dataesr/scientific-paragraphs-categorization huggingface dataset:
A designer who synthesizes alternative variants needs to minimize objectives such as cost, performance and failure rate. Examples of objectives include cost and performance, redundancy level for failure rate, and allocation for performance. [5]  and  [6]  rely these design decisions on a pure optimization problem and separate them from design representation. Conversely, the paper discusses a MBSE approach based on SysML language  [3]. With OOSEM and SysML 1. 5, OMG introduced stereotypes to allow trade-off analysis in the form of objective functions and measure of effectiveness (moe). For each variant of the system, a «moe» stereotype models the values to be optimized, and an external tool computes the objective functions values. A component variant is explicitly defined by inheritance from generic components, and only a limited number of variants shall be considered.
The typical representation of this problem is the Redundancy and Allocation Problem (RAP) discussed in  [6]  and  [12]. The RAP problem deals with component selection, for cost and reliability optimization at system level. It is formalized as an optimization problem. It is not connected to any MBSE approach. In the RAP problem, the connection topology is fixed as a serial-parallel model. The last degree of freedom generally studied in system design trade-off is the allocation of sources elements onto target elements. This happens with embedded system design, where the applications elements are allocated to the Processing Elements (PE) of the HW platform. In  [13] , the authors compare software architecture optimization methods, few of them using SysML or UML. Concerning the degrees of freedom, component selection/duplication (instance and redundancy) represents 40% of the approaches and allocation represents 33%.
Variant modeling During the trade-off analysis, the designer of a complex system has to evaluate a large number of alternatives, and a specific approach is needed. With variant modeling  [16] , one specifies design alternatives by explicitly modeling them in a single model, and annotating them using variation points. Variant modeling is often associated with software product lines and feature models  [17] , with the intention to create many variants of a product. A feature diagram is hierarchically organized, starting with a feature node at the root position. Another technique is to use a separate variability language, such as CVL  [18]. For optimization, it is essential for the designer to clearly identify the SysML elements subject to variability, rather than to define them in a separated language or diagram.
Allocation problem For the allocation problem, the target MBSE shall implement a mechanism allowing connecting elements at different levels of abstraction, or with different types such as software and hardware. It is possible to allocate an activity to a resource for execution, or a small physical component to a bigger one with a given volume. In order to find the set of optimal allocations, we use the variable allocation depicted by Figure  6. The system under analysis must be characterized by a deployment specification containing all possible allocations of the system. Each allocation can be represented by "VariableAllocateComment" when the source and targets are represented in the same diagram. "VariableAllocate" is used otherwise. In both cases a unique source element can be allocated to a set of target elements with specific parameters, such as a cost for each allocation.
SysML and solver integration 4. 1 Stereotypes for optimization Modeling languages such as UML or SysML are defined using metamodeling, describing the language concepts with metaclasses  [20]. The stereotype is a special type of metaclass, derived from an existing UML concept or from another stereotype. To make the concepts presented in section 3 compatible with the UML and SysML semantics, Figure  10  connects decision points to SysML metaclasses and stereotypes. In Figure  10 , the "DecisionPoint" stereotype extends the UML::Comment metaclass. The "InstanceDecision" is connected to a SysML block, and to an instance specification from SysML. The "StructureDecision" applied to a composite association represents a variable redundancy. A specialization of association "CommentDecisionLink" connects a decision point to a block.
Solver integration We evaluated several CSP and optimization solvers. The goal is to integrate them in an open-source framework, such as Eclipse  [21]  and Papyrus  [22]. We can classify the solvers in two groups. The first group contains black-box tools such as Minion  [23]  or RealPaver  [24]. These solvers are not suitable for us, because we want to integrate them into an open-source framework. The second group includes object libraries and functions dedicated to CSP and optimization problem, written in JAVA or Python language. This is the group of solvers we have investigated: Labix  [25] , PyOpt  [10] , CHOCO  [9]  and ECL I PS E  [26]. The first criterion for choosing a solver is the type of variables it uses. For the previously defined decision points, we need both integer and real variables.
Algorithm 1: decision variable creation from SysML model with Boolean Matrix and capacity constraints From the results presented in Table  4 , we selected the Labix solver for our first experimentations with structure and instance decision problems. The PyOpt solver is suited for variables in continuous domain, which is useful for value decision problems. The CHOCO solver is similar to Labix, but with much more capabilities. CHOCO can handle global constraints, needed for allocation problems with a large exploration space. The ECL I PS E solver was not selected because the solver was coded using a declarative language similar to Prolog.
Papyrus tool and new plug-in To support the methodology depicted by Figure  1 , a plug-in has been added to the Papyrus modeling environment  [22]  with the Eclipse Modeling Framework (EMF)  [21]. The plug-in uses open-source software. The three following features are supported by the plug-in and the profile presented in Figure  18 : Listing 2 : Template expression with XTEND 3. Run the JAVA code generated in step 2, with a project using the selected solver. The results are displayed with a Pareto diagram. Other CSP solvers may be used at this step. In previous work  [31] ,  [29]  we have experimented the Labix solver  [31]  for instance and structure decision points and the PyOpt solver for value decision points. Both solvers are written in Python and can be integrated in the Eclipse environment.
Related Work In  [32] , Min et al propose a multi-objective optimization from SysML model, by using the ModelCenter commercial tool. ModelCenter is a graphical environment for analysis and optimization. The designer provides a structural description of the system with SysML blocks that include properties. Then the block properties are connected one by one to a ModelCenter analysis block in a parametric diagram. This matches one of our decision points, the "Value decision", using connection with a commercial tool. In  [12]  the authors propose a transformation from a feature model  [17]  to a mathematical representation of an optimization problem. Then a solver solves a combinatorial problem. Feature models represent all the products of a software product line. They are used in the whole product line development but not in MBSE with SysML.
Conclusions The paper discusses trade-off analysis in a MBSE approach that associates SysML with so-called "decision points". Whether the approach is instantiated on SysML, the concept of decision points is not specific to one MBSE language and enables covering the four kinds of decisions problems largely found in system engineering: instance decision, redundancy, values and allocation problem. For SysML, the paper proposes new stereotypes (decision points, global constraints and optimization context) extending the initial model without variants for trade-off analysis. Then, the algorithm proposed in the paper transforms the extended SysML model into a CSP optimization problem (CSMOP); the process includes decision variables, constraints and objectives functions. Several solvers have been benchmarked in order to address this CSMOP problem: CHOCO  [9] , PyOpt  [10]  and Labix  [25].
The designer selects a solver, stored in the model, according to the kind of decision points, and according to the strategy required by the problem resolution. The methodology was tested on a multi-core UAVs model and validated with the plug-in that we have developed using Papyrus and Eclipse. Unlike approaches published in  [12]  [32]  [33] , the one discussed in this paper allows to model the entire problem at the SysML model level, without programming code at the optimization tool level. Instead of manually connecting the SysML model with an optimization solver, it is possible to generate the problem description file using a SysML extension and our plug-in. This is particularly useful at early stage of design, when the exploration space is very large. With the proposed methodology, the design space exploration is more efficient, reducing the number of possible solutions before a detailed analysis such as scheduling analysis.
Beyond the model fit, the algorithm calculates an importance score for each explanatory variable that is based on permutation. The respective variable was permuted, while the other variables remained unchanged. The larger the increase in the prediction error due to this permutation, the higher the variable importance  (Liaw and Wiener 2002). We tested the significance of the random forest models using a permutation test according to  Murphy, Evans, and Storfer (2010) , with 9999 permutations. In order to test for significant differences in body size (prosoma width in mm) in females and males between the two populations of P. palustris, we performed a two-sided Wilcoxon rank sum test. All statistics were implemented in R (R Core Team 2016) using the r-package randomForest  (Liaw and Wiener 2015)  for the regression analysis and the stats r-package (R Core Team 2016) for the Wilcox test.
Study procedures Recruitment The clinical team will inform potentially eligible patients about the on-going trial, and offer them further information (written and verbal from the research team). Eligible patients will be recruited postoperatively as inpatients, with formal written consent taken by a member of the research team. Baseline information gathered will include: reason for admission, index procedure and date, significant co-morbidities-including history of diabetes or immunosuppression, as well as age and Body mass index (BMI). Participant contact details (mobile telephone number) will be entered into a secure, online data collection tool (Research Electronic Data Capture (REDCap) database). 13 ndomisation and blinding Participants will be assigned in a 1:1 ratio to the intervention or control arms and provided with the appropriate information packs prior to discharge.
30-day follow-up Both arms will receive a follow-up face-to-face or telephone consultation 30 days postoperatively (alternatively, written follow-up is also available for those with significant hearing impairment). This consultation will follow a standardised format and will be conducted with an independent clinical researcher blinded to the intervention status. The clinical researcher will gather data on postoperative course, any symptoms related to the wound and any treatment offered. They will also have access to electronic patient record (including all microbiology results from swabs taken in the community or hospital) and any wound logs returned. On the basis of these three sources of information, two independent, blinded clinical researchers will determine if an infection has been present (trained using the CDC criteria to diagnose infection).
14 ata on patient experience and service usage-ED and GP attendances, as well as contact with emergency surgery nurses-will also be collected via a separate questionnaire delivered alongside the 30-day follow-up (table  2 ). data analysis plan All analysis will be carried out on an intention-to-treat basis. We do not anticipate missing data in patient demographics. However, any missing data values will be handled using multiple imputation. The volume of missing outcome data will be recorded for the control and intervention arms, and any differences in drop-out rate noted. Thereafter patients with missing outcome data will be excluded from analysis.
Correlation analysis will be performed using Kendall tau rank test. This will assess the accuracy of the algorithm in stratifying risk, as compared with a clinician, and will indicate what additional benefit may be gained from photographic analysis. The secondary outcome measure will be use of services: GP and ED attendances, as well as contact with emergency surgery nurses. This data will be gathered at 30 days. Differences in number of attendees to GP and ED will be compared using a χ 2 test. Differences in the number of attendances will be assessed using the Mann-Whitney U test. Data on patient experience will also be gathered via a follow-up questionnaire and analysed separately (table  2 ). This will help determine if an online questionnaire delivered via a smartphone has a positive impact on patient experience of care, and if it helped facilitate their access to care.
data protection and management All participant data will be stored securely in a REDCap  13  database that has controlled access and designed as compliant with Health Insurance Portability and Accountability (HIPAA) security guidelines. This data will be anonymised, and only available to researchers listed on the protocol. Participant responses to the questionnaire will be reported directly to the REDCap database and will Open access not be stored on patient phones. However, patients will be advised to review the security setting on their phone if they intend to store their wound photographs. Patient details will be recorded in a trial log should any safety concerns arise necessitating they be contacted.
Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. We assessed the availability of the pancreatic cancer TCGA data (TCGA_PAAD) from several repositories and investigated the nature of each sample and how non-PDAC samples impact prognostic biomarker studies. While the clinical and genomic data (n = 185) were fairly consistent across all repositories, RNAseq profiles varied from 176 to 185. As a result, 35 RNAseq profiles (18. 9%) corresponded to a normal, inflamed pancreas or non-PDAC neoplasms. This information was difficult to obtain.
By considering gene expression data as continuous values, the expression of the 5312 and 4221 genes were significantly associated with the progression-free and overall survival respectively. Considering the cohort was not curated, only 4 and 14, respectively, had prognostic value in the PDAC-only cohort. Similarly, mutations in key genes or well-described miRNA lost their prognostic significance in the PDAC-only cohort. Therefore, we propose a web-based application to assess biomarkers in the curated TCGA_PAAD dataset. In conclusion, TCGA_PAAD curation is critical to avoid important biological and clinical biases from non-PDAC samples.
Introduction Consortium efforts, such as those of the Cancer Genome Atlas (TCGA) or the International Cancer Genome Consortium (ICGC), to massively sequence thousands of tumors from multiple types have led to a much better understanding of tumor biology. While the data were freely accessible, their use was restricted in practice to teams with great expertise in bioinformatics. Further efforts from centers such as the Broad Institute (https://gdac. broadinstitute. org) or the University of California Santa Cruz (http://xena. ucsc. edu) allowed easy access to TCGA normalized RNAseq, methylation and clinical data, often readily available in excel files. Finally, multiple web-based platforms were launched with "one-click" capabilities to give users direct access to the prognostic role of the gene expression level, the frequency of any mutation, the protein level expression and the networks of genes and proteins, etc.
Main platforms include TCGA (https://gdc. cancer. gov), the Broad Institute Cancers 2019, 11, 126 2 of 10 (https://gdac. broadinstitute. org), the University of California Santa Cruz (http://xena. ucsc. edu), cBioportal (http://www. cbioportal. org) and the Human Protein Atlas (https://www. proteinatlas. org). These platforms have given everyone access to use these data as exploratory or validation sets. Pancreatic cancer is a generic term often misused as a surrogate for the most common malignant tumor entity in this organ, the ductal adenocarcinoma (PDAC). Other malignant tumor entities such as the neuroendocrine neoplasms or the acinar cell carcinomas can also be found in the pancreas  [1]. While these tumors are uncommon, they often fall under the umbrella term of "pancreatic cancer", and are as such defined by completely different biology (mutational and transcriptional profiles) and clinical outcomes from the classical PDAC  [2, 3].
If such tumors were unnoticeably included within a dataset mainly composed of PDAC, they may introduce a strong bias in data analysis and lead to false conclusions regarding the prognostic value of a DNA mutation or an mRNA expression level. Depending on the source, 176 to 185 samples compose the TCGA study dedicated to the pancreas (TCGA_PAAD) and the multiomic analysis of these samples from the TCGA group was restricted to 150 samples  [4]. In a recent study, Peran et al. highlighted that the TCGA_PAAD cohort, which mostly comprised patients who underwent surgery, displayed a much better survival rate than that of the unselected cohort SEER  [5]. They further demonstrated that a failure to exclude non-PDAC samples might introduce a bias in the gene expression analyses leading to false conclusions being drawn when assessing the prognostic value of several mRNAs.
In this study, we aim to gather and compare the available data concerning the TCGA_PAAD from all the main repositories and clearly establish a list of suitable TCGA_PAAD samples for the PDAC centered studies. We then compare the TCGA_PAAD cohort to a large multicentric consecutive cohort of surgical PDAC. Using the key DNA mutation and the whole transcriptome, we then assessed the potential for bias based on survival analyses when using an uncurated sample list. Finally, we designed a web-based tool to assess the prognostic value of any gene expression on the curated TCGA_PAAD dataset.
Data Comparison from the Repositories for the TCGA_PAAD Across most platforms queried, the number of patients within the TCGA_PAAD study was consistent and set at 185 (TCGA data portal n = 185, UCSC Xena n = 185, Broad Institute Firehose n = 185, The Human Protein Atlas n = 176 (only patients with available RNAseq data were considered) and cBioportal n = 185). In all platforms, clinical data were available for the 185 patients. Depending on the platforms, mutation and copy number data were available for 184 or 185 samples, DNA methylation data were consistent and available for 184 samples, and RNAseq data were the most discordant, ranging from 176 to 185 samples (cBioportal n = 185, UCS Xena n = 183, Broad Institute Firehose n = 178, TCGA data portal n = 178 and The Human Protein Atlas n = 176).
TCGA-derived RNAseq data were the most frequently used. As a result, we carefully investigated the nature of the samples to explain the discrepancy in the available number of samples depending on the platforms. RNAseq data were not available for seven patients and the number of patients with RNAseq data (list in Table  S1 ) was set at 178. For four patients, RNAseq data from the normal adjacent pancreas were available and included in the datasets from the platforms with more than 183 samples. These samples could be identified, as they have the same ID number as the tumor sample but with "-11" at the end of their ID instead of "-01" (TCGA-HV-A5A3-01 and TCGA-HV-A5A3-11, for instance). For one patient, RNAseq data from the primary tumor and the metastasis was available. The metastasis is identifiable by its "-06" at the end of the sample ID TCGA-HZ-A9TJ-01 (primary tumor) and TCGA-HZ-A9TJ-06 (metastasis).
Curation of the TCGA_PAAD Dataset The recent study from the TCGA group focused on pancreatic ductal adenocarcinoma and only included 150 samples. Therefore, we carefully reviewed the clinical and histological data of all the cases gathered through the repositories and by viewing the virtual slides. Ten samples presented the pancreas as normal with atrophy, eight samples were neuroendocrine neoplasms, four samples were tumors derived from other organs (duodenum-ampulla in three cases and undefined location in one case), two samples were intraductal papillary neoplasms, one sample was an acinar cell carcinoma, one was a ductal adenocarcinoma but had received neoadjuvant chemotherapy and one had a normal ampulla. It should be noted that for several patients, while the analyzed specimens were not PDAC (atrophic pancreas, PanIN, etc.
), these patients did have a PDAC. These clinical data may therefore be used, but not the omic data. One additional case was excluded, as no single nucleotide variation data was available (TCGA-L1-A7W4-01). This sample was listed as a PDAC and treated with adjuvant gemcitabine, a classical drug for this tumor. The examination of the frozen section showed a poorly differentiated tumor, which was difficult to clearly identify as a PDAC or a neuroendocrine carcinoma. Copy number abnormalities (SMAD4 deletion, MYC amplification, no alteration on TP53, CDKN2A and RB1) did not help in further assuring the diagnosis. The flow chart presenting the TCGA-PAAD sample curation is presented in Figure  1  and the full list of the 150 proper PDAC sample and the 28 non PDAC sample are provided in Table  S1.
Clinical Relevance of the TCGA_PAAD Curated Sample List Samples constituting of the TCGA cohorts were collected from multiple institutions, which may have introduced some heterogeneity in patient management and clinical data collection. In addition, while these cases were all surgical resections, they were not consecutive. To assess how representative the curated TCGA_PAAD cohort was, we compared it to our well characterized cohort of 471 Cancers 2019, 11, 126 4 of 10 consecutive resected PDAC in five centers collected over a 13 years period  [6]. Basic clinical and pathological comparison is presented in Table  1. For this comparison, we used the consensual 150 cases, but the seven PDAC cases with no RNAseq data could have been added (results unchanged, data not shown). Both cohorts were comparable on most criteria.
Both cohorts were comparable on most criteria. Tumors from the TCGA_PAAD study tended to be slightly more aggressive with larger and more poorly differentiated tumors (p < 0. 05). Progression-free survival (PFS) was comparable in both cohorts (TCGA_PAAD 16. 75 months vs. 14. 51 months in our cohort, ns) (Figure  2a ). In contrast, overall survival (OS) was much shorter in the TCGA_PAAD cohort (19. 54 vs. 33. 09 months, p < 0. 0001) (Figure  2b ). We assessed whether the clinical or biological data from the non-PDAC patients impacted survival analyses performed on the TCGA_PAAD cohort. While the median PFS and OS of the uncurated cohort was marginally longer than that of the pure PDAC cohort (median PFS: 17. 0 versus. 15. 9 months and median OS 20. 1 vs. 20. 2 months respectively), the non-PDAC patients had a much longer PFS and OS compared to the pure PDAC cohort (n = 150) (median PFS: 27.