior.
Finally, we performed an offline analysis to compare the self-calibration online results with a standard calibration using the same signals acquired during the online experiment. The number of calibration trials required for each subject was estimated by splitting the dataset into a training set formed by 300 trials, and a test set composed of the remaining ones (200). Then, a classifier was trained using an incremental number of trials from the training set and evaluated on the test set. This process was repeated ten times shuffling the order of the trials. The number of trials required for calibration was selected as the one for which the accuracy reached a plateau for at least five consecutive trials. Once a classifier was trained using the computed number of trials, the ErrP-based control algorithm (see subsection 2.
Thus, the proposed approach could serve as a complement rather than an alternative to standard calibration approaches. For instance, the self-calibration algorithm could be run after a standard calibration of few minutes where the user gets accustomed to the protocol and data can be recorded in a supervised manner. In fact, the results showed how several subjects (bottom left subjects in Fig 4b ) were affected by the self-calibration, and thus might have benefited from a supervised warm-up period. An avenue for future research is how to exploit constraints to develop self-calibration BCIs for other tasks or brain signals. On one hand and regarding the control signals, there have been numerous works in the invasive and non-invasive community showing how it is possible to control devices for reaching tasks (e.
This work has been partially supported by  INRIA ,  Conseil Regional d'Aquitaine  and the  European Research Council  grant  EXPLORERS 24007  (JG, PYO, ML); by the  Spanish Ministry of Science  via  DPI2011-25892 , and by  Diputacion General de Aragon-FSE  grupo  T04  (JO, LM); and by  Swiss National Centres of Competence in Research (NCCRs) "Robotics ," and by the  "EPFL Fellows" fellowship  programme co-funded by  Marie Curie ,  FP7  Grant agreement no. 291771  (II).
Acknowledgments The authors thank  Claire Chambers  and  Daniel Pressnitzer, PhD  advisor of Chambers, for sharing with us the PhD thesis and unpublished experimental results and for their interest in our modeling work. Chambers' findings inspired us to develop the model. This work is supported by  NIH   K18-DC011602  to JR and by an  Advanced ERC  and  NIH  grant ( R01 DC005779 ) to SS.