dataesr/scientific-paragraphs-categorization huggingface dataset:
Reliability measurements We measured three key aspects of atrophy measurement techniques as defined by  Fox et al. (2011)  that focus on plausibility and consistency. For all analyses, the data was grouped according to disease (healthy control and AD) in order to determine whether the behaviour of these measurements was different based on the population. First, volumes of brain, lateral ventricles, left and right hippocampi from the identified baseline scans were compared between the two disease groups. The next assessment was to measure change over short interval scans. Specifically, the mean atrophy between the identified baseline and the other baseline scan, where available, was calculated. Two-tailed t-tests were performed on the null hypothesis that the mean change was zero. A similar analysis was performed using the identified baseline scan and the two-week scan.
Where more than 97. 5% of these comparisons were in favour of one of the methodologies the difference was judged statistically significant at the 5% level (two-sided test). For most techniques and most structures the models specified in Eqs. (  5 ) and (  9 ) converged, yielding reliable parameter estimates when fitted in Stata 12. However on occasions convergence problems were encountered either when fitting the model to the full data or to bootstrap samples drawn from the data. This occurred when one of the components of variance was estimated to be zero or close to zero. For six structure-technique combinations (BAUMIP for left and right hippocampi, CSIRO for left hippocampus, INRIA for right hippocampus, MAYO_BSI for whole brain and MNI for right hippocampus) such convergence problems were encountered in more than 1% of the bootstrap resamples.
Results The Supplemental Table shows the extent of all of the data collected in the MIRIAD study and previously reported, highlighting the aforementioned 9 scans and 128 corresponding image pairs from 7 subjects that were excluded here on quality grounds. Subjects were seen at baseline, 2, 6, 12, 26, 38, 52, 78 and 104 weeks with repeat scans taken on three occasions (baseline, 6 and 38 weeks). All 23 controls and 44 out of the 46 AD cases attended the 52-week follow-up with high retention rates at the visits in the intervening period. A subset of the subjects returned for additional scans at 78 and 104 weeks that were not part of the original imaging protocol. Each subject provided measures of "direct" change for up to sixty-six (12 × 11/2) scan pairs with three of these being same day scan pairs.
Discussion We have performed an extensive statistical analysis making comparisons among what we believe to be the largest collection of different imaging techniques ever compared head-to-head in a blinded single analysis. The comparative analysis has been carried out using the MIRIAD dataset, which although not the largest data-set in terms of numbers of subjects, has the advantage of including multiple measures of atrophy over a range of different intervals from two weeks up to two years. This makes the MIRIAD dataset particularly useful for accurately quantifying the between and within subject variability in rates of atrophy; this variability is a key component of required sample sizes for hypothetical clinical trials in AD, our primary outcome measure. All participants provided measurements for every dataset in the challenge, allowing for a more accurate comparison between methods.
In an effort to encourage researchers to provide clearer comparisons between different methods and research studies on the most widely used cohort in the community, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has created standardized analysis datasets  (Wyman et al. , 2012). The fact that all techniques were assessed on exactly the same data, with only minimal exclusion of poor quality scans is a strength of our analysis. In other comparative studies, the number of images included for each technique has not always been controlled. For example, in the analysis reported by  Holland et al. (2012) , the number of subjects included in the analysis ranged from 572 to 733 depending on the method. As well as being consistent across techniques we felt that, since our aim was to compute required sample sizes for hypothetical treatment trials, the number of exclusions should be small.
This is because an important consideration in assessing the reliability of results from any clinical trial is the extent of missing data since this has the potential to introduce bias. For example, a study by  Hua et al. (2013)  illustrated that selective data exclusion, albeit based solely on post-hoc removal of data with implausible atrophy rates, will result in a bias towards lower required sample sizes. A further strength of our analysis is our use of linear mixed effect models. The MIRIAD data has been used in a similar manner before  (Smith et al. , 2007)  to compare BSI, SIENA, and SIENAX. In their study, they also obtained measures of repeatability using the same day scan and transitivity. However, instead of using linear mixed effect models, the comparison of atrophy rates was done by comparing t-statistics on whole brain atrophy measures of the first and last scans for each subject.
In validation studies on different brain segmentation algorithms  (Eskildsen et al. , 2012; Leung et al. , 2011a) , voxels on the cortical surface boundary, especially those near the temporal lobe, seemed to be the most likely source of mis-segmentation. In both whole brain and ventricles, resulting sample size estimates correspond well with the sample sizes estimated from the ADNI cohort using the same sample size formula and atrophy measurement methods. For example, the estimated sample size for a one year study using the UCL KN-BSI method in the brain region was 242 (152 to 422) in this study and 220 (148 to 353) for ADNI  (Leung et al. , 2010b). This was replicated by  Holland et al. (2012)  who used the KN-BSI data uploaded to ADNI from UCL and obtained a two year sample size estimate of 180 (129 to 276) for two years; in this cohort, the estimate was 188 (114 to 336).
For ventricles, the sample size estimate is 190 (118 to 320) for MIRIAD and 257 (177 to 417) from ADNI. The slightly larger differences may be that Holland and colleagues did not have access to the KN-BSI results in ADNI for the ventricles. Holland et al. study also provided sample size estimates for FreeSurfer using the ADNI data, though this might have been performed on a different software version compared to the challenge. For whole brain, the estimate for ADNI was 696 (393 to 1693) and for MIRIAD 432 (220 to 1108) and 646 (276 to 7832) for the two submissions. For ventricles, the values were 255 (174 to 427) for  ADNI and 186 (112 and 312)  and 182 (112 to 314) for the challenge. More formal comparisons of these sample size estimates across data cohorts will be of interest and the focus of future work.
The least consistent structure across all assessments was the hippocampi, with required sample sizes sometimes varying as much as tenfold between techniques. This variability between techniques is likely due to the numerous definitions of the hippocampus used in segmentation protocols. Fig. 5  provides example delineations of the same image from all the challenge techniques for a single subject. This discrepancy has lead to an EADC initiative  (Boccardi et al. , 2011; Frisoni et al. , 2015)  to create a unified hippocampal segmentation protocol based on previous protocols from the literature and additional input from numerous experts during iterations of the universal protocol. Despite this variability, the results are in-line with a previous study using manual delineations of hippocampi in 36 patients and 20 controls from this dataset  (Barnes et al.
The techniques providing the sample sizes using hippocampal atrophy were similar in magnitude to those for ventricular atrophy. In general, compared to sample sizes reported using the ADNI dataset, hippocampal sample sizes are larger in MIRIAD. Using the UCL BSI technique in the hippocampi, sample size estimates were 490 (290 to 794) in MIRIAD and 135 (79 to 301) for ADNI  (Leung et al. , 2010a) ; and for FreeSurfer 284 (158 to 698) for MIRIAD and 217 (150 to 355) in ADNI  (Holland et al. , 2012). These higher sample sizes could be due to coarser resolution of the T1 weighted images in MIRIAD, more modern scanners and coils available in many of the ADNI sites or underlying differences in the AD population between the two cohorts. For all analysis regarding required sample sizes, it is important to consider uncertainty in these estimates, as the point estimate may provide a very low sample size, but the resulting confidence intervals may be very wide.
(2006). The method of blinding implemented in this challenge could be considered an obstacle in terms of getting the largest effective size. In terms of a tradeoff between bias and variability, we erred more on the side of removing potential sources of positive bias by blinding the participants to disease group, as well as the time between scans. As a result, it was more difficult for the participants to identify potential outliers and take corrective action. Many of these methods have been developed and validated using the ADNI datasets, acquired at both 1. 5 T and 3 T, whereas all of the data from the MIRIAD atrophy challenge was acquired on a 1. 5 T scanner using different parameters than those used in ADNI. Whilst most methods show good agreement whether the data was acquired on 1. 5 T or 3 T scanners, such that they are often pooled for analysis in clinical trials, it is possible that the difference in protocols resulted in slightly different behaviour than that seen in ADNI.
Our results suggest that sample size estimates based on ventricular expansion rates are more consistent than those from whole brain atrophy, and both are markedly more stable than those derived from hippocampal atrophy measures. By providing comparisons between techniques based on sample size, our aim is not to determine which techniques should or should not be used for any given trialnoting that factors other than sample size alone need to be taken into account when choosing an imaging techniquebut to provide the clinical trials community with robust sample size estimates for trials based on contemporary techniques; and in the absence of a ground truth, to provide the imaging community with a means of comparison. To this end the MIRIAD datasetin both a blinded (challenge) and unblinded (ordered) formwill remain publicly available at http://www.
Acknowledgments The public release of the dataset used for the challenge is made available through the support of the  UK Alzheimer's Society. The original data collection was funded through an unrestricted educational grant from  GlaxoSmithKline  and funding from the  UK Alzheimer's Society  (to  Dr Schott ) and the  Medical Research Council  (to  Professor Fox ). The Dementia Research Centre is an  Alzheimer's Research UK Co-ordinating Centre  and has also received equipment funded by the  Alzheimer's Research UK ,  Brain Research Trust  and  The Wolfson Foundation. This work was supported by the  NIHR UCL/UCLH Biomedical Research Centre  and  NIHR Queen Square Dementia Biomedical Research Unit. The  BAUMIP Team  is financially supported by the  Scientific and Technological Research Council of Turkey  under the grants  BIDEB 2215  ( L.
O. Iheme ) and Career Grant  111E083  ( D. Unay  and  M. Kandemir ). The INRIA team was funded by the  European Research Council  through the  ERC Advanced Grant MedYMA , and the  French ANR "programme blanc " number  ANR-09-BLAN-0332. The  MNI group  ( N. Guizard ,  Dr. Fonov  and  Dr. Collins ) was funded by the  MS Society of Canada  (BioMed PhD studentship, 691) and the  Canadian Institutes of Health Research  ( MOP-111169 & 84360 ). The UCL team receives support from the  UCL Leonard Wolfson Experimental Neurology Centre  ( PR/ylr/18575 ) and  EPSRC  ( EP/H046410/1 ) and the  Alzheimer's Disease Neuroimaging Initiative. The UPENN team would like to acknowledge funding from the  NIH  grant  R01 AG037376. S. O. receives funding from the  EPSRC  ( EP/H046410/1 ,  EP/J020990/1 ,  EP/ K005278 ), the  MRC  ( MR/J01107X/1 ), the  EU - FP7  project  VPH-DARE@IT  ( FP7-ICT-2011-9-601055 ), the  NIHR Queen Square Dementia Biomedical Research Unit  and the  NIHR UCL/UCLH Biomedical Research Centre  ( NIHR BRC UCLH/UCL High Impact Initiative-BW.
In contrast, ITS1 and scR1 RNAs (expected to be cytoplasmic;  [59] ) were found in the cytoplasmic fraction (Figure  4b ). Altogether, these data strongly indicate that subTERRA localize to the nucleus and might play a role in telomere maintenance through their transcription or through direct action of RNA molecules. and nuclear fraction (NUC) were separated on 1% formaldehyde/MOPS agarose gel (upper panel) rRNA and tRNA species are stained with ethidium bromide. RNAs were transferred to nylon membrane and indicated ncRNAs were detected with specific radio-labeled probes (oligonucleotides/single-stranded for ITS1, PMA1, scR1 or double-stranded for subTERRA).
To prove this hypothesis we used subTERRA overexpression from its chromosomal loci (Figure  S6a ) to determine whether subTERRA plays a direct role in telomere clustering. Upon galactose induction, GFP-Rap1p foci were quantified and no significant effect of this trans-expression of antisense subTERRA was observed (data not shown). We conclude that despite the possible role of subTERRA-XUTs in telomere clustering its action could be limited to cis mode of regulation and therefore undetectable in a global manner.
Blots were washed twice with 0. 1 × SSC and 1% SDS for 20 min at 37 °C or 65 °C, respectively. Probes were obtained by randomprimed labeling (Prime-It Random Primer Labeling Kit, Stratagene, Agilent Technologies France, Les Ulis, France) of specific DNA fragments generated by PCR or kinase labeling of locus-specific oligonucleotides. PCR primers are available upon request. 18S-rRNA, ACT1, RPO21 and scR1 RNAs were used as loading controls. Northern blot signals were quantified using Quantity One software (Biorad).
Data Mapping Fifty nucleotide-long sequence reads (performed on an ABI SOLiD ® v4 sequencer) were identified using the standard SOLiD base-calling software and then aligned to the reference genome (Saccharomyces cerevisiae S288c retrieved from the Saccharomyces Genome Database (SGD)  [97]  using Bowtie (v0. 12. 7) software allowing up to three mismatches.
On this artificial Y′ genome we performed segmentation process (ZINAR script;  [98] ). Briefly, it is based on the number of tags and a sliding window that detects changes in density of tags. Overlapping reads are summed beyond 10 reads; one segment is formed of more than 150 nt. Based on RNA-seq profiles we have manually identified four different RNAs. Sequence data are publicly available at Gene Expression Omnibus (GEO) accession number: WT -1sra/SRX046705 and 2sra/SRX046709; xrn1∆ -1sra/SRX046706, 2sra/SRX046710; trf4∆ and xrn1∆ trf4∆ -GSE67585 at http://vmgb. curie. fr/marta/XUT_trf4/. Differential expression analysis was performed using DESeq  [99]. Telomeric genes category consist of EST1, CDC13, RAT1, SIR2, EST2, STN1, NMD1, SIR3, EST3, TEN1, NMD2, SIR4, TLC1, MEC1, NMD3, SAS2, RIF1, MTR10, SAS4, RIF2, TEL1, SAS5, RAP1, PIF1, SET1, MRE11, DOT1, RAD50, RAD6, XRS2, RAD52, RAD51, YKU70, YKU80, ESC1.
Green fluorescent images were acquired with a GFP filter block (excitation: band pass (BP), 465-500 nm and dichroic, 506 nm; emission: BP, 516-556 nm; Semrock, Rochester, New York, USA). All fluorescent images are a z projection of z-stack images, acquired with an axial (z) step of 200 nm. Before quantification, deconvolution was made using the Meinel algorithm in Metamorph (eight iterations; Σ = 0. 8; frequency 3; MDS Analytical Technologies, Sunnyvale, CA, USA). Analyses have been performed using a home-made Matlab (MathWorks, Natick, MA, USA) application (Q-foci)  [35]. For manual counting (Figure  S5 ) cells were grown to exponential phase (OD600 = 0. 8) in YPD at 30 °C. 3D-pictures were acquired as described above and Maximum Intensity zprojection (ImageJ) was applied to manually count GFP-Rap1p foci.
Acknowledgments We thank students for technical help ( S. Licheheb ,  V. Roche ,  M. Piganeau ,  N. Kapetanakis , and  F. Poulain ). Morillon and Taddei's groups for helpful discussions. We thank  D. Shore ,  S. Marcand ,  K. Mizuta ,  B. Luke ,  C. Mann ,  T. Teixeira  and  V. Géli  for kindly providing strains, plasmids and antibodies. We thank  M. Pinskaya  for critical reading of the manuscript and  A. Murthi  for English improvements. Our financial supports are CNRS, IC,  ATIP-CNRS, ANR-REGULncRNA  and  ERC EPIncRNA. Highthroughput sequencing was performed by the  NGS platforms of Imagif and of Institut Curie , supported by the grants  ANR-10-EQPX-03  and  ANR10-INBS-09-08  from the  Agence Nationale de la Recherche  ( ANR, Investissements d'avenir ) and by the  Canceropôle Ile-de-France.
Introduction This article describes a search for heavy long-lived 1  multi-charged particles (MCPs) in √ s = 8 TeV pp collisions data collected in 2012 by the ATLAS detector at the CERN Large Hadron Collider (LHC). Data taken in stable beam conditions and with all ATLAS subsystems operational are used, resulting in an integrated luminosity of 20. 3 fb -1. The search is performed in the MCP mass range of 50-1000 GeV, for electric charges 2  |q| = ze, with the charge numbers z = 2, 3, 4, 5, and 6. The observation of such particles possessing an electric charge above the elementary charge e would be a signature for physics beyond the Standard Model. Several theories predict such particles, including the almost-commutative model  [1] , the walking technicolor model  [2] , and the left-right symmetric model  [3] , which predicts a doubly charged Higgs boson.
Uncertainties due to selection The uncertainty on the selection efficiency is evaluated by varying the requirement values used in the analysis. Several reasons motivate these variations. For example, the uncertainty on the amount of material in front of the MS, which is found to be about 1%  [26] , propagates into an uncertainty on the selection efficiency due to the slowing down of particles, and its effect is covered by the effect of varying the p T requirement. The following variations of the nominal requirements are studied: p T value by ±3% because of an uncertainty on the track p T measurements and the uncertainty on the amount of material; f HT value by ±25% due to pile-up dependence, S (pixel dE/dx) by ±10%, S (TRT dE/dx) by ±5% and S (MDT dE/dx) by ±15% because of the observed disagreement of the mean and root-mean-square width of these distributions in the Z → µµ events in data and simulation, as well as of any potential mismodelling of these ionisation estimators.
Conclusion This article reports on a search for long-lived multi-charged particles produced in proton-proton collisions with the ATLAS detector at the LHC. The search uses a data sample with a center-of-massenergy of √ s = 8 TeV and an integrated luminosity of 20. 3 fb -1. Particles with electric charges from |q| = 2e to |q| = 6e penetrating the full ATLAS detector and producing anomalously high ionisation signals in multiple detector elements are searched for. Less than one background event is expected and no events are observed. Upper limits are derived on the production cross-sections and are interpreted as mass exclusion limits for a Drell-Yan production model from 50 GeV up to 660, 740, 780, 785, and 760 GeV for charges |q| = 2e, 3e, 4e, 5e, and 6e, respectively.
The highest IR DNA copy number was found in the RCCC diabetes group, which suggests that such DNA alteration may be engaged in the early stages of tumor initiation. Also, this genome-wide research reported differences in DNA copy number variations (CNV) between kidney tissue samples derived from patients with RCCCdiabetes, diabetes, and RCC. Diabetic patients showed increased CNV, which may suggest that hyperglycemia or hyperinsulinemia is likely to cause genomic alterations, possibly leading to oncogenesis. Patients with RCC and coexisting diabetes had most of the genomic variations. The Database for Annotation, Visualization and Integrated Discovery (DAVID) screen revealed shared pathway disturbances between diabetes and RCC groups, which comprise changes in transcriptional, nuclear, chromosomal parts, and protein-DNA complexes organization.
AtNPC1 Encodes a Phospholipase C that Cleaves PC It is believed that NPCs encode proteins with PLC activity. However, NPC3 shows lysophosphatidic acid phosphatase activity instead of PLC activity  (Reddy et al. , 2010). Therefore we studied the enzyme activity of recombinant NPC1. We prepared recombinant NPC1 by inserting AtNPC1 cDNA into the expression vector pET-30a(+)(pET30). This vector contains a 6xHis tag. The cleavage site between the signal peptide and the coding region was identified using SignalP 4. 1 (Supplementary Figure  S1A ); the signal sequence was removed. The NPC1 produced in E. coli accumulated in inclusion bodies. To obtain greater solubility, we had to use a lower concentration of isopropyl β-thio-galactopyranoside (IPTG; 0. 1 mM) and lower temperature (16 • C) for a longer induction time.
The Alterations of NPC1 Level has No Impact on ROS Accumulation or Transcript Level of HSP18. 2, HSP70, MBF1c and DREB2A To understand the molecular mechanism by which NPC1 is involved in thermotolerance, we first focused on HS signaling; NPC1 may serve as a signaling protein. We measured the accumulation of ROS and the expression of HSPs as early and late heat signaling markers. Reactions to stress are mostly coupled with the accumulation of ROS. A relationship also exists between ROS and the HSR  (Ruelland and Zachowski, 2010; Wang et al. , 2014). We assessed ROS accumulation using 2 , 7 -dichlorofluorescein diacetate as a ROS detection reagent according to  Wang et al. (2014)  after 30 min periods of HS (45 • C). No difference in ROS accumulation pattern among WT, npc1-1 and NPC1-OE seedlings was observed (data not shown).
(2011). No differences in the expression of any of the genes tested were observed between WT, npc1-1 and NPC1-OE seedlings (data not shown). FIGURE 7 | The effect of HS on the root length of Arabidopsis WT and npc1-1 seedlings. Seven-day-old Arabidopsis seedlings were grown on agar plates (four seedlings of WT and five seedlings of npc1-1 on one plate) at 22 • C. The plates were exposed to HS (42 • C, 30 min), then returned to the control conditions. Root elongation was determined 14 days after HS. Each value is the mean ± SE. Asterisk denotes significant difference compared with the WT (two-tailed Student's test; n = six plates; * P < 0. 05). This experiment was repeated three times with similar results.
Over the last two decades, the literature on polyphenols has grown exponentially following the recognition of their antioxidant, anti-inflammatory, and anti-carcinogenic properties and more evidence for their potential beneficial effects upon health has been accumulated  [4]. However, epidemiological data on the relationship between polyphenol intake and the risk of chronic diseases and mortality are still limited, especially in prospective studies, and largely concern flavonoids and lignans  [2, [5] [6] [7]. To calculate polyphenol intake, most of these studies have used the US Department of Agriculture (USDA) databases on polyphenol contents in foods  [8] [9] [10] , which only contain data on flavonoids expressed as aglycones. The more recently published Phenol-Explorer database (www. phe nol-explorer.