dataesr/scientific-paragraphs-categorization huggingface dataset:
The demonstration evaluated the above scenarios that have all been considered for the validation of the MC services of the 5G ESSENCE architecture. Three situations have been identified, briefly discussed as follows: In the first situation, the 5G ESSENCE platform provided the required network slices (in terms of radio frequencies, data rate, cloud resources and QoS) in the area of the CESC cluster. This situation has been considered as a baseline to deploy the second situation, where an emergency in the area occurs. In this case, the CESCM was able to react to the new service requirements and satisfying the needed requirements of first responders in terms of capacity and computation resources, because the PS communications provider may require additional service in order to cope with an increased number of first responders or additional types of services such as mission-critical video transmissions.
This work has been realised in the scope of the 5G-ESSENCE European Research Project and has been supported by the Commission of the European Communities under the H2020/5G-PPP Grant Agreement No. 761592. DeclarationsConflict of interestThe authors declare no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
Funding This work has been realised in the scope of the 5G-ESSENCE European Research Project and has been supported by the Commission of the European Communities under the H2020/5G-PPP Grant Agreement No. 761592. DeclarationsConflict of interestThe authors declare no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
, 2018) , allowing the generation of higher-resolution matrices with fewer empty bins (Figure  S1E ). Finally, we included a streptavidin-mediated pull-down of biotinylated interactions immediately after sonication and size-selection of the library (Figure  1A ). The preparation of the sequencing library was then done on the streptavidin beads carrying biotinylated chimeric DNA fragments  (Orlando et al. , 2018; Rao et al. , 2014) , facilitating a further 3. 5-fold enrichment of informative reads and a ~10-fold increase overall (Figure  S1F ). To evaluate the quality of the data generated through this approach, the resulting bacterial contact maps (Figure  1B  and C) were compared to published datasets from different laboratories, processed using the same computational pipeline (Table  S1 ).
Discrete DNA loops scattered throughout the chromosomes Finally, the increased resolution facilitated the visualization and characterization of finer structures such as intra-chromosomal boundaries (Figure  3D ) and DNA loops (Figure  3E ). To detect and quantify these structures we utilized Chromosight, a computer vision-based program recently developed to call de novo DNA motifs in Hi-C maps  (Matthey-Doret et al. , 2020). Chromosight detected an average of 26 borders in the Hi-C contact maps of WT H. volcanii cells across three biological replicates (Figure  3F ), in good agreement with the 23 detected by the DI analysis (Figure  2C ). Furthermore, and more intriguingly, Chromosight detected an average of 64 loop-like structures in the H. volcanii genome (Figure  3F ), most of which are on the main chromosome.
Wide-field microscopy and DNA staining of H. volcanii cells DNA was labeled using the fluorescent Hoechst 33342 dye (excitation 361nm / emission 486 nm). Exponentially growing cells were centrifuged and resuspended in an equal volume of 18% SW containing 5 µg per ml of Hoechst 33342. Cells were incubated for 10 min in the dark and then mounted onto glass slides covered with a thin layer of 1% agarose (suspended in 18% SW solution). DIC and fluorescent images were obtained at room temperature using a ZEISS Axio Observer microscope equipped with a 40x, 1. 4 NA oil immersion objective. 365 nm excitation at the maximum available intensity (2 W. cm -2 ) and filter set 49 (EX G 365, BS FT 395, EM BP 445/50) were used for fluorescence imaging of Hoechst signal. A Z-stack (30 slices) centered on DIC focus was performed and data was collected sequentially for DIC and Hoechst signals at each plane.
Correlation of Hi-C contact maps and transcription To investigate the correlation between transcription and chromosome contacts in WT H. volcanii, we took the corresponding RNA-seq dataset and mapped the reads against the reference genome. Only reads with a mapping quality > 30 were conserved. The signal was then binned to match the binning of the corresponding scn normalized  (Cournac et al. , 2012)  contact maps (5kb) and the two datasets plotted according to their genomic coordinates (Figure  2E ). Both the Hi-C contact and transcription signals were smoothed with a Savitzky-Golay filter and the Pearson coefficient was determined.
Differential expression analysis The sorted BAM files and a GFF annotation file of the reference genome were used as inputs for HtSeqcount  (Anders et al. , 2014)  to obtain read counts per transcript. HTSeq-count was run with default parameters, except that the strand option was set to suit dUTP libraries (-s reverse) and the mapping quality score was increased (-a 30). The analysis was performed using RStudio  (RStudio Team, 2020)  (version: 1. 3. 959) and R (version: 4. 0. 1). DeSeq2  (Love et al. , 2014) (version: 1. 28. 1) was used to import read counts for the investigation of transcriptomic differences between different strains/treatments. Differentially expressed genes were defined as those with an adjusted p-value < 0. 05 and log2 fold change in expression of >2 or < -2. The full list of differentially expressed genes for each condition has been uploaded to the SRA database (Key Resources Table ).
For the Differential expression analysis of actinomycin D compared to the untreated control, 26% of genes were upregulated and 31% of genes were downregulated. We mostly focused on regions of the genome that showed a decrease in gene expression, but one would assume that the upregulated genes are involved in the stress response. However, because many of the genes in the H. volcanii genome are yet to be assigned a function, we were unable to determine if this was indeed the case. When stationary phase cells were compared to exponentially growing cells, 54% of genes showed >2-fold change in gene expression. In smc mutant cells < 5 % of genes with a >2-fold change in gene expression compared to WT cells in the same growth phase. Full details of all R scripts are available upon request.
Identification of active / inactive compartments and compartment-like structures The compartment index was calculated as described previously  (Lieberman-Aiden et al. , 2009). To facilitate the comparison of Euryarchaeal compartments with those previously published in Crenarchaea  (Takemata et al 2019) , the raw reads from the latter were downloaded from the SRA database (https://www. ncbi. nlm. nih. gov/sra) and re-processed using our pipeline (See Key Resources Table  and Table S1 ). For each Hi-C matrix, a distance-normalized matrix was generated. These matrices were then converted into Pearson correlation matrices and from this, the eigenvalues and first eigenvector were calculated. The eigenvector allows the A and B compartments to be attributed. Positive and negative eigenvalues were arbitrarily chosen to correspond to the A and B compartments respectively.
Genes  Dev. 29, 1661 -1675. Wendoloski, D. , Ferrer, C. , and Dyall-Smith, M. L. (2001). A new simvastatin (mevinolin)-resistance marker from Haloarcula hispanica and a new Haloferax volcanii strain cured of plasmid pHV2The GenBank accession number for the sequence reported in this paper is AF123438. (Microbiology Society,).
Acknowledgments We thank  Olivier Espéli ,  Frédéric Boccard , and  Vicky Lioy  for the careful reading of the manuscript. We thank  Jacques Oberto  for his help in running the T. kodakaraensis experiment with support from  Project EVOMOBIL  ( ERC Grant  Agreement no. 340440 ). We also thank the members of the  RSG lab  and especially  Martial Marbouty  for insightful discussions about experiments, as well as  Théo Foutel-Rodier  for computational guidance, and  Cyril Matthey-Doret  and  Axel Cournac  for providing access to the  Chromosight program. This research was supported by funding to R. K from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreement  771813 ).
Medical questionnaires are a valuable source of information but are often difficult to analyse due to both their size and the high possibility of having missing values. This is a problematic issue in biomedical data science as it may complicate how individual questionnaire data is represented for statistical or machine learning analysis. In this paper, we propose a deeply-learnt residual autoencoder to simultaneously perform non-linear data imputation and dimensionality reduction. We present an extensive analysis of the dynamics of the performances of this autoencoder regarding the compression rate and the proportion of missing values. This method is evaluated on motor and non-motor clinical questionnaires of the Parkinson's Progression Markers Initiative (PPMI) database and consistently outperforms linear coupled imputation and reduction approaches.
Introduction Data representation is a critical problem in biomedical data science, in which the data available concerning an individual patient can be simultaneously large, uncertain, heterogeneous, and incomplete. It is common knowledge in the machine learning community that accurately curating data and providing a uniform representation are often critical to the success of later approaches. Medical questionnaires are one of the major ways of assessing the clinical state of a patient, providing crucial information necessary to diagnose and monitor patients. Extracting this information is crucial to ensuring patient care in the era of computerised, personalised medicine. Unfortunately, medical questionnaires databases often suffer from missing values for various reasons: some tests cannot be performed by some patients or aren't performed at each visit, problems can occur when data is computerised, or paper records can be lost.
Data Imputation Imputation can be conceptually split into methods that are applied prospectively, where a possibly complete or incomplete training database is used to estimate missing values for an incomplete and previously unseen data vector, and methods that are applied retrospectively, where information from an incomplete database is extracted in order to estimate its own missing values. In a clinical context, prospective imputation is of greater utility, allowing new patient records to be processed, although retrospective is more commonly used in research contexts in which an entire database is often analysed at the same time. The most common way of performing retrospective imputation is case deletion, in which every sample with at least one missing value is removed from the database. This method has the benefit of being easy to use, but suffers from two main drawbacks, in addition to not being capable of prospective use.
First, if missing values are distributed amongst a large number of samples, it can substantially reduce the size of the database, limiting the power of any statistical or machine learning method. Secondly, if the probability of which values are missing is not independent or changes based on the value the variable would have otherwise taken, removing incomplete lines can introduce bias into the study  [16]. Until recently, the most frequently used method for imputation was meanor median-replacement in which the missing variables are replaced with a constant, specifically the mean or median of the database as a whole  [17, 18]. Once a prior database has been collected, it is possible to use this technique prospectively, and it is relatively simple to implement and update as more patients enter the database.
However, it is easy to see how this reduces the variability in a database and thus can bias down-stream statistical methods  [18]. A common but more nuanced method for data imputation is fitting a linear model to the data. The most commonly used method now is arguably Multiple Imputation through Chained Equations (MICE) in which individual variables are sequentially imputed in an entire database using simple linear regression, starting with the variable with the least number of missing values and using complete datapoints to initiate the process  [19]. The downside of this approach is that it is designed specifically for retrospective use and it is unclear how accurate it would be on unseen data that does not contribute to the construction of the model. Alternatively, Principal Component Analysis (PCA) in particular can be naturally extended to perform prospective imputation by removing the PCA eigenvector components corresponding to the missing values when calculating the PCA scores but using the full eigenvector when transforming the scores back into the data space.
Assuming the training database also contains missing values, the PCA decomposition can be determined through several methods  [20]. Pairwise correlation PCA (PPCA), for example, computes the mean vector and correlation matrix from all the data vectors available with the corresponding values  [20]. Iterative PCA (IPCA) is an expectation-maximisation algorithm that iterates a process of PCA decomposition and imputation until the PCA decomposition converges  [21, 22]. These methods are designed to preserve the mean and covariance of the observed data through the process of imputation. The fact that PCA is also a common dimensionality reduction method makes it even more suitable for patient normalisation, although their linear nature may be problematic as aspects of the question may be coupled in a fundamentally non-linear manner.
Unfortunately, the regularisation term which encourages this correlation requires a low number of modalities and complete data vectors for training which limits their applicability to medical questionnaires where this may not be the case. Nevertheless, neural networks can be difficult to design and to train for various reasons. Primarily, the shape of the neural network highly affects its performance. For example, even assuming low parameterisation and risk of overfitting, a shallow neural network may not be able to capture the non-linearities of the inputs, while a too deep neural network could suffer from training issues such as inability to propagate gradients effectively. In this paper, we propose a method relying on an autoencoder architecture with a densely-connected encoder and decoder consisting of fully-connected layers, which we call a Fully Connected Autoencoder (FCAE), to address both data imputation and dimensionality reduction.
for higher depths to be used to capture non-linearities, and has already been used successfully for data imputation  [30]. The final layer of the encoder is a dense layer used to estimate the internal representation. The decoder is constructed similarly to the encoder, with a series of computational blocks each receiving the internal representation and the output of all previous decoder layers as input. Each dense layer is composed of 10 neurons and both the encoder and decoder have a depth of 7 layers, leading to an overall depth of 15. Dropout (5%) was applied to the input of each layer except the final one. Rectified Linear Units (ReLU) were used as the activation functions for each dense layer. The proposed structure (shown in Figure  1 ) is similar to that of multimodal autoencoders  [31]  with the exception of our concatenation structure and that the noise operator is performed within the network as a layer rather than used to augment the dataset prior to training.
In order to improve the convergence of the autoencoder during training and to minimize the effects of random weight initialisation, each FCAE began with an initialisation step. The bias layer was initialised to replace missing values with the mean value of the respective variable. Each encoder layer was then greedily initialised to the PCA transform that preserved the largest amount of information from its input. Each decoder layer was initialised with linear regression to create the optimal reconstruction given the input to that particular output layer. This initialization guarantees that the training set performance of the FCAE is at worst equivalent to that of the pairwise PCA variant. This step was performed using the entirety of the training dataset simultaneously, but did not involve data augmentation.
The hyperparameters for the network were determined in a two-step process. First, we defined the topology of the network using a grid search, in order to optimize the number of neurons per dense layer and the depth of the network while keeping the other parameters at a constant value. Second, we optimized training-related parameters (keeping the network structure fixed), such as learning rate, batch size and dropout rate, in a Bayesian manner using a Gaussian processes as a surrogate model and expected improvement as the criterion. The networks were implemented in Keras using a TensorFlow back-end with NAdam as the optimizer. We have made available the code to build and compile our proposed autoencoder, at https://github. com/m-prl/PatiNAE.
Accuracy, Loss and Regularisation Metrics For the purpose of evaluating each compressing data imputation approach, two measures of accuracy based on the input, x, and reconstruction values, x, were used: A 1 = 1 K N i=1 M i=j (x (i) j -x (i) j ) 2 * M (i) j (1) A 2 = 1 U N i=1 M j=1 (x (i) j -x (i) j ) 2 * (1 -M (i) j ) ( 2 ) where K is the number of known values and U the number of unknown values in the dataset being evaluated, and M (i) j is a mask identifying the known values. For A 2 , additional unknown values must be inserted into the dataset in order for their ground-truth value (x (i) j ) to be known for evaluation purposes. For training, the loss metric, analog to the one used by Sanchez et al. [5] , used a weighted mean squared reconstruction error using both a binary mask to identify missing data (M miss ) and a second binary mask to identify data that has been dropped in the process of data augmentation (M drop ): L = 1 K + U N i=1   M i=j (x (i) j -x (i) j ) 2 * (1 -M (i) miss,j ) + 4 * M i=j (x (i) j -x (i) j ) 2 * M (i) drop,j   (3) The weighting factor of is equivalent to put a higher importance into reconstructing the dropped data and thus preferentially improves the network's A 2 accuracy.
PPMI Questionnaire Database The Parkinson's Progression Markers Initiative (PPMI)  [32]  is a program sponsored by the Michael J. Fox Foundation for Parkinson's Research. It is an observational clinical study which tracks cohorts of subjects with different forms of Parkinson's disease for up to 8 years with the goal of identifying biomarkers of disease progression using MR imaging, biologic sampling as well as clinical and behavioural assessments. This work is focused on the clinical and behavioural assessments which were designed to provide a satisfactory amount of information regarding the patient's motor, cognitive, neuro-behavioural and neuro-psychological state. The database was built collecting and merging data from most of the motor and non-motor tests. The tests presented on the database are: MDS-UPDRS (all parts), Physical Activity Scale for the Elderly -Household Activity, Modified Schwab & England ADL, SCOPA-AUT, Clinical Cognitive Categorisation, Geriatric Depression Scale (Short), Questionnaire for Impulsive-Compulsive Disorders (QUIP), State-Trait Anxiety Inventory, Benton Judgement of Line Orientation, Hopkins Verbal Learning Test, Letter-Number Sequencing (PD), Montreal Cognitive Assessment (MoCA), Semantic Fluency, Symbol Digit Modalities Text, Epworth Sleepiness Scale, Features of REM Behaviour Disorder.
Some parts were discarded because they where performed too sparsely (on too few patients or just at one visit). We built our database by pooling the tests of each cohorts at each visit, on April 2018. The constructed PPMI database has 281 columns and 7490 rows across 1011 patients. 42. 9% of the data is missing in a highly heterogeneous manner. The questions used in these tests mostly permit ordinal answers. These tests do not include questions that permit categorical answers, but there are a few that permit continuous-valued ones. Figure  3  shows the first item of part 3 of the MDS-UPDRS test, which is a question with an ordinal answer. Table  1  gives statistics regarding missing values and modalities for each questionnaire used in this study. The names of the questionnaires have been shorten for clarity purpose, and are in the same order than presented in this section.
This table shows a great heterogenity in the way that values are missing. Nonetheless, the major cause of missing values is when the whole modality is missing en bloc. Note that the large majority of missing values are due to the protocol design, as not all tests are performed, nor are intended to be performed, at each visit for each cohort. This is well described in the PPMI database's protocol information. An extensive study on the MDS-UPDRS questionnaire (present in the PPMI database) as been performed Goetz et. al. [33] , showing that the loss of information is consequential even with only a few missing values. In their analysis, removing 0-27% of answers completely at random drastically reduces the coherence of the remaining answers. This scenario is obviously worsened by the removal of entire modalities, rather than individual questions, a not uncommon occurrence as shown in Table  1.
Experiments We have three hypotheses to experimentally verify: • H1: there is an optimal internal representation (IR) size to minimise error. That is, there is a degree of flexibility in terms of the networks' performance that diminishes the network's A 2 performance. This is expected for PCA in which, after a certain point, additional variables allows the IR to "remember" missing values as the mean of their corresponding variable rather than impute them. • H2: the network reconstructs data vectors more accurately when they are more complete, decreasing in performance as data becomes missing. • H3: there is a benefit from learning from incomplete data points. Although complete data vectors are unarguably better, using case deletion to restrict the training dataset to only those data points reduces performance.
H1: Optimal IR Size This experiment consists of training and testing autoencoders (and comparative PCA approaches) with IR sizes ranging from 1 to 10. In order to handle the low number of data-points, 20-fold cross-validation was performed to estimate the error. After splitting the dataset into 20 folds, one fold was iteratively selected as the testing dataset. The testing dataset was randomly corrupted with 10% chance of a modality being removed. This corruption process was repeated  40 times in order to have 40 differently corrupted versions of each testing dataset and total number of 800 datapoints for comparison. The datasets were saved to ensure that the same ones were used for evaluating each method, allowing for paired tests. The remaining 19 folds were again split at each iteration with 80% as training data and 20% as validation data.