dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, using a model to simulate different levels of signal and noise in the data, we show that the benefit of noise correlations for decoding is a broadly applicable property of fMRI data, and illustrate how various network parameters influence this finding. Taken together, although other studies have proposed similar ideas from a theoretical perspective, to our knowledge no prior study has validated them at this level of detail, using both empirical and simulated data, and shown how they play out in practice in the context of a widely used multivariate decoding strategy. In this study, we focused on the classification of face and scene information from ventral temporal cortex as a canonical example of the kind of problem for which MVPA has proven effective. Moreover, this dataset was well suited to an initial exploration of the influence of noise correlations because it contained multiple rest and task runs for each participant allowing for independent definitions of selectivity and noise correlations.
We expect that our conclusions will apply to multivariate decoding with brain imaging data more generally. Indeed, the findings from our model-where we observed a similar pattern of results with artificial datasets generated from simulated populations of voxels with a range of physiologically realistic selectivity profiles and noise correlation structures-provide an initial validation of the general applicability of our conclusions. Nevertheless, it will be important for future studies to apply the approach outlined here to other datasets and brain regions. Another caveat relates to our inability to draw conclusions at the level of neurons from fMRI data. Each voxel in fMRI likely reflects the activity in thousands of neurons, with the exact sampling of neural responses by voxels inaccessible to analysis.
Functional runs Each participant completed two face/scene "localizer" runs, each of which consisted of an alternating on-off block design, with 18-s blocks of stimulation interleaved with 18-s blocks of "blank" passive fixation. Stimulation blocks contained 12 1-s presentations of either face or scene images (the order of face and scene blocks was counter-balanced across participants), each separated by a 500-ms inter-stimulus interval. Face images consisted of 24 photographs from the NimStim dataset (http://www. macbrain. org/resources. htm, neutral expressions) and scene images consisted of 24 photographs of single houses collected from the internet and stock photograph discs  [59]. Images were presented in grayscale, cropped using a circular mask, and subtended 6˚of visual angle in radius.
In one run, face and scene stimuli were presented in the left visual field, and in the other run, face and scene stimuli were presented in the right visual field. Each run began with a 9-s fixation period and included a total of 12 blocks of stimulation (6 face, 6 scene), which lasted 7m 21s. During blank periods, participants were presented only with a central, white point to fixate (radius = 0. 2˚). Data from two "rest" runs were also collected for each participant, during a second session. Each rest run had the same duration as the localizer runs, but with only the central fixation point. Participants were instructed to passively view the fixation point without performing any overt task.
Image acquisition fMRI data were acquired with a 3T scanner (Siemens Skyra) using a 16-channel head coil. Functional images for both the localizer and rest runs were acquired with a T2 Ã gradient-echo echo-planar imaging sequence (repetition time [TR] = 1. 5 s; echo time [TE] = 28 ms; flip angle [FA] = 64˚; matrix = 64 x 64; resolution = 3 x 3 x 3. 5 mm), with 27 interleaved axial slices aligned to the anterior/posterior-commissure line. TRs during the localizer were time-locked with the presentation of photos. In addition, a high-resolution T1 MPRAGE anatomical scan was acquired for spatial registration. To improve registration, an additional T1 FLASH anatomical scan was acquired at the end of each session, co-planar to the functional scans. To correct for B0-field inhomogeneity, phase and magnitude field maps were collected at the end of all sessions, co-planar to the functional scans and with the same resolution.
Image analysis fMRI data were analyzed using FSL (http://fsl. fmrib. ox. ac. uk/fsl/) and Matlab (MathWorks). All functional images were skull-stripped to improve registration, and registered to the anatomical images, and the MNI standard brain. The volumes from the initial 9-s fixation period were removed and the remaining volumes were corrected for slice-acquisition time and head motion, high-pass filtered (100-s period cutoff) and spatially smoothed (5 mm FWHM). Despite potentially blurring the spatial activity patterns used for classification, we applied spatial smoothing for two reasons: First, we wanted our preprocessing and analysis steps to replicate as closely as possible a standard fMRI study in order to quantify how noise correlations influence decoding performance in a situation frequently encountered in cognitive neuroscience.
Labeling voxels based on selectivity Data from the localizer and rest runs were masked to include the temporal occipital fusiform cortex and the parahippocampal gyrus (posterior division), defined anatomically from the Harvard-Oxford cortical atlas in standard MNI space. These regions were chosen because of their general preference for face and scene stimuli, respectively. This mask produced a median of 5875 voxels, which varied less than 2% across participants because of small changes in head position. To identify voxels as face-or scene-selective, we fit a GLM to the BOLD activity observed across the masked ventral temporal voxels during one of the localizer runs (counterbalanced across participants). The GLM contained two main regressors, one for face blocks and the other for scene blocks, as well as six nuisance covariates (one for each motion direction).
scene classification) did not affect the pattern of results. To quantify classification accuracy, we averaged the results of 6-fold cross-validation. The classifier in each fold was trained on 5/6 th of the data and tested on the left-out 1/6 th of the data. Because only one localizer run was used for this cross-validation (the other was used to independently define selectivity), these divisions of the data into training and test sets occurred in the same fMRI run. Data from the same run can have dependencies, both locally when activity in the previous block spills over into the current block, and globally as a result of non-task factors like head motion or arousal. Despite this, our within-run approach was unbiased. With respect to local dependencies, all conditions being classified were present in each run and alternated between each other, and thus any spill-over (into a period with a different label) would hurt performance.
Model simulations We developed a simple model of face/scene selectivity in BOLD data from human ventral temporal cortex to examine the separate influence of noise correlations and selectivity on MVPA. We simulated a set of 30 face-selective voxels and 30 scene-selective voxels. Mean activity in each of the face-selective voxels took on a larger value in response to a face stimulus (M FF ) than in response to a scene stimulus (M FS ), and vice versa for scene-selective voxels (M SF and M SS ). Based on the empirical dataset, these parameters were set at baseline to (in arbitrary units): M FF = 708, M FS = 705, M SS = 740 and M SF = 735. Pairwise correlations in the activity of voxels took on different values within the face-selective pool of voxels (c FF ), within the sceneselective pool of voxels (c SS ), and across the two pools (c FS , c SF ).
27  Altogether, these data indicate that MOZART1 may contribute to the recruitment of gTuRCs to microtubule-organizing centers (MTOCs), and to the activation of microtubule nucleation. [20] [21] [22] [23] [24] [25] [26] [27]  Consistently, defects in MOZART1 lead to abnormalities in mitotic spindle assembly and in cell division and development. [20] [21] [22] 24, 25, 27  The biochemical and structural description of MOZART1 and its interaction with GCP3 and other GCPs may significantly advance our understanding of gTuRC functionality. Unfortunately, recent studies on yeast and plant homologs of MOZART1 have shown that despite its small size, Mzt1/GIP1 protein represents a challenging target for detailed characterization in vitro. 20, 25  In this article, we report the expression and purification of recombinant human MOZART1 and the structural characterization of the purified protein using a combination of DLS, SEC-MALS, and NMR experiments.
4 (C) and Supporting Information, Fig. S2 ). Similar boundaries were obtained from the analysis of chemical shift values of C a , C b , C 0 , H N , H a , and N resonances with the TALOS1 software,  29  in which torsion angle values characteristic of a-helix were observed for residues A16-L35, M41-E52, and P57-A76. The three helices were systematically predicted by structure prediction programs such as Lomets,  30  Phyre2,  31  or CS-Rosetta  32  with comparable boundaries (Supporting Information, Fig. S2 ).
Dynamic light scattering (DLS) Samples of 150 mM 15 N-labeled MOZART1 protein in 50 mM sodium phosphate pH 6. 0, 150 mM NaCl with or without 25 mM sulfobetaine-12, and 10 mM DTT were analyzed at 20. 58C using a DynaPro NanoStar instrument (Wyatt Technology) and 4 mL disposable cuvettes (Wyatt Technology). The data were analyzed with Dynamics software (Wyatt Technology).
Size exclusion chromatography coupled with multiangle light scattering (SEC-MALS) 15 N-labeled MOZART1 protein buffered in 50 mM sodium phosphate pH 6. 0, 150 mM NaCl was analyzed on a Superdex 200 Increase 5/150 GL (GE Healthcare) column with multiangle light scattering (MALS). The column was equilibrated in a 50 mM sodium phosphate 0. 1 lm filtered buffer (pH 6. 0 and 150 mM NaCl) on an Agilent 1260 Infinity LC chromatographic system (Agilent Technology). Data were collected using a DAWN 81 and Optilab T-rEX refractive index detector (Wyatt Technology). Of 150 mM protein sample, 70 mL was loaded on the column and the separation was performed at a flow rate of 0. 4 mL/min at 158C. Results were analyzed using the ASTRA 6. 1 software (Wyatt Technology).
0, 50 mM NaCl, 2 mM DTT, and 30-50 mM sulfobetaine-12 (sulfobetaine-12:MOZART1 molar ratio was always >60:1). NMR spectra were recorded at 378C on Bruker Avance III or Bruker Avance II spectrometers equipped with TCI cryoprobes and operating at 600 or 950 MHz 1 H frequencies, respectively. All NMR spectra were processed with Topspin 3. 2 (Bruker Biospin) and analyzed using NMRFAM-SPARKY. 38  1 H N ,  15  N,  13  C a , 13 C b , and C 0 resonance assignments were obtained from HNCA, BEST-HN(CO)CACB, BEST-HNCACB, HNCO, and HN(CA)CO experiments. 39, 40 1] [42]  The assignment was deposited in BMRB under accession number 27093. For NMR titration and interaction with the Nterminus domain of GCP3, 2D SOFAST-HMQC experiments  43  of MOZART1 at the concentration of 12-20 lM were recorded at 298 K after each incremental addition of unlabeled SUMO-GCP3  -His 6.
Acknowledgments SEC-MALS, DLS equipments, and  Bruker 500 MHz , 600  MHz , and 700 MHz NMR spectrometers belong to the  IBiSA Integrated Screening Platform of Toulouse (PICT, IPBS, CNRS -Universit e de Toulouse ). The R egion Midi-Pyr en ees and European structural funds are acknowledged for funding the NMR equipment. The authors acknowledge TGIR-RMN for access to a Bruker 950 MHz NMR spectrometer in Grenoble and  Drs Bernhard Brutscher  and  Adrien Favier  for their support and discussions. They thank  Dr Jens L€ uders  ( IRB Barcelona ) for providing pGEX-MOZART1 plasmid.
Many studies attempting to solve this equation have been published, some with specific focus on particular terms of the equation or the use of new approaches based on different mathematical models (e. g. ,  [23] [24] [25] [26] [27] [28] [29] ). The Ozma project (1960) and the Frank Drake study (1961) are at the origin of the Search for Extra-Terrestrial Intelligence (SETI) programme looking for intelligent signals using, in particular, the Arecibo telescope to collect data for the seti@home project (http://setiathome. berkeley. edu/). Nevertheless, in astrobiology, the search for extraterrestrial life is not limited to intelligent civilisations. On the contrary, research in this domain mostly focuses on microbial life, particularly with respect to the Solar System. Thus, S. Seager undertook a broader approach to investigate the detectability of life on exoplanets located in the habitable zone of their star  [30, 31].
Acknowledgments: We thank the  CNES  for funding. We thank  Allain-Gérald Faux  and his colleagues from the  Maison Pour La Science, Orléans , and  Arnaud Lepot  for their help and participation to the training course on Astrobiology organized at CBM since 2016. We thank the reviewers for their useful comments. Author Contributions:  Frédéric Foucher  proposed the model and wrote the paper. Keyron Hickman-Lewis ,  André Brack , and  Frances Westall  contributed in the reflection and discussion about the model and in the the writing of the paper.
The objective of this work is for the verification of large experimental (EXFOR) and evaluated nuclear reaction databases (JEFF, ENDF, JENDL, TENDL. ). The work is applied to neutron reactions in EXFOR data, including threshold reactions, isomeric transitions, angular distributions and data in the resonance region of both isotopes and natural elements. Finally, a comparison of the resonance integrals compiled in EXFOR database with those derived from the evaluated libraries is also performed.
Introduction The Nuclear Energy Agency (NEA) Data Bank gathers and updates the data from different organisms for all nuclear reactions, and makes them available to scientists and engineers. The objective of this work is to improve the overall quality of these data. In a previous work, the NEA Data Bank and the Société de Calcul Mathématique co-developed statistical methods to cross-check the consistency between experimental (EXFOR) and evaluated libraries (ENDF/B-VII. 1 (USA), JEFF-3. 2 (OECD/NEA), JENDL-4. 0 (Japan), TENDL-2014 (Netherlands)). In the present work we update this method to give a consistency ranking to each EXFOR entry that can be compared across all the reactions. The comparison is performed separately for each nuclear reaction between each experimental subentry and each evaluator.
Database description The work is applied to induced neutron reactions in EXFOR data, including threshold reactions, isomeric transitions, angular distributions and data in the resonance regions. It is applied for isotopes and natural elements. In this work, a total number of 7,468 reactions were checked, corresponding to 26,634 EXFOR subentries and 3,955,254 points. Each nuclear reaction is represented by two parameters: the abscissa corresponds to the incident energy a e-mail: gottfried. berton@scmsa. eu (or the angle in angular distributions) and the Y axis corresponds to the cross-section (which may be considered as a probability of neutrons interaction). Data contained in EXFOR are the result of the experimentations and considered as a set of points, whereas the data presented by the different evaluated libraries (e.
95% ] as follows (see Fig. 1  at left): distance = maxQ EXFOR or ENDF 5% -min Q EXFOR or ENDF 95% +. The confidence intervals are computed from the standard deviations, σ , of the cross-section denoted by: Y E N DF -1. 96 × σ E N DF , Y E N DF + 1. 96 × σ E N DF. To take into account the horizontal uncertainty in EXFOR, one discretizes the 95% horizontal confidence interval and computes the minimal distance over the interval as shown at right in Fig. 1. The higher the distance, the less consistency there is between the experimental data and the evaluated curve. A ratio is computed between this distance and the vertical uncertainty of EXFOR or ENDF (set to 10% when not specified): ratio = distance max(σ E X F O R , σ E N DF ). For example, if the distance between the curve and the point is more than 5 times the vertical uncertainty, the ratio is equal to 5.
Results Only 2% of the subentries have been classified in the lowest rank "E". The numbers of subentries that have obtained a ratio superior to several thresholds are presented below: -Ratio > 2: 3665 subentries; -Ratio > 3: 2937 subentries; -Ratio > 5: 1932 subentries; -Ratio > 10: 284 subentries; -Ratio > 20: 45 subentries. For natural elements, 3118 subentries were checked: 173 obtained a ratio > 8, and 20 have ratio > 20. All the subentries that have obtained a rank of D or E (ratio > 3) with all evaluators are reported for each type of reaction in the Table  1. Based on this checking, the most suspicious data are (n,n α), (n,He), (n,2p), (n,nonelastic), (n,nX) and (n, γ ). The proportion of potential outliers might seem high, but the method is based on uncertainties, so a point can receive a bad ranking when the uncertainties of both ENDF and EXFOR are very small with respect to the distance, and this situation is very frequent.
In this work, a comparison of 790 EXFOR recommended (RECOM) and calculated (CALC) entry values to some major nuclear data libraries is carried out. Few deficiencies in EXFOR entries (wrong compilation of energy limits. ) were found and reported to NRDC. Table  2  summarizes this comparison. Then, large differences between evaluations have been found. As an example, Table  3  shows the large overestimation given by JEFF-3. 2 for the calculated integral resonance of 50 Ti(n,γ ). In Fig. 6 , the cross-section of 50 Ti(n,γ ) is illustrated showing large difference between JEFF-3. 2 and other evaluations.
Conclusion The algorithm allowed to focus on the most suspicious data among the whole set of data of 26,636 EXFOR subentries. More than 459 subentries have been classified in the lowest rank. This analysis might be useful for NRDC to find potential outliers, and for evaluators to improve current evaluations. Future activities will be focused on the development of a new methodology to tackle the difficulties to assess the resonance energy range. Finally, this study has been applied to induced neutron reaction, but can be extended to other induced reactions (e. g. charged particles,. ).
The Alzheimer Cohorts Consortium The Alzheimer Cohorts Consortium is a collaboration of nine prospective cohorts studies from the United States and Europe including: the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study, the Atherosclerosis Risk in Communities (ARIC) study, the Cardiovascular Health Study (CHS), the Cognitive Function and Ageing Studies (CFAS), the Framingham Heart Study (FHS), the Gothenburg population studies, the Personnes Agees QUID (PAQUID) study, the Rotterdam Study, and the Three-City Study (3C). All cohorts are population-based and comprise of prospectively collected data on dementia (and in most studies information on clinical subtypes), in addition to genotyping, and extensive (cardiovascular) phenotyping.
The PAQUID cohort is a populationbased study in the southwest of France of 3777 individuals aged 65 years or older recruited in 1988  [19]. There have been nine waves of data collection at 1, 3, 5, 8, 10, 13, 15, 17, 20, 23, 25, and 27 years after the baseline assessment. The RS is a prospective population-based cohort study comprising 14,926 subjects aged 45 years or older  [20]. Baseline data of 7983 participants were collected between.
A participant is included in a particular age group if they were dementia-free at start of the age group category. Since all the cohorts have repeated visits with participants, when data was available, a single person could contribute to IRs of multiple age groups. To account for this, we employ robust sandwich estimators to calculate the 95% CI around the IRs. Five-year cumulative hazards and hazard ratios are being assessed individually in each cohort and not combined across studies because of differing timing of examinations. Non-overlapping epochs are defined based on examination cycles and are specific to each cohort. Five-year cumulative hazard and hazard ratios (HRs) are being calculated using a Cox proportional hazard regression model and adjusted for age and sex in non-stratified models using a robust sandwich estimator for covariance structure  [22].
Participants who did not experience a dementia diagnosis are censored at the last date they were known to be free of dementia, or 5 years after the beginning of the epoch, whichever was sooner. Hazard ratios are being computed for each epoch as compared to the first epoch followed by trend per decade. We do this by assigning to each epoch an index value equal to median time in years since the beginning of the first epoch. For example, if epoch 1 was 1995-1999 and epoch 2 was 2000-2005 then the index variable would be 2. 5 and 7. 5 respectively. The index variable is then used in the Cox proportional hazard regression to assess a linear change in hazard of dementia over the epochs or linear trend. To ensure the analyses are identical across cohorts, statistical code using both SPSS and SAS was developed and tested using the Rotterdam Study dataset to ensure results matched between statistical software programs and then provided to each cohort for analyses.
Results Descriptions of all cohorts are summarized in Table  1. In all cohorts combined there is data on almost 69,000 people at risk of dementia with the range of follow-up years between 2 and 27. The average age at baseline is similar across cohorts ranging between 72 and 77 (Table  2 ). Each cohort is made up of [ 50% females, ranging from 56. 8% in FHS to 76. 3% in the Gothenburg studies (Table  2 ). All cohorts collect information on incident dementia and all but one cohort (CFAS I/II) also collecting information on incident AD.
Cohort enrolment, resampling, and survival bias Most cohorts contributing data to these analyses use a closed-cohort design with single enrolment, while two of the cohorts, FHS and the Rotterdam Study, are expanded during the study period, including additional individuals from the source population and one set of cohorts, CFAS I and CFAS II is composed of two comparison cohorts each with a follow-up assessment. Single enrolment in closed cohorts will limit the number of comparable individuals within the same age range, as the cohort on average becomes older over time. We intend to utilize the full potential of this collaboration by including all available data, i. e. expansion cohorts as well as the originally defined cohorts. On a participant level, we allow a single participant to be included in multiple epochs as long as they are free of dementia at the start of the epoch.
Within the Alzheimer Cohorts Consortium nine prospective population-based cohort studies leverage conscientiously collected data over a 25-year period with the aim to determine trends in the incidence of dementia and to unravel underlying causes. Uniting a wide range of diseasespecific and methodological expertise in research teams within and beyond these cohorts, the first analyses within the Alzheimer Cohorts Consortium are underway to tackle outstanding challenges in the assessment of time-trends in dementia occurrence.
Description of the cohorts included in the Alzheimer Cohorts Consortium ARIC-NCS Atherosclerosis Risk in Communities Neurocognitive Study, CFAS Cognitive Function and Ageing Studies, CHS Cardiovascular Health Study, PAQUID Personnes Agees QUID, AD Alzheimer's disease, N/A not available Trends in the incidence of dementia: design and methods in the Alzheimer Cohorts Consortium 1990 and 1993, with subsequent cohort expansions in 2000 (3011 individuals) and 2006 (3236 individuals). Participants have been examined once every 4 years. In addition, the entire cohort is continuously under surveillance for disease outcomes through linkage of electronic medical records with the study database. The 3C is a longitudinal population-based study of the relation between vascular diseases and dementia in persons aged 65 years and older [21].