dataesr/scientific-paragraphs-categorization huggingface dataset:
This is due to the nature of this retrospective cohort in a centre where intermittent infusion is current standard practice in the ICU. Third, all patients in the model development and both external datasets had a serum albumin concentration below 35 g/L. Therefore, extrapolation of this model to patients with higher serum albumin concentrations should be performed with caution. Nevertheless, our data represent an important step forward, as this study is the first to develop and validate a PK model incorporating both total and unbound flucloxacillin. This resulted in several relevant findings that are easily applicable to the optimization of flucloxacillin dosing regimens in daily clinical practice.
Transparency declarations R. M. vH. has provided consultancies for Nordic Pharma. R. J. M. B. has served as a consultants to  Astellas Pharma, Inc. ,  F2G ,  Amplyx ,  Gilead Sciences ,  Merck Sharp & Dohme Corp. , and  Pfizer, Inc. , and has received unrestricted and research grants from  Astellas Pharma, Inc. ,  Gilead Sciences ,  Merck Sharp & Dohme Corp. , and  Pfizer , Inc. All contracts were through Radboudumc, and all payments were invoiced by  Radboudumc. J. L. has received honoraria from  Pfizer  and  MSD. J. A. R. wishes to acknowledge funding from the  Australian National Health and Medical Research Council for Centre of Research Excellence  ( APP1099452 ) and a  Practitioner Fellowship  ( APP1117065 ). J. A. R. has provided lectures/consultancies or had grant funding provided to his institution from  MSD, Astellas, BioMerieux ,  Accelerate Diagnostics and Cardeas Pharma.
Spectral imaging of potato plants The image (a single hyperspectral image of approximately 4 plants) was selected from an experiment for the early detection of diseased potato plants. The image was captured with a V10e spectral line-scan camera from Specim (Oulu, Finland) with top view as illustrated in Fig. 1. Illumination was provided by a 15 W halogen light source. The camera provided a 500 x 656 x 193 data cube with 2 spatial dimensions, and a spectral dimension including 193 bands acquired in the range of 400e1000 nm, at a resolution of 3 nm (FWHM). For illumination a fibre-optic line light was used, using a tungsten halogen light source. The image was corrected with white (>98% reflectivity Spectralon) and dark reference samples. However, it should be noted that this correction is carried out using a flat white calibration reference, therefore despite this correction, spectral images of plants suffer from unavoidable illumination effects due to plant geometry.
K-means clustering analysis K-means clustering is used here for data modelling in order to demonstrate the effect of the different pre-processing techniques. K-means is a vector quantisation method which partitions n observations into k number of clusters in which each observation belongs to the cluster with the closest barycentre. Any number of clusters can be defined; however, it is always necessary to perform an optimisation to find the optimal number of clusters. The cluster number optimisation protocol includes defining a criterion that minimises the within-cluster distances and maximises the between-cluster distances. In the present work, the K-means clustering was implemented using MATLAB's 'Statistics and machine learning toolbox' and the criterion used to determine the optimal number of clusters was the 'Calinski Harabasz' index, which is defined as the ratio between the within-cluster dispersion and the betweencluster dispersion.
Nonetheless, some issues with inflated FDR in DEA have been previously reported in the literature  [11, 12, 13, 14, 15] , but those warnings have made little apparent impact on DEA practices. Inflation of the empirical FDR in DEA can have numerous causes, from inadequate preprocessing of the data to violations of the DEA method's underlying assumptions. In particular, edgeR, DESeq2 and limma-voom make potentially strong distributional assumptions on RNA-seq data. This type of model-based inference may be required when RNA-seq studies include only a small number of observations. However, these methods' parametric assumptions are not typically verifiable in practice. Any deviation from the hypothesized distribution of test statistics will translate into ill-behaved p-values and therefore uncontrolled FDR.
dearseq is a robust approach that uses a variance component score test and relies on nonparametric regression to account for the intrinsic heteroscedasticity of RNAseq data. In the Results section we compare the performance of dearseq to the three most popular state-of-the-art methods for DEA: edgeR, DESeq2 and limma-voom. We demonstrate that dearseq enforces strict control of Type-I error and FDR while maintaining good statistical power in a realistic and extensive simulation study where knowing the ground truth facilitates benchmarking the properties of the different methods. We also present a comparative re-analysis of a real-world Tuberculosis data set from Singhania et al. [16]  studying apparent false positives identified by the leading DEA methods compared to dearseq. dearseq can efficiently identify the genes whose expression is significantly associated with one or several factors of interest in complex experimental designs (including longitudinal observations) from RNA-seq data while providing robust control of FDR.
Synthetic simulation study As highlighted by both Conesa et al. [17]  and Assefa et al. [15] , engaging in realistic yet clear simulations is difficult. One has to find the right balance between the controlled settings necessary to know the ground truth, and the realism necessary to be convincing that the results would translate in real-world analyses. In an attempt to cover as broad a spectrum as possible, we present a performance evaluation of our methods under three data-generating scenarios : a) a negative binomial parametric assumption for RNA-seq data, b) a highly non-linear model designed to violate most modeling assumptions, and c) a resampling from SEQC data  [18]  with truncated Gaussian noise. Scenario a) may be favorable to edgeR and DESeq2 as it relies on their parametric assumption of a negative binomial distribution for RNA-seq count data.
Scenario b) may be unfavorable for all three compared methods (edgeR, DESeq2 and limma-voom) since it features a high degree of non-linearity, deviating from any assumed model. Scenario c) is likely the most realistic of the three because it relies only on resampling real RNA-seq samples from the SEQC study  [18] , similarly to what was done in Germain et al. [13]. A multivariate truncated Gaussian noise (using the estimated covariance stucture across the observed genes) was added to enable the generation of larger sample sizes while preserving the count nature of the data. We simulated 1,000 synthetic data sets at different sample sizes using each one of these three scenarios. For scenarios a) and b), 10% of genes were generated as truly DE while the remaining 90% were not DE. For the scenario c), since it is based on resampling from homogeneous samples, it was impossible to induce truly DE genes without making further parametric assumptions (which would have made the scenario less realistic).
tuberculosis antigen specific IFN-γ release assay (IGRA). Healthy control participants were recruited from volunteers at the National Institute for Medical Research (NIMR, Mill Hill, London, UK) and were negative to both TST and IGRA. In total, 54 participants were included, of whom 21 were active TB patients, 21 were LTBI patients, and 12 were healthy controls. The signature was derived by contrasting active tuberculosis (TB) patients on the one hand against healthy individuals (Control) or those with a latent infection (LTBI) on the other hand (see Fig. 3 ). Their original analysis applied edgeR to their Berry London RNA-seq data, which included 14,150 normalizedgene counts measured across 54 samples after preprocessing (see Singhania et al. or supplementary material in additional file 1 for more information on this preprocessing) available from GEO (GSE107991).
Discussion The proposed method dearseq represents an innovative and flexible approach for performing gene-wise analysis of RNA-seq gene expression measurements with complex design. As demonstrated in our simulation study, edgeR, DESeq2 or limma-voom can all fail to control the Type-I error and the FDR when the 6 sample size increases, while our method behaves correctly. Moreover, the reanalysis of the London Berry Tuberculosis data set revealed that the differentially expressed genes identified by dearseq are highly predictive of active Tuberculosis status, while results from the three state-of-the-art methods (including the original edgeR analysis) likely include numerous false positives. It is important to note that edgeR, DESeq2 or limma-voom will not systematically have inflated FDR. As illustrated by our simulation studies, there are some scenarios in which, for some given sample sizes, they control the FDR adequately.
However, we have shown here that this is no guarantee, and in practice it is very difficult to know under which circumstances a data analysis is taking place. Because dearseq solely relies on the Central Limit Theorem convergence for its asymptotic test to work, it guarantees a control of the FDR without needing any model to hold as long as the sample size is large enough. For lower sample sizes, where convergence is not reached, a robust permutation test can be used instead. Using Phipson & Smyth's  [21]  correction, it adequatly controls the FDR regardless of the sample size and exhibits good statistical power in our simulation study (the only trade-off is its increased computation time). Among the three state-of-art methods compared here, DESeq2 seems to fail to control the FDR most often.
In particular, even under its model assumption of a Negative Binomial distribution for the data, it can suffer from inflated FDR. This seems counter-intuitive as our synthetic data were generated under the Negative Binomial distribution, and this should advantage DESeq2 and edgeR -since both methods assume this model. As has been noted previously, this behavior can be caused by non-uniformity in the distribution of the p-values arising from DESeq2 or edgeR (especially when combined with a multiple testing correction such as Benjamini-Hochberg procedure)  [22, 12, 23, 24]. DEA can have numerous preprocessing steps, and the various possibilities can complicate the fair comparison of different methods. Since here our primary goal was to compare to the original edgeR analysis, we used the edgeRpreprocessed data as input to dearseq.
For DESeq2 and limma-voom we used the raw counts. Indeed both edgeR and DESeq2 assume the input data to be strictly counts (i. e. integers), due to their Negative Binomial distribution assumption, though edgeR also has some support for so-called "non-integer counts". While this seems sensible given the nature of RNA-seq data, recent innovations in RNA-seq alignment methods such as salmon  [25]  or kallisto  [26]  return pseudocounts that are not integers. If the loss of precision is likely not severe when rounding up pseudo-counts, this same limitation prevents the use of already pre-processed (i. e. normalized or transformed data) and forces the DEA practitioner to stick to the specific processing of the methods. In that regard, dearseq is extremely flexible and offer to use either raw or transformed data (the default applies a log-cpm transformation similarly to limma-voom).
These results have important implications for the field, as DEA of RNA-seq data has become widespread. The distributional assumptions and model-based inference inherent to DESeq2, edgeR and limma-voom can underestimate the number of false positives in realistic settings. Users should be aware of the possibility of inflated FDR when using these procedures and should consider the use of dearseq which gives theoretical and empirical control of the FDR without sacrificing its statistical power. Given the results of both our simulations and our real-world data re-analysis, we thus formulate the following recommendations: i) do not rely on a single DEA method and compare the results across several tools, as this strategy may likely eliminate the majority of false positives ; ii) for your main analysis, we recommend using dearseq or limma-voom over DESeq2 or edgeR.
Acknowledgements MG is supported within the  Digital Public Health Graduate 's school, funded by the  PIA 3 (Investments  for the Future -Project reference:  17-EURE-0019 ). The project is supported through  SWAGR Inria Associate-Team from the Inria@SiliconValley program. Computer time for this study was provided by the computing facilities  MCIA (Mésocentre de Calcul Intensif Aquitain) of the Université de Bordeaux  and of the  Université de Pau et des Pays de l'Adour.
With this procedure, we found the four compensators d i1 , i ∈ 1. 4. The partial fraction decomposition can be found using the residue function in Matlab using the transfer functions. Step 1. 5 -Matrices A, B and C, for each transfer function, are not included in this paper due to space limits but they can be easily obtained using Matlab functions ss, c2d and ssdata from the transfer function. Step 2. 1 and 2. 2 -The control theory diagram of the obtained system is similar to the one showed in Figure  1  (a) where there are four Q jj boxes that execute the transfer function in the position (j, j) of the matrix Q(s) and each one is connected to four D ij that execute the transfer function in the position (i, j) of the matrix D -1 (s). Step 2. 3 -This point describes the controller dynamic behavior.
Step 3 -Using the Matlab function dlyap it was verified that condition (  12 ) is met and in consequence, the system will be stable under unrestricted switching. Results. To validate the approach, we implemented a numeric simulation with Simulink. From all the possible derived models, we choose V of them in an aleatory way to create a reduced proof of concept of the resilient CPS and analyze how the system reacts to adversaries with different capabilities. In this case, V is set up to six. However, in Section V-A, we analyze the possible model generation for a process n × n. The feedback loop was implemented using a Linear Quadratic Gaussian (LQG) approach. Results are shown in Figure  2. All the plots depicted in Figure  2  assume that time (cf. x-axis) is normalized between 0. 0 and 1. 0, representing the temporal percentage of multiple experimental runs.
We present ReDU (https://redu. ucsd. edu/), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking. The availability of public mass spectrometry (MS)-based metabolomics data continues to grow, but leveraging these data has been difficult. It is arduous to find relevant files scattered among different datasets and analyze them in a consistent and meaningful manner. Therefore, we developed the Reanalysis of Data User (ReDU) interface (https://redu.
In total, 6,663 nodes in the molecular network (created from 399,826 MS/MS spectra) were annotated (Fig. 2b ) via spectral library matching (level 2 or 3; ref. 8  ). While the annotation percentage was relatively low (7. 58% of nodes), molecular networking linked chemicals with similar MS/MS patterns. As MS/MS patterns are often coupled to chemical structure, one can propagate annotations via analogy in combination with mass differences, exact mass and manual interpretation of the MS/MS spectra. Simply put, repository-scale molecular networking improves the ability to annotate unknown chemical analogs   across different datasets or sample types. For example, we propose clindamycin analogs (compounds 2-9) through propagation (for example, on the basis of changes in m/z ratio and MS/MS spectral interpretation), some of which match reported metabolites such as clindamycin sulfoxide (compound 4; ref.
16  ), from the annotation of clindamycin (compound 1). The clindamycin analogs (compounds 2-9) were linked to clindamycin (compound 1) across human urine, blood and fecal data originating from different datasets (Fig. 2c , Supplementary Discussion and Supplementary Figs. 1 2 3 4 ). Lastly, all data in ReDU, including the metadata and annotation information, are available for download from the homepage. The annotation information was used for molecular cartography  17  at the repository scale, which was used to plot the location of drugs in human samples (Extended Data Fig. 4  and Supplementary Video 1). We envision that this information will be invaluable to researchers. ReDU's utility will continue to grow as more data are uploaded to GNPS/MassIVE and as public MS/MS reference libraries expand, scaling in breadth and depth.
ReDU is a resource developed for the community and strives to embody the findable, accessible, interoperable, and reusable (FAIR) principles  18. Repository-scale molecular networking and library search. Users can reanalyze with public data by clicking the 'Reanalyze Public Data at GNPS' text (Supplementary Fig. 5j ), which links to the ReDU file selector (https://mwang87. github. io/ReDU-MS2-Documentation/). The ReDU file selector allows one to select (and filter) files based on the sample information and place multiple types of files into one of six different groups (G1-G6) for molecular networking via GNPS. Library search without molecular networking, providing annotations only, can be formed via GNPS; however, all files should be placed in G1, as groups are not supported. Upon completion of data selection, the user can launch the 'Reanalyze with GNPS Molecular Networking' or 'Reanalyze with GNPS library search' buttons, which populate the GNPS molecular networking or GNPS library search launch pages, respectively.
The suggested parameters for molecular networking and library search are detailed in the GNPS documentation (https://ccms-ucsd. github. io/GNPSDocumentation/). A maximum of 5,000 files for molecular networking is suggested. Note that a free account on GNPS is required and the user must be logged in before attempting to launch reanalyses in GNPS. In the example shown in Fig. 2 , molecular networking was performed in GNPS after selecting human blood plasma and serum (n = 711), human urine (n = 307) and human fecal (n = 5,097) files in the ReDU file selector (https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=a75aa494e927481dae6de12608e5e4a0). The data were filtered by removing all MS/MS peaks with m/z values ±17 with respect to the precursor's m/z. MS/MS spectra were window filtered by choosing only the top six peaks in windows located ±50 m/z with respect to each peak throughout the spectrum.
The data were then clustered with MS-Cluster with a precursor m/z tolerance of 0. 02 and an MS/MS fragment ion (that is, product ion) m/z tolerance of to create consensus spectra. Further, consensus spectra that contained fewer than five spectra were discarded. A network was then created where edges were filtered to have a cosine score above 0. 7 and more than five matched peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top ten most similar nodes. The spectra in the network were then searched against GNPS' spectral libraries. The library spectra were filtered in the same manner as the input data. All matches kept between network spectra and library spectra were required to have a score above 0. 7 and at least five matched peaks.
The network was opened in Cytoscape (3. 7. 1; https:// cytoscape. org/)  19 , and the networks were output as a. pdf and assembled in Adobe Illustrator. The molecular networking component associated with clindamycin was analyzed using the in-browser network visualization at https://gnps. ucsd. edu/ ProteoSAFe/result. jsp?view=network_displayer&componentindex=2892&task= a75aa494e927481dae6de12608e5e4a0#%7B%7D. Universal spectrum identifiers were generated (Supplementary Table  1 ) and used to plot the spectra displayed in Supplementary Figs. 2  and 4. MolNetEnhancer 4 was launched from the results page of the molecular networking job (https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task= 5ce4c3be9f5a4adfa1c50c9e99c4aeaf; Extended Data Fig. 3 ). Upon completion, the molecular network was downloaded and opened in Cytoscape.
The code and materials needed to recreate this analysis and plots are available on GitHub (https:// github. com/mwang87/ReDU-MS2-GNPS/tree/master/examples). Co-analysis of user data with public data using molecular networking. Users can co-analyze their data with public data by clicking the 'Co-analyze Your Data with Public Data at GNPS' text, which links to the ReDU file selector (Supplementary Fig. 5k ). Once the user has selected the public files they wish to include, a click of the 'Co-analyze with GNPS Molecular Networking' or 'Co-analyze with GNPS Library Search' button will load the public files into a GNPS molecular networking or GNPS library search launch page, respectively, at which point the user can add their own files to the appropriate group and submit the job. Details on molecular networking and library search can be found in the GNPS documentation (https:// ccms-ucsd.
github. io/GNPSDocumentation/). A maximum of 5,000 files for molecular networking is suggested. Note that a free account on GNPS is required and the user must be logged in before attempting to launch reanalyses in GNPS. If more than 5,000 files are to be co-networked, then we suggest contacting the authors, as more computing resources will need to be allocated. Illustrative use of the ReDU database: molecular cartography. In the example shown in Extended Data Fig. 1 , the ReDU information (MSV000084206) was downloaded and the latitudinal and longitudinal data were cleaned of any non-adherent formatting. The number of unique files associated with each latitude and longitude coordinate was calculated as well as the number of chemical annotations. The sum of the chemical annotations per latitude and longitude coordinate was divided by the number of unique files associated with the coordinates.
Files lacking coordinates were removed. The values were log 10 scaled to aid in visualization. The data were plotted in R ('ggmap' and 'map' packages were used) onto a world map. The code and materials needed to recreate this analysis and plots are available on GitHub (https://github. com/mwang87/ ReDU-MS2-GNPS/tree/master/examples). In the example shown in Extended Data Fig. 4 , the ReDU information (MSV000084206) was downloaded and merged with the sample information Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank the individuals involved in the funding, administration, sample collection and data acquisition of the public data used in ReDU. We recognize the financial support of the  US National Institutes of Health  ( P41 GM103484 ,  R03 CA211211 ,  R01 LM013115  and  R01 GM107550 ), the  Sloan Foundation (R. K. ) , the  Gordon and Betty Moore Foundation  (P. C. D. , N. B. , K. L. M. ), a  FAPESP fellowship  ( 2018/24865-4 ), the  American Society for Mass Spectrometry  (A. K. J. ),  NSF  grants  IOS-1656481  (P. C. D. and A. M. C. R. ) and  ABI-1759980 ,  Netherlands eScience Center  no. ASDI. 2017. 030 (J. J. J. v. d. H. ), the  Krupp Endowed Fund (R. Coras) , the  US Office of Naval Research  ( N00014-15-1-2809 ) and the  University of California, San Diego, Center for Microbiome Innovation SEED  grants.