dataesr/scientific-paragraphs-categorization huggingface dataset:
For the co-expression analysis, we preferred mixture models to correlation-based approaches. Mixture models aim at identifying an underlying structure in modeling the unknown distribution by a weighted sum of parametric distributions, each one representing a group of co-expressed genes. Gaussian mixture models were relevant for microarray data and were applied with success on several datasets  [36, 37]. For RNAseq data, which are discrete,  Rau et al. [18]  rst concluded that normalized expression pro les modeled with a Poisson mixture are relevant for co-expression analysis. However, in the Poisson mixture, the dependence structure between samples is not considered and can mislead the results. To tackle this problem, they proposed then a Gaussian mixture after a transformation of the normalized expression pro les  [19].
This model seems to be more suitable for RNAseq co-expression analysis by providing a proper identi cation of the groups of co-expressed genes because it accounts for per-cluster correlation structures among samples. For these reasons, we chose this Gaussian mixture implemented in the coseq R-package. In conclusion, using these neutral comparison studies, we combined the most adapted tools for each step of a standard RNAseq analysis. DiCoExpress is a workspace, illustrated in Figure  1 , to be installed on a computer to create a user-friendly workspace for analyzing RNAseq datasets. The directory Data will store all the projects, and the directory Results will contain a subdirectory per project with all the results of the different steps. The directory Sources contains the R functions used by DiCoExpress.
Implementation To create DiCoExpress, we use the R programming language and several R-packages from CRAN and Bioconductor  [39]. Each step of the analysis has a dedicated function available in the directory Sources. Seven functions compose the core of DiCoExpress (Fig. 2 ), and they are combined in a script, stored in the directory Template_scripts for each project to specify the steps of the analysis and the parameters to use. A full description of these seven functions is available in Additional File 1.
Input les and data quality controls To run DiCoExpress on a project, the user has to provide only two input les: one containing a count table summarizing the mapped reads for each gene, named Project_Name_COUNTS. txt, and a second one with a description of the project design according to the experimental factors, named Project_Name_TARGET. txt. If functional gene annotations are available in a le, the user has the option to upload it. This information will be integrated into the result tables and can also be used to perform enrichment tests. The (1) Load_Data_Files function allows the user to upload the Project_Name_COUNT. txt and Project_Name_TARGET. txt les. A check is done to be sure that both les are adequately built: the samples in Project_Name_COUNT. txt le must be organized in the same order as the rows of the Project_Name_TARGET.
txt. If it is inconsistent, then the columns of the Project_Name_COUNT. txt are reorganized according to the column of the target le. DiCoExpress performs analysis for a complete and balanced experimental design. If this condition is not veri ed, then an error message appears, and the script stops running. A lter option in Load_Data_Files is proposed to extract a subset of samples leading to complete and balanced design, thus avoiding manual modi cations of the expression le. The ltering rules are described according to the Project_Name_TARGET. txt (see section Results for an example). The (2) Quality_Control function produces several representations of the dataset before and after ltering low expressed genes and correction of the library size effect. This step is optional, but we advise the users to perform it to evaluate the quality of the RNAseq data before further analyses.
The EM algorithm used to estimate the model parameters is known to be sensitive to the initialization point. Coseq uses a small-EM strategy, and in DiCoExpress, we go further to get robust results. First, mixture models with 5, 10, 15, 20, 25, and 30 subpopulations are estimated 5 times each to identify an interval for the nal number of co-expressed gene clusters. A second collection of models on this interval of a subpopulation is then estimated 40 times each (per default). The best mixture model is the one minimizing the Integrated Completed Likelihood (ICL). The ICL curve is expected to be a convex function of the number of subpopulations, and we use this criterion to assess that the chosen model ts well the data. A different behavior of the ICL curve means that the dataset is too heterogeneous.
In this case, we advise users to modify the dataset removing some genes that show a too at normalized pro le. For the co-expression analyzes, we recommend using a powerful calculation server. The RData object of the second loop is saved at each iteration so, if the function is stopped, the analysis can be resumed. The RData of the selected model is also saved. Moreover, several tables and graphics are saved in order to check the analysis quality and to explore the co-expression results.
Results And Discussion We illustrate the use of DiCoExpress by analyzing a dataset associated with the publication of Haddad et al. [43]. This RNAseq dataset describes gene expression in roots and mature leaves of Brassica napus with or without silicon (Si) treatment. Three biological replicates are available. The experimental design can be described by two biological factors Tissue and Treatment and a technical factor Replicate with three modalities (Fig. 3 ). To illustrate the outputs of enrichment tests, we used the annotation of B. napus v. 5 from the Brassica genome database  [44]  to perform enrichment analyzes. We tested DiCoExpress on the full dataset available in contrast to Haddad et al. , who only focused on the root samples. The procedure is described in Additional File 2 as a tutorial of DiCoExpress.
In this interaction analysis, we found 106 genes differentially expressed, and an example of a gene in this list is shown in Supplementary Fig. 2B. The hierarchical clustering on the top 50 DEGs ranking on their p-values for this contrast is also proposed by DiCoExpress (Supplementary Fig. 2C ). On the bottom of this plot, we observe groups of genes with a clear opposite behavior between the two tissues. For the others, the behavior is more variable, but all these genes are declared to be the most impacted genes by the treatment and in different ways in the two tissues. As users often need to compare DEG lists, in DiCoExpress, we propose the Venn_Intersection_Union function to generate these lists quickly. In the Brassica napus dataset, we unite three contrasts: [MatureLeaf_NoSi-MatureLeaf_Si], [Root_NoSi-Root_Si] and [MatureLeaf_NoSi-MatureLeaf_Si]-[Root_NoSi-Root_Si] to study genes impacted in their transcription by the treatment (Supplementary Fig.
In cluster 2 (85 genes) and cluster 5 (130 genes) are grouped genes showing a small difference in expression levels between the two tissues. For both clusters genes show over-expression following the treatment in roots (more apparent in cluster 5), but no signi cant change in leaves. The cluster composition based on probabilistic modeling of the normalized gene pro les is, as it could be expected, different from the groups of genes found with the DEG list comparisons (Additional File 3). Following the co-expression analysis, that nishes the statistical analysis of this dataset used to illustrate the use of DiCoExpress, we performed enrichment analyses on these 7 clusters, and also of the 106 DEGs for the interaction contrast, and they are available in the tutorial (Additional File 2). Further interpretation and discussion of the biology behind these enrichments are beyond the scope of our presentation of the DiCoExpress usage.
Conclusions DiCoExpress is a user-friendly workspace for analyzing e ciently multifactorial RNAseq transcriptome experiments from quality controls to co-expression analysis through differential expression analysis. We based the development of DiCoExpress on neutral comparison studies combining the most performant statistical approaches for each step of a standard RNAseq analysis. In DiCoExpress, we used generalized linear models (GLM) implemented in the R-package edgeR for differential gene expression analysis and Gaussian mixture models implemented in the R-package coseq to perform the co-expression analysis. DiCoExpress simpli es the GLM analysis proposing automated writing of all possible contrasts and optimizes the co-expression analysis with the re-estimation of the collection of Gaussian models.
DiCoExpress produces a collection of les to visualize the results and multiple summary les of the data for further data exploration. The integrated enrichment analysis with the hypergeometric test gives the user the rst glimpse at potential biological functions underlying the different gene lists. In conclusion, DiCoExpress allows biologists without advanced statistical knowledge and programming skills to use these two packages in a pre-existing and organized workspace. where for each project, the input les (target and count tables) must be saved. If an annotation le used to describe biologically the different result tables as well as a reference le for the enrichment analysis expression analysis using generalized linear models is performed by using the (3) GLM_Contrasts and (4) DiffAnalysis_edgeR functions.
Recently, many smartphones manufacturing companies have been focusing on implementing biometric technologies on smartphones (e. g. , fingerprint, iris scanning, facial recognition). However, biometric systems are made up of vulnerable components such as capture devices, communication channels, and databases, which are susceptible to several attacks  [34]. The protection of the biometric data is crucial since biometric systems introduce vulnerabilities that can be exploited by hackers to break into the system. The authors of ref. [27]  proposed a real-time biometric recognition system that is based on Orthogonal Line Ordinal Features (OLOF) extraction technique. The processing of the authentication is distributed on the mobile device and the server. The use of palm print that can be easily captured provides more security than any other authentication methods.
For instance, many military and governmental agencies maintain policies stipulating use of smart cards by military and civilian personnel for purposes of authenticating the holder's identity (e. g. , a smart card may employ public key infrastructure (PKI))  [38]. Existing smartphones are not equipped with a smart card reader; thus, they suffer from their inherent limitations in relation to two-factor authentication using smart cards. Several of companies have attempted to introduce solutions to address this limitation. Thursby Software  [39] , for example, has developed the PKard reader that can be connected to Android and Apple devices. PKard readers are plug-in readers that support a wide range of smartphones from different vendors. Additionally, ACS [40] has developed smart card readers that use NFC contactless technology with Bluetooth technology.
Registration Phase Prior to registration, the user obtains a digital certificate by registering with a digital certificate authority. The digital certificate comprises the public key and the digital signature of the user, along with its identity information. The registration phase is carried out only once unless the user re-registers. At this stage, the user enters their user ID and chooses a password. The password will be necessary later, and, in future logins, to ensure that the user is the legitimate owner of the smart card, i. e. , is not an imposter. The user ID and password are processed and sent securely to the server. On receipt of the user ID and password the server checks its user database to see whether the user is an existing user or a new user. If it is a new user, then the server registers the user and computes security parameters that are unique to the user.
Server Registration As soon as the remote server receives the user's registration, the following events are performed by the server: 1. Record the time of receipt of registration T reg. This is used to identify if the user is a registered user and, if so, when s/he was registered, and also to generate the nonce; 2. Check if the user is a new user by checking its database of existing users. This is done to protect against user duplication; 3. If the user is a new user, the server checks the validity of U's digital certificate in order to guarantee its authenticity and it checks if the certificate belongs to the user being registered (if the user is already registered, then they will be rejected); 4. Obtain U's public key from the digital certificate, which is used to encrypt future communication with the client; 5.
U's access policy file, including U's usage policy and access level, is concatenated with U's digital certificate, and the result is hashed using a suitable one-way function creating the message digest MD u = h 0 (User policy || User certif icate). MD u is used for mutual authentication with the client, and it can also be used to identify the user; 6. Generate the S secret keys x s and y. x s is used by the remote server to generate security parameters for future communications with the smart card. y is stored by the remote server in the smart card to be used by the smart card to generate security parameters for future communication with the server; 7. Create a new user account in the user database with the following parameters stored on the database: ID i , T reg , MD u ; 8. Compute NB. A i and V i are used to enhance the security methods for the smart card, to locally authenticate the user's password, without actually storing the user's password on board the smart card.
Server Authentication Once receives the authentication message, it carries out the following tasks: 1. Register the time of message arrival * ; 2. Calculate the time difference between arrival time * and authentication message sent time , extracted from the received authentication message, i. e. , ∆ = * -. If the time window is acceptable, the message is accepted, else it is rejected. The time difference between the time of arrival of the message and the time the message sent is important to avoid replay attacks; 3. Extract 's password ℎ0( ǁ ) = ⊕ ℎ0( ⊕ ⊕ ); 4. Compute = ℎ0( ⊕ ℎ0( ǁ )); 5. Compute = ( ⊕ ⊕ ⊕ ); 6. Check if * =. If so, then it proceeds to the next step, else, the user is rejected. This process is used to ensure the authenticity of the message received from ; 7. Use the shared column reference # to locate 's record in the server database; 8.
Use to decrypt {{ } ǁ } in to obtain and { }; 9. is also used as a seed for to generate ℎ, which is used to decrypt { }; 10. The decrypted is compared with the user stored in the server database. If they match, then the user is known to be a legitimate user, else the user authentication message is rejected; 11. For mutual authentication, signs using the cloud's private key; 12. uses 's public key to encrypt { ', { }, } and sends to the user, where ' is the current server time, and S is the server identity.
Use the shared column reference #CF to locate U's record in the server database; 8. Use T K to decrypt {{MD u } || SI} in D i to obtain SI and {MD u }; 9. T K is also used as a seed for CPRNG to generate Kauth, which is used to decrypt {MD u }; 10. The decrypted MD u is compared with the user MD u stored in the server database. If they match, then the user is known to be a legitimate user, else the user authentication message is rejected; 11. For mutual authentication, S signs MD u using the cloud's private key; 12. S uses U's public key to encrypt {T , sign {MD u }, S} and sends to the user, where T is the current server time, and S is the server identity. NB. and S in {T , sign {MD u }, S} are used to prevent impersonation and replay attacks.
where x s is S's secret key; 11. Personalize the smart card by storing V i , A i , N i , MD u , h(. ), y, and #CF, where #CF is the column number in the database where U's records are stored; 12. Send the smart card with the reader securely to the client with instructions on how to: (a) use the smart card with the smart card reader, (b) keep the smart card secure, (c) change the password, (d) unlock the card, (e) report any loss or theft of the card.
Face processing viewed through long-range spatial correlations 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 a publicly available fMRI dataset (see Methods), and show that our methodology allows us to find specific spatial patterns of the activity following the presentation of facial visual stimuli. These patterns are transmitted to the contralateral FFA with high correlation in both group and individual level analysis, 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 both robustness and a high specificity of our approach.
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 of the face  22. 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 significant correlation in the orbitofrontal cortex in our findings indicate that the social and emotional dimensions that are carried by faces are represented as spatial patterns of activity. Similarly, we also found significant cerebellar correlations for face-stimuli -an observation that is in agreement with studies which have implicated the cerebellum in facial information analysis  20, [26] [27] [28].
However, we observed stable task-related gradients of activity at the group level  12  , necessitating the existence of the stimulation-related processes which act to maintain the described spatial gradients. 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 favour 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  39. 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 stimulation, we demonstrated that the mean spatial overlap of the fMRI differential activity with EEG and MEG may be about 80%.
fMRI dataset In order to analyze functional pattern similarities in the brain during a given task, the freely available data were chosen from a work of Wakeman and Henson  48  (ftp://ftp. mrccbu. cam. ac. uk/personal/rik. henson/wakemandg_hensonrn/). The Matlab scripts attached to the original dataset were used for pre-processing 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. In the original 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.
Data pre-processing We put the obtained result (crosscorr_final. nii) (see supporting information) into the same MNI space as the original image using the 3D matrix in the intial SPM contrast image. 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 verified 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. The resulting files were then renamed (Ztransf_Crosscorr_final_co_transl.
Data analysis SPM12 toolbox (https://www. fil. ion. ucl. ac. uk/spm/software/spm12/) was used to analyze the data. We used the two-sample t-test to compare the obtained z-transformed images and the randomized images (FWE correction, p < 0. 05, extent threshold = 0). In order to ensure that there were no spurious correlations, we also used the two-sample t-test on the z-transformed images to compare the visual and auditory seed regions from the same initial contrast image. Given the nature of the stimuli used by Wakeman and Henson, we chose two regions of interests in our analysis: an occipital and a temporal one. In the occipital region, we considered the coordinates corresponding to the peak of the brain activity in the contrasts. In the temporal one, as a control region, we choose the coordinates of the primary auditory area which should not show activity during visual stimulation.
We used the contrasts con_0006. img (Faces (familiar + unfamiliar) > scrambled) and con_0005. img (scrambled > baseline) of every subject (the numbers of the contrasts are indicated according to the attached to the dataset Matlab scripts). We considered two seed points, the first one at the peak of the activity (Fig. 3c  and d   We used the matrix coordinates to perform the cross-correlation analysis with a radius of 5 voxels. When the activity peak is at the periphery of the brain, it is not useful to take the center of the pattern at the peak of the activity because in this case a large part of the pattern would be located outside the brain. When peaks are peripheral, it is better to center the pattern deeper into the brain with respect to the peak of activity so that the periphery of the pattern includes the peak (see Fig.
Likewise, at the strongest peak of significance in the group analysis, we verified in each subject if the correlations at these coordinates are not different from 1 (Table  3 ). In addition, given the distribution of the resulting data, which may not always respect the normal one, we also replicated the same analysis with a non-parametric approach using the SnPM toolbox (https://warwick. ac. uk/fac/sci/statistics/staff/academicresearch/nichols/software/snpm) (two sample t-test, number of permutations = 256, variance smoothing = [8, 8, 8]). In this case, we did not apply the z-transformation to the images; we only put them into the same MNI space as the original contrast and performed the abovementioned translation. This analysis is included in the supporting information part.
This chapter presents the contribution of databanks to the development of biodiversity knowledge through the example of marine biodiversity databanks. Focusing on the marine field allows us to insist on the imbalance of the unknown vs. the better known part. The chapter emphasizes the role of taxonomic and genetic databanks as well as the ongoing transformations that databanks are submitted to in order to answer pressing demands due to the biodiversity crisis. It aims to analyse the requirements biodiversity databanks have to satisfy in order to help both researchers and conservationists in their respective endeavors. It begins by pointing out the main characteristics and limits of biodiversity knowledge and defend the view that databanks are well-suited to overcome these limits as soon as they are widely accessible and interoperable.