dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated data We conducted two simulation experiments. In both, we simulated a hypothetical histone mark across conditions 1 and 2, and along human chromosome 1 only (from the hg19 assembly). We set the fragment length to 150 bp and read length to 76 bp. We simulated the ChIP-seq histone modification region's length randomly from 1 to 20 kb. The simulated signal covered 10% of chromosome 1 in each condition, and differential regions represented 25% of the simulated signal. For each condition, we simulated three replicates. We simulated the technical variation between the replicates by controlling the noise level and GC-content bias in each replicate. For the noise level, we simulated its values by picking a random value from a normal distribution N ∼ (μ, σ ), (μ denoting the mean, σ the standard deviation), where we set μ 1 = 0.
75 for condition 1 and μ 2 = 0. 5 for condition 2, with σ = 0. 1 for both. We use a 'noise level' parameter to control fold change between signal and background; a higher noise level corresponds to lower antibody efficiency. In all our simulations, we set minimal fold change between signal and background equal to 2. To simulate GCcontent bias, we used six different GC-content bias profiles from different ENCODE datasets (see Supplementary Tables S1 and S2 for more details on the parameters for simulated data). In the first simulation (simulation 1), all regions had a normal copy number, while in the second (simulation 2) we simulated differences in copy number between conditions. Reads were simulated using a ChIP-seq data simulation tool that accompanied the HMCan method  (10). To simulate differential regions in simulation 1, we controlled the ratio of ChIP-seq read counts across regions to reflect the state of the simulated regions (C1, C2 or 'no difference'); we considered a region to be in a differential state when its read ratio was higher than 2.
Experimental data We used H3K27me3 and H3K27ac ChIP-seq data, and RNA-seq data, to evaluate the performance of HMCandiff in two experiments: (i) lung adenocarcinoma (A549 cell line) compared to normal lung tissue; (ii) human breast adenocarcinoma (MCF7 cell line) compared to primary human mammary epithelial cells (HMEC cell line). The A549 data were generated using the Diagenode polyclonal antibody specific to H3K27me3 (C15410069) and the Abcam antibody specific to H3K27ac (ab4729). Data is deposited in GEO with accession number GSE75903. Chromatin preparation and ChIP were performed with the Ideal ChIP-seq kit for histones according to the supplier's protocol (Diagenode). Data for the lung tissue were obtained from the epigenomic roadmap database, and data for the MCF7 and HMEC cell lines were obtained from ENCODE.
Evaluation on simulated data First, we compared HMCan-diff with other relevant tools: ChIPDiff (16), DiffBind (13), MACS2, MAnorm  (19) , MEDIPS  (21) , ODIN  (20)  and RSEG  (18) , on simulated data. ChIPDiff uses an HMM with a beta-binomial fixed emission distribution, and learns transition probabilities using the Baum-Welch algorithm  (29). DiffBind initially preprocesses ChIP-seq reads that fall into peak regions by subtracting the input read count; it then uses the edgeR package  (31)  to detect differential regions. MACS2 first calls peaks and constructs read pileup files for both conditions, then determines differential regions by assessing the fold change between conditions. MAnorm constructs an MAplot from reads falling in the common peak regions between the two conditions, then uses this plot to normalize data between conditions; after normalization it identifies differential peaks.
MEDIPS uses statistical methods developed in the edgeR package  (31)  to identify differential regions from various assays including ChIP-seq. MEDIPS also provides a threshold-based procedure to account for copy number variation in the analyzed data. ODIN uses HMMs to identify differential peaks; it models the emission distribution using a mixture of Poisson distributions. RSEG also uses HMMs; it models emission probabilities as the difference of two independent variables following the NB distribution. We evaluated the accuracy of each method by constructing precision-recall (PR) curves  (32)  based on the predictions of each tool. To construct PR curves, we sorted predictions of each algorithm based on their scores or p-values, then for each method we applied different thresholds to get different predictions sets.
We investigated different parameter sets for methods in both simulations (Supplementary Figure  S1  and Supplementary Figure  S2 ). Then, we selected parameters that yielded the best performance (Supplementary Table  S5 ). When we compared calculated PR-curves based on data simulated without copy number bias (Figure  2A ), we found comparable accuracy of most methods, with a slightly lower value for HMCan-diff compared to DiffBind (F-measure HMCan = 0. 95 and DiffBind = 0. 96). The poor performance of RSEG in simulation 1 was likely due to not accounting for different antibody efficiencies simulated in our in silico experiment. When we compared PR-curves calculated on a dataset where copy number bias was present (Figure  2B , simulation 2), we noticed a significantly lower performance of tools that did not directly account for copy number bias, unlike HMCan-diff, which maintained good prediction accuracy (Table  1 ).
Even though MEDIPS and DiffBind may account for possible copy number alterations (i. e. , DiffBind subtracts input to reduce the copy number effect while MEDIPS includes copy number at the final filtering step), our comparison on both simulated and experimental datasets showed that HMCan-diff gave in most cases a dramatically superior performance. The remaining methods do not include any component to handle variable copy number, and are not able to accurately detect differential regions when copy number variation is present. We noticed some decrease in performance of HMCandiff in simulation 2 compared to simulation 1 (corresponding, respectively, to the presence and absence of genomic rearrangements between the two conditions). This decrease e58 Nucleic Acids Research, 2017, Vol. 45, No. 8 PAGE 8 OF 9 was due to a series of normalizations applied by HMCandiff.
It applied some density values in regions with relatively weak simulated ChIP-seq signals close to or below the differential threshold. HMCan-diff accounts for dispersion present in ChIP-seq data by initializing HMMs based on local Poisson distributions or negative binomial distributions, and defining the emission probability for HMMs as the joint empirical distribution over density values. Also, HMCan-diff assumes that each condition has a single copy number profile, so when using HMCan-diff with replicates, only one copy number profile is estimated per condition. Similar to other differential peak detection methods, HMCan-diff is designed to work with homogenous samples only. In future, we plan to extend HMCan-diff functionality to handle a mixture of cancer and normal cells. This will enable us to extend the analysis from cancer cell lines to primary tumors.
SGCCA The classical hypothesis of imaging genetics, which is depicted on Fig. 2a  as a sequential model, states that imaging phenotypes are intermediate between genetics data and disease diagnosis. We proposed a first alternative hypothesis, named complete model, represented on Fig. 2b. The complete model would not impose any order in the contribution of genetic and imaging blocks of data to determine the subject phenotype (control vs. patient). We also tested the possibility of a reversed-sequential model (Fig. 2c ), where transcriptional data would be intermediate between imaging data and the disease diagnosis. To test which model fits better with our biological data, we applied SGCCA to the three designs. The performance of the multiblock analysis was evaluated by an external cross validation loop.
MRI acquisition Data were acquired on a 3-T MEDSPEC 30/80 AVANCE imager (Bruker). After an initial localizing scan, functional data were acquired using a T2*weighted gradient-echoplanar imaging sequence (number of repetition = 200; TR = 2400 ms; TE = 30 ms; FOV = 19. 2 × 19. 2; 64 × 64 matrix; flip angle 81. 6°; voxel size 3 × 3 × 3 mm³; slices = 36) along the anterior-posterior commissure plane with a continuous slice thickness of 3 mm. Following the functional magnetic resonance imaging (fMRI) scans, high-resolution anatomical images were acquired with a sagittal T1-weighted MP-RAGE sequence (TR = 9. 4 ms; TE = 4. 42 ms; TI = 800 ms; 256 × 256 × 180 matrix; flip angle 30°, voxel size 1 × 1 × 1 mm³). fMRI data analysis Data analysis was conducted as previously described. 51 Prior to analysis, the quality of the functional images was assessed using tsdiffana (http:// imaging.
mrc-cbu. cam. ac. uk/imaging/DataDiagnostics). Functional images were subjected to spike artifact detection. The quantitative quality indicators (signal-to-noise ratio, scaled variance, scaled mean voxel, slice by slice variance) were examined to ensure the stability of the signal over time and the lack of abrupt variation between successive slices. All data were analyzed using SPM8 software (Wellcome department of Cognitive Neurobiology, University College London; http://www. fil. ion. ucl. ac. uk/spm/ software/spm8). The first 4 volumes of each session, corresponding to signal stabilization, were excluded from the analysis. The remaining scans were corrected for differences in slice acquisition time. To reduce the effect of head motion, whole images were realigned to the mean scan of each session.
Realignment plots were examined to ensure the absence of excessive movements during the scan. Data were discarded from further analysis if movements in any axis were superior to 3 mm and/or 2°. The structural scan was co-registered to the functional images, and all images were transformed into a standardized coordinate system corresponding to the Montreal Neurological Institute (MNI) space. The normalized images were spatially smoothed with an isotropic Gaussian kernel (full width at half maximum of 6 mm). Finally, each preprocessing step was check using the Check Registration function implemented in SPM. The preprocessed functional images were analyzed using an eventrelated approach. Hemodynamic responses were modeled using a canonical function and convolved with the onsets and durations of each condition to form the general linear model.
Six movement parameters were included in the analysis as regressors of no interest. A 128 s high-pass filter was applied to the data to remove low-frequency noise. For each participant, first-level contrast images were calculated to estimate BOLD signal changes due to variation in emotional valence (negative vs. positive valence conditions), emotional congruency (incongruent vs. congruent conditions), and attentional level (attention to the scene [high] vs. attention to the face [low]). The first-level contrast images were then entered into a second-level one-sample t-test with a random effects statistical model to examine the main effects of the task at the group levels. We used a region of interest (ROI) approach, obtained from the 3 conditions of the fMRI paradigm, focusing on regions involved in the two main aspects of the task, visual processing: right and left thalamus (Thalamus_R/L), right and left inferior occipital gyrus (Occipital_Inf_R/L), right and left fusiform gyrus (Fusiform_R/L), right and left para hippocampal gyrus (Parahippocampal_R/L), and right and left superior temporal gyrus (Temporal_Sup_R/L), as well as areas previously implicated in emotion processing: the right and left amygdala (Amygdala_R/L), dorsal and rostral ACC (Cingulum_Ant_R/L), and right and left DLPFC (Fronta-l_Inf_Tri_R/L + Frontal_Mid_R/L).
These 16 ROIs were anatomically defined using the Automated Anatomical Labeling software implemented in the WFU PickAtlas. 87 We identified healthy group local maxima coordinates within each of these ROIs at a statistical threshold of P < 0. 001 voxel-wise (uncorrected for multiple comparisons). Using the MARSBAR toolbox,  88  we built 10-mm radius spheres (5 mm for the amygdala) centered around these coordinates. We then extracted for each subjects the mean activity beta value within each sphere that we entered in the RGCCA model. Blood mRNA extraction 8-10 ml of venous blood was collected from fasting SCZ patients and matched-HCs in EDTA tubes between 7:00 and 9:00 a. m. , I. E. just preceding MRI testing, and processed within 2 h. Peripheral blood mononuclear cells (PBMCs) were isolated from the blood by Ficoll density centrifugation.
Total RNA was extracted from the PBMCs with the mirVana miRNA isolation kit (Ambion, Austin, TX) according to the manufacturer's protocol. RNA concentration was determined using a nanodrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). RNA integrity was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), and only samples that exhibited an RNA integrity number (RIN) superior to 8 were further processed. 7 samples were excluded (1 patient and 6 controls) because of RIN <8. For the genetic imaging study, the samples corresponding to participants whose data could not be analyzed for fMRI were also excluded for gene expression. Finally, gene expression was evaluated on 25 SCZ patients and 20 healthy controls.
After verifying on DataAssist software (Applied Biosystems, v3. 0) that our 5 selected reference genes were stably expressed among all the samples from MDE patients and controls tested by RT-qPCR, we set-up 5 windows of expression intensities to normalize target gene expression for tLDA data (Ct < 22; 22 < Ct < 24; 24 < Ct < 25. 5; 25. 5 < Ct < 28. 5; Ct > 30) (Supplementary Table  5 ). CRYL1 was universally used as a reference gene for the individual amplification of the second set of genes. The expression level of each candidate gene was calculated as 2 - ΔΔCt with DataAssist. In this method, each candidate gene is quantified relative to the expression of one or two reference genes, to calculate a proximal level (i. e. difference between the target and the reference mRNA <2 Ct) of expression compared to the target gene.
SGCCA 4] [95]  RGCCA is a multivariate method adapted to the analysis of several blocks of variables. In a nutshell, RGCCA aims to extract the linear relationships that best explain the correlated structure across datasets. In this formalism, each dataset is called a block and represents a set of measurements obtained on the same individuals: in our case, we have three different blocks, one phenotypic block (e. g. age, gender, smoking status, clinical status etc. ), one block of gene expression data and one block of imaging data. Our method reduces the RNA and imaging blocks of variables to a few meaningful components, akin to principal components, that are computed such that they capture the variability of their own block while taking into account the variability in the other blocks. Being a component-based approach, RGCCA requires the estimation of block components y j ¼ X j a j ; j ¼ 1; ; J obtained such that (i) block components explain well their own block and/or (ii) block components that are assumed to be connected are highly correlated.
Simulation side The purpose of this work is to combine the Monte Carlo code FIFRELIN (described in the Sect. 3. 1) with the Brosa model (see Sect. 3. 2 for details) in order to get the kinetic energy distribution per mass. A procedure of energy loss correction was developed to compare the simulated data with the experimental ones (see Sect. 3. 3) and conclude on the compatibility between the observed components in the experimental kinetic energy distributions and the Brosa modes.
The repartition of the total excitation energy is done by using a temperature ratio law with two free parameters: RT min and RT max. Then, the angular momentum of the fission fragments (J ), are sampled according to the following distribution: P(J ) ∝ (2J + 1) exp - (J + 1/2) 2 2σ 2 (1) where σ 2 stands for the spin cut-off parameter. • The third step is to build the nuclear structure scheme for each fission fragment created. Above a cut-off energy (E RIPL cut-off ), the discrete experimental nuclear scheme coming from the RIPL-3 database  [14]  is completed with discrete levels with a model of level density (here CGCM  [15] ) up to a second cut-off energy (E bin ). Above this energy, the nuclear level scheme is described as a continuum. • The last step is to determine the de-excitation cascade which involves γ and neutron emission.
The probability to decay through the emission of a neutron is calculated using neutron transmission coefficients which were determined by using an external code (TALYS/ECIS  [16] ) and depends on optical model parametrizations. Thanks to the models of γ strength function (here EGLO  [17] ) the probability for an initial nuclear state to decay to a final step by emitting a γ -ray is calculated. In this work, only the post-neutron emission kinetic energy distributions are considered in order to compare FIFRELIN results with experimental kinetic energy distributions.
Conclusion and perspectives This work presents the path from Brosa fission channels to experimental kinetic energy distribution through the use of the Monte Carlo code FIFRELIN. It shows that both standard modes (StI and StII) combined with an energy loss calculation reproduce with a good agreement the experimental data points. The characteristics of the superlong mode (SL) are still under consideration as they fail to reproduce the data in symmetric region. Different ways of improvement were highlighted. Taking into account the time dependency of the target thickness through the parameter e should improve the agreement between simulated and experimental data. Using the experimental kinetic energy data to determine the characteristics of Brosa modes and look at the consequences on other observables calculated with FIFRELIN (yields, sawtooth,.
Methods Participants. Fifty-nine healthy volunteers with normal or corrected-to-normal vision participated in the study. Average age: 27 (MSE: 0. 71) years; 30 females. All participants signed an informed consent form. The study was approved by the ethics committee of the Tel Aviv Sourasky Medical Center (Israel). All the methods were carried out in accordance with the these guidelines and regulations. Participants received either monetary payment or psychology course credit points for their participation in the experiment. All experimental procedures were performed in accordance with the guidelines provided by the ethics committee. Apparatus and software. Samsung notebook (NP350U2A), screen size 12. 5", 1366 × 768 resolution, and refresh rate 60 Hz, Microsoft Windows 7 Home Edition, Service Pack 1 operating system was used.
MRI data acquisition. Structural MRI data (SPGR sequence) were collected for each participant using a 3 T GE MRI scanner (8-channel head coil) located at the Sourasky Ichilov Medical Center in Tel Aviv, Israel. Scanning resolution was 1 × 1 × 1 mm, providing full brain coverage, with TE = 3. 52 ms, TR = 9. 104 ms. Data Analysis. Behavioral data analysis. Data analysis was performed in MATLAB (R2009B version). The raw result values across several repetitions of the illusion were averaged. This resulted in a single raw value per participant/illusion. The magnitude of the illusory effect was calculated as a ratio between participant's perceptual estimation and physically correct stimulus properties  22, 31. The ratio values were subsequently transformed using the binary logarithm (base 2) to correct for potential non-linearity of the data  57.
To assess the significance of the correlations between illusions, Bonferroni multiple-comparison correction was applied based on the number of illusions. Confidence intervals were calculated for the percentiles by taking into account the Bonferroni multiple-comparison correction (i. e. , 99. 5% confidence interval for ten comparisons). Principal Component Analysis (PCA) for the magnitudes of the illusions (five illusions) was conducted using princomp MATLAB function (the input matrix: 59 participants × 5 illusions). Prior to submitting the data to PCA, for each illusion separately, the log-transformed magnitudes of the illusions were z-standardized (mean = 0, standard deviation = 1). The explained variance of each component was calculated as an eigenvalue of each component divided by the sum of eigenvalues.
To define this region, a single binary mask of the bilateral parahippocampal and hippocampus was constructed based on the aal masks of these regions  65  (http://fmri. wfubmc. edu/software/PickAtlas). The models estimated for the main analysis (Fig. 5 ) were restricted to this binary mask. To establish statistical significance, in all analyses we applied cluster-level correction using the "Non-Stationary Cluster Extent Correction for SPM" toolbox  64  (http://fmri. wfubmc. edu/cms/NS-General). We applied non-stationary correction, since it has been suggested that the use of standard cluster-based random field theory might be inappropriate because there is local variation in smoothness in structural images  66. In the VBM analyses of individual illusions, localization of significant clusters was conducted using Bonferroni multiple comparison correction for the number of illusions (p < 0.
These regions were defined as a sphere with a radius of 8 mm, centered in coordinates previously reported in the literature. In particular, the coordinates (in MNI space) for the PPA and RSC were used from ref. For the significant clusters found using VBM analysis we extracted gray matter density using the MarsBar region of interest toolbox for SPM  74  and custom code  75. The extraction of gray matter was performed for each participant, resulting in one data point per participant/per cluster. The extracted gray matter density was correlated with the behavioral illusory effect of each illusion. We took special precautions to avoid potential circular ("double-dipping") analysis  35. Our general strategy was as follows: For the clusters that were identified using the same or partly the same behavioral data (i.
e. , variable 1) and the behavioral scores of the Muller-Lyer illusion (i. e. , variable 2). Then, gray matter volume in this cluster was correlated with the behavioral scores of the let's say Ponzo illusion (i. e. , variable 3). Because variables 2 and 3 are independent, the selection of a cluster based on the correlation between variables 1 and 2, does not introduce a bias for the correlation between variables 1 and 3. In the supplementary material, we provide a MATLAB simulation code that illustrates this idea. Finally, for the clusters identified using the Ebbinghaus illusion, no statistical inference was conducted in correlation analysis between the local gray matter and the Ebbinghaus illusion magnitude (Fig. 6B ). The statistical analysis was conducted for the correlations between local gray matter and four remaining illusions (Fig.
Study Design This study reports on a secondary analysis of data collected during a previously conducted pilot RCT among homedwelling older adults. Data came from individual interviews exploring the components of the IWSS's acceptability after three-month, pilot RCTs conducted between August 1, 2014, and March 31, 2015. The trials involved using an IWSS intervention, as part of a tele-health protocol in the daily practice of CHNs, to rapidly detect health issues among older adults receiving home healthcare.