dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). When filtering, it is important to take into account the frequency response and impulse response of the filter. The default filter used adapts the filter length and transition band size based on the cutoff frequencies, as done in the EEGLAB software  (Parks and Burrus, 1987; Ifeachor and Jervis, 2002; Widmann et al. , 2015)   6  Although no default parameters will fit all analysis requirements, MNE chooses parameters that aim to achieve reasonable stop-band attenuation without excessive filter ringing. To illustrate this point, we compare filters across MNE versions using frequency response and impulse response plots in Figure  3. The stop-band attenuation and transition bandwidth in Figures 3A,B are less restricted in the newer versions, which results in less steep attenuation but also less temporal ringing in the impulse response (see Figures  3C, D ).
Epoching In event-related M/EEG studies, a trigger channel (in this data STI101) contains binary-coded trigger pulses to mark the onset/offset of events. These pulses can be automatically extracted from the data during analysis and the values on the trigger channel are mapped to the event IDs. MNE offers the possibility to extract events when the signal in the trigger channel increases, decreases, or both. It also allows the construction of binary masks to facilitate selecting only the desired events. We masked out the higher order bits in the trigger channel when extracting the events as these corresponded to key presses. After extraction, events can be freely manipulated or created as necessary by the user, as they only require (i) the sample number, and (ii) some integer code relevant for the experiment or analysis.
As a next step, we extracted segments of data from the continuous recording around these events and stored them as single trials, which are also called epochs, in MNE. The Epochs object can store data for multiple events and the user can select a subset of these as epochs[event_id] 8. Moreover, MNE offers the possibility for the user to define a hierarchy of events by using tags (similar in flavor to hierarchical event descriptors by  Bigdely-Shamlo et al. , 2013). This is done using event_id which is a dictionary of key-value pairs with keys being the tags separated by a forward slash (/) and values being the trigger codes 9. For the paradigm used in this study we used: e v e n t s _ i d = { ' f a c e / famous / f i r s t ' : 5 , ' f a c e / famous / i m m e d i a t e ' : 6 , ' f a c e / famous / l o n g ' : 7 , ' f a c e / u n f a m i l i a r / f i r s t ' : 1 3 , ' f a c e / u n f a m i l i a r / i m m e d i a t e ' : 1 4 , ' f a c e / u n f a m i l i a r / l o n g ' : 1 5 , ' s c r a m b l e d / f i r s t ' : 1 7 , ' s c r a m b l e d / i m m e d i a t e ' : 1 8 , ' s c r a m b l e d / l o n g ' : 1 9 , } At the highest level of hierarchy are "face" and "scrambled.
Caveats Although it is quite standard in EEG pipelines to subtract conditions without taking into account the number of trials, this needs to be carefully thought through. When subtracting two evoked responses with substantially different number of trials, one evoked response will be more noisy, and therefore the subtraction will have a noise level that should be properly estimated. This computation of adjusted SNR and noise variance is done by the mne. combine_evoked function, which was used in this study with the option weights='equal'. By using this option, if one considers a dataset with 100 trials in condition A and 10 trials in condition B, by weighting the averages across each condition equally, a single trial from condition A is effectively weighted 10 times less than a single trial from condition B.
This has the effect of amplifying noise from condition B but maintaining the amplitude of the underlying signal. However, this function can also be used with option weights='nave'. In that case, the number of trials averaged together will be taken into account in the contrast so that each trial, in either condition, will have the same weight. In this case, with the above example, the individual trials from the first condition will effectively be weighted the same way as trials from the second condition, which means that any underlying responses in condition A will be 10 times larger than those in condition B. This has been historically recommended by the MNE software for source estimation, but can be problematic when working with certain experimental protocols (for example, oddball tasks) which, by design, produce many more trials for one condition than for the other.
SOURCE RECONSTRUCTION The MNE software relies on the FreeSurfer package  (Dale et al. , 1999; Fischl et al. , 1999)  for the processing of anatomical MRI images. This automatic procedure is run using the command recon-all on the T1 MRI of each subject. This provides many useful pieces of information, but the most critical here are the cortical reconstruction (a high resolution triangulation of the interface between the white and the gray matter) and the inner skull surface. For inverse source reconstruction and beamforming, we must first compute the forward solution, often called a gain or lead field matrix  (Mosher et al. , 1999). Computing the gain matrix, which is a linear operator, requires having a so-called source space of dipole locations, a conductor model for the head, and the sensor locations relative to those dipoles.
Source Space As we expect most of our activations of interest to be due to cortical currents  (Dale et al. , 2000) , we position the candidate dipoles on the cortical mantel. It is worth noting that for certain datasets, this assumption may not necessarily be true. For instance, in the case of brainstem or cerebellum activations one could add these structures to the model as proposed by the MNE software. We chose a cortical source space obtained by recursively subdividing the faces of an octahedron six times (oct6) for both the left and right hemispheres. This leads, for each subject, to a total of 8196 dipoles evenly spaced on the cortical surface (See Figure  6  in  Gramfort et al. , 2014).
Head Conductivity Model MNE can use simple spherical conductor models but when the MRI of subjects are available, the recommended approach is to use a piecewise-constant conductivity model of the head. Tissue conductivities are defined for each region inside and the segmented interfaces forming the inner skull, outer skull and the outer skin. It corresponds to a so-called three layer model, however a single layer is possible when using only MEG data. The default electrical conductivities used by MNE are 0. 3 S/m for the brain and the scalp, and 0. 006 S/m for the skull, i. e. , the conductivity of the skull is assumed to be 1/50 of that of the brain and the scalp. With such a head model, Maxwell equations are solved with a boundary element model (BEM). In addition to the T1 MRI image, fast low-angle shot (FLASH) images are provided in the present dataset.
Group Source Reconstruction To analyze data at the group level, some form of data normalization is necessary, whereby data from all subjects is transformed to a common space in a manner that helps compensate for inter-subject differences. This procedure, called morphing by the MNE software, exploits the FreeSurfer spherical coordinate system defined for each hemisphere  (Dale et al. , 1999; Fischl et al. , 1999). In our analysis, the data are morphed to the standard FreeSurfer average subject named fsaverage. The morphing procedure is performed in three steps. First, the subsampled data defined on the high resolution surface are spread to neighboring vertices using an isotropic diffusion process. Next, registration is used to interpolate the data on the average surface. And finally, the data defined on the average surface is subsampled to yield the same number of source locations in all subjects (here, 10242 locations per hemisphere).
Once the morphing is complete, the data is simply averaged. What is presented in Figure  11  is the group average of the dSPM and LCMV beamformer solutions on contrast between faces and scrambled at 170 ms post-stimulus. Looking at these results, one can observe that both methods highlight a peak of activation on the right ventral visual cortex known to be involved in face processing  (Grill-Spector et al. , 2004 , 2017; Wakeman and Henson, 2015). To discuss the agreement between these seemingly different methods, we would like to point to  Mosher et al. (2003). While LCMV uses an empirical estimate of the data covariance to compute linear spatial filters, MNE postulates it. It is therefore totally possible that the both inverse methods offer comparable activations on a specific dataset. Note however that we do observe here that the dSPM peak is slightly more anterior.
Alternatives When strong hypotheses exist with regard to spatial, temporal and spectral regions of interest, it may be preferable to test the experimental hypotheses on fewer well-chosen signals. In the context of a group analysis, a linear multilevel modeling approach may provide an interesting option for obtaining joint inference at the single subject and group level  (Gelman, 2006; Baayen et al. , 2008; Dowding and Haufe, 2017). However, such methods are not yet available in MNE and were therefore not considered in our present analysis. We however, consider that by providing all our analysis code online, such methods could easily be tested and advertised by modifying very little code. Eventually, we hope that such a method will be implemented by an existing or a new MNE contributor.
It presents a fully reproducible group analysis of the publicly available dataset from  Wakeman and Henson (2015). All code and results are publicly accessible http://mne-tools. github. io/mne-biomaggroup-demo/. The study provides contextualized in-depth discussion of all major steps of the analysis with regard to alternative options, caveats, and quality control measures. As a rare contribution to the M/EEG literature, this study illustrates in comparative figures, the experimental results obtained when changing essential options at different steps in the analysis. In the following, we want to share some insights that we obtained from working together on this study.
We encourage the reader not to take the analysis presented here as a direct justifications for parameter choices used in their analyses, but instead learn the principles underlying the choices made in our examples. The general rule is: assess your options and chose the optimal measure at each processing step, then visualize and automate as much as you can. Practical recommendations: 1. Know your I/O. Make sure to have a clear idea about the metadata available in your recordings and that the software package knows about relevant auxiliary channels, e. g, stim, EOG, ECG. Use custom MNE functions and other libraries to add quick reading support if I/O for a file-type is not readily supported. 2. Understand the origin of the noise. Inspect your raw data and power spectra to see if and how much denoising is necessary.
Prefer automated scripts as opposed to interactive or manual pipelines wherever possible. 7. Keep software versions fixed. In an ideal world, software (and hardware) versions would not matter, as each operation necessary for data analysis should be tested against known results to ensure consistency across platforms and versions. However, this ideal cannot always be met in practice. To limit difficulties, do not change software versions, hardware or operating system versions in the middle of your analysis. Keep in mind that MNE is based on several other pieces of software. Updating them can have an impact on the outcome of MNE routines. Once data analysis is complete, cross-checking on different platforms or with different software versions can be useful for community feedback and identifying fragile or problematic steps.
Finally, we would like to emphasize once more that there is no single recipe for all data. Indeed, this approach can be dangerous and lead to surprising results. In that sense, there might be other equally valid pipelines recommended by other academic softwares  (Brunet et al. , 2011; Dalal et al. , 2011; Litvak et al. , 2011; Oostenveld et al. , 2011; Tadel et al. , 2011). The aim of this paper is to help the reader assess the right alternative at each stage of the analysis rather than recommend a single pipeline. In order to facilitate the reproduction of all the results presented in this manuscript, all the code used to make the figures in this paper, but also much more, is available at http://mne-tools. github. io/mne-biomag-group-demo/.
ACKNOWLEDGMENTS We would like to thank the many members of the  MNE community  who have contributed through code, comments, and even complaints, to the improvement and design of this software. During the preparation of this manuscript, we benefited greatly from discussions with other software package maintainers, such as Brainstorm maintainers who suggested that we visualize PSDs on a channel-by-channel basis to more effectively identify bad sensors. We thank  Sami Aboud  for discussions on the manuscript and proof-reading. This work was supported by  National Institute of Biomedical Imaging and Bioengineering  grants  5R01EB009048 ,  NIH   R01 MH106174  and  P41RR014075 ,  National Institute on Deafness and Other Communication Disorders  fellowship  F32DC012456 ,  NSF  awards  0958669  and  1042134 , the  French National Research Agency  ( ANR-14-NEUC-0002-01 ) and the  European Research Council Starting  Grant  SLAB ERC-YStG-676943.
Introduction Large-scale genomics projects, such as The Cancer Genome Atlas (TCGA), have greatly advanced biomedical research by generating, curating and publicly releasing multiple omics datasets collected from thousands of samples  (Weinstein et al. , 2013; Koboldt et al. , 2012; McLendon et al. , 2008; Omberg et al. , 2013). Beyond facilitating specific, hypothesis driven research, data from TCGA offer the unprecedented opportunity to conduct genome-wide integrative analyses that may offer insights not available from more directed 'look-ups' of the data. In this spirit,  Jiang et al. (2015)  integrated transcription factor (TF) profiles collected from the ENCODE project with tumor data from 6576 TCGA samples in 17 different cancer types. They found that tumor-specific gene expression is highly influenced by TF regulatory potential after controlling for local genomic factors such as promotor methylation and nearby copy-number alterations (CNAs).
TCGA data acquisition Processed TCGA Level 3 data on gene expression methylation, copy number alterations, somatic mutations and microRNA abundance from a total of 3288 samples of self-reported European ancestry and 17 cancers were downloaded from the Broad Institute Genome Data Analysis Center (GDAC) Firehose on March 18, 2017 using the TCGA2STAT R package  (Wan et al. , 2016). Raw genotyping image files (. CEL files from the Affymetrix 6. 0 platform) taken from matched normal tissue (i. e. non-cancer tissue) were downloaded for each of the 3288 samples from the National Cancer Institute's Genomic Data Commons. For all analyses, we only considered tumor samples for which data on gene expression, methylation, copy-number alterations, microRNAs, somatic mutations and germline genetic variation were available (Table  1 ).
Genetic variation Germ-line genetic variation was available via controlled data access through the Genomic Data Commons (GDC). We downloaded all Affymetrix 6. 0 image files (. CEL) files from the GDC derived from normal tissue. For each cancer, we performed genotype calling with the crlmm package in R. After genotype calling, we performed standard quality control steps including (i) set to missing any genotype called with a quality score less than 0. 8; (ii) remove samples with a subsequent missing rate greater than 3%; (iii) remove markers with a missing rate greater than 3%; (iv) remove markers with an minor allele frequency (MAF) less than 1%; and (v) remove any markers with a P-value for testing Hardy-Weinberg Equilibrium less than 5 Â 10 -8. For each gene, we considered genetic variants within 1 mega-base of the transcription start site of the gene.
Transcription factors Transcription factors (TFs) play an essential role in regulating gene expression. However, TCGA did not generate data directly measuring transcription factor abundance. In order to integrate TFs into our analyses, we used the expression of the gene (described above) that encodes the TF as a proxy for TF abundance. By crossing the combined lists of TFs provided by Ingenuity Pathway Analysis (IPA; QIAGEN Inc. , https://www. qiagenbioinformatics. com/products/in genuity-pathway-analysis) and the TRRUST database  (Han et al. , 2015)  with the genes for which RNA-Seq expression data were available, we thus characterized the expression of 877 different TFs.
Variable selection for TFs and miRNAs TFs and miRNAs can each potentially target multiple genes, and a substantial body of work has emerged to develop methods and databases to predict these TF-target and miRNA-target pairs, for example using text-mining  (Han et al. , 2015)  or bioinformatics-driven approaches  (Wong and Wang, 2015). However, as these regulatory interactions are complex and knowledge of the biological processes driving them is far from complete, identifying a definitive mapping of TFs and miRNAs to genes from the large lists of available predicted targets is not straightforward. For both TFs and miRNAs, several hundred expression measures were available, meaning that the inclusion of all measured TFs and miRNAs in each of our gene-level models was not possible. As such, rather than making use of databases of predicted TF-and miRNAtarget pairs to reduce the dimensionality of our data, we adopted an alternative approach.
To explore how TFs potentially regulate PTPN14 expression, we examined the existing ENCODE database to identify transcriptional regulators that bind to the PTPN14 promoter in T47D breast cancer cells, which revealed binding sites for GATA3 and FOXA1 (Fig. 4A ). Likewise, the 'TF contribution' tab in the EDGE in TCGA tool revealed that GATA3 and FOXA1 expression were strongly correlated with PTPN14 expression in BRCA and suggested an inverse relationship (Fig. 4B ). The ability of GATA3 and FOXA1 to repress the PTPN14 promoter was tested using a PTPN14 promoter-luciferase reporter assay. Compared with the empty vector control group, the PTPN14 promoter activity was significantly downregulated by co-expression of GATA3 ($5-fold, P < 0. 001) and FOXA1 ($3-fold, P < 0. 001) (Fig. 4C ), suggesting that GATA3 and FOXA1 indeed repress the PTPN14 promoter.
Second, we provide a tool for comparing the relative importance of fixed effects (MUT, CNA, METH, miRNA, TF) and random effects (GEN) on the expression of a specific gene. We do not provide any measures of absolute importance and the results should not be interpreted in this way. For example, the effects shown on the TF zoom plots (Fig. 2 ) do not represent effect sizes that estimate an absolute quantity; a value of À3 on this plot means that the associated TF down-regulates expression of the gene 3 times more than a TF with a value of À1. As more large-scale omics data continue to be generated [for example, through the National Heart, Lung and Blood's (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program], there will be renewed interest in 'integrative' analyses that bring together data on germ-line genetics, gene expression, methylation, proteomics and metabolomics.
Funding AR was supported by the  AgreenSkillsþ fellowship program , which received funding from the  EU 's  Seventh Framework Program  under grant agreement number  FP7-609398  (AgreenSkillsþ contract). MJF was supported by the  NCI  ( R01CA193343 ), the  Mary Kay Foundation  (Grant No. 024-16 ), the  Advancing a Healthier Wisconsin Endowment , the  Dr. Nancy Sobczak Fund for Breast Cancer , the  Wisconsin Breast Cancer Showhouse  and the  MCW Cancer Center.
This trade-off between time and accuracy (quality) requires big-data analysis architectures to support both Batch Data Analysis Processes (for latent but quality solutions) as well as Streaming Data Analysis (for near realtime situation awareness). The deployment and provision of efficient-management systems for b data applications will greatly benefit from a cloud-like middleware infrastructure, which could be formulated to create a combined environment consisting of multiple private (municipalities, enterprises, research organisations) and public (Amazon, Microsoft) infrastructure providers to deliver on-demand access to emergency management applications. Cloud computing (CC) assembles large networks of virtualised ICT services such as hardware resources (such as CPU, storage, and network), software resources (such as databases, application servers, and web servers) and applications.
Pytim is a versatile python framework for the analysis of interfacial properties in molecular simulations. The code implements several algorithms for the identification of instantaneous interfaces of arbitrary shape, and analysis tools written specifically for the study of interfacial properties, such as intrinsic profiles. The code is written in the python language, and makes use of the numpy and scipy packages to deliver high computational performances. Pytim relies on the MDAnalysis library to analyze the trajectory file formats of popular simulation packages such as GROMACS, CHARMM, NAMD, LAMMPS or Amber, and can be used to steer OpenMM simulations. Pytim can write information about surfaces and surface atomic layers to VTK, CUBE, and PDB files for easy visualization. The classes of Pytim can be easily customized and extended to include new interfacial algorithms or analysis tools.
[5, 6] ore recently, the field has been stirred up by the necessity of investigating the structure of fluid/fluid interfaces from an intrinsic point of view, namely, by removing the smearing effect of thermal capillary waves,  [7]  and several methods focused on liquid/liquid interfaces were developed, such as Willard and Chandler instantaneous liquid interfaces method,  [8]  the circular variance method of Mezei,  [9]  the method of Škvor and colleagues  [10]  or the method of Yesylevskyy and Ramseyer for the calculation of the local curvature. [11] he Pytim package includes a series of algorithms to identify phases and surfaces or surface atoms, and perform various types of analyses related to interfaces. The main algorithms implemented in Pytim are: (1) the identification of truly interfacial molecules  [12]  (ITIM) for macroscopically planar systems; (2) the generalized ITIM  [13]  (GITIM), for arbitrarily shaped systems; (3) the algorithm of Willard and Chandler,  [8]  to compute continuous surfaces based on a smoothed estimate of the atomic volumetric density; (4) the Lee-Richards algorithm  [3]  to compute the SASA and the associated solvent-exposed molecules; (5) an efficient cutoff-based clustering algorithm to distinguish liquid and vapor phases,  [14]  and (6) an improved, density based clustering algorithm for highly miscible systems.
[15] ll algorithms that identify surface atoms can be used not only to compute the interfacial layer, but also the successive layers beneath it. The algorithms and the peculiarities related to their implementation in Pytim are briefly reviewed in the next section. The underlying philosophy of Pytim is to provide a flexible, extendable, and easily scriptable system for the calculation of interfaces and interfacial properties in molecular systems that free the user from the burden of writing ad hoc code for different simulation packages. Pytim is built on top, and extends, the MDAnalysis library,  [16]  which provides the backend for reading different trajectory formats. All algorithms are implemented by making use of the efficient numerical libraries wrapped by numpy and scipy that take advantage of the SIMD architecture of modern processors.