dataesr/scientific-paragraphs-categorization huggingface dataset:
In our experiment the models RMSE values for snow water equivalent and snow precipitation amounts also when compared to the observation-based dataset HISTALP as a reference over the same period (not shown). A careful evaluation of these datasets is thus recommended before using them, especially in mountain areas. The GEOTOP model provides the best snow depth estimates when forced by MeteoIO, ERA5 and ERAI-LR. However, all these forcing datasets have a cold temperature bias, and GEOTOP model is affected by a sistematic underestimation error on snow depth. These errors offset each other, with the effect that the RMSE on snow depth simulations is smallest compared to the other models. Conversely, when using ERA-Interim forcing, GEOTOP performances are the worst ones due to the positive temperature bias of the reanalysis dataset, which increases the underestimation of snow depth simulations.
In this set of experiments GEOTOP model show weaknesses in reproducing both snow water equivalent and snow density, thus calling for a check of its snow scheme. The UTOPIA and HTESSEL models perform as well as the most sophisticated model SNOWPACK with optimal forcing, but they require less input data, for example they do not need surface temperature. These models can be employed when no information on snowpack internal structure and stratification is needed. UTOPIA and CHTESSEL provide good performances also with low temporal resolution forcings up to 6 hours and with ERA-Interim forcing. However, lower skills are found when employing other low-accuracy input dataset (TIME-12h, MeteoIO), suggesting that the UTOPIA and HTESSEL models can be sensitive to the bias in the meteorological forcing.
Spatial interpolation of meteorological forcings from neighboring stations The spatial interpolation of ground meteorological observations represents the most frequent practice in the operational applications of hydrological models. In order to test the models performance in such a conditions an interpolated dataset (  The algorithm used for the interpolation is the inverse distance weight (IDW) as first choice for all the parameters. The interpolations takes into account also for vertical gradients in both temperature and precipitation. In MeteoIO is possible to assign different types of algorithm interpolation for each parameter, the Table  xx1  shows the settings used in this study for the 2D interpolation. Please refers to Bavay&Egger 2014 for the full comprehension of the settings table and further details on this library.
Parameter [Interpolations2D] setting  Appendix A: Spatial interpolation of meteorological forcings from neighboring stations In hydrological and snow modeling the spatial interpolation of ground meteorological observations is commonly employed to derive spatially continuous meteorogical forcing to drive the models. In this work, we evaluate the response of snow models with such forcing. An interpolated dataset for Torgnon monitoring site has been prepared exploiting the MeteoIO library  (Bavay and Egger, 2014). The meteorological data are interpolated from six neighboring stations, over a squared digital elevation model.
depth are generally comparable to those obtained with Torgnon data at 12-hourly resolution. GLDAS, and to a lesser extent https://doi. org/10. 5194/hess-2019-511 Preprint. Discussion started: 25 November 2019 c Author(s) 2019. CC BY 4. 0 License. ERA5, are affected by large temperature and precipitation biases at the Torgnon gridpoint, probably owing to difficulties of these datasets in simulating processes in high-elevation regions. For example, over the Alpine region, ERA5 compared to ERA-Interim is characterized by larger daily precipitation amounts during the season December-to-April, probably in relation to its finer spatial resolution and enhanced orographic processes (average over the period 1980-2014, not shown). ERA5 shows larger.
Acknowledgements. This work received funding from the  Italian Project of Interest NextData of the Italian Ministry for Education, University and Research  and from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 641762  (ECOPOTENTIAL). Part of this work was performed in the framework of the  MEDSCOPE (MEDiterranean Services Chain based On climate PrEdictions )  ERA4CS  project (grant agreement no. 690462 ) funded by the  European Union. ERA5 :  Copernicus Climate Change Service  ( C3S ) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Ser-.
Materials and methods The following experimental protocol was designed by ANSES (French Agency for Food, Environmental and Occupational Health and Safety) and the DGAL (French Directorate General for Food), and funded by the French Ministry of Agriculture. It was authorised by the European Commission. Data were issued from French herds in which non-negative results to skin test screening (SIT or SICCT) were observed between 2013 and 2015. These herds were included in the study on farmer's voluntary basis. The skin tests were performed on cattle by veterinarians, as part of the usual official screening programme (in compliance with Council Directive 64/432/EEC). In the case of SICTT, non-negative were only those with doubtful results, positive SICTT animal at screening were considered infected and thus not included in the study.
Results to MIX (interpreted separately from PPD) were considered positive when the per cent OD was ≥0. 03, and results to PPD (interpreted separately from MIX) were considered positive when OD was ≥0. 05. Data were processed using Excel and Access (Microsoft). McNemar's tests for paired data were performed using R, and considered significant when P<0. 05. In the database, 2851 animals had non-negative results to ST screening. At the end of the study, detailed results were available in the database for 40 infected animals from 29 farms, and 1825 bTB-free animals from 744 farms (figure  1 ). Sensitivity and specificity values were estimated in herds where non-negative results to preliminary ST screening were observed: they were thus conditional to a non-negative result to ST D0.
The location in all views (lesions were delineated) and characterization (morphologic appearance and histologic features) of cancers and of findings that led to false-positive recalls was recorded. According to the reference standard, the median size of the cancers at mammography was 13 mm 2 (interquartile range, 4-22 mm 2 ). Abbreviations AI = artificial intelligence, AUC = area under the ROC curve, BI-RADS = Breast Imaging Reporting and Data System, CAD = computer-aided detection, POM = probability of malignancy, ROC = receiver operating characteristic.
Materials and Methods This retrospective study was compliant with the Health Insurance Portability and Accountability Act. Our study was performed with anonymized, retrospectively collected digital mammographic images obtained from screening examinations. Women were included from two institutions: one in the United States (collection center A) and one in Europe (collection center B). The requirement to obtain informed consent and ethical approval to use anonymized data was waived after review of the institutional review board at collection center A and under national law at collection center B. The study was financially supported by ScreenPoint Medical (Nijmegen, the Netherlands). The authors who were not employees of or consultants for ScreenPoint Medical had control of the data and information submitted for publication at all times.
Finally, proprietary algorithms are used to combine the scores of all detected regions in craniocaudal and/or mediolateral oblique right and/or left breast images into the examination-based score (the Transpara score), which ranges from 1 to 10 (with 10 indicating the highest likelihood that cancer is present on the mammogram). The Transpara score is calibrated such that the number of mammograms in each category is roughly equal in a screening setting (eg, 10% of screening mammograms fall into category 1, 10% in category 2). The AI system is trained, validated, and tested by using a database containing more than 9000 mammograms with cancer (one-third of which are presented as lesions with calcifications) and the same number of mammograms without abnormalities. The mammograms originate from devices from four different vendors (Hologic, Siemens, GE Healthcare [Waukesha, Wis], and Philips Healthcare [Sölna, Sweden]).
Statistical analysis was performed with SPSS software (version 24; IBM, Armonk, NY), and open-access Obuchowski-Rockette and Dorfman-Berbaum-Metz software (version 2. 5; Medical Image Perception Laboratory-University of Iowa, Iowa City, Iowa; available from http://perception. radiology. uiowa. edu/). ROC performance. -ROC curves and their AUCs were computed by using the POM score. The Obuchowski-Rockette and Dorfman-Berbaum-Metz mixed-model analysis of variance yielded a P value for rejecting the null hypothesis that readings performed unaided or with AI support have equal performance  (26) (27) (28) (29). P ,. 05 was indicative of a statistically significant difference between both reading conditions. Secondarily, to identify possible strengths and weaknesses of the study as a function of the different types of mammograms and readers used, five subgroup subanalyses were also performed: (a) subgroups of examinations according to lesion type (soft tissue or calcifications); (b) subgroups of examinations according to digital mammography system used (Hologic or Siemens); (c) subgroups of examinations according to breast density (lower density [BI-RADS categories a and b] or higher density [BI-RADS categories c and d]); (d) equal subgroups of radiologists based on years of experience (lower 50% vs higher 50%); and (e) subgroups of radiologists based on the use of the AI system integrated in the workstation or on a separate viewer.
Reading time. -The reading time per case was automatically measured by the workstation software used for the observer evaluation. Average reading times per case were compared between reading conditions with a generalized linear model similar to the one described for sensitivity and specificity but with use of reading time per case as the dependent variable. For this analysis, outliers, defined as values extending beyond 1. 5 times the standard deviation of the data, were removed. These were considered unreliable because readers might have been interrupted. P ,. 05 was indicative of a statistically significant difference between reading conditions. A learning curve for the AI system in relation to reading time was evaluated. The generalized linear model analysis for reading times was repeated for two subsets of the data, representing data from the first reading session (first-time use of AI system) and the data from the second reading session, after the washout period (second-time use of AI system).
Statistical Analysis GraphPad Prism 6. 0b software (GraphPad Prism, San Diego, CA) was used to analyze the data. Results are presented as means ± standard errors. Longitudinal data analysis was conducted using a 1-way ANOVA or a 2-way ANOVA with repeated measures and the Bonferroni post-test. To assess significance, p < 0. 05 was considered statistically significant.
Acknowledgements We thank  Edward Gill ,  Laura G. Smith ,  Hernan Rios ,  Richard Tang  and the  Bioimaging Core facility  for their help conducting the MRI acquisitions. The authors also wish to thank the funding agencies for their support of the study:  NIH/NIAMS   R01AR066517  (DL, DG, ZG),  California Institute for Regenerative Medicine Discovery  Award  CIRM-DISC1-10643  (DS),  NIH/NIAMS   K01AR071512  (DS).
Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability.
Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e. g. , sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.
Therefore, further developments are needed to combine time-course modeling with multivariate approaches to start exploring microbial interactions and dynamics. In addition, current statistical methods have mainly focused on a single microbiome dataset, rather than the combination of different layers of molecular information obtained with parallel multi-omics assays performed on the same biological samples. Data derived from each omics technique are typically studied in isolation and disregard the correlation structure that may be present between the multiple data types. Hence, integrating these datasets enables us to adopt a holistic approach to elucidate patterns of taxonomic and functional changes in microbial communities across time. Some sparse multivariate methods have been proposed to integrate omics and microbiome datasets at a single time point and identify sets of features (multiomics signatures) across multiple data types that are correlated with one another.
For example,  Gavin et al. (2018)  used the DIABLO method  (Singh et al. , 2019)  to integrate 16S amplicon microbiome, proteomics, and metaproteomics data in a type I diabetes study;  Guidi et al. (2016)  used sparse PLS  (Lê Cao et al. , 2008)  to integrate environmental and metagenomic data from the Tara Oceans expedition to understand carbon export in oligotrophic oceans, and  Fukuyama et al. (2017)  used sparse canonical correlation analysis  (Witten et al. , 2009)  to integrate 16S and metagenomic data. However, methods or frameworks to integrate multiple longitudinal datasets including microbiome data remain incomplete. Zhou et al. (2008)  used principal component analysis (PCA) to summarize functional data, with the PC scores used for model fitting, prediction, and inference. However, only pairwise relationships were investigated and for a single type of data.
Other type of modeling (loess regression) was used by  Ribicic et al. (2018)  in combination with sparse PCA to explore the link between chemistry and microbial community data in the biodegradation of chemically dispersed oil, but their approach was not designed to seek for multi-omics signatures. We propose a computational approach to integrate microbiome data with multi-omics datasets in longitudinal studies. Our framework, described in Figure  1  includes smoothing splines in a linear mixed model framework to model profiles across groups of samples and builds on the ability of sparse multivariate ordination methods to identify sets of variables highly associated across the data types, and across time. Our framework encompasses data pre-processing, modeling, data clustering, and integration.
Pre-Processing of Microbiome Data We assume the data are in raw count formats resulting from bioinformatics pipelines such as QIIME  (Caporaso et al. , 2010)  or FROGS  (Escudié et al. , 2017)  for 16S amplicon data. Here, we consider the operational taxonomic unit (OTU) level, but other levels can be considered, as well as other types of microbiomederived data, such as whole genome shotgun sequencing. The data processing step is described in Lệ  Cao et al. (2016b)  and consists of: 1) Low count removal: Only OTUs whose proportional counts exceeded 0. 01% in at least one sample were considered for analysis. This step aims to counteract sequencing errors  (Kunin et al. , 2010). 2) Total sum scaling (TSS) can be considered as a "normalization" process to account for uneven sequencing depth across samples.
Straube et al. , 2015  showed that the proportion of profiles fitted with the different models increased in complexity with the organism considered. Different types of splines can be considered in models (  2 )-(  4 ), including a cubic spline basis  (Verbyla et al. , 1999) , a penalized spline and a cubic penalized spline. A cubic spline basis uses all inner time points of the measured time interval as knots and is appropriate when the number of time points is small (≤5), whereas the penalized spline and cubic penalized spline bases use the quantiles of the measured time interval as knots; see  Ruppert (2002). In our case studies, we used penalized splines. The LMMS models are implemented in the R package lmms  (Straube et al. , 2016).
Measure of Association for Compositional Data Compositional data arise from any biological measurement made based on relative abundance  (Lovell et al. , 2015; Gloor et al. , 2017). Microbiome data in particular are compositional for several reasons, including biological, technical, and computational. Thus, interpretation based on correlations between profiles must be made with caution as it is highly likely to be spurious. Proportional distances have been proposed as an alternative to measure association. The compositional data analysis field is an active field of research, but methods are critically lacking for longitudinal data. Here, we adopt a practical and post hoc approach to evaluate pairwise associations of microbial and omics profiles once they have been assigned to their clusters.
We used the proportionality distance φ s proposed by  Lovell et al. (2015)  and implemented in the "propr" R package  (Quinn et al. , 2017). For two LMMS profiles x i and x j , we define the pairwise proportionality distance as ϕ s i j i j i j x x x x x x ,. ( ) = - ( ) + ( ) var var (6) A small value indicates that, in proportion, the pair of profiles is strongly associated. We calculated the distance φ S on the logtransformed LMMS modeled profiles within each identified cluster to exclude potentially spurious correlations and further guide the interpretation of the results. In addition, to evaluate the quality of our clustering approach, we compared the pairwise distances of the profiles within a particular cluster and profiles outside the cluster.
Integration Multiblock Projection to Latent Structures Methods To integrate multiple datasets (also called blocks) measured on the same biological samples, we used multivariate methods based on projection to latent structures (PLS) methods  (Wold, 1975) , which we broadly term multiblock PLS approaches. For example, we can consider generalized canonical correlation analysis (GCCA,  Tenenhaus and Tenenhaus, 2011; Tenenhaus et al. , 2014) , which, contrary to what its name suggests, generalizes PLS for the integration of more than two datasets. Recently, we have developed the DIABLO method to discriminate different phenotypic groups in a supervised framework  (Singh et al. , 2019). In the context of this study, however, we present the sparse GCCA in an unsupervised framework, where input datasets are spline-fitted matrices.
We denote Q data sets X (1) (TxP 1 ), X (2) (TxP 2 ), …, X (Q) (TxP Q ) measuring the expression levels of P q variables of different types (taxa, "omics, " continuous response of interest), modeled on T (interpolated) time points, q = 1,…,Q. GCCA solves for each component h = 1,…,H: max cov a a q j q j Q q j h q h q h h Q c X a ( ) ( ) ,. , , , , ( ) ( ) , 1 1 = ≠ ∑ X X a a a h j h j h q h q q ( ) ( ) ( ) ( ) ( ) , ( ) = ≤ s. t. and 2 1 1 λ (7) where λ (q) is the ℓ 1 penalization parameter, a h q ( ) is the loading vector on component h associated with the residual (deflated) matrix X h q ( ) of the data set X (q) , and C = {c qj } is the design matrix. C is a Q×Q matrix that specifies whether datasets should be correlated and includes values between zero (datasets are not connected) and one (datasets are fully connected).
Thus, we can choose to take into account specific pairwise covariances by setting the design matrix (see  Rohart et al. , 2017  for implementation and usage) and model a particular association between pairs of datasets, as expected from prior biological knowledge or experimental design. In our integrative case study, we used sparse PLS, a special case of Eq. (  7 ) to integrate microbiome and metabolomic data, as well as sparse multiblock PLS to also integrate variables of interest. Both methods were used with a fully connected design. The multiblock sparse PLS method was implemented in the mixOmics R package where the ℓ 1 penalization parameter is replaced by the number of variables to select, using a softthresholding approach (see more details in  Rohart et al. , 2017).
Infant Gut Microbiota Development The gastrointestinal microbiome of 14 babies during the first year of life was studied by  Palmer et al. (2007). The authors collected an average of 26 stool samples from healthy full-term infants. As infants quickly reach an adult-like microbiota composition, we focused our analyses on the first 100 days of life. Infants who received an antibiotic treatment during that period were removed from the analysis, as antibiotics can drastically alter microbiome composition  (Dudek-Wicher et al. , 2018). The dataset we analyzed included 21 time points on average for 11 selected infants (vaginal delivery = 6, C-section = 5; see Figure  3 ). Samples were collected daily during days 0-14 and weekly after the second week. We separated our analyses based on the delivery mode (C-section or vaginal), as this is known to have a strong impact on gut microbiota colonization patterns and diversity in early life  Rutayisire et al.
Time is indicated on the x-axis in days. As delivery method is known to be a strong influence on gut microbiome colonization, the data are separated according to either C-section or vaginal birth. acetic and propionic acid in the bioreactors (5 time points). Microbial dynamics were profiled with 16S RNA gene metabarcoding as described in  Poirier et al. (2016)  and included 4 time points and 90 OTUs. A metabolomic assay was conducted on the same biological samples at four time points with gas chromatography coupled to mass spectrometry GC-MS after solid phase extraction to monitor substrates degradation  (Limam et al. (2010). The XCMS R package (version 1. 52. 0) was used to process the raw metabolomics data  (Smith et al. , 2006). GC-MS analyses focused on 20 peaks of interest identified by the National Institute of Standards and Technology database.
Data were then log-transformed. The purpose of the study was to investigate the relationship between biowaste degradation performance and microbial and metabolomic dynamics across time. The aim of our statistical analysis was to identify highly associated multiomic signatures characterizing waste degradation dynamics in the three bioreactors. This study involves the integration of two omics datasets and degradation performance measures; thus, we applied sPLS and multiblock sPLS, as shown in our workflow in Figure  2.