dataesr/scientific-paragraphs-categorization huggingface dataset:
3. Metabolome and metagenome integration strategies. pairing samples across groups according to the covariates and excluding the unpaired samples from further analyses  [41] ; or 3) adjusting the omics variables for covariates using generalised linear model methods  [155]. Before using any data analysis method, datasets need to be preprocessed to improve the interpretability of the models obtained with these methods. Due to the distinct nature of both data, the preferred preprocessing strategies for each data type are also different. Metabolomics data are typically first transformed (log-, glog-, or powertransformed) and then scaled (e. g. , auto-scaling, pareto scaling)  [156]. Additional steps of metabolomics data cleaning can be added on the metabolomics preprocessing (e. g. imputation of missing data, removal of variables present in the blanks and those with a large variability in the quality controls)  [157].
Metagenomics data are preprocessed with more adapted methods to address their skewness and sparse nature (e. g. , trimmed mean of M-values and relative log expression)  [130]. Nevertheless, in human cohort studies, both datasets are often transformed with the rank-based inverse normal transform method  [42, 158]  which converts the distribution of each continuous variable to appear more normally distributed. Statistical analysis. The simplest data-driven integration strategy consists of performing bivariate statistics, i. e. , correlating one -omic variable of each set (metabolomics and metagenomics) at a time. When associations between all possible pairs of metabolomics and metagenomics variables are evaluated systematically at once in a brute-force manner, this is referred to as Metabolome-and Metagenome-Wide Association Study (MMWAS)  [159].
In Segata et al. (2019), random forest was used to predict colorectal cancer samples from metagenomics data  [169]. Multi-block analysis. The multivariate methods presented above can be regarded as single-block chemometric methods, where models are built from one descriptor matrix only (e. g. , a single omics data (metabolomics or metagenomics), or a data matrix containing all of the different sets of omics data. As a consequence, single-block methods do not account for the intrinsic properties of each omics data (e. g. , data distribution, number of variables) nor the differences in these properties among the omics data. To address this situation, several multi-blocks methods have been proposed. The multi-block method ComDim enables an exploratory analysis of the variance common to all of the omics data  [170].
0 includes a significantly larger number of compounds, totalling 217,920. We anticipate that ongoing and forthcoming research in metabolomics will contribute to the enhancement of biological pathway maps, consequently leading to important improvements in these tools. Metabolic modelling. Bioinformatic tools that use the biological knowledge of the enzymes encoded in the gut microbiome as input have been developed to predict whether the variations in the metabolite profiles can be explained by the metabolic potential of the microbial communities as inferred from the KEGG KOs they encode. Examples of these tools are MIMOSA  [178]  and AMON  [179]. Other bioinformatic tools predict the metabolite contributions of the microbes present in the gut by using metagenome-scale metabolic models. One of the most popular tools is COBRA, a system biology framework that employs curated (meta)genome-scale reconstructions of organisms to predict their metabolic fluxes after imposition of condition-specific constraints (for instance, calculated from experimental metabolomics data and/or from nutritional data)  [180].
Perspectives, challenges and limitations In gut microbiome research, metagenomics and metabolomics can be regarded as two interdependent analytical strategies, where advancements in one can contribute to the progress of the other. On the one hand, better insight of the microbial composition can be informative of the chemical diversity in the metabolomics samples, and therefore be used as an assessment to design experimental methods that optimally capture this chemical information. In this line, tools capable of predicting the chemical diversity from metagenomics data can potentially be used to guide the development of the future LC-MS metabolomics methods. On the other hand, the combination of metabolomics with metagenomics can also improve microbial genome assembly, gene prediction and the identification of genes that are induced or repressed under specific conditions thanks to the detection of metabolites using metabolomics  [7].
Acknowledgments M. -E. D. is supported by the  NIHR Imperial Biomedical Research Centre ,  GutsUK, Diabetes UK  and by  Isite ULNE  ( R-002-20- TALENT-DUMAS ), jointly funded by  ANR  ( ANR16-IDEX-0004-ULNE ), the  Hautsde-France Regional Council  ( 20002845 ) and by the  European Metropolis of Lille (MEL). The  European Genomic Institute for Diabetes  is funded by a grant from the  French National Research Agency  ( ANR-10-LABX-46 ,  European Genomics Institute for Diabetes). The  National Center for Diabetes Precision Medicine -PreciDIAB , is jointly supported by the  French National Agency for Research  ( ANR-18-IBHU-0001 ), by the  European Union (FEDER) , by the  Hauts-de-France Regional Council  (Agreement  20001891/NP0025517 ) and by the  European Metropolis of Lille  (MEL, Agreement  2019_ESR_11 ).
Our starting point is a natural weakest pre-expectation semantics for PWhile in the form of an expectation transformer et P , capturing also natural program constructs such as lexically scoped local variables, procedure parameters, unrestricted return statements, etc. Our main contribution lies in a novel term representation of the aforementioned expectation transformer, denoted as infer  [P]. Its definition follows the pattern of et P , but notably differs in the definition of procedure calls and loops, where we replace the underlying fixed-point constructions via suitably constrained first-order templates. Through this step, we manage to tightly over-approximate the precise, but higher-order semantics via first-order constraint generation susceptible to automation. Second, we establish a new and original automation of this quantitative analysis in our prototype implementation ev-imp.
The crux of turning such a calculus into a fully automated analysis lies in automatically deriving closed forms for expected values of loops and recursive procedures. Related problems have been extensively studied in the literature, eg. [Contejean et al. 2005; Fuhs et al. 2007; Podelski and Rybalchenko 2004; Sinn et al. 2014]. One prominent approach lies in assigning templates to expected value functions, by which the definition of the expectation transformer can be reduced to a set of constraints treatable with off-the-shelf SMT solvers, like Z3 [de  Moura and Bjørner 2008]. Following this approach, we express values as linear combination • of base functions (or norms) with variable coefficients , mapping program valuations to (non-negative) real numbers. Norms serve as a numerical abstraction of memories and encompass a variety of common abstractions, for example the absolute value of a variable, the difference between two variables and more generally arbitrary polynomial combinations thereof.
These three constraints can be met by taking 1 , 0 = 1 /5, 2 , 1 = 1, and 0 = 0. We re-obtain (unsurprisingly) that the expected return value of procedure balls is given as 1 /5 •. We emphasise that parameterising templates through logical variables is crucial, even for relatively simple examples such as balls. Since the program is not tail-recursive, the continuation changes after each recursive call. Parametricity allows us to vary templates across recursive calls, as we have done above through instantiating x by 0 + 1 • x. et balls (. 1 /5 • K x balls ( + 1) + 4 /5 • K x balls ( )) ( -1) ≤ H 0 + 1 •x balls ( - We have successfully automated this approach in our prototype implementation ev-imp, see Section 5. Automation is based on our first contribution, the development of a term representation of the expectation transformer inducing an inference method that describes the generation of constraints.
The bounds for the procedures in Listings 1(a) and (b) are precise, respectively. For the procedure every we, however, only manage to derive a (sound) upper bound. The latter is not surprising. As shown in the textbook proof the expected number of throws is given as Θ( • log( )) in general. Our template approach does not (yet) support logarithmic functions. Hence, we cannot hope to derive the precise bound fully automatically. To the best of our knowledge, our prototype ev-imp is the only existing tool able to fully automatically analyse the expected result value (or expected cost for that matter) of probabilistic, recursive programs, if formulated in an imperative language. The complete evaluation of our prototype implementation is given in Section 5.
As argued by  Kaminski et al. [2018]  an expected value analysis is in general unsound for expected cost analysis. However, if the program under consideration is almost-surely terminating (AST for short), then an expected cost analysis can be recovered from an expected value analysis by counter instrumentation. In this context, an expected value analysis constitutes a strict extension of an expected cost analysis. Note that all benchmarks considered in Table  2  (f) and (g) are AST (even positive almost-sure terminating). We have incorporated recursive examples from  Kaminski et al. [2018] , suited to our possibilities, cf. Table  2  (e). While procedure geo can be handled instantly with ev-imp, the growth rate of the (expected) value of faulty_factorial is non-polynomial. We thus suited a variant-dubbed faulty_sum-to our benchmarks replacing the multiplication by a sum, thus featuring polynomial growth but similar algorithmic complexity.
In contrast to  [Chakarov and Sankaranarayanan 2014; Katoen et al. 2010] ,  Wang et al. [2018]  and the very recent  Bao et al. [2022]  provide fully automated (linear) invariant generation methodologies. Wang et al. [2018]  provide a compelling algebraic framework for the program analysis of probabilistic programs. Apart from the here relevant linear invariant generation, interprocedural Bayesian inference analysis and the Markov decision problem are conducted. Wrt. linear invariant generation, we have considered all provided examples in our benchmarks and obtained comparable results, while improving the speed of the analysis by a magnitude (in comparison to the analysis times reported in  [Wang et al. 2018] ), cf. Section 5. Automation of the method developed in  [Wang et al. 2018 ] is based, like ours, on a template approach.
Wrt. precision, however, we often derive the same bounds on expected costs. Further, and as argued, our methodology focusing on expected value analysis is more general and our implementation incorporated the highly non-trivial handling of recursive programs. Technically the closest comparison is to work on amortised cost analysis of functional languages (eg. [Leutgeb et al. 2022; Wang et al. 2020] ), as resource parametricity  [Hoffmann 2011 ] has been studied in this context. In comparison to  Wang et al. [2020] , our tool ev-imp cannot infer higher-order moments but concerning expectations seems to have better support for recursion. For instance, we were not able to reproduce an expected cost analysis of the balls procedure in their RaML prototype. Similarly, ev-imp is no match to  Leutgeb et al.
CONCLUSION We established a fully automated expected result value analysis for probabilistic programs in the presence of natural programming constructs, in particular recursion. Our analysis is in the form of an expectation transformer et • and its syntactic representation infer  [•]. As argued, automated inference of upper invariants is challenging for PWhile, due to the presence of recursion. We have overcome these challenges and implemented the established methodology in our novel prototype implementation ev-imp. In future work, we aim to incorporate (i) more program features and , like eg. support for dynamic uniform distributions; (ii) improve the constraint solving capabilities of our prototype implementation, to handle the analysis of further natural probabilistic programs and data structures fully automatically.
AVAILABILITY OF DATA AND SOFTWARE Our prototype implementation ev-imp is publicly available via the following Zenodo link: https: //doi. org/10. 5281/zenodo. 7706691. The artifact  [Avanzini et al. 2023b ] is given as a Docker image running a minimal Linux distribution, containing the sources in directory /evimp, as well as precompiled executables. A second, larger, image contains the source distribution as well as the complete toolchain to compile ev-imp. We have successfully tested the images under Linux, MacOS and Windows, as long as these were non-ARM architectures. For ARM64 architectures, the artifact additionaly provides instructions to install from source. Detailed instructions on the use of ev-imp are provided as well.
Material The virtual environment was built in our lab using Unity software. The environment was designed to immerse participants in a close-to-daily-life urban landscape with rich ambient sounds, animations, and scenes. The virtual environment was displayed on an HTC Vive Pro headset equipped with headphones (stereoscopic). We further circumscribed three paths of approximately equal length (~ 5mins) at different locations in the virtual environment (Fig. 1 -a ). A bright white line was visible on the ground to delineate each path and indicate the path to follow. Each path included 12 short, animated scenes representing everyday-life events for a total of 36 scenes (Fig. c, d ). The scenes depicted characters, animals, or objects engaged in daily-life situations (e. g. , a street musician playing the guitar, a trashcan on re, or a dog barking aggressively) and were evenly distributed around the white line so that as many events were on the left, right, or in front of the participants.
Statistical analysis In our statistical analysis, we performed a mixed model analysis that included the within factors of Condition (Synch, Asynch, and No Body). We also examined the interaction between Condition and Delay (Immediate or Delayed) for the memory tests by adding Delay as an additional within factor. Mixed models are well suited for repeated measure designs as they enable dependencies in the data to be considered. Models were computed using the lmer function from the lme4 package in R studio version 4. 0. 4  [45]. When data residuals were not normally distributed (as assessed by a Shapiro-Wilk test), we used a robust version of the lmer function named rlmer using the robustlmm package  [46]. Estimates, con dence intervals, and p-values for xed effects estimates were computed using a Wald t-distribution approximation using the parameters function of the performance package  [47].
The novelty of this solver is the coupling of BEM method with equivalent circuit  7, 8. Like many modelling software, it demands experimental studies especially for the interfaces' representation and their evolution according to different corrosion scenarios encountered in real conditions. Among the different scenarios investigated, the behavior of hull under natural and accelerated ageing conditions is studied with a focus on the unpainted area (defects). These results will be integrated as boundary conditions in the computational software "CorOns"  7, 8. One of the points discussed in this article, is the verification of certain assumptions generally used in modelling software to represent the different metal/electrolyte interfaces under cathodic protection. The verifying assumption presented here, concerns the painted steel modelling by a pure insulator when no visible defect is observed (i.
This assumption is not valid in our case presumably because the resistance is linked to the porosity of the paint, and this resistance depends on the electrolyte unlike the capacitance, which depends on the dielectric characteristics of the paint itself  21. Therefore, we use this Power-Law model to model the CPE paint. In Fig. 2 , we can observe a good agreement between the experimental points and the modelling of the impedance diagrams (Nyquist and Bode Fig. 2A , B respectively) with the EEC Power-Law model obtained with SIMAD® software validating the chosen EEC to represent a freshly paint hull. Thanks to this model, a value of the porosity resistance (R porosity ) is estimated at around 2. 2 × 10 +12 ± 5. 6 × 10 +11 Ω. cm 2. The very high value of this R porosity confirms once again the very protective and insulating character of this freshly deposited paint.
Surface characterization and EIS investigation For whole study presented here, two computational software are used for impedance modelling: SIMAD® and EC-LAB®. SIMAD® software is a homemade software developed at the "Laboratoire Interfaces et Systèmes Electrochimiques" at "Pierre et Marie Curie University (UMPC)" in France. EC-LAB® software is developed by the Biologic® Company. Both software are developed to model impedance datas (diagram), thank to Electrical Equivalent Circuit and analytical expressions. The SIMAD® software is used as power-law model could be implemented to fit EIS data in comparison with EC-LAB® software that is a commercial one in which we could not use power-law model. Three repetitions are realized for each test (continuous or intermittent cathodic protection). To characterize the metallic interfaces, SEM (Scanning Electron Microscopes) measurements are performed with FEG-SEM JEOL ® (Field Emission Gun-Scanning Electron Microscopes) equipped with an energy-dispersive X-ray detector (Energy Dispersive X-ray spectroscopy detector -Bruker Nano XFlash 6|60).
We cluster the data using the weather features available in the dataset (solar irradiation, temperature and humidity for the whole year) using K-means clustering in combination with dynamic time warping (DTW) to create the clusters. DTW has shown superior performance when applied to time-series datasets, because it is able to detect similar patterns even if they do not occur in the same time period  [45]. Forecasting Models. We enrich the clustered datasets with the historical information about the PV power production and energy consumption of the prosumer in the system. For each of the four clusters we then generate an individual machine learning forecast model. As a predictor we use LightGBM, which is a gradient boosting decision tree (GBDT) algorithm  [46]. LightGBM is constructed differently than conventional GBDT algorithms, as it grows leaf-wise to find a leaf with the largest split gain instead of iterating over all previous leaves.
PV power generation and energy consumption prediction The development of the predictors as well as the distribution of the prediction deviation is divided into three main steps: (1. ) clustering of the datasets with weather information, (2. ) development of the predictors and (3. ) estimation of the prediction deviation. As a first step, we conducted the weather type clustering as described in Section 4 with the available weather features: solar irradiation, temperature and humidity for the whole year of data. With this we retrieved four sub-datasets for each weather condition (sunny, partly overcast, overcast and rainy). Additionally to the weather features, we enrich these datasets with the historical information about the PV power production and energy consumption of the prosumer in the system.
The sub-datasets for the four different clusters were divided into a training (80%), validation (10%) and test (10%) dataset. A LightGBM model was trained for the energy consumption and PV power production of each prosumer and weather cluster, individually. The predictor takes 48 h as input to forecast the next 24 h. Additionally, we used grid-search to select the best hyperparameters for each model. To estimate the deviations of the prediction we used the QR and QS as described in Section 4 using 0. 90 for the upper boundary and 0. 10 for the lower one to build a 80% confidence interval.
Optimization In our case study, we schedule day-ahead energy exchange where each time slot is equal to 1 hour; with time horizon of 𝑁 = 24 hours, i. e, the decision is made by solving the optimization problem for the     next 𝑁 = 24ℎ with a time window from 0: 00 to 23: 59. Hourly import and export prices were obtained from Nord Pool market data. 2  We use the ML predictions for the PV production, the consumption and their deviations as the input to our optimization models, i. e, the reference values. The model is implemented in Python (RSOME library 3  ) and solved by Gurobi. 4.
Price of robustness To show the impact of data uncertainty on the objective value, i. e, the MG operational cost, and to demonstrate the effectiveness of the proposed approach against it, we simulate 10 000 scenarios of random yields for users production and then we compare the robust solutions generated by varying the level of the uncertainty budgets 𝛤 𝑃 𝑉 𝑡 and 𝛩 𝑃 𝑉 𝑡. For simplification and without loss of generality, we fix 𝛤 𝑃 𝐶 and 𝛩 𝑃 𝐶 𝑡 to zero. We evaluate the constraint violation probability and the Price of Robustness. We define the PoR rate as the relative difference between the costs realized by the robust solution and the nominal solution where we do not protect against the uncertainty. The PoR is used to quantify the extra MG cost required to cover for the robust solution.
Acknowledgements This work has been partly funded by the  Energy Agency of Sweden (Energimyndigheten)  through the  AI4-ENERGI project  (Projektnr:  50246-1 ) and the project  Solar Electricity Research Centre, Sweden (SOLVE) , grant number  52693-1. Additional funding has been provided by the  Bavarian State Ministry for Science and Art through the Hightech Agenda (HTA).
The cell suspension was strained twice. Strainers were washed with additional DMEM (Gibco # 41966029) with 5% FBS and cells were pelleted by centrifugation at 300 x g for 4 minutes at 4°C. Cell pellets were carefully resuspended in 300 uL of ACK lysis buffer (Lonza # BP10-548E) for 1 minute in ice. After incubation cells were washed with 1mL of DMEM with 5% FBS and pelleted. Cells concentration was determined using 0. 4% trypan blue stain (Invitrogen) and Countess II automated cell counter (Invitrogen). 1 × 10^7 cells were processed for dead cell depletion using Dead Cell Removal Kit Single-cell RNA sequencing: data processing and analysis Genomic references for C57BL/6 (GRCm38) was generated using CellRanger mkref (v7. 0) using sequence and gene annotations from Ensembl (release 94). For mapping of C57B6 / CAST/EiJ F1 mouse data, a joint reference was constructed based on the GRCm38 reference in which SNP positions between mm10 and CAST/EiJ were N-masked.
These SNPs were derived from (Keane2011, ftp://ftp-mouse. sanger. ac. uk/current_snps/mgp. v5. merged. snps_all. dbSNP142. vcf. gz). Filtered count matrices were generated using CellRanger count (v6. 1. 1) using default settings, which includes reads mapped to introns. Low-level analysis of scRNA-Seq data was then performed, largely using functions from the scran (v1. 24. 1) and scater (v1. 20. 1) R packages  17, 18. First, cells with less than 500 UMIs and 500 detected genes were removed. Next, counts were normalised using the computeSumFactors function and log-transformed. For cell type annotation, we used mutual nearest neighbour-based batch correction using the function MNNcorrect with the library as batch variable to exclude species-specific and technical variation across samples  19.