dataesr/scientific-paragraphs-categorization huggingface dataset:
Cows were 4. 7 ± 0. 9 yr old, weighed 662 ± 62 kg, and were 71 ± 10 DIM at the beginning of the study. Diets were assigned to the cows during 4 successive periods in a 4 × 4 Latin square design. Each experimental period lasted 28 d and consisted of 23 d of adaptation to the diet and 5 d of measurements. A transition period of 3 d was applied between each experimental period. Animals were fed twice daily at 0900 and 1700 h. Diets had similar net energy values and proportion of forage to favor similar DM intake and avoid confounding effects on digestibility between diets due to level of intake. Diet composition is shown in Supplemental Table  S1  (https: / / data. inrae. fr/ dataset. xhtml ?persistentId = doi: 10. 15454/ FFFRL2,  Morgavi, 2021). Daily DMI and milk production throughout the study was, respectively, 20.
Data Processing The MS data were converted to NetCDF format using 3. 4 Datanalysis software (Bruker Biospin), and MS data from untargeted approach were processed using the open-source web-based interface Galaxy instance Workflow4Metabolomics running under R3. 5. 2  (Giacomoni et al. , 2015). Further pre-processing steps were also applied to remove noise and unwanted variation, including filtrations and normalization. The MS data were first filtered on retention time, and signals outside the range (lower than 0. 4 min or higher than 22 min) were considered as noise and removed. Background noise was also removed by removing masses found in blank samples (solvents). Because we had 2 types of QC (1 per type of collection), MS data of each collection were then normalized within batch using a linear regression model  (van der Kloet et al.
Metabolite Annotation Two-dimensional (2-D) NMR data were acquired on a Bruker AVANCE I spectrometer operating at 500 MHz and equipped with a 5-mm inverse-triple tuned (TXI) 1H/13C/15N with z-gradient coil probe (Bruker Biospin Wissenbourg). For 1-dimensional (1-D) 1 Hspectra, a standard 1-D spectroscopy sequence (zgpr) was used with low power irradiation of the water resonance during the recycle delay of 5 s. Then, 128 scans were collected with a 90° impulsion time of 10 µs, an acquisition time of 3. 2 s, and a spectral window of 10. k Hz, and 64 K data points were zero-filled to 128 K before Fourier transformation with 0. 3-Hz line broadening. Two-dimensional homonuclear (1H-1H COSY, 1H-1H TOCSY, 1H-1H Jres) and heteronuclear (1H-13C HSQC and HMBC) experiments were performed with quadrature phase detection in dimensions, using state-TPPI (time proportional phase incrementation) or QF (quadrature fixed) detection mode in the indirect experiment.
For each of 512 increments in the indirect dimension, 2 K data points were collected, and 16 or 32 transients were accumulated in the direct dimension. The 13C decoupling (GARP) was performed during acquisition time for heteronuclear experiments. A π/2 shifted square sine-bell function was applied in the both dimensions before Fourier transformation. All spectra were recorded at 300 K and treated with TopSpin version 3. 1 software. For untargeted MS data, mass spectra were first manually annotated for each discriminant ion to identify the parent ion. The redundant m/z peaks corresponding to different ions (fragments and adducts) were used to help confirm the molecular ion, and then removed. For identification, an in-house database containing more than 1,000 metabolites analyzed under the same conditions was first queried based on both retention time and mass obtained from the time-of-flight-MS detector.
This identification was based on both their retention time and mass. For the rest of unidentified metabolites, 4 online chemical databases were queried by accurate mass to search for hypotheses of chemical structures as follows: the Human Metabolome Database (HMDB, www. hmdb. ca), Kyoto Encyclopedia of Genes and Genomes (KEGG, http: / / www. genome. jp/ kegg/ ), Metlin (http: / / metlin. scripps. edu/ ), and the Bovine Metabolome Database (http: / / www. rumendb. ca/ ). To obtain exact masses, pooled urine samples were analyzed using a high resolution Thermo Scientific LTQ Orbitrap Velos hybrid mass spectrometer (Thermo) using the same LC conditions as the LC-MS acquisitions. The NMR metabolites identification based on the 1-D and 2-D NMR spectra were performed by querying an in-house database, public databases HMDB  (Wishart et al.
, 2018) , Biological Magnetic Resonance Data Bank  (Ulrich et al. , 2008) , and relevant publications  (Fan, 1996; Beckonert et al. , 2007; Bertram et al. , 2011). Eleven urinary metabolites that contributed to the separation of diets differing in N level were identified. They were confirmed by NMR 2-D in urine spiked by pure standard. The 2-D experiments were performed on pooled urine samples that were prepared as follows. In the initial step, 5 QC samples (see in the HILIC-MS experiments section the description of their preparation) were pooled together in an Eppendorf and centrifuged at 10,000 × g at 4°C for 30 min on a tabletop centrifuge (Sigma 112). The supernatant was transferred into a 15-mL polypropylene tube and lyophilized. The lyophilized sample was then dissolved into 1. 4 mL of a 39 mM potassium phosphate buffer (pD* ~7.
Before statistical analyses, both NMR and MS data were processed and subsequently normalized to the total intensity to avoid bias due to normal variations in concentration between cows and due to the collection method. Data were then subjected to univariate scaling and analyzed using SIMCA-P software (Umetrics, v. 13. 01). Unsupervised principal component analysis (PCA) was first used to visualize trends in metabolite profiles between TC and SC, followed by supervised orthogonal partial least squares (OPLS)-discriminant analysis (DA) to discriminate N diets. Validation of the OPLS models were performed by considering samples from each day collection independently. Discriminant variables were selected according to the variable's importance in projection (VIP; VIP ≥ 1. 0). These discriminant variables were then subjected to annotation (Supplemental Methods; https: / / data.
inrae. fr/ dataset. xhtml ?persistentId = doi: 10. 15454/ FFFRL2,  Morgavi, 2021). The characteristics of the OPLS models were assessed by the following: proportion of the dependent variables that is explained by the model (R 2 X), cumulative predictive ability parameter (Q 2 ), and the number of VIP ≥1, indicating the contribution of variables to the discrimination between N diets. All OPLS models were tested for overfitting with a 200-time permutation analysis [see  Worley and Powers (2013, 2016)  for further details and examples of the statistical analyses]. Further univariate analyses (2-way ANOVA, cow, and N diets) were performed to assess the variability of the 9 discriminant metabolites of N diets identified by the untargeted MS method. Data from targeted methods were normalized by creatinine before univariate analyses (2-way ANOVA, N diets, and sampling techniques with interaction) using XlStat-Biomed (v.
Sampling Techniques Markedly Influenced Urinary Metabolite Profiles To compare the number of MS features extracted in each sampling technique, we processed the MS data from TC and SC separately. We obtained similar number of unfiltered features, 3,582 for TC and 3,506 for SC, with 88% of them common to both sampling techniques (Figure  1 ). Notwithstanding, when TC and SC data sets were jointly processed to obtain a quantitative comparison of shared features, there were differences between the 2 sampling techniques. After filtration and variable selection, the number of variables was reduced from 4,662 unfiltered features to 2,177 filtered features with more than half (n = 1,331) of these features being significantly different between sampling techniques (paired parametric test, Benjamini-Hochberg false discovery rate corrected, P < 0.
Both Sampling Techniques Equally Discriminated Dietary Groups Despite the difference in metabolite profiles (Figure  2 ), both sampling techniques were able to discriminate diets in terms of N content when samples were analyzed by HILIC-ToF/MS and 1 H NMR. The PCA models built from MS data showed better separation between diets in terms of N content than NMR in both sampling techniques (Figure  3 ). It is also noted that discriminant metabolites were almost the same between sampling techniques (see below) and that the same metabolic pathways were affected by diet independently of the data set analyzed (i. e. , SC or TC; MetaboAnalyst pathway analysis, data not shown; http: / / www. metaboanalyst. ca). Several studies reported variability throughout the day in the metabolic profile of urine obtained by SC compared with the average profile obtained from TC samples  (Lee et al.
, 2019). As stated in the previous section, there were differences due to time spans considered in the collection that were mainly caused by feeding. This difference was also compounded by daily individual differences in feeding behavior, nutrient absorption, and use rate as well as by the time when the animal last urinated. To overcome this drawback, urine outputs from SC were normalized with creatinine as recommended  (Shingfield and Offer, 1998; Tebbe and Weiss, 2018). Another normalization method is based on osmolarity  (Jacob et al. , 2014) ; whereas other authors recommend repeated sampling throughout 1 or more days  (Lee et al. , 2019; Wilson et al. , 2019). Also, we investigated whether sampling over several days improved the robustness and precision of the SC models. To do that, we compared the quality parameters of PCA and OPLS-DA models of TC built with data obtained in the HILIC-ToF/MS analytical platform from 1 d (TC-d1) with those built from 1 d of SC samples (SC-d1) or the average data of 2, 3, 4, or 5 SC samples taken once a day.
Both sampling techniques were able to differentiate diets when using PCA models built with data of d 1. The separation of N diet levels was obtained in the first component, whereas separation of energy diets was obtained in the second component. Discrimination of N diets was achieved with 26. 5% and 20. 2% of variance explained on component 1 for TC-d1 and SC-d1, respectively (Figure  4 , Table  2 ). A stronger separation [R 2 X(1) = 24%] between N diet levels was observed when combining data from the 2 first collection days. This value increased marginally when data from more days were averaged, up to 28% when 5 d were averaged. The OPLS-DA models showed high value of the predictive parameter (Q 2 ) for all the models with a slight increase from 2-d-average. Mean values within a column and within the same sampling technique with different superscript letters differ (P < 0.
05). 1 HN = high nitrogen (14% CP); LN = low nitrogen (11% CP). 2 β aminobutyric acid. Although good discrimination is obtained at d 1 with a Q 2 value similar to that of TC (SC-1d = 0. 81; TC-1d = 0. 73), we observed a better discriminative power from 2-d-average (SC-2d = 0. 89) that remained stable over 5 d of collection (Table  2 ). The number of VIP ≥1, indicating the contribution of variables to the discrimination between N diets, was similar among models, with the exception of model built with data from d 1 that is smaller. These observations suggested that multiple-day collection improved the model quality, and that 2 consecutive days seemed enough to achieve stable results. This hypothesis is consistent with the Venn diagram (Supplemental Figure  S1 , https: / / data. inrae. fr/ dataset.
Within each energy source, a clear separation of diets differing in N content was obtained with both sampling techniques with a good predictive capacity (Q 2 ) for MS and NMR data (Supplemental Figure  S2 , https: / / data. inrae. fr/ dataset. xhtml ?persistentId = doi: 10. 15454/ FFFRL2,  Morgavi, 2021). Twenty metabolites discriminating N diets were identified as follows: 11 by NMR (Supplemental Table  S2 , https: / / data. inrae. fr/ dataset. xhtml ?persistentId = doi: 10. 15454/ FFFRL2,  Morgavi, 2021)  and confirmed by NMR 2-D in urine spiked with pure standards (Supplemental Figure  S3 , https: / / data. inrae. fr/ dataset. xhtml ?persistentId = doi: 10. 15454/ FFFRL2,  Morgavi, 2021) , and 9 by HILIC-ToF/MS (Supplemental Table  S3 , https: / / data. inrae. fr/ dataset. xhtml ?persistentId = doi: 10.
15454/ FFFRL2,  Morgavi, 2021). These discriminant metabolites were common to both sampling techniques, and followed the same pattern, (i. e. , increase or decrease). Notwithstanding, in agreement with  Lee et al. (2019) , higher variability between cows was observed for some metabolites detected by MS in SC compared with TC samples (Supplemental Figure  S4 , https: / / data. inrae. fr/ dataset. xhtml ?persistentId = doi: 10. 15454/ FFFRL2,  Morgavi, 2021). We also compared both sampling techniques using a quantitative HILIC-MS/MS method targeting 9 markers of nitrogen status as follows: allantoin, uric acid, xanthine, hypoxanthine, β-aminobutyric acid, β-alanine, creatinine, creatine, and urea  (Boudra et al. , 2012). Results showed no difference (P > 0. 05) of the sampling technique in the concentration of these metabolites, except for allantoin and uric acid that were significantly (P < 0.
, 2019)  and should be considered depending on the objective of the study as discussed below. Similar to untargeted results, quantification of all metabolites in individual cows followed similar pattern in both collections (Supplemental Figure  S5 , https: / / data. inrae. fr/ dataset. xhtml ?persistentId = doi: 10. 15454/ FFFRL2, Morgavi, 2021). Supporting these results, linear regression analyses showed a strong correlation between sampling techniques for metabolites analyzed by the targeted method  (Boudra et al. , 2012). Human clinical studies showed that single urine SC samples collected in the morning and analyzed by the untargeted NMR method allowed the discrimination between healthy and sick groups  (Saude et al. , 2011). Alternatively,  Wilson et al. (2019)  showed that there was no significant loss of information when TC was substituted for SC in human studies.
, 2005; Huhtanen et al. , 2015). Further regression models based on these 22 discriminant metabolites were constructed to investigate the relationships between these metabolites and the N-use efficiency at the digestive, metabolic, and rumen levels. There was no relationship except for the overall milk N-use efficiency (Supplemental Figure  S6 , ttps: / / data. inrae. fr/ dataset. xhtml ?persistentId = doi: 10. 15454/ FFFRL2,  Morgavi, 2021) , likely due to the inherent errors associated with digestive and metabolic measurements compared with the milk N-use efficiency. This work showed that SC is as good as TC for obtaining robust metabolomics results in lactating cows. When applied to assess the effect of dietary N level, urine samples obtained from SC effectively discriminated diets with high or low N levels.
ACKNOWLEDGMENTS This work was funded by the seventh EU framework project "Innovative and practical management ap- proaches to reduce nitrogen excretion by ruminants (REDNEX)," FP7, KBB-2007-1. We thank David Alvarez and Ulli Hohenester (UMRH, INRAE) for their technical assistance. We also thank the personnel of the experimental animal facilities for the care of animals. The authors have not stated any conflicts of interest.
Before investing resources into the development of a precision dosing (modelinformed precision dosing [MIPD]) tool for tacrolimus, the performance of the tool was evaluated in silico. A retrospective dataset of 315 de novo kidney transplant recipients was first used to identify a one-compartment pharmacokinetic (PK) model with time-dependent clearance. MIPD performance was subsequently evaluated by calculating errors to predict future concentrations, which is directly related to dosing precision and probability of target attainment (PTA). Based on the identified model residual error, the theoretical upper limit was 45% PTA for a target of 13. 5 ng/ml and an acceptable range of 12-15 ng/ml. Using empirical Bayesian estimation, this limit was reached on day 5 post-transplant and beyond. By incorporating correlated within-patient variability when predicting future individual concentrations, PTA improved beyond the theoretical upper limit.
Classical goodness of fit (GOF) metrics, such as mean prediction error or root mean squared prediction error (RMSE%), show how well a model can fit existing data. Recently, prospective evaluation was proposed to evaluate these metrics on future concentrations, based on only the concentration samples collected up to that point. 10 Unfortunately, none of these metrics directly translate into the expected individual PK, PD, or clinical outcomes when using MIPD. Simulated data are preferred over GOF metrics for two reasons. First, predicted individual data can be condensed to clinical benefit (e. g. , avoidance of rejection), and this benefit is weighed against the implementation cost. Should pharmacogenetic information be included, how many additional blood samples are needed, and should MIPD even be implemented at all? Quantifying the potential clinical benefit is key in justifying investment into MIPD.
Source data A retrospective study  11  of 315 kidney allograft recipients transplanted between 2004 and 2014 was repurposed for this work. For an in-depth description, we refer to the work by Vanhove et al. The dataset consisted of trough concentrations measured on days 0-14 posttransplantation under the standard of care (i. e. , leading to a dose adaptation by experienced transplant physicians targeting trough concentrations between 12 and 15 ng/ ml). Extensive data management was required to prepare this dataset for modeling (described in Supplementary Materials). Missing covariates were imputed as the population median.
Model development Based on literature review, a one-compartment model with oral absorption was selected as the appropriate starting point for this sparse dataset. Following the approach from other studies,  [12] [13] [14]  the absorption rate constant (ka) was fixed to 4. 5/h,  15  as it cannot be reliably estimated from trough data alone. A two-compartment model and addition of lag time were investigated as possible improvements. The use of a hematocrit-standardized model  16  was investigated, including concentrationdependent binding of tacrolimus to erythrocytes. The whole-blood concentration C wb is related to hematocritstandardized concentration C std through a concentrationdependent proportionality factor R, with C stdmax reflecting the maximum binding capacity, and C std50 the concentration associated with half maximum binding.
Bayesian feedback and modelpredictive control Tacrolimus exhibits high PK variability, not only between patients but also within a single subject. This variability can be described by the residual error model or through IOV, depending on whether rich individual data is available. To the best of our knowledge, all models previously used to describe tacrolimus PKs assume a random variability. This variability is not entirely random, however, and previous studies have reported a correlation of the error between subsequent occasions. 17 This seems intuitive: an increased clearance on day N should indeed carry over to the following day N + 1. This was quantified in the dataset by calculating the autocorrelation of the residual error from a standard Bayesian fit. For a detailed discussion, please see Supplementary Material.
The full dosing history and concentrations at each iteration were used to fit individual parameters η, after which the PK model f(η, D rec,j , t i+1 ) was used to predict future trough concentrations. For each future administration, the dose was adapted such that the trough concentration was equal to the target concentration C target of 13. 5 ng/ml. This is visualized in Figure  1a. We made sure computer dosing would require minimal changes to the current clinical workflow at UZ Leuven. Therefore, the loading dose was not adapted. Only dosing amounts were adapted. Planned dosing times and formulations (Advagraf or Prograft) were retained from the source dataset. Doses of 0 mg were not recorded in the source dataset, therefore a dummy dose of 0 mg was added at 08:00 if Advagraf was previously administered, or 08:00 and 20:00 if Prograft was previously administered.
To ensure fair comparison with physician-based dosing, only doses 6 h after a concentration sample were considered for adaptation, as concentrations were generally only available in practice 3 h post-sampling and adapting a dose close to administration time was not deemed practical. Doses were rounded to 0. 5 mg. To simulate the resulting concentrations Y′ after applying MIPD, the following procedure was used (visualized in Figure  1b ). As we cannot go back in time and administer the recommended dose D rec to the actual patient, we used the best prediction available: we simulated using a fitted η orig on all observed concentrations for this patient in the historic dataset f(η orig , t i+1 ) and re-applied the original residual error ϵ. This exercise was performed on all 315 patients for all available trough samples.
Missing concentration samples were reused as missing data. This resulted in two parallel datasets: a first dataset of dose and resulting concentration per patient per day as originally performed in reality by physicians in standard of care, and a second hypothetical dataset where the dose was calculated through MIPD. Both arms could then be compared graphically and using statistical methods. In collaboration with physicians, an improvement by MIPD was qualitatively defined as: more patients with (11) Y � i+1 = f orig , t i+1 + i+1 F I G U R E 1 (a) Observations 1 to i were used to calculate fit f(η) (solid green line). This was then used to find dose D_rec that makes f(D_rec, t_(j + 1)) = C_target (dotted green line). (b) All observations 1:n were used to calculate fit f(η_orig) (solid purple line).
Clinical trial simulation Finally, the candidate trial with N = 200 patients at 2:1 allocation was evaluated as a clinical trial simulation. Standard of care was not simulated, but rather sampled without replacement from the available N = 315 profiles in the retrospective dataset. The MIPD arm was similarly sampled from the profiles in the simulation previously performed. Dropout and missing data were considered as represented realistically in the retrospective dataset. This was repeated 1000 times to characterize the distribution of possible clinical trial outcomes and evaluate Probability of Study Success (PoSS).
Software Monolix 2019  19  was used to perform modeling, using the Stochastic Approximation Expectation Maximization (SAEM) algorithm complemented with importance resampling to determine -2 log-likelihood. The R version 3. 5. 2 was used for all data management and simulation tasks, using tdmore version 1. 1. 20 Tdmore can be freely downloaded at https://github. com/tdmore-dev/tdmore.
There is an ongoing debate on appropriate PK targets for tacrolimus, with some evidence pointing to AUC as a superior metric. 4 Our trough dataset cannot be used to accurately predict AUC. 29 With rich data and an appropriate model, the presented approach may be applied to evaluate the accuracy (and improvement over standard of care) when using one, two, or more blood samples per day. CYP3A5 genotype is missing from our current model, as it was not generally measured in transplant recipients at UZ Leuven hospital. It is worthwhile to evaluate the inclusion of this covariate, and to quantify the potential improvement in MIPD dosing accuracy. If this covariate is not available, a mixture model could be used to estimate individual CYP3A5 expression probability. However, due to the low number of CYP3A5 expressors in the target population, we expect the impact to be low and transient.