dataesr/scientific-paragraphs-categorization huggingface dataset:
| RE SULTS Data for 129 patients, 50 of whom were hypertensive, were analyzed (Table  1 ). Patients with hypertension were older, had a lower estimated glomerular filtration rate, and higher pulse pressures. Also, type 2 diabetes and treated dyslipidemia were more common in hypertensive patients compared to normotensive patients. The hospital outcome occurred in 36. 00 % of patients in the hypertensive group and in 26. 58% of patients in the normotensive group (P =. 26). On admission, the level of inflammatory markers and the severity of pneumonia as assessed by CT scan were not significantly different according to hypertension status. It is important to note that the level of inflammation on admission was highly correlated to blood pressure within three days after admission for normotensive patients while no significant correlation was observed in patients with hypertension (Table  2 ).
| Limitations and strengths of the study A limitation of the study was due to limited availability of biomarkers of inflammation because of the retrospective design of the study. These biomarkers are needed to explore the interaction between inflammatory tone and hypertension status on blood pressure and outcomes. Certainly, it would be of interest to analyze the impact of inflammation on blood pressure using biomarkers such as soluble CD14, von Willebrand factor, and interleukin-6 to disentangle the biological pathway responsible for the interaction. Importantly, we analyzed all patients admitted to our hospital for infection with SARS-CoV-2 during the period March 2020-April 2020, except patients admitted directly to an intensive care unit, so there is no selection bias. Furthermore, all data required for analyzing the interaction between CRP and hypertension status on blood pressure and the prognosis were rigorously obtained in the context of patients hospitalized for SARS-CoV-2 infection.
Full a posteriori verification of the correctness of modern software systems is practically infeasible due to the sheer complexity resulting from their intrinsic concurrent nature. An alternative approach consists of ensuring correctness by construction. We discuss the Rigorous System Design (RSD) approach, which relies on a sequence of semantics-preserving transformations to obtain an implementation of the system from a high-level model while preserving all the properties established along the way. In particular, we highlight some of the key requirements for the feasibility of such an approach, namely availability of (1) methods and tools for the design of correct-by-construction high-level models and (2) definition and proof of the validity of suitable domain-specific abstractions. We summarise the results of the extended versions of seven papers selected among those presented at the 1st and the 2nd International Workshops on Methods and Tools for Rigorous System Design (MeTRiD 2018-2019), indicating how they contribute to the advancement of the RSD approach.
Proving that the assumptions made at the modelling level to justify the separation of concerns hold, indeed, at the platform level, guarantees that all the properties established throughout the design process also hold for the generated code. Thus, the RSD approach applies-on the higher abstraction level of the system design process-the same principles as those provided by standard compilers for the generation of machine-executable code from programs written in languages such as Java or C++. It consists in decomposing the argument justifying the correctness of the entire process into several independent arguments justifying the correctness of individual transformations. Furthermore, it provides flexibility w. r. t. the target platforms by postponing design choices as far as possible and allowing for different transforma-tions of the same model to be applied at every design stage.
However, in drawing this parallel, it is appropriate to differentiate between commonly used compilers such as gcc, where the public trust originates mainly from the extensive usage experience, and verified compilers such as CompCert  [29] , where preservation of semantics at the various stages is formally verified. Although the second scenario is currently preferable for RSD tool sets due to lack of usage history comparable to that of compilers such as gcc, both can be relevant in practice. Applications. Although the RSD approach as formulated by Sifakis  [33]  originates from the development of the BIP framework for the Embedded Software design (see, for example,  [5, 6] ), it is applicable in a much broader variety of domains, whereof we will mention below just a few. Key issues of applying the RSD approach to the Cyber-Physical Systems (CPSs), which comprise components with both discrete and continuous underlying dynamics, are discussed in  [11].
This issue This special issue contains the extended versions of selected papers presented at the 1st and the 2nd International Workshops on Methods and Tools for Rigorous System Design (MeTRiD 2018-2019) held as satellite events of the corresponding European Joint Conferences on Theory and Practice of Software (ETAPS 2018-2019). The goal of the MeTRiD workshop is to promote crossfertilisation between research in academia and practical applications in the industry. On the one hand, we hope that, through the publication of research and tool papers, the workshop will contribute to raising awareness of the methods and tools available among the industrial players. On the other hand, presentation and exchange of realistic case studies should allow academic researchers to better fit their tools to industrial needs, thereby improving the dissemination of results.
The first, 2018 edition was a traditional workshop with peer-reviewed proceedings published as volume 272 of the Electronic Proceedings in Theoretical Computer Science (EPTCS)  [10]. MeTRiD 2018 accepted three categories of papers: regular, tool and case-study papers. MeTRiD 2019 was by invitation with presentations of, both, already published results and ongoing work, and no proceedings. This issue comprises 7 papers addressing the key elements outlined above. Papers  [3, 18]  contribute to the design of correct-by construction high-level models by defining a highlevel modelling formalism  [18]  and by providing an approach for debugging CPS models  [3]. Papers  [9, 14, 22, 24, 28]  contribute to the design and proof of domain-specific abstractions. They provide techniques for ensuring the correctness of randomised consensus protocols  [9] , program block parallelisation  [14] , usage control policies  [22] , and for ensuring optimality of partition schedules  [24]  and energy consumption  [28].
It presents an extension of Behaviour-Interaction-Priority (BIP) framework called Dynamic-Reconfigurable BIP (DB-RIP), which allows dealing with reconfigurable systems including different types of dynamism. The technical contribution of the paper consists of the formal definition of DR-BIP, i. e. its syntax and semantics. In addition, the paper describes a prototype implementation of the language in Java and illustrates the application of the approach to model 3 different examples from different domains. -The paper "Model-based optimization of ARINC-653 partition scheduling" by Han, Zhai, Nielsen, and Nyman  [24]  is an extension of the MeTRiD 2018 paper  [23] , by the same authors. This work introduces a framework for generating optimal ARINC-653 partitioned schedules, i. e. system partitions scheduled in mutual exclusion in order ensure temporal isolation of applications in safety-critical systems.
123 sising optimal and budget parameters for the partitions is the scalability problem, since the parameter space for larger systems rapidly grows in size and cannot be exhaustively explored. The proposed framework excludes, as early as possible, non-schedulable combinations of parameters by applying (in order of sequence) global schedulability tests, statistical model-checking and model-checking in Uppaal. Finally, an evolutionary algorithm for parameter exploration is applied that generalises the yes/no verdict of the schedulability question into a numeric fitness evaluation. -The paper "Correct Program Parallelisations" by Blom, Darabi, Huisman, and Safari  [14]  is an extension of the FASE 2015 and NFM 2017  [13, 15]  papers by the first three authors. This paper presents a verification technique to reason about the correctness of compiler directives indicating which program blocks may potentially be executed in parallel without changing the behaviour of the program.
To this end, the authors introduce an intermediate language for representing such programs, its formal semantics and a soundness proof for deductive proof rules that build on that semantics. The paper also discusses how OpenMP programs can be translated into the intermediate language and how the verification methodology can be implemented on top of the VerCors/Viper verification tool infrastructure. -The paper "Energy characterization of IoT systems through design aspect monitoring" by Lekidis, and Katsaros  [28]  is an extension of the MeTRiD 2018 paper  [27]  and a related presentation in MeTRiD 2019, by the same authors. This work proposes a model-driven approach for the energy cost estimation of Internet of Things design aspects (availability, reliability, dynamicity and security), by monitoring them.
) EDX-System. To examine oxidation states and to identify elemental composition of the surface area, X-ray Photoelectron Spectroscopy (XPS) measurements were performed using a PHI 5000 Ver-saProbe II (ULVAC-PHI, Inc. ). Detailed information about measurement parameters, sample preparation, and data evaluation are described in the supplementary data (SD). X-ray absorption spectroscopy was carried out on selected samples to identify the Se oxidation state, coordination as well as the molecular short-range structure. Se K-edge X-ray Absorption Fine Structure (XAFS) spectra were collected at the Rossendorf Beamline (ROBL) at ESRF (Grenoble, France). Measurement parameters and sample preparation are described in detail in the SD. Fluorescence deadtime correction, energy calibration and averaging of scans was performed with the software package SixPack (www.
sams-xrays. com/sixpack), while normalization and extraction of the EXAFS chi function was performed with the software WinXAS according to standard procedures  (Ressler, 1998). The k 3 -weighted EXAFS data were fit using theoretical back-scattering amplitudes and phase shifts calculated with FEFF 8. 2  (Ankudinov and Rehr, 1997). Statistical analysis of spectra was performed with the ITFA program package  (Rossberg et al. , 2003). Spectra of Se reference samples were taken from  Scheinost and Charlet (2008).
Effects of selenium adsorption on the properties of the solid phase To assess the impact of selenite or selenate adsorption on the properties of the magnetite phase, selected samples from adsorption experiments were characterized by various solids analysis methods. A detailed description and evaluation of these analyses can be found in the SD Chapter 3. 2. In short, XRD and SEM/EDX analyses demonstrated that neither the mineralogical composition (Fig. S4 ) nor the morphology (Fig. S5 ) of the initial magnetite nanoparticles was affected by the adsorption of selenite or selenate. EDXRF data (Table  S2 ) and SEM/EDX analysis (Fig. S5 ), however, proved that the examined solid samples contained significant Se quantities after adsorption, whose proportions depend on the initial Se speciation and concentration.
660 keV), indicating that the initial tetravalent oxidation state remained unchanged. Furthermore, a statistical analysis of the spectra using the ITFA software package  (Rossberg et al. , 2003)  showed that the three sample spectra contained only one spectral component commensurate with Se(IV), and not with any other Se oxidation state tested (Se (VI), Se(0), Se(-II)). Hence, contributions from these latter oxidation states are smaller than 5 %  (Charlet et al. , 2007). XANES data are thus in line with the XPS results which showed that the adsorption processes caused neither selenite reduction nor oxidation. An immobilization due to selenite reduction and formation of sparingly soluble reduced Se compounds such as elemental Se 0 or selenide minerals can therefore be excluded not only at alkaline but also at acidic pH conditions.
Acknowledgements This work is part of the  IMMORAD  project, funded by the  German Federal Ministry of Education and Research (BMBF)  under grant No. 02NUK019B. Additional financial support was provided by the  Graduate School for Climate and Environment (GRACE)  at  KIT. The authors would like to thank  Dr. Peter Weidler  and  Volker Zibat  for BET and SEM analyses. We also thank  Dr. Utz Kramar  and  Claudia Mößner  for their help with XRF and ICP-MS analyses. The  ESRF  and the team of the  Rossendorf Beamline  ( BM 20 ) are gratefully acknowledged for the provision of beam time and their support during the XAFS measurements.
Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e. g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, Spar-seDOSSA's model captures marginal microbial feature abundances as a zero-inflated lognormal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions.
Conditional on feature relative abundances and total read depth, count observations are modeled with a standard multinomial sampling procedure, and per-sample read depth is modelled with a log-normal distribution. For implementation, we adopted a penalized Expectation-Maximization procedure for model fitting, and we have evaluated and provided options for cross-validated selection of the optimal penalization parameter (Methods). SparseDOSSA 2 is implemented as an R/Bioconductor package (http://huttenhower. sph. harvard. edu/sparsedossa2). Note that this differs somewhat from our previous implementation of SparseDOSSA that was already publicly available; we provide a summary of changes between the versions in Methods. Sparse-DOSSA can be accurately fit to a wide variety of different microbial community structures to capture both (inferred) absolute and relative count observations (Fig  1B  and 1C , details below).
SparseDOSSA accurately recapitulates real-world microbial community structures We validated SparseDOSSA's ability to accurately capture realistic microbial community feature profiles by quantifying its performance across a variety of real-world datasets (Fig  2  and  S1 and S2 Tables). The studies used include: 1,2) taxonomic profiles from shotgun sequenced metagenomes of healthy human stool and posterior fornix samples from the HMP1-II, hereafter referred to as "Stool" and "Vaginal"  [23] , 3) shotgun sequenced stool metagenomes of inflammatory bowel disease (IBD) patients from the HMP2 Inflammatory Bowel Disease Multi-omics Database (IBDMDB, abbreviated as "IBD")  [2] , and 4) 16S rRNA gene sequenced murine distal gut communities after diet perturbation  [24]. By evaluating the model in different cohorts, we established its robustness under different community phenotypes, habitats (i.
e. body sites), overall ecological structures, and sequencing technologies (S1 Table ). SparseDOSSA 2 captured community parameters and re-simulated microbial profiles with overall community structures that accurately reflected those of the original, real-world ecologies, better than alternative methods (Fig  2A  and 2B ), across all human datasets (murine study results reported in separate section). Overall, simulated communities yielded the same patterns of global beta-diversity as were contained within each modeled dataset (Fig  2A ). This was quantitatively compared against alternative models (Dirichlet-multinomial, DM  [20]  and gamma-MGH, namely metaSPARSim  [18] ) with the PERMANOVA R 2 statistic  [25]  (Methods). We calculated ecological Bray-Curtis dissimilarities between real-world microbial profiles and those simulated by each evaluated method.
We then quantified the total variability in the combined dissimilarities that could be attributed to real-world versus simulation  ). Notably, this was consistent in both the human gut (Stool, IBD), where community structure forms continuous "gradients" of microbial composition  [9] , and the human vaginal environment (Vaginal), where communities are often characterized by a few discrete types with dominant species  [26]. Only for the Stool dataset did SparseDOSSA slightly underperform when compared to metaSPARSim in terms of R 2 statistic, while still outperforming with respect to per-feature distributions (Fig  2D ). Additionally, metaSPARSim's  ). R 2 compared against randomly split original real-world data are included as baseline controls. C) Representative features from each environment are similarly distributed between real-world and SparseDOSSA simulated samples, as shown in empirical cumulative distribution functions (CDFs) of log-10 relative abundances (with pseudo value 1e-6 to visually represent zeros).
D) Per-feature Kolmogorov-Smirnov summary statistics quantify that SparseDOSSA outperforms existing methods in simulating realistic feature-level relative abundance distributions. First, the similarity between the model-simulated feature abundance distribution versus that in the real-world dataset is quantified with K-S statistics. Then, the K-S statistics for SparseDOSSA and the other two models (DM and metaSPARSim) are plotted on the x-and y-axis, respectively (each point representing one feature, smaller K-S statistics represent better approximation). Lastly, the K-S statistics of SparseDOSSA versus other models are formally tested using Wilcoxon signed rank tests (p-values are significant and included in S4 Table ). https://doi. org/10. 1371/journal. pcbi. 1008913. g002 simulation procedure can take as much as ~10x longer than SparseDOSSA 2 (S1 Fig) , which is prohibitive for realistic data sizes (especially for benchmarking or power-analysis efforts requiring multiple simulations per parameter configuration, or for Monte-Carlo calculations).
modeled real-world communities. Smaller K-S statistics thus indicate better performance of the model. SparseDOSSA better approximated the targeted real-world per-feature distributions than existing methods across all evaluated datasets (Fig  2D ), reaching statistical significance in each case (S4 Table, Wilcoxon signed rank tests p < 0. 05). In addition to simulating existing microbial features, SparseDOSSA 2 also provides the functionality to simulate new features that resemble the targeted environment's ecological characteristics (Methods) and was validated to generate "Stool-like", "Vaginal-like", or "IBD-like" new features in terms of prevalence, abundance, and variability for each of the tested datasets (S2 Fig). Thus both in overall community structure modeling and in per-feature models, SparseDOSSA was able to accurately capture and re-simulate realistic microbial observations better than alternative approaches.
Second, associations with both non-zero (abundance) and zero-inflated (prevalence) components of microbial features can be captured, along with clearly defined effect sizes (fold change or odds ratio, see Methods). This enables rigorous evaluations of, for example, differential abundance testing methods for their statistical performance (e. g. power or false positive rates). Based on models fit to the Stool and Vaginal communities, SparseDOSSA 2 accurately introduced, or "spiked-in" associations for control phenotypes in a new, simulated population (Figs  3  and S3 ). Specifically, for the Stool dataset, we introduced a binary covariate (similar to e. g. a case / control contrast) with non-zero effects on 16 (5% of the total 332) microbial features' abundances (Fig In addition to modeling associations between microbial features and external covariates, Spar-seDOSSA can also model community ecological interactions (i.
) or power analysis (Fig 4B). To demonstrate SparseDOSSA's use for benchmark comparison of microbial community statistical tests, we again simulated synthetic datasets based on the Stool profiles with "phenotypic" associations spiked-in for 5% of features at varying effect sizes (as in Fig  3A ). Multiple replicates of the same parameter set were performed to provide performance metric mean and standard errors (Methods). Using the resulting gold standards, the performances of three different association tests-limmaVOOM  [30] , ANCOM  [31] , and MaAsLin 2 [29]-were similar for power, but false discovery rates varied strikingly (Fig  4A ). Notably, the MaAsLin 2 generalized linear model showed good FDR control at small to moderate effect sizes. At higher effect SparseDOSSA can add feature-phenotype and feature-feature associations to modeled microbial community simulations.
This highlights the important difference between true differential abundance effects corresponding to microbes' biological variation, versus changes post normalization that are driven by other features. In contrast, ANCOM  [31]  was designed to account for compositionality and draw inference about hidden absolute abundances; it successfully and maintained FDR under moderate to strong effect sizes. Arguably as a result, however, its performance suffered for small to null effects, presumably because in such cases it is difficult to distinguish between "driver" microbial features with true absolute effects versus those with changes in their relative abundances due to compositionality. Lastly, limmaVoom  [30] , designed primarily for RNA-Seq data, had inflated FDRs across all cases. To demonstrate SparseDOSSA's use for power analysis during microbial community study design, we focused targeted simulation datasets with spiked-in effects on a feature modeled on Escherichia coli, as a microbe commonly associated with dysbiosis in the human gut  [2].
The SparseDOSSA model was also able to model and synthetically replicate changes in "Bacteroidetes" and "Firmicutes" phyla in response to raw vs. cooked diets, including B) overall community alpha-diversity (Shannon index), C) the resulting "Firmicutes" vs. "Bacteroidetes" ratio, and D) overall whole-community effective biomass. These correspond to  [24] 's Fig 1F  -1H , respectively. TRF = raw tuber (free-fd); TCF = cooked tuber (free-fed); TCR = cooked tuber (restricted ration). https://doi. org/10. 1371/journal. pcbi. 1008913. g005 exactly the same relative abundance profiles. These real-world application results highlight SparseDOSSA's adaptability to community phenotypes and treatment effects, as well as demonstrate its performance for amplicon sequence datasets and microbial communities associated with non-human hosts.