dataesr/scientific-paragraphs-categorization huggingface dataset:
We do not fixate on a single type of error, nor do we emit a definitive judgement on peak quality, as it is impossible without supervision. This is made possible by using high-quality ReMap data; indeed, unsupervised anomaly detection presupposes a low proportion of anomalies. We have validated our approach using artificial data designed to model correlating elements and a noise of atypical peaks. The model autonomously learns multiple n-wise correlation groups of sources in both artificial in real biological data. As the underlying task is compression we use comparatively small, simple networks which nevertheless perform well. Hence, our method can be readily used on a laptop from training to application.
Furthermore, when comparing other ChIP-seq data to those tracks, similarity (meaning peaks for the same TFs in the same cell line at the same position) to high scoring peaks suggests this is a typical event. But if the data matches a low scoring peak, or no peak at all, it is atypical. What this means precisely depends on the data being studied, as discussed in the previous paragraphs: it can be noise, or if the user is confident in their data it could be an interesting anomaly worth studying. While we provide scores for selected ReMap data, our model can be reused to denoise any kind of region database with multi-view sources such as in the ReMap data format (where peaks are independant between cell lines and each have a TR and a source dataset, cf. documentation). Our hierarchical multi-view approach is a type of intermediate fusion, where a first latent space is learned based on one type of combination, followed by learning combinations of it across another dimension.
The full tensor represents the CRM; the latent learning by the convolutional kernels is focused on local combinations analogous to CREs (local clusters). Scaling the information budget is crucial to learn the appropriate correlation groups, and databases with more experiments will require larger models. Furthermore, models with higher budget will be able to remember more patterns, and as such will more rarely give low scores (since fewer patterns will have been discarded). To do so, we propose a Q-score based on whether known correlations influence the rebuilding. We introduce a group-based normalization to correct rebuilding biases and introduce interpretability. We believe these contributions could be applied to other latent variable models, and more generally to any black-box model with arbitrary complex correlations.
Generalization ChIP-seq protocols, and subsequent quality, can vary wildly. Since our approach learns how experiments are corroborated by others, such differences in quality are self-correcting. Hence, we do not require unified protocols like large consortia (ENCODE) would, and can work with heterogeneous data from multiple provenances. That being said, having more and larger genomic datasets for each Transcriptional Regulator will help. Other methods may take different perspectives, such as identifying not only shared elements but also those that are specific to specific cell types  [30]. In contrast with the latter, atyPeak marking a peak as atypical (low score) only means its local configuration is unusual among the training examples. It does not necessarily mean that its configuration is specific to a given cell line or specific to its source.
However, such information may be gained through another potential usage that we find intriguing. It would be imply training atyPeak on one type of genomic region to learn what their typical combinations are, and use this trained model on another type of regions. This would detect local configurations that are different from those observed in the training regions. For example, training on promoters and processing enhancers to identify enhancer-specific correlation patterns; peaks with those patterns will be given a low score. One just needs to be mindful that, like with all ML approaches, sufficient amounts of data must be available for the model to learn from. While our study focuses on ChIP-seq data, our approach can be generalized to any type of data consisting of a series of peaks, or more generally corroborating timeseries intervals from multiple datasets.
In genomics, this includes ChIP-exo, ATACseq, as planned for future ReMap releases, or even otherwise determined regions like promoters of overexpressed genes in a certain condition. But it could also be used, for example, to compare weather forecasting models. The atyPeak approach could also be applied to many multi-omics problems by changing the meaning of the dimensions, i. e. , integrating different assays for different cell lines instead of different datasets for different TRs. More generally, we propose to leverage typical combinations between sources to perform anomaly detection by representing multi-view data as K-dimensional tensors (for K views) and using structures designed to consider those combinations. To our knowledge, our approach shows the first use of a large-scale meta-analysis of ChIP-seq datasets to corroborate them with each other, using deep learning methods to integrate them in complex combinations.
Data sources In this study, data is provided by ReMap 2018  [21]. ReMap endeavors to identify and characterize regulatory regions from a large-scale integrative analysis of DNA-binding protein experiments. The 2018 human update uniformly annotated and processed 3180 ChIP-seq experiments, including some biological replicas, creating a catalogue from the analysis of 35. 5 million peaks (after merging) for 485 TRs in a variety of cell types and tissues. The regions of interest or Cis-Regulatory Modules (CRM) selected for this study are defined as a region binding at least two different regulatory proteins in all the cell lines and tissues of ReMap, in order to mitigate variation coming from non-standardized sources. A CRM can contains from two to a few thousand peaks, in one (or more) Cis-Regulatory Elements(s).
Model architecture The structure of the model is detailed in Fig. 1b. Our model has two parts: an encoder creating a latent representation and a decoder retrieving the original tensor. As with all autoencoders, the model is trained to try to rebuild the original CRM representation as its output. The full model parameters are available on GitHub. Our model was implemented using Keras 2. 3  [41] , with Tensorflow 1. 15 and NumPy 1. 18. 1  [42].
Convolutional encoding The CRM representations are viewed through sliding convolutional filters, to focus on correlations between the TRs and datasets. A convolutional filter gets as input a slice of a matrix and outputs a weighted sum of its elements, with the weights forming the filter proper. The first layers are two successive convolutions with two different types of kernels (combinations of datasets, then combinations of TRs). Let n be the number of TRs in the cell line, m the number of datasets and k the size of the convolutional kernels. As a result, the kernel shapes are (k, m, l, channel = 1) for the first, and (k, l, n, F) in the second where F is the number of filters in the previous layer. As there is no ordering to the datasets or TR, we perform a depthwise convolution and read the entire dimension at once.
Integrative layers and decoding The convolutional layers are followed by 4 regular (Dense) integrative layers, to learn complex combinations. On each layer, only the last dimension (filters) provides weights, resulting in Time-Distributed layers with no communication along the X dimension. The ReLu activation function is used. We obtain an encoded dimension in the fourth Dense layer. For the decoding, we consider each element of the non-human-readable encoded dimension as a latent variable. A first layer of convolutional filters reads the entire dimension once to produce a first decoding, and a second and final layer has one filter per source (TRdataset pair). This is done because unlike classical images, there is no order to the features. The final layers perform a reshaping of the result back to the original tensor shape.
Normalization of correlation group biases This normalization aims to correct bias in rebuilding based on the learned correlation groups. We calculate a weight for each source (meaning each {dataset, TR} pair), based on the following steps, to be applied to all anomaly scores computed for this source. μ X (T) designates the 2D matrix of the mean across the X axis (region size) of the 3D tensor T, and max X (T) its maximum. M[s] designates the value of the matrix M for the current source. h(T) is the tensor obtained as output when passing as input the T tensor to a trained atyPeak model. We note F t a "full CRM" which is a 3D tensor representation where all possible sources with abundance higher than zero are present along its length with a value of 1. Let F = μ X (F t ). The correlation group of a source can be estimated (see Interpretability) by preparing a CRM containing only a peak for the given source along its entire length denoted U s.
The final weight is k = k 1 * k 2 * k 3. To prevent overcorrection of sources that were not learned by the model, all k are capped at 10. For now, having a CRE with more peaks than average results in higher rebuilt values, as we consider that for CREs in particular more TRs mark denser/better CREs. This assumption could be changed here by penalizing values above the corrected average quality. The final step consists of centering and reducing/normalizing the scores by TR, under the assumption that no TR is inherently of a better quality than the others. Having more correlators (i. e. , data less sparse for the same dimension, more datasets per TRs) is a benefit. For each peak, if their score at this step is s their final score is s f = 750 * 1 + s-µ TR 2σ TR , where μ TR and σ TR are respectively the mean and standard deviation of scores observed for this source's TR at the previous step.
Impact of data characteristics/scaling on required information budget As we discussed in Results, the information budget determines the aggressivity of the compression. It depends on the relative information budget. It is the ratio between the quantity of information to be learned in the data (itself a function of the number of TR and/or dataset combinations) and the model's entropic capacity (how much information can be stored in the compressed representation). Adequate hyperparameter tuning is a widespread problem in deep learning as a higher information budget will predictably increase the model's Vapnik-Chervonenkis dimension and make it more prone to overfitting. In our case, the entropic capacity is mostly increased by increasing the dimension of all Dense layers and the number of convolutional filters on one side (more is higher).
But also by diminishing the learning rate (LR) on the other which was often necessary to reach lower losses, even with all other parameters constant. We saw in Fig. 3  that to achieve the same aggressivity, the required entropic capacity scales up with the quantity of information in the data. Figure  2B , on the contrary, is an example of overprecision. Larger dimensions (more datasets and/or TRs in the database) require a higher information budget, even with no additional information (Additional file 3: Fig. S3B ). However, Lower Learning Rates are more of a necessary condition than larger models to reach higher precisions with higher dimensions (Additional file 3: Fig. S3C ). Learning larger correlation groups (composed of more sources) is also harder. The most frequent sources are learned in more precise groups, while the rarer ones appear often grouped together in more "background" groups.
Q-score quantifies the quality of the reconstruction Autoencoders are usually evaluated based on the reconstruction error  [50]. Here, we instead propose a new method more adapted to the problematic. To rigorously choose the information budget, we propose to verify that the model correctly learns generated pairwise correlations. On one hand, if two dimensions (datasets or TF) correlate, finding them both together in the region of interest should result in a higher score for them than when they are found alone; on the other hand if they do not correlate, this should have no impact. To estimate this we design a Q-score, which is lower in better models. The Q-score is defined as: where ∀ i, j ∈ �, Q = i,j A i * A j * (P + R) C = R i, j > μ(R) Here Λ is a set of all TRs and all datasets (so all possible Y and Z dimensions, excluding the X dimensions of peak position) and the brackets are Iverson brackets denoting indicator variables.
Note that we only compare TRs with other TRs and datasets with other datasets, because a dataset and a TR are not mutually exclusive and issues can arise when considering a dimension that is only present as noise when another is present. For the same reason, we consider only positive correlation coefficients later. C asserts whether the Pearson correlation coefficient between the two considered dimensions is higher than the mean correlation coefficient. It is calculated on the tensor representations of the CRMs at the nucleotide level. For P and R, we take 10thousand CRM tensor representations T and their rebuilding h(T). For each of them, we record the values for the (i, j) dimensions of interest (averaged across X axis). We compare the average rebuilt value of A in different scenarios: For P, when a peak of i was present in T, does presence of j in the same CRM result in a higher rebuilt value for i? And for R, when i was absent, does the presence of j result in higher phantom values than when j is absent? To perform these comparisons, we use a Welch test to determine whether the means are different.
See Additional file 1: Fig. S1  for an illustration of the dimensions. First, we place a control peak called a watermark along all the length of the CRM for the 1st TF in the 1st dataset, representing ubiquitous TRs. It will be very frequent but not particularly correlated with other sources and so form its own correlation group. It has a customizable probability (default 75%) of appearing, to prevent the model from learning it and only it when it is too frequent. Second, we want to place a stack of correlating peaks from different TRs and datasets, at roughly the same positions. The stack will belong to one of two or more TR "correlation groups". The groups are made by splitting the set of all in TRs in two, or more, or by making groups of 4. Group choosing probabilities are equal by default but can be weighted.
Only one such group is picked per generated artificial CRM. We then pick a common center for the peaks, uniformly randomly across the region. Now, we pick K + 1 datasets without replacement among all predetermined reliable datasets (by default, the last half of them). In these datasets we will place N + 1 peaks. For each peak to be added, we randomly select P + 1 TRs from the current correlation group with replacement. N, K and P are random variables. For each TR selected, separately move the center by a distance j d (uniform R. V. between -200 and + 200), take a peak length randomly of L (L is a log-normal RV of μ = 250, σ = 0. 25) and finally, write the exact same peak among all the datasets selected previously. Note that since artificial data draws peaks at random, there is a larger number of possible combinations than there is usually in real data of the same dimensionality.
Third, noise peaks are placed uniformly randomly from all datasets and TRs to represent false positives and atypical peaks, which by nature do not respect existing correlation groups. To represent false negatives, each peak has a probability t = 1/4 of being removed at this tep. Then, we randomly position F + 1 peaks (F is a R. V. ) by drawing randomly their characteristics like previously. Noise cannot be placed in the watermark. group. As a result, sources within the G1 or G2 group will correlate with each other, but will not significantly correlate with sources from outside their group. We then add noise uniformly randomly that can belong to any dataset and TR, representing anomalies. Finally a control watermark peak is added with, usually, 75% probability, representing ubiquitous TRs. All values in the tensor are 1, denoting presence.
Availability of data and materials The datasets analyzed during the current study are based on ReMap 2018 and are available in our GitHub repository at <https:// github. com/ qferre/ atype ak>, along with the model source code. Treated data files with scores for the considered ReMap peaks are available as a UCSC session at <http:// genome-euro. ucsc. edu/s/ qferre/ atyPe ak_ hg38> (Fig. 5 ), or as BED files with supplementary diagnostic data at <https:// github. com/ qferre/ atype ak-files> and on the ReMap website at <http:// remap 2020. univ-amu. fr/ downl oad_ page>.
Technological developments in radiation therapy are at a steady pace, which can be the cause of difficulties in the use or mastery of equipment by professionals, especially as this equipment is increasingly complex  (IRSN, 2011). As part of a research project to understand the appropriation of a new system, we analysed the effects of introducing a new medical imaging system (MIS) on the safety barrier of "control of patient positioning by imaging" (IRSN, 2021). One of the hypotheses of the study is that the period of appropriation of the new system destabilises work practices and thus reveals the points of fragility of human safety barriers, which will then be "masked" by experience. Data from the study were collected at a Cancer Centre between 2014 and 2017. These data reflect the experience and organisation of this radiotherapy centre at the time of collection.
The dose delivered to the patient is greater. The second point of fragility is related to the fact that the new MIS no longer makes it possible to acquire a single image after a readjustment. This technical limit goes against optimising the dose delivered to the patient since radiographers are required to acquire two images even if only one is sufficient to control the positioning. Furthermore, the new MIS increases the workload for both radiographers and medical physicists by requiring them to program one anterior image and one lateral image for each patient upstream to treatment sessions, in addition to the two systematically programmed image pairs. A strategy that involves the use of OFFLINE Review software has been developed to allow radiographers to create only one image if it is sufficient.
Molecular dynamics simulations The [C 16 py][AmB] formulation was parameterized using the CL&P atomistic force eld,  30  an extension of the AMBER and OPLS force elds,  31  specially designed to study ionic liquids and their homologous series. Water, ergosterol, cholesterol, AmB and [AmB] À anion were modeled using the SPC model  32  and the OPLS force eld, respectively. 5] [36] [37] [38]  The runs in DL_POLY started from lowdensity congurations built with the PACKMOL package 39 and were performed using 2 fs time steps and 2 nm cut-off distances. All simulations were subjected to equilibration runs under isobaric isothermal ensemble conditions (p ¼ 0. 1 MPa and T ¼ 300 K with Nosé-Hoover thermostats and barostats with relaxation time constants of 1 and 4 ps, respectively) for 200 ps. Therefore, Gromacs simulations were performed using 2 fs time steps and 2 nm cut-off distances, with Ewald summation corrections performed beyond the cut-offs.
First, the different types of interaction centers were dened. In type (i) analyses the selected interaction centers were all non-hydrogen atoms in the ergosterol or cholesterol molecules. Type (ii) analyses were conducted by selecting all carbon atoms of the alkyl chain of the cation or carbon atoms of the unsaturated part of the AmB or [C 16 py] [AmB], and all non-hydrogen atoms in the ergosterol or cholesterol molecules. Second, a connectivity threshold for each case was established by considering the corresponding g ij (r) data between the selected interaction centers. 40 In cases (i) and (ii) the threshold was set to 0. 5 nm, corresponding to average intermolecular contact distances between non-hydrogen atoms evaluated from the corresponding g(r) functions. Third, the use of the threshold criteria allowed the computation of closestneighbor lists for each interaction center for all recorded congurations in the molecular dynamics trajectories, thus ascertaining the connectivity between the selected species.
Data were acquired using Empower 2 soware, 2006 (Waters Corporation). Sample injections of 50 mL were made using a 100 mL loop operated in partial loop mode. The mobile phase, at a ow rate of 1. 2 mL min À1 , consisted of 90% methanol and 10% acetonitrile. Each sample was run for 17 min. Ergosterol was detected at 282 nm and eluted at 13 min. Ergosterol was quantied by peak area according to a calibration curve. Three replicates of each condition were performed. Ergosterol content in the supernatant of mixtures of AmB, [C 16 py][AmB] or [C 16 py]Cl was taken relative to the control.
Acknowledgements This work was nancially supported by  Fundação para a Ciência e a Tecnologia (FCT ) by Project  MOSTMICRO ITQB  with refs  UIDB/04612/2020  and  UIDP/04612/2020 , and by the  European Research Council  through grant  ERC 2014-CoG-647928. The authors thank  FCT  for funding the  Research Unit Centro de Química Estrutural  ( UIDB/00100/2020  and  UIDP/00100/2020 ) and project  PTDC/QUI-QFI/29527/2017 , and  PT2020   Partnership Agreement  for project  Lisboa/01/0145/FEDER/028367. The authors thank  Associate Laboratory for Green Chemistry-LAQV  which is nanced by national funds from  FCT/MCTES  ( UIDB/ 50006/2020 ) and co-nanced by the  ERDF  under the  PT2020 Partnership Agreement  ( POCI-01-0145-FEDER -007265 ), to FCT ( PTDC/QUI-QOR/32406/2017 ) and  MAR2020  ( MAR-02.
Procedure We adopted a cross-sectional online questionnaire through a secure survey platform (Sphinx software), with a 9-month enrolment period. The study and consent procedures were approved by the ethics committee of the University (Comité d'Ethique de la Recherche Tours-Poitiers, n°2019-09-01). Online advertisement and snowballing were used to recruit participants. All participants endorsed an online informed consent and were subsequently directed to the online questionnaire. Measures were taken between 1-month to 12-months postpartum. Male or female participants completed the online survey assessing demographics, obstetric history, psychological history, birth experience, symptoms of anxiety, depression, PTSD, perceived parental bonding, and social support. Participants were instructed to answer about their experience during the birth of their most recent baby.
Measures Biographical data were collected using sociodemographic questions, medical, psychological, obstetrical history, and traumatic life events. A self-reported psychometric assessment allowed to measure the PTSD-FC, postnatal depression, social support, and perceived mother-infant bond. Sample description. The information regarding obstetric and birth-related history were collected through several questions such as the number of pregnancies, the number of children, the sex of the last child, the mode of the last birthemergency caesarean, vaginal vacuum, vaginal (no instrument) and vaginal (forceps). We also investigated the experience of distressing events during childbirth by asking "Have you experienced particularly upsetting or traumatic event during childbirth?" We also asked if the participants have lost a child before (i.
We also performed Bravais-Pearson correlations to calculate the bivariate associations between the MOS, the MIBS, the EPDS, the PCL-5, the CBTS total scale and subscales for mothers and for partners. The Bravais-Pearson correlations could allow us to explore the relationship between several biopsychosocial factors (i. e. , depression, perceived social support, mother-infant bonding) and CBTS total and subscales (possible PTSD-FC). Descriptive and statistical analyses were performed using SPSS 24. 0 software (IBM).