dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , PICRUSt; (63)] thus suffer limitations, and identity and functions should ideally be investigated in tandem. Functional capabilities can be evaluated through metagenomic sequencing, but genes and functions that are expressed at specific time points are better evaluated by metatranscriptomic or metaproteomic approaches. Metabolomics, which map metabolites, are another important tool that profiles ongoing metabolisms, and thus informs functions  (64, 65)  although, as for all of the above, the improvement of databases supporting metabolite identifications will be critical  (66). The integration of these approaches in multi-omics appears challenging, yet particularly promising for revealing the causal role of the microbiome and mechanisms involved in contaminants metabolism and toxic effects  (67, 68).
Indeed, metabarcoding datasets produce taxa relative abundances tables, and their variations. In these, an increase in one group thus cannot be properly interpreted, as it may as well represent a lower decrease relative to other groups in a globally shrinking population. Bacterial densities in guts of distinct lineages of rainforest ants were for example shown to vary by orders of magnitude based on qPCR quantifications; interestingly, absolute abundance variations were better correlated with habitat (arboreal or terrestrial) and trophic position than actual community compositions  (71). Antibiotics, which are reported to affect relative abundances in bacterial communities, act first by affecting the total number of bacteria present, as was clearly demonstrated for streptomycin and sancomycin, this being their major influence on the microbiome  (72, 73).
The CANCOR (also abbreviated CCorA) is a multivariate analysis that reveals the co-inertia between two tables that describe the same set of observations (here the SNPs) with two different sets of possibly covarying variables (here environmental and phenotypic variables). This approach analyzed simultaneously the environment at sites of origin of populations, their phenotype assessed in experimental gardens and the allelic frequencies of populations in order to identify the environmental variables imposing selection, the adaptive phenotypic responses and the adaptive loci. An extensively annotated gene set can help to identify climate adaptive genes and gene functions under selection. In view of that, we also improved the published perennial ryegrass gene set  (Byrne et al. , 2015)  by de novo gene prediction and functional annotation of our genomic dataset.
Accepted Article individuals per population were pooled before DNA extraction. Variants were called using the draft reference genome sequence of  Byrne et al. (2015). Further details are available in the Supporting Information of  Blanco-Pastor et al. (2019). We merged the two datasets (GBS and HiPlex) for analyses and performed a stringent filter on the minor allele frequency (MAF) to reduce the proportion of low frequency alleles. We retained SNP loci if their MAF was greater than 5% in at least 10 populations. The final merged dataset comprised alternative allele frequencies (AAFs) of 189,968 SNP loci in the 469 natural populations  (Data S1 in Blanco-Pastor et al. , 2020). The genotype data included 7. 81% missing values that were imputed by using the mean allele frequency across populations.
To identify putative adaptive loci, we used a GEA linear mixed model similar to that of  Yoder et al. (2014)  (Supporting Information, Methods S4). We additionally used a GWAS linear mixed model to assess individual locus effect on a given phenotypic trait (Supporting Information, Methods S5). Both GEA and GWAS were run using the GWAS function of the 'rrBLUP' R package  (Endelman, 2011). Among the significant loci revealed by the GEA analysis, we only considered as GEA-GWAS outlier those also significantly associated with a phenotypic trait in GWAS. We used a liberal threshold of False Discovery Rate (FDR) of 0. 2 in both GEA and GWAS because a SNP needed to be significant in the two independent analyses to be considered as outlier. But we also report results using the more conservative FDR = 0.
Accepted Article one hand (Y, Fig. 2b ) and on phenotypic traits on the other hand (X, Fig. 2b ), as the two sets of input variables to analyze (Supporting Information, Methods S6). We also ran an additional CANCOR in order to discern the general structure of correlation between environmental and phenotypic variables at the population level. In this analysis, the populations were considered as the experimental units and the value of environmental variables at sites of origin of populations on the one hand, and the population means of phenotypic traits on the other hand, as the two sets of input variables to analyze. We ran the CANCOR analysis using the R package 'vegan'  (Oksanen et al. , 2018). We tested the significance of outlier loci using a  2 test on Mahalanobis distances following the method of  Luu et al.
Accepted Article axis 1 replaced by negative sign and negative sign on the axis 2 replaced by positive sign). Then we used these modified canonical loadings to calculate a matrix of pairwise Euclidean distances between SNPs. For each of the two groups, we used undirected graph networks to visualize modules of SNPs. Nodes were connected by edges according to three different thresholds of pairwise Euclidean distances (d) (< 1, < 0. 5 and < 0. 1). Co-association networks were visualized using the 'igraph' R package  (Csardi & Nepusz, 2006). To demonstrate the utility of CANCOR for investigating the genetic basis of complex traits, for each of the two groups of SNPs, we performed two independent Gene Ontology (GO) enrichment analyses in the largest modules obtained with threshold of d < 0. 1. The GO enrichment analyses were performed with agriGO 2.
Gene prediction and functional annotation The EVidenceModeler (EVM)  (Haas et al. , 2008)  was used to improve completeness, without losing gene model accuracy, of the previously published set of 28,182 genes annotated on the L. perenne draft genome sequence  (Byrne et al. , 2015). For this, the annotation set was complemented with a less conservative set of gene predictions, orthology-guided transcript assemblies  (Ruttink et al. , 2013)  and aligned proteomes of closely related species (Brachypodium distachyon, rice, maize and sorghum). All evidence tracks were generated using the GenomeThreader  (Gremme, Brendel, Sparks, & Kurtz, 2005)  with default settings and used as input for the EVM. The completeness was estimated using BUSCO  (Simão, Waterhouse, Ioannidis, Kriventseva, & Zdobnov, 2015)  and the PLAZA 2.
The CANCOR approach The CANCOR using the loci as experimental units (Fig. 3b-c ) found the first 14 canonical correlations ('CanCorr' elements of the CCorA function of the R package 'vegan') larger than 0. 9. The environmental input variables (regression slopes of AAFs on environmental variables) were highly correlated (|r| > 0. 7) to the first and second environmental canonical variates for 44 and 35 environmental variables, respectively. Likewise, the phenotypic input variables (regression slopes of AAFs on phenotypic variables) were highly correlated (|r| > 0. 7) to the first and the second phenotypic canonical variates for 35 and 37 phenotypic variables, respectively. In contrast, the CANCOR using populations as experimental units (Fig. 3d-f ) only found the first two canonical correlations larger than 0.
Acknowledgements This work was funded in the frame of the project GrassLandscape awarded by the 2014  FACCE-JPI ERA-NET+ call Climate Smart Agriculture. Funding was granted by the  European Commission (EC)  (grant agreement nº  618105 ), by the  Agence Nationale de la Recherche (ANR)  and the  Institut National de la Recherche Agronomique (INRA -métaprogramme ACCAF) in France , the  Biotechnology and Biological Sciences Research Council (BBSRC)  in the  United-Kingdom , the  Bundesantalt für Landwirtschaft und Ernährung (BLE) in Germany. J. L. Blanco-Pastor  has received the support of the EC in the framework of the  Marie-Curie   FP7 COFUND People Program , through the award of an  AgreenSkills+ fellowship  (grant agreement nº  609398 ). Support to  J. L. Blanco-Pastor  came partially from RéGàTe, a project funded by the  French Ministry of Agriculture  through the 2015  CASDAR program.
Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz.
Recently, machine learning has received significant interest in clinical neuroscience for its potential to predict from such heterogeneous multimodal brain data  (Woo et al. , 2017). Unfortunately, the effectiveness of machine learning in psychiatry and neurology is constrained by the lack of large high-quality datasets  (Woo et al. , 2017; Varoquaux, 2017; Bzdok and Yeo, 2017; Engemann et al. , 2018)  and comparably limited understanding about the data generating mechanisms  (Jonas and Kording, 2017). This, potentially, limits the advantage of complex learning strategies proven successful in purely somatic problems  (Esteva et al. , 2017; Yoo et al. , 2019; Ran et al. , 2019). In clinical neuroscience, prediction can therefore be pragmatically approached with classical machine learning algorithms  (Dadi et al.
, 2019) , expert-based feature engineering and increasing emphasis on surrogate tasks. Such tasks attempt to learn on abundant high-quality data an outcome that is not primarily interesting, to then exploit its correlation with the actual outcome of interest in small datasets. This problem is also known as transfer learning  (Pan and Yang, 2009)  which, in its simplest form, is implemented by reusing predictions from a surrogate-marker model as predictors in the small dataset. Over the past years, predicting the age of a person from its brain data has crystalized as a surrogate-learning paradigm in neurology and psychiatry  (Dosenbach et al. , 2010). First results suggest that the prediction error of models trained to learn age from brain data of healthy populations provides clinically relevant information  (Cole et al.
Brain scans from healthy people between 17 and 90 years old were used to build the computer model. The experiments showed that combining all three methods predicted brain age better. The predictions also correlated with the cognitive fitness of individuals. People whose brains were predicted to be older than their years tended to complain about the quality of their sleep and scored worse on memory and speed-thinking tasks. Crucially, Engemann et al. tested how the algorithm would hold up if some data were missing. This can happen in clinical practice where some tests are required but not others. Positively, prediction was maintained even with incomplete data, meaning this could be a useful clinical tool for characterizing the brain. such as the putative speed of attention  (Richard Clark et al.
We, therefore, call it opportunistic stacking model. At this point, there are very few multimodal databases providing access to electrophysiology alongside MRI and fMRI. The Leipzig Mind-Brain-Body (LEMON) dataset  (Babayan et al. , 2019)  includes high-quality research-EEG with MRI and fMRI for 154 young subjects and 75 elderly subjects. The dataset used in the present study is curated by the Cam-CAN  (Shafto et al. , 2014; Taylor et al. , 2017)  and was specifically designed for studying the neural correlates of aging continuously across the life-span. The Cam-CAN dataset is currently the largest public resource on multimodal imaging with high-resolution electrophysiology in the form of MEG alongside MRI data and rich neuropsychological data for more than 650 healthy subjects between 17 and 90 years.
Opportunistic prediction-stacking approach We begin by summarizing the proposed method. To build a model for predicting age from electrophysiology, fMRI and anatomical MRI, we employed prediction-stacking  (Wolpert, 1992). As in  Liem et al. , 2017 , the stacked models, here, referred to different input data instead of alternative models on the same data. We used ridge regression  (Hoerl and Kennard, 1970)  to linearly predict age from high-dimensional inputs of each modality. Linear predictions were based on distinct features from anatomical MRI, fMRI and MEG that have been commonly associated with aging. For extracting features from MEG, in a first step, we drew inspiration from EEG-literature on aging and considered evoked response latencies, alpha band peak frequency, 1/f slope topographies assessed in sensor-space.
Previous work on neural development and aging  (Khan et al. , 2018; Gola et al. , 2013 ) and Alzheimer's disease  (Gaubert et al. , 2019)  has pointed at the importance of spatial alterations in stationary power spectra which can be exploited using high-dimensional regression techniques  (Fruehwirt et al. , 2017). In this work, we have adapted this reasoning to the more general problem of predicting age while exploiting the advanced source-modeling options supported by the Cam-CAN dataset based on MEG and the individual MRIs. Therefore, it was our principal effort to expose the geometry of stationary power spectra with minimal distortion by using source localization based on the individual head geometry  (Sabbagh et al. , 2019)  to then perform high-dimensional regression. As a result, we predicted from the spatial distribution of power and bivariate interactions between signals (connectivity) in nine frequency bands (Table  1 ).
Opportunistic stacking approach. The proposed method allows to learn from any case for which at least one modality is available. The stacking model first generates, separately for each modality, linear predictions of age for held-out data. 10-fold cross-validation with 10 repeats is used. This step, based on ridge regression, helps reduce the dimensionality of the data by generating predictions based on linear combinations of the major directions of variance within each modality. The predicted age is then used as derived set of features in the following steps. First, missing values are handled by a coding-scheme that duplicates the second-level data and substitutes missing values with arbitrary small and large numbers. A random forest model is then trained to predict the actual age with the missing-value coded age-predictions from each ridge model as input features.
7 years (SD ¼ 0:55, P 2:5;97:5 ¼ ½3:77; 5:74). Note that dependable numerical p-values are hard to obtain for paired model comparisons based on cross-validation on the same dataset: Many datasets equivalent to the Cam-CAN would be required. Nevertheless, the uncertainty intervals extracted from the cross-validation distribution suggests that the observed differences in performance were systematic and can be expected to generalize as more data is analyzed. Moreover, the out-of-sample ranking between the different models was stable over cross-validation folds (Figure  2 -figure supplement 1) with the full model achieving the first rank 71/100 times and performing at least 80/100 better than the MRI + fMRI or the MRI + MEG model. This emphasizes that the relative importance of MEG and fMRI for enhancing MRI-based prediction of age can be expected to generalize to future data.
, 2015; Cole et al. , 2018). Does improved performance through stacking strengthen effect-sizes? Can MEG and fMRI help detect complementary associations? Figure  3  summarizes linear correlations between the brain ageD and the 38 neuropsychological scores after projecting out the effect of age, Equations 6-8 (see Analysis of brain-behavior correlation in Materials and methods for a detailed overview). As effect sizes can be expected to be small in the curated and healthy population of the Cam-CAN dataset, we considered classical hypothesis testing for characterizing associations. Traditional significance testing (Figure  3A ) suggests that the best stacking models supported discoveries for between 20% (7) and 25% (9) of the scores. Dominating associations concerned fluid intelligence, depression, sleep quality (PSQI), systolic and diastolic blood pressure (cardiac features 1,2), cognitive impairment (MMSE) and different types of memory performance (VSTM, PicturePriming, FamousFaces, EmotionalMemory).
Points depict single-case prediction errors. Boxplot-whiskers show the 5% and 95% uncertainty intervals. When performance was degraded, important modalities were absent or the number of cases was small, for example, in MEG sens where only sensor space features were present. age-prediction by multimodal modeling on 674 subjects from the Cam-CAN dataset, the currently largest publicly available collection of MEG, fMRI and MRI data. Our results suggest that MEG and fMRI both substantially improved age-prediction when combined with anatomical MRI. We have then explored potential implications of the ensuing brain-age D as a surrogate-biomarker for cognitive and physical health. Our results suggest that MEG and fMRI convey non-redundant information on cognitive functioning and health, for example fluid intelligence, memory, sleep quality, cognitive decline and depression.
While this interpretation may be enticing, an important caveat arises from the fact that fMRI signals are due to neurovascular coupling, hence, highly sensitive to events caused by sources other than neuronal activity  (Hosford and Gourine, 2019). Recent findings based on the dataset analyzed in the present study have shown that the fMRI signal in elderly populations might predominantly reflect vascular effects rather than neuronal activity  (Tsvetanov et al. , 2015). The observed complementarity of the fMRI and MEG in age prediction might, therefore, be conservatively explained by the age-related increase in the ratio of vascular to neuronal contributions to the fMRI signal, while MEG signals are directly induced by neuronal activity, regardless of aging. Nevertheless, in the context of brain-age prediction these mechanisms are less important than the sensitivity of the prediction, for instance, regarding behavioral outcomes.
5 in units of standard deviations of the normalized predictors and the target. On the other hand, it is important to consider that the Cam-CAN sample consists of healthy individuals only. It, thus, appears as rather striking that systematic and neuropsychologically plausible effects can be detected. Our findings, therefore, argue in favor of the brain age D being a sensitive marker of normative aging. The effects are expected to be far more pronounced when applying the method in clinical settings, that is, in patients suffering from mild cognitive impairment, depression, neurodevelopmental or neurodegenerative disorders. This suggests that brain age D might be used as a screening tool for a wide array of clinical settings for which the Cam-CAN dataset could serve as a normative sample.
It will, therefore, be critical to benchmark and improve learning from power topographies in clinical settings. Finally, it is worthwhile to highlight that, here, we have focused on age in the more specific context of the brain age D as surrogate biomarker. However, the proposed approach is fully compatible with any target of interest and may be easily applied directly to clinical end points, for example drug dosage, survival or diagnosis. Moreover, the approach presented here can be easily adapted to work with classification problems, for instance, by substituting logistic regression for ridge regression and by using a random forest classifier in the stacking layer. We have provided all materials from our study in form of publicly available version-controlled code with the hope to help other teams of biomedical researchers to adapt our method to their prediction problem.
Limitations For the present study, we see four principal limitations: availability of data, interpretability, nonexhaustive feature-engineering and potential lack of generalizability due to the focus on MEG. The Cam-CAN is a unique resource of multimodal neuroimaging data with sufficient data points to enable machine learning approaches. Yet, from the point of view of machine learning, the Cam-CAN dataset is a small dataset. This has at least two consequences. If the Cam-CAN included many more data points, for example beyond 10-100 k subjects, the proposed stacking model might possibly be of limited advantage compared to purely non-linear models, for example random forests, gradient boosting or deep learning methods  (Bzdok and Yeo, 2017). At the same time, the fact that the Cam-CAN has been unique so far, hinders generalization testing to equivalent multimodal datasets from other sites based on alternative scanning methodologies, protocols and devices  (Engemann et al.
Finally, while MEG and EEG share the same types of neural generators, their specific biophysics render these methods complementary for studying neuronal activity. At this point, unfortunately, there is no public dataset equivalent of the Cam-CAN including EEG or, both, EEG and MEG. Such a data resource would have enabled studying the complementarity between MEG with EEG as well as generalization from stacking with MRI and MEG to stacking models with MRI and EEG. We hope that our method will help other scientists to incorporate the multimodal features related to their domain expertise into their applied regression problems.
Sample We included MEG (task and rest), fMRI (rest), anatomical MRI and neuropsychological data (cognitive tests, home-interview, questionnaires) from the CAM-Can dataset  (Shafto et al. , 2014). Our sample comprised 674 (340 female) healthy individuals between 18 (female = 18) to 88 (female = 87) years with an average of 54. 2 (female = 53. 7) and a standard deviation of 18. 7 (female = 18. 8) years. We included data according to availability and did not apply an explicit criterion for exclusion. When automated processing resulted in errors, we considered the data as missing. This induced additional missing data for some cases. A summary of available cases by input modality is reported in Table  3. For technical details regarding the MEG, fMRI, and MRI data acquisition, please consider the Cam-CAN reference publications  (Shafto et al.
fMRI features Functional connectivity Large-scale neuronal interactions between distinct brain networks has been repeatedly shown to change during healthy aging. Over the past years, for fMRI-based predictive modeling using functional atlases from about 50 to 1000 ROIs have emerged as a fundamental element for mitigating heterogeneity and dimensionality reduction, especially in small-to medium-sized datasets such as the Cam-CAN with less than 1000 observations  (Dadi et al. , 2019; Abraham et al. , 2017). To estimate macroscopic functional connectivity, we deviated from the 197-ROI BASC atlas  Bellec et al. , 2010 used in Liem et al. , 2017. Instead, we used an atlas with 256 sparse and partially overlapping ROIs obtained from Massive Online Dictionary Learning (MODL)  (Mensch et al. , 2016).