dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, with respect to the storage of long bronchoscopic video recordings, the DICOM standard is usually not used. However, the newest version of the standard introduces the first extensions toward storage of long medical records. For diagnostic purposes, the most popular format is AVI. The most commonly used video codec is the (almost lossless) MJPEG, which results in broadband streams that are not suitable for streaming. By contrast, lossy codecs generally produce desirable narrow-band video streams. Unfortunately, as mentioned above, lossy codecs are hardly ever used for compression of surgery multimedia data if the decompressed images are meant to support a diagnostic process. These codecs are usually designed with the assumption of introducing a significant loss if used for effective compression (the decompressed image may differ significantly from the original).
Video Metadata Besides the medical data itself, some information (ie, related to the video, patient, pathology, etc) should be recorded in parallel. These metadata are essential to identify the hospital, surgeon, patient, and all information relevant to the surgery. These additional data increase the size of the total data to be recorded or transmitted. Moreover, these data are subject to specific constraints, as they must be processed using standard algorithms. These are also often sensitive data and must be compliant with regulations such as General Data Protection Regulation. Finally, the data are coming from different sources and suffer from a lack of integration, making it difficult for the expert to access the data in their daily practice in some cases. Despite all of these constraints, exploitation of these metadata offers several advantages and should be of interest for medicine at present and in the future.
In particular, this would enable improvement of the everyday practice of experts during the identification of the disease, diagnosis, and follow up. One way to circumvent the constraints mentioned above, especially the increase in data size and the lack of integration, could be to use data-hiding techniques to hide metadata in the medical video without increasing the total amount of data. In this way, the metadata would never become separated from the recording. Data hiding can be used to hide different types of information such as text, signature, code, image, video, or audio. It is crucial to include the patient metadata relevant to the recorded procedure in the video, including preexamination results, disease history, and linking to the patient data in a national or hospital database. Such an approach would enable having the data that are most important for follow-up treatment always available with the video.
The cross-correlation technique provides a unique correlation value per voxel, which summarizes the resemblance of spatial activity in the vicinity of this voxel with the seed pattern. In addition to the increasingly popular temporal connectivity analysis, our approach could be considered as the first analysis of spatial connectivity in functional neuroimaging, which searches for the existence of similar spatially encoded information (in the form of 3D time-averaged activity patterns) between brain areas. In this article, we present an implementation of such a crosscorrelation analysis and use it to analyze datasets which span two sensory modalities. Firstly, we simulated random datasets to confirm the robustness of our analysis. Then, using a dataset of contrast images for face and non-face stimuli, we showed that face-specific representations are shared so as to form a network of similar spatial patterns across the brain.
Due to the rotation, the seeds (which are cubes) span a spherical volume. Since both the rotations and the cross-correlation analysis are computationally intensive, it is important to accelerate the analysis using Fourier transform. The analysis of one subject took about 43 min on the PC optimized for performance (available in Windows options). The whole analysis on 16 subjects took about 4 hours. Importantly, we did not smooth the contrast images, which were used for cross-correlation. Smoothing was used only as part of preprocessing of the images to improve spatial correspondence and signal to noise ratio  ( Wang et al. , 2005 )  before making contrasts. In our analysis pipeline, we took the seed at the site of the significant activity peak in the classical group level analysis reported in the used datasets, thus we followed the same pre-processing steps as the published results.
12 voxels for the 1 st fMRI dataset (WH dataset) and 3. 74 voxels for the 2 nd one (ST dataset), so inferior to the distance of 5 voxels. At the most statistically significant peak in the group analysis, which should correspond to the seed location, we verified in each subject if the correlations at these coordinates are not different from 1. Furthermore, we verified the angles of rotations at the most significant locations at the group level and found that these maximum cor-Fig. 1. Schematic representation of the procedure of cross correlation. (A) The seed pattern is turned then compared with target patterns. (B) Activity in the seed pattern is crosscorrelated with the entire brain activity. (C) An example of a peak of activity where the pattern is centered deeper in the brain with respect to the peak.
(D) An example of a pattern that has a large part outside of the brain (indicated by the word 'Out' with arrow) -the situation to be avoided by moving the pattern deeper. In both C and D the square represents the pattern and the dot inside the location of the peak of activity. MRI-Cron software was used to display the figures ( https://www. nitrc. org/projects/mricron ). relations are not defined by the geometry of the cortex in the target region but fluctuate with a rather high inter-subject variability. For example, the rotation with a maximum correlation in the orbitofrontal cortex corresponds approximately to 60-90°of rotation around the X axis (SEM = 13. 8). Our analysis produces images containing the information on the rotational angles corresponding to the maximal correlation.
Processing the results of spatial cross-correlation We put the obtained result (crosscorr_final. nii) (see supplementary information) into the same MNI (Montreal Neurological Institute) space as the original image using the 3D matrix in the initial SPM contrast image (3 mm isotropic voxels). In addition, given the presence of a small shift between the cross-correlation image and the initial contrast, we used a translation of the obtained result in MATLAB (v 2017a). The obtained file was renamed (crosscorr_final_co_trans. nii). In order to verify if the latter has correctly been realigned to the initial contrast, we superimposed both images in SPM and checked that the peak in the cross-correlation image corresponds to the chosen coordinates in the initial contrast. Furthermore, we z-transformed the data after subtracting the mean to approximate the normal distribution.
Statistical analysis of the results of spatial cross-correlation SPM12 toolbox ( https://www. fil. ion. ucl. ac. uk/spm/software/spm12/ ) was used to analyze the data; statistical analysis was applied to the maximal values of normalized cross-correlations per voxel after ztransform. We used the two-sample t-test to compare the obtained z-transformed images and the randomized images (5% FWE (Family-Wise Error) inference with P = 0. 001 cluster-forming threshold, extent threshold = 0). These p-values are corrected for the multiple dependent comparisons and are based on the probability of obtaining clusters with k, or more, voxels above a threshold on the basis of Random field theory with Euler characteristics  ( Nichols and Hayasaka, 2003 )  (here, k is the number of voxels passing the threshold).
Correlation coefficients at the significant peaks were assessed using bootstrap with bias corrected accelerated confidence intervals (resampling 10000 times with a bias corrected and accelerated (BCa) percentile algorithm for confidence intervals  ( Carpenter and Bithell, 2000 ). A difference is judged to be statistically significant if confidence intervals do not overlap. In order to ensure that there were no spurious correlations, we also used the two-sample t-test on the z-transformed images (see details above) to compare the visual and auditory seed regions from the same initial contrast image. In addition, for both WH and ST datasets, given the distribution of the resulting data, which may not always respect the normal distribution, we also replicated the same analysis with a nonparametric approach using the SnPM toolbox ( https://warwick.
fMRI datasets and their analyses Firstly, we created simulated random images of brain activity as control datasets to verify whether or not statistically significant spatial patterns could be found in random brain activity and whether the shape of the brain could influence the results. In order to analyze functional pattern similarities in the brain during a given task, we chose two fMRI datasets, each of them coming from studies that explored different questions and sensorial modalities. In the first fMRI dataset  ( Wakeman and Henson, 2015 ) , authors used visual stimuli (facial and non-facial ones) on human subjects and explored the cortical response to face and non-face stimuli presentation. The second fMRI auditory dataset comes from a work where the authors  ( Strelnikov et al. , 2011 )  used complex auditory stimuli (words) to study cortical responses.
Simulated random datasets (RANDOM datasets) In order to perform a statistical control analysis for our crosscorrelation method, we generated 100 random datasets, which simulated brain NIFTI images having the same dimensions (sizes: x = 53, y = 63 and z = 46 voxels), shape and 3D smoothing as the 1 st fMRI dataset ( WH, 2015 , see below). These images preserved the shape of the brain in the MNI space; randomization consisted in the filling of brain shapes with random values from the uniform distribution in the range of the real values from the datasets, afterwards smoothing was applied. After the smoothing, the inclusive spatial mask was applied once again to restore the brain shape. We analyzed cross-correlation with occipital and temporal seeds as in the 1 st fMRI dataset in these random simulated images to test the possible effects of brain shape and smoothing on the results of cross-correlation (occipital coordinate: x = 16, y = 13 and z = 15 voxels ( ⇔ x = 33, y = -76 and z = -8 mm); temporal coordinate: x = 10, y = 32 and z = 21 voxels ( ⇔x = 51, y = -19, z = 10 mm)).
This direct effect of smoothing was statistically tested on the group of 16 images. In addition, we took the difference between random smoothed images to simulate 16 random "stimulation vs. baseline " contrasts; this simulation is close to our approach on the real contrasted images. Afterwards, we performed an analysis on 100 randomly formed different groups of images using the occipital seed (occipital coordinate: x = 16, y = 13 and z = 15 voxels ( ⇔ x = 33, y = -76 and z = -8 mm). We took the difference between random smoothed images to simulate 16 random "stimulation vs. baseline " contrasts per group. To exclude autocorrelations, double the volume of the seed was masked out. After the same analysis as in the real datasets (see Analysis pipeline), we assessed the fraction of false positive voxels at p < 0.
fMRI dataset 1 ( WH 2015 ) The freely available data were chosen from a work of Wakeman and Henson  ( Wakeman and Henson, 2015 )  ( ftp://ftp. mrccbu. cam. ac. uk/personal/rik. henson/wakemandg_hensonrn/ ). The MATLAB scripts attached to the original dataset were used for preprocessing and statistical analysis (using the MATLAB SPM toolbox), resulting in contrast images in the MNI space with 3mm isotropic voxels smoothed by a 3D 8mm isotropic Gaussian kernel before creating the contrasts. In this study, the subjects (n = 16) were presented gray scale images of familiar and unfamiliar faces, and faces scrambled by a 2-D Fourier transform. Original and scrambled faces were cropped using a mask built on the basis of a combination of familiar and unfamiliar faces. This dataset is composed of 2 contrasts: con_0005.
fMRI dataset 2 (ST dataset) The second fMRI dataset (indicated in the current work as fMRI auditory) was for auditory processing and originated from our work on speech processing done on 15 subjects  ( Strelnikov et al. , 2011 ). Subjects lay in the scanner with eyes closed and listened to dissyllabic words. One out of 13 stimuli was randomly repeated, and the subjects were instructed to press the button when they heard a repetition. The dataset comprises the contrasts of words versus silent baseline. In the present work we considered only 14 subjects. The 5 th subject has been discarded from the analysis because we suspected an anomaly regarding the shape of the brain of this subject even after spatial normalization to a standard MNI space. To verify whether the exclusion of this subject influences the general statistical results, we used one sample t-test for this dataset with and without this subject and looked for statistical differences.
Simulated random datasets (RANDOM datasets) To check the role of brain shape and smoothing, we performed the cross-correlation analysis with random smoothed values within the brain mask, which preserved the shape and external borders of the brain. In the smoothed random images, the only significant effect was observed for the coordinates of the initial occipital and temporal seeds. Similarly, in the random groups with smoothed contrasted random images the only significant effect was observed for the coordinates of the initial seeds. Thus, no significant effects outside the seed regions were found in simulated random datasets. At the level of p < 0. 05, uncorrected, the false positive rate in 100 random groups was 4. 4 ± 0. 4 % (mean ± SD) and at the level of p < 0. 05, FWE corrected (used in this study), the false positive rate was 0.
fMRI Dataset 2 (ST dataset) In order to verify the replicability of our approach, we conducted a cross-correlation analysis in an independent dataset putting the seed coordinates near the peak of activity in the left STG. As expected, the most significant peak in the group analysis was found at the location, which corresponds to the coordinates of the seed pattern. In addition, significant correlations were found in areas involved in word processing, for example: L and R STG, temporo-polar areas and supramarginal gyri (Tables  S10  and S12 ), indicating a high specificity of our approach. Furthermore, similar to the first fMRI dataset (WH dataset), all individual values of the cross-correlation exactly at the seed coordinates were equal to one. The values of the cross-correlations per subject for significant non-seed areas had confidence intervals of 0.
48-0. 59 according to the bootstrap analysis at p < 0. 05 ( Fig. 5  ). Moreover, we performed an additional cross-correlation analysis taking the seed in the contralateral hemisphere to the previous analysis position (in the R STG). In this reverse-seed analysis, the most significant correlation peaks were at the original seed (R STG), its vicinity and in the L STG (Tables  S11  and S13 ) indicating, as in WH dataset, that a pattern in the opposite hemisphere can be detected whatever the side of the seed pattern used for the analysis.
Discussion In the present work, we propose a new approach to analyze fMRI data using spatial correlations. As a proof-of-concept, we apply our approach to publicly available fMRI datasets (see Methods), and show that our methodology allows us to find specific spatial patterns of the activity following the presentation of visual (facial, in WH dataset) and auditory stimuli (ST dataset). The validity of our statistical analysis is confirmed by the fact that it always detects the seed pattern at the right locations and with the highest significance both in real and simulated random datasets.
Face processing viewed through long-range spatial correlations In the first dataset, we found that spatial patterns are shared with the contralateral FFA with high correlation in both group and individual analyses, indicating the conservation of spatial information regarding faces in both left and right FFA. In addition, by performing reverse-seed correlations (setting the seed in the contralateral area) we found the same results in the FFA -confirming the robustness of our approach. In our results, we found that the correlations are high (up to 0. 8), suggesting that a large amount of spatial information in brain activity is shared between these areas. The FFA is known to respond to face stimuli  ( de Souza et al. , 2008 ; Ghuman et al. , 2014 ; Kanwisher et al. , 1997 ; Posamentier and Abdi, 2003 ; Sergent et al.
, 2009 ; Troiani et al. , 2016 ; Willis et al. , 2014 ). Barat and collaborators (2018)  showed that orbitofrontal face cells encode facial stimuli by, first, discriminating them from non-facial ones, and thereafter categorizing them according to their social and emotional dimensions. Face neurons encode these aspects despite differences in face stimuli concerning, for example, identity or head position  ( Barat et al. , 2018 ). In the present study, we used the dataset from a work where the presented face-stimuli have various expressions (generally, happy or neutral). The presence of the significant correlation in the orbitofrontal cortex in our findings indicates that the social and   ( Carpenter and Bithell, 2000 ). In parentheses, MNI coordinates of the areas (x,y,z) are indicated. emotional dimensions that are carried by faces are represented as spatial patterns of activity.
Complex auditory information (words) is distributed as spatial patterns of brain activity Analyzing the second dataset, we found significant correlations (0. 5-0. 6) between areas involved in word processing, mostly in both L and R STG, temporo-polar areas and supramarginal gyri. These areas, involved in speech processing pathways, are known to process lexical information  ( Davis, 2016 ; Davis and Gaskell, 2009 ). Phonetic forms are coded in the STG and, thereafter, lexical information is integrated with semantic representations in the anterior parts of the temporal lobe  ( Davis, 2016 ). In addition, we found significant correlations in the supramarginal gyrus, which is considered an auditory-motor interface that organizes speech information into premotor and auditory representations  ( Davis, 2016 ; Davis and Gaskell, 2009 ).
No meaningful spatial patterns were found in random images of brain activity (RANDOM datasets) As a control analysis, we simulated smoothed random brain activity in order to verify whether the analysis of these simulated images would lead to significantly high correlations due to smoothing or to the brain shape. Our analysis in the Random datasets preserved brain shapes, the absence of significant results in the same statistical analysis as for the real datasets indicates the absence of the effects of cortical folding  ( Tijms et al. , 2012 ). Given the same coordinates of the seed, the significant differences in the contrast "face-specific vs. non facespecific " further indicate that anatomical structures do not influence the analysis. Besides, we performed cross-correlation on "stimulation vs.
These oxygenation related gradients are likely to be driven by electrical gradients, which encode the flow of local information. The results from our spatial-correlation analysis are in favor of this interpretation. We compared fMRI, EEG and MEG spatial differential activity during different tasks to see what amount of fMRI differential activity corresponds to the electromagnetic differential activity  ( Strelnikov and Barone, 2014 ). Distributed source reconstruction was used to obtain 3-dimensional models of electric and magnetic activity in EEG and MEG prior to spatial differentiation. Using independent datasets with the same stimuli, we demonstrated that the mean spatial overlap of the fMRI differential activity with EEG and MEG may be about 80%. In addition, about 93% of divergence (spatial sources) in fMRI corresponded to the EEG and MEG divergence.
Appendix. Supplementary data: Data generated and analyzed during the current study as well as the cross program and the MATLAB scripts are provided as a source data file. We also provide a package for the users with detailed instructions and examples how to conduct the analysis. Download link: https://osf. io/ze6bp/?view_only = 84670d16805 c4886b37d3ed69f441760 or https://osf. io/ze6bp/.
The aim of the present work is to specifically study iron homeostasis. In particular, we want to highlight key genes, which are systematically deregulated when C. glabrata faces decreased or increased bioavailability of iron. We performed transcriptomic experiments (microarray technology) to monitor gene expression changes of C. glabrata to ferrous iron (Fe 2+ ) deficient and overload conditions at 30 • C and 37 • C. These temperatures are respectively the usual temperature at which C. glabrata is cultivated in laboratories and the temperature at which C. glabrata develops in the human body. The resulting dataset was analyzed to (i) clarify the potential effect of temperature on iron homeostasis, (ii) identify iron responsive genes, i. e. genes significantly up-or downregulated in at least one iron imbalanced situation and (iii) define a new set of genes, referred to hereafter as 'iron homeostasis key genes' (iHKG, Figure  1 ).
These genes are good candidates to be chief components of iron homeostasis. Our exploration of the datasets was facilitated by the inference of functional networks of co-expressed genes, which can be accessed through a web interface (https: //thomasdenecker. github. io/iHKG/). The philosophy of this work is to empower experimental researchers by providing access to all transcriptomics data and by generating easily interpretable graphical outputs. This should facili-tate deep exploration of genome-wide functional data in the pathogenic yeast C. glabrata to advance our global understanding of iron homeostasis.
RNA preparation, labeling and microarray hybridization. RNAs were isolated using RNeasy Mini Kit for purification of total RNA (QUIAGEN, GmbH, Germany) according to the manufacturer's instructions. Concentration was determined using a Nanodrop 2000 instrument. RNA quality was checked using the Agilent 2 bioanalyzer Nanochip system according to the manufacturer's instructions. Microarrays were manufactured with eArray (https://earray. chem. agilent. com/earray/) from Agilent Technologies. There are described in the Gene Expression Omnibus (GEO) database  (30)  under 'Platform GPL27653'. Technical information is provided in Supplementary Note S8. Probe preparation, labeling and hybridization were performed with Agilent Technologies according to the manufacturer's instructions. The reference sample is a mixture of RNA extracted from all different growth conditions.
(C) Schematic representation of the two types of iHKG based on the de-regulations observed in our dataset, respectively in 'low' (-) and 'high' (+) iron conditions. 'Type I' are iHKG with opposite deregulations in low and high iron conditions, whereas 'Type II' are iHKG with constant (or parallel) deregulation in low and high iron conditions. Microarray data acquisition, inter-channel normalization and public access. Microarrays were scanned using the Agilent Array Scanner according to manufacturer's instructions. Pre-processing and inter-channel (Cy5 and Cy3) fluorochrome normalization were performed with MAnGO software  (31). Artefactual spots were eliminated from the analysis, duplicated spots were averaged. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  (30)  and are accessible through GEO Series accession number GSE139363 (https://www.