dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-Seq Data Processing and Differential Expression Analysis We assessed FASTQ sequence data quality using FastQC v0. 11. 5 and trimmed the reads to remove adapter sequences and low-quality bases using Trimmomatic v0. 36  [17]. The resulting trimmed paired-end reads were aligned using STAR v2. 5. 3a [18]  to the GRCh38 human genome release. Obtained BAM files were indexed using Sambamba (v0. 6. 6) after ordering them by coordinates. Aligned reads were counted with StringTie v1. 3. 1c using GENCODE annotation. Differentially expressed genes (DEGs) of different conditions were identified using the DESeq2 (v1. 18. 1) R-package. The number of raw sequence reads, remaining reads after trimming, and mapping rates for each sample are shown in Supplementary Excel File S1. Differentially expressed genes were extracted at the fold-change cutoff ≥2 and an FDR (False Decovery Rate)-corrected p-value of 0.
05 (named after FDR). We used the Ensembl human gene IDs identified in the DESeq2 analyses as input for further analyses. The raw RNA-Seq data and raw count matrix were deposited at the NCBI Gene Expression Omnibus (https://www-ncbi-nlm-nih-gov. gate2. inist. fr/geo/ (accessed on 23 June 2021) under the accession GSE173251.
Gene Ontology Analysis Overrepresentation test analyses were performed using enrichGO from the R-package clusterProfiler (v3. 10. 15). We identified biological pathways (BPs) with an FDR < 0. 05. Results are presented as bar plots, where the x-axis represents the log10(FDR), the corresponding GO terms are shown on the y-axis, and the percentage of DEGs associated with a given GO term is at the top of each bar plot.
Quantitative RT-PCR Reverse transcription of 1 µg of total RNA was performed using the Superscript IV First-Strand cDNA Synthesis kit (Life Technologies, Waltham, MA USA). We used a mix of oligo dT and random hexamers to target all types of RNA and followed manufacturer's instructions to synthesize cDNA. The remaining RNA was degraded by incubation with Escherichia coli RNase H (Life Technologies) at 37 • C for 20 min. Primers were designed using Primer Blast and Primer3 (Supplementary Table  S2 ). Real-time PCR amplification was performed on a LightCycler 480 (Roche) using the SYBR green master mix. All PCRs were performed using a standardized protocol and data were analyzed with the LightCycler 480 software version 1. 5. 0. 39 (Roche). Primer efficiency was determined by absolute quantification using a standard curve.
RNA-Seq Data Analysis RNA-Seq data were analyzed using MOGAMUN, a multi-objective genetic algorithm that identifies active modules (i. e. , highly interconnected subnetworks with an overall deregulation) in a multiplex biological network (i. e. , a network composed of different layers, where each layer represents physical and/or functional interactions). We performed an analysis of differential expression of our RNA-Seq data, to compare patients vs. controls with DESeq2. We used as input for MOGAMUN the resulting FDR-corrected p-values, and a multiplex biological network composed of three layers or undirected interactions from  [19]. The first layer corresponds to physical protein-protein interactions, and it was obtained by merging the CCSB Interactome database  [20]  and several databases from the PSICQUIC portal  [21].
The second layer contains data of pathways, obtained using the R-package graphite  [22]. The third layer corresponds to the absolute Spearman correlation superior or equal to 70%, calculated using the RNA-Seq expression data of 32 tissues and 45 different cell lines  [23]. We ran MOGAMUN 30 times with the default parameters. The resulting accumulated Pareto front was composed of several active modules that contained different numbers of genes, including significant ones (considering an absolute log fold change >1 and FDR <0. 05). Active modules were visualized using Cytoscape v. 3. 8. 2. Genes corresponding to each node were analyzed using g: Profiler (https://biit. cs. ut. ee/gprofiler (accessed on 23 May 2021))  [24]  to define the corresponding molecular function and obtain the corresponding p-value.
BAMS-Specific DEGs Are Associated with Cell Migration and Communication As NCSCs contribute to the development of facial structures, which are strongly impacted in BAMS patients, we looked more specifically at the 657 DEGs that are unique to BAMS cells (Figure  3A ). In BAMS NCSCs, BPs associated with these DEGs correspond to, among others, "cell development", "cell morphogenesis", "axon guidance", "telencephalon cell migration" and "neural crest cell migration" pathways (Figure  3A ). BPs corresponding to "embryonic eye development" and "cartilage development" also appear highly interesting in light of BAMS patients' phenotypes. BPs related to "pattern specification process" and "extracellular matrix organization" are shared between FSHD and BAMS cells. C-H ) Representative active modules sampled from the accumulated Pareto front of 30 runs for the different data sets using the MOGAMUN algorithm.
The color of the edges (links) denotes the type of interaction/relationship between each pair of genes, specifically, protein-protein interactions (blue links), biological pathways (orange links), and correlation of expression data (yellow links). Upregulated nodes are colored in red and downregulated ones in green, the intensity of the color reflects the fold change. Nodes with a dark border are significant (i. e. , False Decovery Rate (FDR) < 0. 05 and -2 > FC > 2). (C,D) Active modules containing genes related to Extracellular Matrix (ECM) organization and function in FSHD2 NCSCs with a majority of upregulated genes. (E-H) In BAMS NCSCs, MOGAMUN uncovered different active modules comprising genes related to ECM organization and functions, with a majority of downregulated genes, except for COL6A2 (E) or CALM1 (F).
Neural Crest Stem Cell (NCSC) Differentiation Reveals Impairment of Extracellular Matrix Synthesis Complementary to classical RNA-Seq analyses, the expression data were analyzed with MOGAMUN  [15] , using the lists of DEGs as seeds. MOGAMUN is a recently developed algorithm that integrates expression data with multiplex biological networks to identify active modules. For our experiments, we considered protein-protein interactions (blue edges), biological pathways (red edges), and correlation of expression (yellow edges) in the multiplex biological networks. Depending on the samples, 9 to 19 subnetworks (active modules) were retrieved. We first selected similar gene sets, studied their information content, and grouped the subnetworks based on their associated BP terms, determined using g:Profiler.
BAMS Neural Crest Cells Display a Defect in Cell Migration Given the redundancy of PDGFRα and β in the different active modules retrieved by MOGAMUN and their implication in neural crest cell migration, we confirmed their marked decreased expression by RT-qPCR in NCSCs derived from additional BAMS patients (Figure  5A ). By analyzing the proportion of NCSCs expressing PDGFRα, β or both in control and BAMS cells from three different patients by flow cytometry and immunostaining at day 11 post-differentiation, we observed a significant decrease in the proportion of PDGFRβ-positive cells in BAMS cells but a similar percentage of PDGFRαpositive cells. We further showed that the proportion of cells expressing both membrane receptors was also significantly decreased in BAMS cells (0. 2 to 14. 5%) compared to controls (37.
As network-based approaches have been highly efficient for identification of biological processes perturbed in diseases, we performed in-depth analysis of our RNA-Seq data using MOGAMUN, a novel algorithm designed to identify active modules by integrating expression data with one or more networks of biological interactions  [42]. By comparing the different active modules found in the different conditions using DEGs as seeds, we identified genes and pathways that appeared highly meaningful with regard to neural crest cell differentiation and BAMS pathophysiology. Indeed, MOGAMUN retrieved several networks highlighting changes in the extracellular matrix organization and related function in cell adhesion in the different conditions. Markedly in BAMS, the top-perturbed pathways implicated PI3K/AKT, PDGFR, and ERBB3 signaling, with a striking mirror effect between BAMS (downregulation) and FSHD2 or FSHD1 (upregulation) cells.
This is achieved using a saturation algorithm, which is run on a valuated graph implementing the inclusion lattice of the point powerset, labeled by the minimum and maximum rank. Once the saturation graph is built, it is traversed to build a Coq proof script which actually proves the statement at stake. Technical details about the implementation can be found in  [4]  and the current implementation of the prover as well as several examples of applications are available in the git repository: https://github. com/pascalschreck/MatroidIncidenceProver. In the next sections, we show how to integrate such a tool so that it can be simply used as a tactic in Coq. 3 Translating a Coq statement into the input language of the prover Provided that we define the theory of ranks in Coq (see Appendix A), as we first did in  [10] , we can state geometric properties as Coq lemmas using equalities on the ranks of some sets of points.
2 , layers are a legacy tool which was used to decompose the context at stake. This allows to build smaller proof scripts which can be easily checked by Coq. In the current version of the prover, each deduced statement is stated as a lemma and thus layers are not needed anymore. Once the input file is generated, we simply execute the external prover from Coq using the Unix. system primitive of the OCaml library. It produces a Coq file (. v), with the appropriate prelude, which is ready to be verified by Coq. An implementation of this translation as well as the mechanisms to use the produced proof inside Coq is available in this git repository: https://github. com/magaud/projective-prover. It works with Coq 8. 12. 2 (December 2020) and we are currently updating it to the most recent release of Coq.
Discussion We choose a simple but efficient approach to run the prover inside Coq. One of the main advantages of this approach is that it does not require the designer of the external prover to have any understandings of the internals of Coq. It simply requires the developer to be knowledgeable of the way Coq scripts are written. This is a skill every Coq user has. Such an approach thus makes every Coq user a potential tactic writer, provided we can build an interface linking Coq statements to the expected inputs of such an external prover. Indeed, the critical part of our tool is the translation of the statement from Coq to the input language of the prover. The proof generation does not need to be formally verified. Indeed, Coq will reject the proof script if it does not actually prove the statement at stake (either because statements do not coincide, or because the proof script is incorrect).
Conclusions and Perspectives We successfully embed the external prover Bip as a simple tactic to be used in Coq using Ltac. We proceed in two steps: we first develop a prover, which is completely independent from the internals of Coq. The second step consists in connecting this external prover to Coq. We call the prover from Coq and retrieve the external proof script it generates. We then simply feed this script to Coq to type-check it. The API of Coq is not intended to make it easy to embed some Coq proof scripts generated externally to prove a goal. We believe it would be a nice feature to ease this process by allowing a tactic to simply run a snippet of Coq code loaded from a file. In the longer run, improving our prover requires to make it able to take synthetic geometry statements as input rather than sets of points and ranks.
This applies to several critical processes within Smart Grid management, and in particular to the billing application, a major target for disclosure attacks  [8]. Examples of attacks targeting a billing application are: (i) exfiltration of sensitive data contained in the billing database, (ii) escalation of privileges within the application itself, allowing a user with readonly privileges to access parts of the data and obtain write access, or even administrator accesses for the application. Detecting security incidents at the application level raises a number of concerns. The first one is that each billing application is different and has its own log mechanism, introducing the need of specific detection rules. In addition the log itself is sensitive and typically cannot be shared, posing a challenge that the people aware of the specific business and application risks do not have access to the data they need for defining and tuning detection rules.
These annotations form a database that can be queried to select a set of services compatible with the declarative model objectives  [12]. The Big Data consultant uses the MBDAaaS platform to connect these services, obtaining an abstract workflow of BDC. The generated workflow, jointly defined by Big Data Developers and Engineers, represents the procedural model (Workflow Definition in Fig. 1 ). Starting from the procedural model, our MBDAaaS platform generates a platform-dependent workflow, called deployment model (MBDAaaS Compiler in Fig. 1 ).
Test and Validation. Upon retrieving the deployment model, MBDAaaS platform executes the analytics on the target Big Data platform. The result of the execution of a service composition is evaluated at Test and Validation decision point. Two possible alternatives can be followed after evaluation: (i) the service composition must be redesigned by substituting (BDC Design and Bootstrap) or refining (BDC Tuning and Refinement) one or more services, (ii) the desired quality has been achieved and the service composition can be deployed in production (End). BDC Tuning and Refinement. The user refines a (subset of) service by developing her own algorithms, either starting from an existing sequential -platform independent -code or completely from scratch. Ad Hoc Service Transformation. The result of the execution of the ad hoc computation is evaluated at Ad Hoc Service Transformation decision point.
More specifically, let us focus on privilege abuse attacks on the part of database administrators who can access all content, including sensitive data. Database administrators are chosen carefully and sign a document upon taking up the role, stating that they will use their account only for performing administrative tasks such as data backups, adding/suppressing users, or modifying user authorizations. Technically, nothing prevents an administrator to read sensitive database tables, but each read request is being logged. It is not possible to deduce from the log whether the currently logged-in user is an administrator or not, but it is possible to mark as malicious those activities where users, usually performing administrative actions, execute a read action on a sensitive table. This incident is usually reported as an occurrence of a nosy admin.
Alice has now access to a high enough amount of data for training and tuning Machine Learning models, such as a Random Forest classifier. During this phase, Alice may choose to add some new services, for example for data preparation, like converting categorical in numerical (String Indexer) data, or representing dates in seconds (Epochs). These services are then added to the catalog, and their links with the classifier are added to the ''knowledge'' of the MBDAaaS platform for realizing the correct service composition  [12]. Once the Random Forest classifier is tuned to an acceptable level of accuracy, it can be added to the Service Catalog with the corresponding mapping to its implementation, and used as a service for the next pipeline design. We note that, when tuning the classifier, Alice could need to adapt the pipeline, for example changing the anonymization algorithm to obtain a better data quality.
4 ). These services are re-used in the production pipeline. Using the output of Bootstrap pipeline and corresponding services, Alice can devise and train a Random Forest model that receives as input an entry and provides as output labels 'benign', 'nosy admin', or 'privilege escalation'. The 'nosy admin' detection pipeline is realized using data ingestion, pseudo-anonymization, data preparation, classifier, and visualization services. Being it run by Bob without the involvement of other roles, there is no need of complex anonymization step, and basic pseudo-anonymization is sufficient. Since all these services are available, both in the catalog and in the target platform, Bob can design and deploy the security application using MBDAaaS platform, without additional support from Alice. Similarly, to adapt the application for including detection of dormant accounts, Bob just needs to replace the previously trained model with the new one available in the platform (it was added to the catalog by Alice together with the corresponding data preparation service Elapsed) and re-deploy the pipeline.
in prep). The ALCS is a large project observing 33 galaxy clusters using the Atacama Large millimetre/sub-millimetre Array (ALMA). The target sources of this study are in the redshift range 5. 9 z 6. 6 and have not been individually detected with [C ii] in the ALCS data. The structure of this paper is as follows. In Sect. 2 we describe in detail the data as well as the studied sample. Sect. 3 outlines the method and the different tools used to carry out this analysis. In Sect. 4 we present the results before discussing their implications, biases, and newly raised questions in Sect. 5. Throughout the paper we assume H 0 = 70 km/s, Ω M = 0. 3, and Ω Λ = 0. 7.
The data were reduced and calibrated using the Common Astronomy Software Applications (CASA) package version 5. 4. 0  (McMullin et al. 2007 ). Data cubes were imaged using the CASA task TCLEAN down to a level of 3σ with a channel size of 60 km s -1. Throughout this paper, we use naturalweighted, primary-beam-corrected, and uv-tapered maps, with a tapering parameter of 2 arcsec (pixel size of 0. 16 arcsec). We use uv-tapered maps so that all the stacked sources share a similar beam size, and hence a similar spatial resolution, which is essential for stacking homogeneity. However, because high-redshift sources may be small compared to the tapered beam size and for completeness, we also performed our stacking analyses on cubes in their native resolution (∼ 1 arcsec; see Fig. A. 4 in the appendix).
2016; Di Criscienzo et al. 2017) , the Hubble Frontier Field Deep Space (HFFDS) catalogue  (Shipley et al. 2018) , and the VLT/MUSE observations catalogue  (Caminha et al. 2019 ). In addition, the regions of interest were queried using As-troQuery  (Ginsburg et al. 2019) , leading to the addition of two sources from the NASA/IPAC Extragalactic Database (NED 2 ). We also manually added the two images of a lensed source in Abell 383  (Richard et al. 2011; Knudsen et al. 2016). HST catalogues are corrected for astrometry offset with respect to ALMA maps following  Franco et al. (2018). To avoid duplicates between different catalogues, sources are removed if they are closer than 1 arcsec from one another. Priority is given to catalogues with higher redshift precision. For homogeneity, and to push the limit of observability towards fainter sources, we only select sources that are not individually detected in our ALMA data.
Sample properties and SED fitting The z ∼ 6 galaxies used in this study are located behind nine clusters from the Frontier Fields and CLASH surveys, for which deep public NIR data are available 3   4. Data from three clusters have been analysed as part of the ASTRODEEP project  (Merlin et al. 2016b) , namely Abell2744, MACSJ0416. 1-2403 and MACSJ1149. 5+2223, and robust catalogues have been released. In addition, Abell2744 and MACSJ0416. 1-2403 as well as MACS1206. 2-0847, MACS0329. 7-0211 and RXJ1347-1145 were analysed as part of the MUSE GTO programme, and complete catalogues were made available through their latest datarelease  (Richard et al. 2021 ). In the following, we use the catalogues from the ASTRODEEP project and the MUSE-GTO data release for these six clusters, and we use our own catalogues for the remaining.
Stacking method The spectral stacking analysis is performed using LineStacker  (Jolly et al. 2020). LineStacker is an open access stacking software developed for stacking interferometric data. It is an ensemble of CASA tasks that allow the stacking of either spectral cubes or extracted spectra. LineStacker comes with a suite of tools, among which are automated weighting routines, which we use in our analysis. 5. 9 6. 0 6. 1 6. 2 6. 3 6. 4 6. 5 6. 6 Sources were stacked pixel to pixel and spectral channel to spectral channel. Both weighted average and median stacking were used. Some sources are located close to the edge of the cubes 7 , making the noise levels of these sources much higher than others. For this reason, we weighted each source proportionally to the inverse of the square of the local RMS.
Such systematic uncertainties would hence only lead to a modest underestimate of the upper limits derived in our analysis. Similarly, stacking positions could also be a source of systematic uncertainties. Systematic astrometric shifts in the source catalogues compared to the ALCS data would lead to a poor -900 -600 -300 0 300 600 900 velocity (km/s) overlap of stacking positions. This impact could be significant if uncertainties are high compared to the [C ii] size of the sources. This is, however, unlikely as offsets between HST catalogues and ALMA are well known (e. g. Dunlop et al. 2017; Franco et al. 2018; Fujimoto et al. 2019; Franco et al. 2020)    for, and MUSE catalogues are properly aligned  (Richard et al. 2021). Nonetheless, a similar effect would be expected from systematic offsets between the [C ii] regions and the optical regions that are the stacking targets.
Acknowledgements. We thank the anonymous referee for their helpful comments. This paper makes use of the ALMA data: ALMA # 2018ALMA #. 1. 00035. L, # 2013ALMA #. 1. 00999. S, and # 2015. 1. 01425. 1. 01425. S. ALMA is a partnership of the  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by the  ESO, AUI/NRAO , and  NAOJ. KK acknowledges support from the  Swedish Research Council  ( 2015-05580 ), and the  Knut and Alice Wallenberg Foundation. K. Kohno acknowledges the  JSPS KAKENHI  Grant Number  JP17H06130  and the  NAOJ ALMA   Scientific Research Grant Number   2017-06B. FEB acknowledges support from  ANID  grants  CATA-Basal AFB-170002 ,  FONDECYT Regular 1190818 ,  1200495  and  Millennium Science Initiative ICN12_009.
DE acknowledges support from a  Beatriz Galindo senior fellowship  ( BG20/00224 ) from the  Ministry of Science and Innovation. GEM  and FV acknowledge the  Villum Fonden  research grant  13160  "Gas to stars, stars to dust: tracing star formation across cosmic time" and the  Cosmic Dawn Center of Excellence  funded by the  Danish National Research Foundation  under then grant No. 140. FV acknowledges support from the  Carlsberg Foundation   Research Grant   CF18-0388  " Galaxies: Rise and Death ".
In supervised learning, classifiers range from simpler, more interpretable and generally less accurate ones (e. g. , CART, C4. 5, J48) to more complex, less interpretable and more accurate ones (e. g. , neural networks, SVM). In this tradeoff between interpretability and accuracy, we propose a new classifier based on association rules, that is to say, both easy to interpret and leading to relevant accuracy. To illustrate this proposal, its performance is compared to other widely used methods on six open access datasets.