dataesr/scientific-paragraphs-categorization huggingface dataset:
6/14 Exploratory analysis To explore results and detect outliers or batch effects, Principal Component Analysis (PCA) can be performed. Individual and variable plots are available to ease the visualisation and interpretation of PCA results (Supplementary Figure  S2 ). Discriminant analysis When the question of interest is to find which metabolites are different between two experimental conditions describing the samples, Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA,  Trygg and Wold (2002) ) can be performed with a dedicated function based on the implementation available in the ropls package  (Thévenot et al. , 2015). To avoid overfitting, a 10-fold cross-validation procedure is implemented. Prediction error and variable importance in projection (VIP) are computed on ten OPLS-DA and averaged over the ten folds.
Pearson correlation between quantifications and dosages were computed for every metabolite directly measured by dosage. These correlations were also compared with the correlations obtained for other quantification methods: Autofit, batman, Bayesil and rDolphin. Contrary to ASICS, these methods were too slow or not automated to allow the quantification for the 283 spectra. Therefore, quantifications were performed on a subsample of 7/14 the original dataset that corresponded to the deciles of the lactate, fructose and glucose dosage to ensure representativity (32 spectra). Computational times were also recorded. For ASICS quantifications, water and urea regions were excluded and the maximum shift, M , was set to 0. 01. In order to perform all quantifications with batman in a reasonable time, its library was reduced to the 160 common metabolites between batman and ASICS reference libraries and the number of iterations was set to 10,000.
Urinary metabolome of Type 2 diabetes mellitus In order to test our method on data acquired with another spectrometer than the one on which the pure metabolite library included in ASICS has been obtained, we used the public datasets from  Salek et al. (2007). The experiment has been designed to improve the understanding of early stage of type 2 diabetes mellitus (T2DM) development. 1 H NMR human metabolome was obtained from 84 healthy volunteers and 50 T2DM patients. Raw 1D Bruker spectral data files were found in the MetaboLights database  (Haug et al. (2013) ; study MTBLS1). In the original study, spectra were normalized by the area under the curve after excluding water (4. 24-5. 04 ppm), urea (5. 04-6. 00 ppm) and glucose  (3. 19-3. 99 ppm, 5. 21-5. 27 ppm)  regions. Finally, a bucketing was performed with a 0.
04-ppm width. The original study used a combination of PLS-DA and statistical tests (t-test, F -test, Kruskal-Wallis test and Kolmogorov-Smirnov test) on buckets (with a manual expert identification) to find differences between the healthy and ill individuals. This dataset allowed us to test the performance of ASICS on a different fluid (urine) in a different species (human). Contrary to  Salek et al. (2007) , we kept glucose region for a quantification with ASICS because the glucose spectrum was available in the library. However, regions of water and urea were excluded. The other parameters of the different methods were set to their default values except for ASICS threshold that was set to s m = 0. 05, because we had observed that this dataset was noisier than the previous one. In addition, to control differences that could originate from the differential analysis method itself, we performed the comparison between the buckets and the ASICS quantifications approaches with OPLS-DA, as for the study about perinatal survival (VIP thresholds set to 1.
On a practical point of view, ASICS has other interesting features: first, it provides an easy way to handle (complement, replace, manipulate) the reference library whereas batman and rDolphin need that information on each multiplet (chemical shift position, multiplicity. ) is specified. A biochemical expertise is thus required for the modification of the reference library in these packages. Autofit is a commercial software that requires the acquisition of a license, which strongly limits its use. Finally, the reference library cannot be modified in Bayesil and this method is only available through a web interface that makes automation of several spectra processing impossible. In terms of computational times, the preprocessing of the library and the metabolite quantification with ASICS takes about 1'30 min per spectrum and can be launched at once in parallel.
Differences between gestational ages of fetuses For the study about fetuses in late gestation, two outliers were detected on the bucket dataset in a preliminary study (Supplementary Figure  S5 ) and were removed from the analysis (Supplementary Figures  S6  and S7 ). OPLS-DA was performed on quantified metabolites and on buckets. Both showed the same predicting power: all samples were perfectly separated according to their stages of gestation. For the bucket analysis, VIP values identified 268 buckets on 781 that were found influential to separate the two groups. Based on this list, a manual identification performed by an NMR expert hightlighted 21 metabolites. The same analysis was performed on the ASICS quantifications and allowed to obtain 22 metabolites. The results obtained by ASICS and buckets analysis are detailed in Supplementary Table  S4.
(2015)  (results also given in Supplementary 10/14 Table  S4 ). Those comparisons highlighted the relevance of ASICS quantification that showed results consistent with previous studies and prior knowledge on Type 2 diabete: some of the metabolites were extracted by ASICS and by bucket quantification, like alanine or acetoacetate (Supplementary Table  S4 ), and have also been identified in  Salek et al. (2007). We were also able to extract other metabolites, like the glucose (D-Glucose), the guanidinoacetic acid or the glycerol, that were not previously described because the glucose region was excluded from the study in  Salek et al. (2007). The glycerol was identified both by ASICS and by  Salek et al. (2007)  in experiments on rats and mice (the glucose region was only excluded in the human dataset and not in the rat and mouse datasets).
Conclusion This article presents an R package, ASICS, integrating a complete analysis workflow of 1 H NMR spectra. This pipeline integrates an automatic metabolite identification and quantification method based on a reference library of pure metabolite spectra. ASICS showed better quantification results than existing methods and allowed to perform a complete study on several hundreds spectra in only a few hours. Its use on two real world datasets exhibited similar results than the standard analysis on buckets followed by expert manual identification but also allowed to provide new information. For both studies, new metabolites, not extracted by expert identification, were found by ASICS, some of them confirmed by previous and independent studies. Obviously, as is the case for other omics data, in coming to a conclusion on whether the metabolites were really present in samples, a validation would be necessary.
Funding This project received financial support from  French National Agency of Research  ( PORCINET grant   ANR-09-GENM005 ). The PhD fellowship of  Gaelle Lefort  is supported by the  Digital Agriculture Convergence Lab  ( #DigitAg , http://www. hdigitag. fr/), by the  INRA Mathematics and Computer Science Division , by the  INRA Animal Genetics Division  and by the  INRA Animal Health Divison.
Search Engine (SE) A SE is a system that uses a specific computer program to collect information from the Internet. A SE is not only a necessary function for users, but also an effective tool for the behavior of a web user. The efficient SE allows the user to find target information accurately and fast  (Antriksha, & Ugrasen, 2011). The search results are usually shown in a series of results, usually called SE result pages. The types of shown information are always different, which include webpages, images, and other types of files. Some SEs can also get important data in the database or open directories. SEs can also run an algorithm on a crawler to maintain the information in real-time from different web directories. The information that a search processes should have high precision and meet the requirement of the user.
Others include removing duplicate webpages, participles, judging the types, analyzing hyperlinks and counting pages important degree/abundance, etc. 3) Providing Search Services: User inputs the keywords then the SE finds the matching pages from the indexed database; except for page, title and URL, it still provides an abstract from webpages and other information to make user's estimate expediently. The work process of SE is shown in Fig. 1. These huge storage devices enable thousands of machines to process much information quickly. When a person searches on any major engine, they request the result immediately; even a one-or two-second delay will cause users' dissatisfaction, so the SE must provide the answer as quickly as possible. The most useful feature of a SE is the relevance of the returned result set.
Quality Criterions High quality sites should provide positive experiences for the visitor. In this paper, the research of quality is divided into four quality sections: Search completion time, Number of webpages shown in a search task, Precision, and Relative Recall. 1. Search completion time：It is a calculated amount of time required for any particular task to be completed. This is a typical metric in usability evaluation. During this research, users were told to read the task and then to click a "start searching" button, which would begin the search session by opening the appropriate search algorithm  (Ya & David, 2009). When the results are shown in the searching webpage, the search task is finished. 2. Number of webpages shown in a search task: is the number of unique SEs or databases used by a participant in a task.
C: No. of irrelevant records retrieved A: No. of relevant records retrieved  We record the number of webpages shown in a search task. The data is shown as Table  4. The comparison of Search completion time is shown as Fig. 4. We record the number of webpages shown in a search task. We select 50 sites from all of webpages for a sample. And we found the No. of relevant records retrieved and No. of irrelevant records retrieved. And we used the definition of precision to calculate the result of precision. The results of Google and Baidu are shown as Tables 5-6 and the comparison of Google and Baidu is shown as Fig. 5. 2,000,000,000 3,000,000,000 4,000,000,000 5,000,000,000 6,000,000,000 7,000,000,000 8,000,000,000 9,000,000,000 We recorded the number of webpages shown in a search task. We used the definition of relative recall to calculate the result of relative recall.
The results of Google and Baidu are shown as Table  7  and the comparison of Google and Baidu is shown as Fig. 6. The present study estimated the four quality features of Google and Baidu SEs, which belong to comprehensive SEs. The result of the study showed that the quality of Google was higher than Baidu. For the number of each keyword searching result, the Google SE was always more than Baidu. Although for the search completion time, Google searched longer than Baidu, but depending on the number of searching results, Google is still better than Baidu. For the precision of each SE, the different keywords lead to a different result. For the relative recall, Google is better than Baidu. We input each keyword to Amazon and JD. COM SE and recorded the finish time. The data is shown as Table  8.
Comparison of Google and Baidu Google Baidu We recorded the number of webpages shown in a search task. The data is shown as Table  9. The comparison of Search completion time is shown as Fig. 8. We recorded the number of webpages shown in a search task. We selected 10 sites from all of the webpages for a sample. And we found the No. of relevant records retrieved and No. of irrelevant records retrieved. And we used the definition of precision to calculate the result of precision. The results of Amazon and JD. COM are shown as Table  10 -11 and the comparison of Amazon and JD. COM is shown as Fig. 9. (4) Relative Recall We recorded the number of webpages shown in a search task. We used the definition of relative recall to calculate the result of relative recall. The results of Amazon and JD.
For the comprehensive SEs, the responding time is less than the vertical SEs and the number of results is much more than the vertical SEs. And the relative recall of comprehensive SEs is also better than the vertical SEs check of what we need. In a word, before we decide to select which type of SE we should use, we should be sure of our purpose or our requirements. Although we selected ten keywords, four quality features, four SEs, and two types of SEs to answer the two research questions that are at the beginning of the paper, the research still contains some shortcomings. The limitations of this paper are as follows: (1) The data is not very precision data. In this study, we selected ten random keywords and used four SEs to research the four quality features. The data we collected is based on the tools for web developers of Chrome.
Blood is a complex fluid in which the presence of the various constituents leads to significant changes in its rheological properties. Thus, an appropriate non-Newtonian model is advisable; and we choose a Modified version of the rheological model of Phan-Thien and Tanner (MPTT). The different parameters of this model, derived from the rheology of polymers, allow characterization of the non-Newtonian nature of blood, taking into account the behavior of red blood cells in plasma. Using the MPTT model that we implemented in the open access software OpenFOAM, numerical simulations have been performed on blood flow in the thoracic aorta for a healthy patient. We started from a patient-specific model which was constructed from medical images. Exiting flow boundary conditions have been developped, based on a 3-element Windkessel model to approximate physiological conditions.
Configuring the MPTT rheological model The Navier-Stokes equations are solved for a laminar and incompressible flow using the free access software OpenFOAM. The walls are considered to be rigid. As a first step, the solution is carried out in the transient mode and for a Newtonian fluid whose dynamic viscosity is g = 0. 0035 Pa-s. In the second stage, the non-Newtonian MPTT model and the generalized Newtonian model of Carreau are used. The MPTT model was implanted in OpenFOAM using the ViscoelasticFluidFoam solver  (Favero et al. , 2010). The constitutive equation for this model is  (Bird et al. , 1980) : Table  1  Analytically determined wall stress for various generalized Newtonian models  (Amblard and Bou-Saïd, 2004).
Patient-specific model In this study, the Digital Imaging and Communication (DICOM) images from MRI were processed with Simpleware ScanIP v7. 0 2000-2014Ó commercial software. A geometric model of the aorta has been meshed with polyhedral elements generated with the commercial Star-CCM +Ó software. A mesh sensitivity study was carried out on the model based on the maximum numerical values of shear stress along the aorta. The study showed that a mesh with elements of about 0. 3 mm 3 was adequate, or 478,000 elements in the model -0. 24% difference on the shear value from a 2,000,000 elements thin mesh.
If the string vibrates against a single point obstacle, the Delassus matrix reduces to a positive scalar and the LCP can be solved directly as: p n+1 c = max 0, - 1 W cc (v n+1 c,free + v n c ). (33) In the case of a distributed obstacle, a LCP solver has to be employed. In this article, we employ the numerical solvers provided by the Siconos software  (Acary et al. , 2016)  which is dedicated to the modelling and simulation of nonsmooth dynamical systems. In Siconos, several numerical algorithms are implemented to solve LCPs. Depending on the number of constraints and the required accuracy, pivoting or projected successive overrelaxation (PSOR) techniques may be used  (Cottle et al. , 1992). Simulations in the present paper are based on the use of a standard pivoting technique for solving LCP, known as Lemke's method  (Cottle et al.
This further step is described in (van Walstijn and Bridges, 2016), and is also implemented for the nonsmooth method. All computations presented in Table III were led with MATLAB on a single CPU with a clock at 2. 4 GHz. One can observe that taking into account only the contact point in the modified compliant method allows one to obtain a significant gain in computational times since a factor two is present as compared to the power-law method with a full S matrix. A more significant gain can be obtained due to the nonsmooth method: computation costs are 12 to 37 times smaller. This is due in particular to the reduced number of matrix vector products as well as a reduced time for solving the LCP (straightforward in the point obstacle case) compared to Newton-Raphson iterations. Note also that computation times has been found to be once again divided by a factor two by running the same code with the SICONOS software in a optimized C/C++ implementation  (Acary et al.
Furthermore, the value of the restitution coefficient does not substantially affect simulations, meaning that, in the present case, the nonsmooth approach would be free of parameters to be adjusted. The simulation of 0. 1 s of signal (time interval recovering all contacts) at F s = 1 MHz lasts 5 minutes with Matlab while it lasts only 30 s with Siconos. Numerical costs are thus found to be divided by a factor 10: once again, an important gain in computation can be achieved with the nonsmooth method on Siconos.
Experimental results We implemented our typechecker as well as our procedure for consistency in a prototype tool TypeEq. We adapted the original prototype of  [28]  to implement additional cases corresponding to the new typing rules. This also required to design new heuristics w. r. t. the order in which typing rules should be applied. Of course, we also had to support for the new bikey types, and for arbitrary terms as keys. This represented a change of about 40% of the code of the software. We ran our experiments on a single Intel Xeon E5-2687Wv3 3. 10GHz core, with 378GB of RAM (shared with the 19 other cores). Actually, our own prototype does not require a large amount of RAM. However, some of the other tools we consider use more than 64GB of RAM on some examples (at which point we stopped the experiment).
Background: Joint modeling of longitudinal and time-to-event data is often advantageous over separate longitudinal or time-to-event analyses as it can account for study dropout, error in longitudinally measured covariates, and correlation between longitudinal and time-to-event outcomes. The current literature on joint modeling focuses mainly on the analysis of single studies with a lack of methods available for the meta-analysis of joint data from multiple studies. Methods: We investigate a variety of one-stage methods for the meta-analysis of joint longitudinal and time-to-event outcome data. These methods are applied to the INDANA dataset to investigate longitudinally measured systolic blood pressure, with each of time to death, time to myocardial infarction, and time to stroke. Results are compared to separate longitudinal or time-to-event meta-analyses.
A recent review of current reporting of single study joint analyses by Sudell et al  8  identified that the number of published joint analyses has been increasing over recent years, suggesting a growing resource of joint datasets. Examples of single study joint models applied in the literature include those of Jacoby et al,  9  Kolamunnage-Dona et al,  10  Lloyd-Williams et al,  11  and Kovanda et al. 12  Glass  13  defined meta-analysis (MA) as the statistical analysis or pooling of results from several studies. Meta-analyses can result in analyses with increased precision and power, while permitting new research questions to be answered. An individual participant or patient data meta-analysis (IPD-MA) utilizes the original data collected in each study, whereas an aggregate data meta-analysis (AD-MA) utilizes study level results, including those available in published reports.
IPD-MA can be one-stage or two-stage. A two-stage meta-analysis fits models to the data from each study included in the meta-analysis and, then, uses standard MA techniques  14, 15  to pool the study specific parameter estimates. A one-stage meta-analysis stores the data from all studies included in the meta-analysis in a single meta-dataset, to which a single model is fitted (which should account for the clustering of data within studies). The literature for meta-analyses is extensive,  14, 15  but research into the meta-analysis of joint longitudinal and time-to-event data is limited to a small number of references. 8, 16 However, it is reasonable that if joint modeling is preferred over separate longitudinal or time-to-event models in certain single study cases (eg, to account for informative dropout in longitudinal study designs  17  or when a time-to-event outcome is influenced by longitudinal outcomes  18  ), the use of joint models rather than separate methods may also be preferred in a meta-analytic setting.
Currently, methodological research has mainly focused on joint models applied to single study datasets (for overviews, see the works of Tsiatis and Davidian  5  and Yu et al  19  ), although a limited number of references exist that deal with multi-center joint data,  20  and multi-level joint models. 21 However, these references did not specifically investigate the meta-analytic case. Multi-center and meta-analytic datasets are similar, in that they have a structure where individuals are nested within studies or centers. However, the number of higher level units differs between cases; meta-analyses often contain fewer studies, each containing a larger number of individuals, whereas multi-center datasets often contain a larger number of centers, each containing a comparatively smaller number of individuals.
As such, the spread of data across the different levels is different for a meta-analytic dataset compared with a multi-center dataset, leading to potentially different approaches being required. This paper extends this methodology by investigating multi-level joint models specifically for use in meta-analytic datasets. Recently, Sudell et al  16  investigated methods for the two-stage MA of joint data. In this article, we investigate one-stage models to analyze individual participant multi-study joint longitudinal and time-to-event data (termed joint IPD). The results of the one-stage meta-analytic joint models are compared with one-stage separate longitudinal or time-to-event meta-analytic models. The article begins with a discussion of the methods employed in the investigation. The presented methods are then applied to an example dataset.
SOFTWARE We developed a flexible R 24 code to fit one-stage multi-study joint models described in this article, which will be available as joineRmeta package; the R codes can currently be downloaded at https://github. com/mesudell/joineRmeta/. This software is an extension of the single study joint modeling package joineR  25  to the multi-study case. Example code and simulated data are available in the supplemental information, demonstrating methods discussed in this article.