dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Christophe Marcireau  ( Molecular Oncology, Sanofi, France ) for cloning the human WT and  G2019 LRRK2 ;  Laurent Dubois  ( Rare and Neurological Diseases Research, Sanofi, France ) for kindly providing compound MLI-2 and for critical comments on the manuscript; and  Laurent Andrieu  ( Biostatistics and Programming Department, Non-Clinical Efficacy and Safety team, Sanofi, France ) for performing the statistical analysis. We are grateful to  Prof. Marcel Leist  ( University of Konstanz ) for tips and useful discussions on the cloning of LUHMES cells.
Basic Concepts There has been a steady increase in the number of breaches reported over the past eight years  [6] , 2019 being an all-time high with 5183 reported breaches as well as 7995 million records lost  [6]. Malicious actors have been responsible for most incidents, but accidental exposure of data on the Internet (e. g. , misconfigured databases, backups, end points, services) has put the most records at risk  [6]. Even though both data breaches and data leaks result in data being exposed to unauthorized entities, the way these data are exposed is different. On one hand, data breaches refer to the unauthorized access of data by exploiting flaws in the security of the breached system. Data breaches can happen with data at rest  [7]  where attackers exploit a flaw to gain access to the data, or in transport  [8] , where attackers exploit a vulnerability to eavesdrop on traffic.
On the other hand, data leaks refer to the exposure of data belonging to an entity, due to the way these data are processed by this entity, by a mistake  [9] , or caused by malicious behavior  [10]. Preventing both forms of data exposure is a challenging problem to tackle, since an exposure can occur in multiple circumstances, caused by hacking, misconfigured databases, malicious insiders, etc. [3]. As attacks were generally assumed to come from a location external to the system via north-south traffic, the traditional way to secure such a system against those attacks was to protect it at the border via gateways, firewalls or programmable switches  [11]. The recent rise of microservices as a paradigm, and their increased use in building large, cloud-based enterprise applications  [12]  has increased the attack surface, meaning protecting the network border is no longer sufficient.
We aim to perform this policy verification on workflows. We define a workflow as a sequence of tasks performed by a set of independent entities  [26]. In practice, workflows are immensely important since they model business processes and define their interactions  [27] [28] [29]. However, workflows can become complex and difficult to manage, especially in the case of multi-party workflows  [30] , which makes the problem of access control even more challenging. One of the most used languages to specify workflows is Yet Another Workflow Language (YAWL)  [31].
Proof of Concept We realized a proof of concept, by implementing the infrastructure described in Section 4. We reproduce the workflow of Figure  2 , with services of the workflow receiving and sending arbitrary data to represent the data of the owner. We use Docker  [67]  for our containers, Kubernetes  [68]  for our orchestration layer, Istio [69] as our service mesh, using Envoy  [70]  for the proxy sidecars and Open Policy Agent (OPA) [71] for the policy sidecars. We also use Kubernetes to provide the services with encrypted volumes. This infrastructure was deployed on Google Cloud Platform (GCP), using one cluster for each actor of the workflow, for a total of five clusters. Each cluster runs one n1-standard-2 node (2 vCPUs, 7. 5 GB of memory), on version 1. 14. 10-gke. 36, except the cluster of the owner which runs two of them, since running the control plane requires additional resources.
The clusters for the owner, color and VFX are in us-central1-f whereas the clusters for HDR and sound are in us-west2-b. The workflow we want to enforce is shown in Table  1 , where each row represents the source of a request, and each column a destination. The agents can also send GET requests, but they are all denied by the policy. The complete data, code as well as guidance to realize this Proof of Concept are publicly available (See github. com/loicmiller/secure-workflow, accessed on 10 December 2021). We also developed a test framework to check that: • Traffic is either encrypted or protected inside a pod by the isolation provided by the pods; • The policy, allowing or denying communications between services, is correctly enforced.
• Bystander loopback and external interface: We need to verify that no communication between the source and the destination is occurring, whether encrypted or unencrypted. We built a tool that automatically extracts the authorization policies from the OPA policy configuration, generates and then tests an access control matrix. For each possible communication in the service mesh, our tool loads all the captures relevant to this communication, identifies them to see what we should verify in each capture, and then proceeds to check if captures are in accordance with the criteria above. It is then easy to evaluate whether the system is compliant with the overall policy. The complete code for the test framework is publicly available (See github. com/loicmiller/secure-workflow, accessed on 10 December 2021).
For each ANOVA, we gathered 40 observations per authorized communication per level of policy (N = 1600 in total). Figure  6  shows the distribution of request duration for each policy size. For intra-region communications, there is a significant difference in request duration among the five scenarios of policy deployments, F(4, 795) = 364. 05, p < 0. 001, η 2 p = 0. 65. For inter-region communications there also exists a significant difference (in request duration) among the five scenarios of policy deployments, albeit with a lesser effect: F(4, 795) = 15. 23, p < 0. 001, η 2 p = 0. 07. See https://github. com/loicmiller/ secure-workflow, accessed on 10 December 2021, for full data, code and statistical analysis in the form of jupyter notebooks. no OPA all allow minimal +100 +1000 0 Although our results suggest that a higher policy size increases request duration, it should be noted that this size should be strongly increased to observe an effect.
Modeling Workflows and Their Policies For our purposes and evaluations, we consider the verification of policies enforcing workflows. In Figure  8 , propositions on the edges constrain the communications. For exam-ple, C 1 can only send data to C 2 and C 3 if the communication is a POST request, and either the tenure of the user is greater than 10, or the request happens between 8 AM and 5 PM. In practice, we consider Yet Another Workflow Language (YAWL)  [31]  as the default language for specifying workflows. Considering YAWL  [31]  as the most representative tool to specify, analyze and execute workflows today, we will show how to transform any of its modeling components (e. g. , composite tasks, conditions) into a metagraph representation.
For each task, irrespective of their order in the YAWL process, relevant elements of the YAWL language are identified. The inputs (outputs) of a task correspond to the invertex (outvertex) of the edge, whereas predicates correspond to propositions in the invertex of the edge. The join code indicates the join operation of the currently processed task, and is used to determine the specific actions in each possible case (AND, OR, XOR). The (non-)completion edge refer to the creation of a new edge from the outputs of the previous task to a proposition signifying the current task has (not) been completed, as we explained earlier with disjoint tasks.
Convert to metagraph elements Figure  12. Flowchart of transformations from YAWL to a metagraph. For each task, relevant elements of the YAWL language are identified, and converted to metagraph elements. The join code indicates the join operation of the currently processed task, which is then used to determine the actions to perform in each possible case (AND, OR, XOR). Figure  11  represents a part of the transformation of our example-the film production process (Figure  10 ) is translated into a conditional metagraph. Edges represent the tasks of the process, where the invertex (outvertex) represents the inputs (outputs) of the task. As explained in the last section, propositions are added (_completed suffix) to make sure that we retain the information of the workflow. In addition to the YAWL format which specifies workflows, we have also added the possibility of specifying a workflow directly in a metagraph-like format.
This stage is applied before the actual matching that is then simply linear in the number of edges. When the algorithm time is predicted, we found that the number of edges (β = 0. 0020; p < 0. 001) is a significant predictor. Indeed, the overall model fit is: R 2 adj = 0. 898, with the post hoc power analysis indicating a power greater than 0. 999. In summary, we can argue that our policy verification method can be efficiently implemented as long as the number of propositions in the policy is reasonable. The complete data, code to generate the measures and results, as well as some guidance are publicly available (See https://zenodo. org/record/4912289, accessed on 10 December 2021). Algo duration (ms) Figure 15. Log regression of the execution time according to the number of edges. When the algorithm time is predicted, we found that the number of edges (β = 0.
This stage is applied before the actual matching that is then simply linear in the number of edges. When the algorithm time is predicted, we found that the number of edges (β = 0. 0020; p < 0. 001) is a significant predictor. Indeed, the overall model fit is: R 2 adj = 0. 898, with the post hoc power analysis indicating a power greater than 0. 999. In summary, we can argue that our policy verification method can be efficiently implemented as long as the number of propositions in the policy is reasonable. The complete data, code to generate the measures and results, as well as some guidance are publicly available (See https://zenodo. org/record/4912289, accessed on 10 December 2021).
Data Availability Statement: See https://github. com/loicmiller/secure-workflow, accessed on 10 December 2021, for complete data, code as well as guidance to realize our Proof of Concept, test framework, as well as full data, code and statistical analysis in the form of jupyter notebooks. See https://zenodo. org/record/4912289, accessed on 10 December 2021, for complete data, code to generate the measures and results, as well as some guidance on policy verification.
In the European Union, more than 100,000 man-made chemical substances are awaiting an environmental risk assessment (ERA). Simultaneously, ERA of chemicals has now entered a new era. Indeed, recent recommendations from regulatory bodies underline a crucial need for the use of mechanistic effect models, allowing assessments that are not only ecologically relevant, but also more integrative, consistent and efficient. At the individual level, toxicokinetic-toxicodynamic (TKTD) models are particularly encouraged for the regulatory assessment of pesticide-related risks on aquatic organisms. In this paper, we first propose a brief review of classical dose-response models to put into light the on-line MOSAIC tool offering all necessary services in a turnkey web platform whatever the type of data to analyze.
, 2019) , from which bioaccumulation factors are automatically derived  (Ratier et al. , 2020). All MOSAIC modules make available a collection of example data sets, allowing new users to practice using the various features. The purpose of this article is to present all the features of MOSAIC in order to guide academics, manufacturers and regulators to benefit from advanced and sound models in ERA in support of their daily work, meeting all expectations in terms of regulatory requirements. The first section gives insights on classical DR analyses, focusing on the last new-born service MOSAIC growth. The second section illustrates how to get bioaccumulation factors from TK models, with a focus on the selection of different models to be compared, and how to fulfil the EFSA workflow regarding the use of GUTS models for ERA  (Ockleford et al.
Few words about modelling When performing standard analyses of toxicity test data in MOSAIC, the mean tendency of the relationship between the observed endpoints and the tested concentrations is first described by a 3-parameters log-logistic model written as follows: f (C) = d 1 + C e b (1) where C stands for the tested concentration, parameter b is a shape parameter translating the intensity of the effect, d corresponds to the endpoint value in control data (i. e. , in absence of contaminant) and e corresponds to the EC 50 , that is the C value leading to 50% of effect compared to the control (i. e. , compared to parameter d): f (e) = d 2. Equation (1) also assumes that lim C→+∞ f (C) = 0. Then, depending on the endpoints that are observed, the variability around the mean tendency is described by an appropriately chosen probability distribution.
MOSAIC 1 To access to the very last version of MOSAIC bioacc that is regularly updated and tested before to be deployed on the official server, please go to https://scharles-univlyon1. shinyapps. io/mosaic-bioacc-gamma/. (aquatic, terrestrial, insect) , more than 66 chemical substances, different exposure routes (water, sediment, soil, food) and several elimination processes (biotransformation and growth dilution). This data collection is presented as a table that summarises the main characteristics of the data (genus, category, substance, accumulation duration, exposure routes, number of data and replicates), as well as a direct link to the reference, and direct links to download the raw data and the full report provided by MOSAIC bioacc. In addition, the table also gives the kinetic bioaccumulation factor estimate (as a median and a 95% uncertainty interval).
MOSAIC GU T S-f it MOSAIC offers two services related to the use of GUTS-RED models to analyze standard survival data as function of both time and exposure concentration: MOSAIC GU T S-f it for step (1) and MOSAIC GU T S-predict for steps (2) and (3). All features of MOSAIC GU T S-f it have already been detailed in  Baudrot et al. (2018b). We just recall here the main highlights: a facilitated uploading of data (either from example data files or from the users themselves), an automatic GUTS fitting analysis for either GUTS-RED-SD and GUTS-RED-IT models, all useful fitting outputs to check the relevance of the results (parameter estimates, fitting curve with its uncertainty, posterior predictive check), and a collection of LC x calculations associated with their uncertainty (Figure  4. A). In the following subsection, MOSAIC GU T S-predict is presented in details, in support of the validation and the prediction steps of the EFSA workflow.
The 20 data sets are standard survival data sets with a first set for 10 different species exposed to chlorpyriphos  (Rubach et al. , 2012) , a second for species Daphnia magna exposed to seven veterinary antibiotics  (Wollenberger et al. , 2000)  and three other data sets  (Forfait-Dubuc et al. , 2012). Each data set was fitted with the three models thanks to the R package morse  (Baudrot et al. , 2021). Note that the entire analysis can be identically reproduced using the MOSAIC platform. For each data set, goodness-of-fit criteria were good enough to support the relevance of the results (see example on Figure  5. A for the data set of D. magna exposed to potassium dichromate). So, for each data set, the LC (x,t) estimates (as medians and 95% uncertainty intervals) were collected for x = 50% and at the end of the experiment, directly from parameter estimates when using the 3-parameters log-logistic model (parameter e in equation (  1 ) above), or asking for the calculation after predicting the dose-response curve with both GUTS-RED models (Figure  5.
The cohort consists of two distinct molecular subgroups of GISTs: KIT-mutant (29 cases) and SDH-deficient (7 cases). All KIT-mutant tumors were characterized by the presence of KIT mutation detected by Sanger sequencing. The SDH-deficient status was assessed by both immunohistochemistry of the SDHB subunit and Sanger sequencing of all the subunits of the SDH complex. Patients' data are reported in Table  1 , and additional details and clinical data are shown in Supplementary Table  S1. Total RNA was extracted from tumor specimens with RNeasy Mini Kit (Qiagen, Milan, Italy) and then processed to be analyzed either on HGU133Plus 2. 0 Affymetrix microarrays or by whole-transcriptome RNA sequencing on Illumina platform. Briefly, for microarray samples, quality-controlled RNA was labelled following the Affymetrix manufacturer's recommendations and then hybridized to HGU133Plus 2.
0 arrays. Gene expression data were normalized and quantified as log2signal by the robust multichip average (RMA) algorithm (package oligo, R-bioconductor). For the whole-transcriptome samples, the cDNA libraries were synthesized starting from 250 ng total RNA with TruSeq RNA Exome (Illumina, San Diego, CA, USA) according to the manufacturer's protocol. Sequencing by synthesis was performed on Nextseq500 sequencer (Illumina) at 75 bp in paired-end mode. An average of 49. 5 million reads per sample were obtained, reaching an average coverage of ~45×. Read pairs were mapped on reference human genome hg38 with STAR (https://github. com/alexdobin/STAR accessed 15 October 2020), duplicates removed, and sorting and indexing were performed with samtools (http://www. htslib. org/ accessed 15 October 2020).
Gene expression was quantified and normalized in two different ways: (1) as count per million (CPM) by adopting the python package HTseq-count to get the raw count (https://htseq. readthedocs. io/ accessed 15 October 2020), followed by the R-bioconductor package edgeR to compute the normalization factors (https://bioconductor. org/packages/release/bioc/html/edgeR. html accessed 15 October 2020); (2) as transcript per million (TPM) using the program kallisto (https://pachterlab. github. io/kallisto accessed 15 October 2020). The two normalization methods are conceptually different and suited to perform different types of downstream analysis; generally CPM are employed to compare between samples while TPM are best suited to compare between genes  [9]. Here, we adopted CPM to perform the principal component analysis (PCA) and the evaluation of differential expression (DE), and the TPM values were considered to estimate the tumor microenvironment composition and to quantify the gene signatures.
This study was approved by the institutional review board of IRCCS-Azienda Ospedaliero-Universitaria Policlinico S. Orsola-Malpighi, Bologna, Italy (approval number 113/2008/U/Tess). Each patient provided written informed consent. The R package prcomp (https://cran. r-project. org/package=nsprcomp accessed 15 October 2020) was adopted to perform the PCA, and the three-dimensional projections, corresponding to the first three components, were plotted with the function plot3d of rgl package (https://cran. r-project. org/package=rgl accessed 15 October 2020). The DE analysis of SDH-deficient versus KIT-mutant GISTs was conducted using the R-bioconductor limma package (https://www. bioconductor. org/packages/release/bioc/html/limma. html accessed 15 October 2020), sequentially adopting the functions lmFit (to produce a fitted model) and eBayes (to compute moderate t-statistic and log2 fold change).
Significantly modulated genes (over-or underexpressed) were defined on the basis of q-value < 0. 05 (adjustment method Benjamini-Hochberg). The methods described for PCA and DE was applied to both microarray and RNA-seq data series. Over-representation analysis was performed separately for over-and underexpressed gene lists to determine whether genes associated to a specific pathway are present more than expected. We adopted the web tool Enrich (https://maayanlab. cloud/Enrichr/ accessed 15 October 2020) focusing on MSigDB Hallmark 2020 to evaluate pathways and Human Gene Atlas to evaluate cell type. As input, we entered the two lists of up-and down-regulated genes obtained by the consensus between microarray or RNA-seq DE results. We included significantly modulated genes (q-value < 0. 05) in microarray data having the same fold change sign and p-value < 0.
01 in RNA-seq analysis (and vice versa). We also performed gene set enrichment analysis (https://www. gsea-msigdb. org/gsea/index. jsp accessed 15 October 2020) adopting the full expression matrix (without any filter) for both microarray and RNA-seq data. We ran Gene Set Enrichment Analysis (GSEA) by selecting the curated gene set carrying "canonical pathways" from Molecular Signatures Database (MSigDB) and adopting the following parameters: number of permutation = 100; enrichment statistic = "classic"; metric for ranking gene = "Diff_of_Classes"; normalization mode = "meandiv". A tumor microenvironment study was performed using CIBERSORT, and immuno-related gene signatures were evaluated as previously described  [10].
Gene Expression Profile of SDH-Deficient GIST Gene expression analysis was performed separately for fresh frozen tissue samples analyzed with microarray and FFPE samples analyzed with RNA-seq. As first step, PCA was adopted to perform an unsupervised analysis with the aim to decompose the high dimensionality of transcriptome data variability into three-dimensional components. The 3D projections in both PCA analyses showed that SDH-deficient GISTs distinctly separate from KIT-mutant GISTs, providing proof of an expression profile typical of this molecular subgroup and profoundly different from KIT-mutant GISTs, supporting the hypothesis that the two GIST molecular groups may derive from two distinct cell types or oncogenic programs (Figure  1A, B ). The analysis of DE was performed for both sample series to discover the set of genes that are significantly overexpressed or down-regulated in SDH-deficient GISTs.
Comparison with SDH-Deficient Pheochromocytoma and Paraganglioma Our analysis showed several interesting signatures overexpressed in SDH-deficient GISTs. To evaluate if the same expression profile and gene signatures were specifically characteristics of this rare subgroup of GIST, or if they were peculiar to neoplasms displaying the loss of function of SDH complex, we comparatively analyzed our microarray GIST series and a set of SDH-deficient pheochromocytoma and paraganglioma analyzed with the same protocol Affymetrix HG-U133 Plus 2. 0 (available at E-MTAB-733 ArrayExpress). This dataset was published by Loriot et al. [18]  within a research paper in which they identified the EMT activation specifically associated with SDHB-mutant metastatic pheochromocytoma and paraganglioma, concluding that this process may be involved in the acquisition of the invasiveness.
Notably, SDH-deficient GISTs showed the up-regulation of hedgehog signaling, another pathway strongly related to cell differentiation and cancer invasion. Similarly to the EMT pathway, we found that hedgehog signaling genes produced clusters that separate KIT-mutant GISTs and SDH-deficient (GISTs and pheochromocytoma/paraganglioma together), as shown in Figure  3C. Finally, following the same procedure, we also performed the hierarchical clustering for hypoxia pathways. The cluster analysis showed that SDH-deficient GISTs and SDHBmutant pheochromocytoma/paraganglioma shared the expression of hypoxia genes that is particularly evident for FOXO3, VLDLR, and ENO2 (Figure  3D ). The hypoxia condition was associated with the overexpression of genes encoding for glutamate receptors  [20] , and in our data, as a matter of fact, we found the glutamate receptor GRIA1 as one of the most up-regulated genes in SDH-deficient GISTs in both RNA-seq and microarray datasets.