dataesr/scientific-paragraphs-categorization huggingface dataset:
Smut Genomes Display Conserved Synteny but Distinct Repeat Dynamics We obtained and annotated the genome sequence of the haploid S. scitamineum strain SscI8. Repeat annotation was done using a database of known repeat sequences combined with de novo annotation (see Materials and Methods). In order to compare the distribution of repeats in distinct genomes, we generated repeat annotations for the five smut fungi as well as the two relatives P. flocculosa, and Ma. globosa. The overall repeat content (excluding tandem repeats) of 3. 23% in S. scitamineum compared with 2. 49% in the closest sequenced relative S. reilianum (table  1 ). This number can be traced back to the higher content of interspersed repeats in S. scitamineum, that is, repeats originating from transposable elements. The compact genome of Ma.
maydis and Ma. globosa  (Boekhout et al. 1998) , and by optical mapping for S. reilianum and U. hordei  (Ka ¨mper et al. 2006; Laurie et al. 2012 ). Gene-wise statistics are based on an updated annotation for these genomes. Annotations for M. pennsylvanicum, P. flocculosa, and Ma. globosa were not modified from their respective original publications. For the sake of consistency, however, CSEP were predicted using the methodology introduced in the present work, and corresponding statistics are reported. trees, one for each family. We performed two independent phylogenetic reconstructions for each gene: one at the protein level using the PhyML software  (Guindon et al. 2010 ) and one at the codon level using the CodonPhyML software  (Gil et al. 2013). While both approaches resulted in very similar conclusions, the codon-based trees had significantly higher support values (0.
The computation is driven by analytical relationships proposed in the literature  [3]. In a local coordinate system, the initial stress tensor is given by Eq. 2: 𝜎 0 ��� = -2𝜇𝜀 𝑝 ��� ( 2 ) where µ is the shear modulus. Implementation of the method. In order to generate residual stress field automatically into a mechanical part, a PYTHON code has been developed and linked to the Finite Element software ABAQUS. This code proposes an efficient way to calculate the residual stress field induced by estimating the initial stress state for each integration point of the model. The method is divided into three main stages: -The depth of each integration point is computed by using nodes and elements data of the surface layer through the connectivity tables of the Finite Element model. -Using SIGINI subroutine, initial stresses corresponding to the plastic strains that would exist in a semi-infinite body are introduced locally in a local coordinate system defined with the normal vector of the surface.
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropathologies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines that extract the most predictive biomarkers from the data.
These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas. Connectomes are then compared across participants to learn patterns of connectivity that differentiate typical controls from individuals with autism. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Good choices of methods for the various steps of the pipeline lead to 67% prediction accuracy on the full ABIDE data, which is significantly better than previously reported results. We perform extensive validation on multiple subsets of the data defined by different inclusion criteria. These enables detailed analysis of the factors contributing to successful connectome-based prediction. First, prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available.
g. [17] ), and even age  [18]. This growing number of studies has shown the feasibility of using R-fMRI to identify biomarkers. However questions about the readiness of R-fMRI to detect clinically useful biomarkers remain  [13]. In particular, the reproducibility and generalizability of these approaches in research or clinical settings are debatable. Given the modest sample size of most R-fMRI studies, the effect of cross-study differences in data acquisition, image processing, and sampling strategies  [19, 20, 21]  has not been quantified. Using larger datasets is commonly cited as a solution to challenges in reproducibility and statistical power  [22]. They are considered a prerequisite to R-fMRIbased classifiers for the detection of psychiatric illness. Recent efforts have accelerated the generation of large databases through sharing and aggregating independent data samples  [23, 24, 25].
It compiles a dataset of 1112 R-fMRI participants by gathering data from 17 different sites. After preprocessing, we selected 871 to meet quality criteria for MRI and phenotypic information. Our inter-site prediction methodology reproduced conditions found under most clinical settings, by leaving out whole sites and using them as newly seen test sets. To validate the robustness of our approach, we performed nested cross-validation and varied samples per inclusion criteria (e. g. sex, age). Finally, to assess losses in predictive power associated with using a heterogeneous aggregate dataset instead of uniformly defined samples, we included a comparison of intraand inter-site prediction strategies.
Datasets: Preprocessing and Quality Assurance (QA) We ran our analyses on the ABIDE repository  [25] , a sample aggregated across 17 independent sites. Since there was no prior coordination between sites, the scan and diagnostic/assessment protocols vary across sites 1. To easily replicate and extend our work, we rely on a publicly available preprocessed version of this dataset provided by the Preprocessed Connectome Project 2  initiative. We specifically used the data processed with the Configurable Pipeline for the Analysis of Connectomes  [34]  (C-PAC). Data were selected based on the results of quality visual inspection by three human experts who inspected for largely incomplete brain coverage, high movement peaks, ghosting and other scanner artifacts. This yielded 871 subjects out of the initial 1112.
Step 3: Connectivity matrix estimation Since scans in the ABIDE dataset do not include enough R-fMRI time points to reliably estimate the true 3 For Harvard-Oxford and Yeo reference atlases, selecting 84 ROIs yields slightly better results than using the full version as shown in Figure  A2. This is probably due to the fact that these additional regions are smaller and less important regions, hence they induce spurious correlations in the connectivity matrices. 4 We identify these ROIs using an approach similar to FSL FIX (FMRIB's ICA-based Xnoiseifier). FIX extracts spatial and temporal descriptors from matrix decomposition results and uses a classifier trained on manually labeled components to determine if they correspond to noise. covariance matrix for a given individual, we used the Ledoit-Wolf shrinkage estimator, a parameter-free regularized covariance estimator  [50] , to estimate relationships between ROI time series (details are given in Appendix B).
Step 4: Supervised learning As ABIDE represents a post-hoc aggregation of data from several different sites, the assessments used to measure autism severity vary somewhat across sites. As a consequence, autism severity scores are not directly quantitatively comparable between sites. However, the diagnosis between ASD and Typical Control (TC) is more reliable and has already been used in the context of classification  [53, 13, 54]. Discriminating between ASD and TC individuals is a supervised-learning task. We use the connectivity measures between all pairs of regions, extracted in the previous step, as features to train a classifier across individuals to discriminate ASD patients from TC. We explore different machine-learning methods 5. We first rely on the most commonly used approach, the 2penalized support vector classification SVC.
2. 7. Experiments: prediction on heterogeneous data ABIDE datasets come from 17 international sites with no prior coordination, which is a typical clinical application setting. In this setting, we want to: i) measure the performance of different prediction pipelines, ii) use these measures to find the best options for each pipeline step (Figure  1 ) and, iii) extract ASD biomarkers from the best pipeline. Cross-validation. In order to measure the prediction accuracy of a pipeline, we use 10-fold cross-validation by training it exclusively on training data (including atlas estimation) and predicting on the testing set. Any hyperparameter of the various methods used inside a pipeline is set internally in a nested cross-validation. We used two validation approaches. Inter-site prediction addresses the challenges associated with aggregate datasets by using whole sites as testing sets.
This scenario also closely simulates the real world clinical challenge of not being able to sample data from every possible site. Note that this cross-validation can only be applied on a dataset with at least 10 acquisition sites, in order to leave out a different site in each fold. We do not apply it on subsample #5 that has been restricted to 3 sites to reduce sitespecific variability. Intra-site prediction builds training and testing sets are as homogeneous as possible, reducing site-related variability. It is based on stratified shuffle split cross-validation, which splits participants into training and test sets while preserving the ratio of samples for each site and condition. We used 80% of the dataset for training and the remaining for testing. Learning curves. A learning curve measures the impact of the number of training samples on the performance of a predictive model.
Computational aspects For data-driven brain atlas models (e. g. ICA), regionextraction methods entail feature learning on up to 300 GB of data, too big to fit in memory. Systematic study requires many cross-validations and subsamples and thus careful computational optimizations. Hence, for K-Means and ICA, we reduced the length of the time series by applying PCA dimensionality reduction with efficient randomized SVD  [57]. MSDL uses algorithmic optimizations  [49]  to process a large number of subjects in a reasonable time 9 (2 hours for 871 subjects). However, it is still the most costly method because of the number of runs required to find the optimal value for its 3 parameters. For reproducibility, we rely on the publicly available implementations of the scikit-learn package  [55]  for all the clustering algorithms, connectivity matrix estimators and predictors.
Overall prediction results For inter-site prediction, the highest accuracy obtained on the whole dataset is 66. 8% (see Table  2 ) which exceeds previously published ABIDE findings  [53, 13, 54 ] and chance at 53. 7% 10. Importantly, performance increases steadily with training set size, for all subsamples (Figures  2  and A6 ). This increase implies that optimal classification performance is not reached even for the largest dataset tested. The main difference between intra-site and inter-site settings is that the variability of inter-site prediction is higher (Table  2 ). However, this difference disappears with a large enough training set (Figure  2 ). This is encouraging for clinical applications. As shown in Figure  3 , the best predicting pipeline is MSDL, tangent space embedding and l 2 -regularized classifiers (SVC-l 2 and ridge classifier).
Effect of the covariance matrix estimator While correlation and partial correlation approaches are currently the two dominant approaches to connectome estimation in the current R-fMRI literature, our results highlight the value of tangent-space projection that outperforms the other approaches in all settings (Figure  3  and Figure  4 ), though the difference is significant only compared with correlation matrices (Figure  4 ). This gain in prediction accuracy is not without a cost, as the matrices derived cannot be read as correlation matrices. Yet, the differences in connectivity that they capture can be directly interpreted  [58]. Among the correlation-based approaches, full correlations outperformed partial correlations, most notably for intra-site prediction. These limitations of partial correlations may reflect estimation challenges  11  with the relatively short time series included in ABIDE datasets (typically 5-6 minutes per participant).
Effect of the dataset size and heterogeneity While not a property of the data-processing pipeline itself, a large number of training subjects is the most important factor of success for prediction. Indeed, we observe that prediction results improve with the number of subjects, even for large number of subjects (Figure  3 ). Despite that increase, we note that prediction on the largest subsample (subsample #1) gives lower scores than prediction on subsample #2, which may be due to the variance introduced by small sites. We also note that the importance of the choice between each option of the pipeline decreases with the number of subjects (Figure  A8 ).
Default Mode Network (Figure  6. a). We observe a lower between left and right temporo-parietal junctions. Both hypoconnectivity between regions on adolescent and adult patients  [59, 60]  and hyperconnectivity in children  [61]  have been reported. Our findings are concordant with  [62]  that observed lower fronto-parietal connectivity and stronger connectivity between temporal ROIs. Pareto-insular network (Figure  6. b). We observe interhemispheric hypoconnectivity between left and right anterior insulae, and left and right inferior parietal lobes. Those regions are part of a frontoparietal control network related to learning and memory  [63]. Such hypoconnectivity has already been widely evoked in ASD  [64, 65]  and previously found in the ABIDE dataset  [13, 66]. These ROIs also match anatomical regions of increased cortical density in ASD  [67].
Discussion We studied pipelines that extract neurophenotypes from aggregate R-fMRI datasets through the following steps: 1) region definition from R-fMRI, 2) extraction of regions activity time series 3) estimation of functional interactions between regions, and 4) construction of a discriminant model for brain-based diagnostic classification. The proposed pipelines can be built with the Nilearn neuroimaging-analysis software and the atlas computed with MSDL is available for download 13. We have demonstrated that they can successfully predict diagnostic status across new acquisition sites in a real-world situation, a large Autism database. This validation with a leave-onesite-out cross-validation strategy reduces the risk of overfitting data from a single site, as opposed to the commonly used leave-one-subject-out validation approach  [53].
It enabled us to measure the impact of methodological choices on prediction. In the ABIDE dataset, this approach classified the label of autism versus control with an accuracy of 67% -a rate superior to prior work using the larger ABIDE sample. The heterogeneity of weakly-controlled datasets formed by aggregation of multiple sites poses great challenges to develop brain-based classifiers for psychiatric illnesses. Among those most commonly cited are: i) the many sources of uncontrolled variation that can arise across studies and sites (e. g. scanner type, pulse sequence, recruitment strategies, sample composition), ii) the potential for developing classifiers that are reflective of only the largest sites included, and iii) the danger of extracting biomarkers that will be useful only on sites included in the training sample.
Counter to these concerns, our results demonstrated the feasibility of using weakly-controlled heterogeneous datasets to identify imaging biomarkers that are robust to differences among sites. Indeed, our prediction accuracy of 67% is the highest reported to-date on the whole ABIDE dataset (compared to 60% in  [53] ), though 13 http://team. inria. fr/parietal/files/2015/07/MSDL_ABIDE. zip better prediction has been reported on curated more homogeneous subsets  [13, 54]. Importantly, we found that increasing sample size helped tackling heterogeneity as intersite prediction accuracy approached that of intra-site prediction. This undoubtedly increases the perceived value that can be derived from aggregate datasets, such as the ABIDE and the ADHD-200  [16]. Our results suggest directions to improve prediction accuracy.
First, more data is needed to optimize the classifier. Indeed, our experiments show that with a training set of 690 participants aggregated across multiple sites (80% of 871 available subjects), the pipelines have not reached their optimal performance. Second, a broader diversity of data will be needed to more definitively assess prediction for sites not included in the training set. Both of these needs motivate more data sharing. Methodologically, our systematic comparison of choices in the pipeline steps can give indications to establish standard processing pipelines. Given that processing pipelines differ greatly between studies  [28] , comparing data-analysis methods is challenging. On the ABIDE dataset, we found that a good choice of processing steps can strongly improve prediction accuracy.
In particular, the choice of regions is most important. We found that extracting these regions with MSDL gives best results. However, the benefit of this method is not always clear cut -on small datasets, references atlases are also good performers. For the rest of the pipeline, tangent embedding and 2 -regularized classifier are overall the best choice to maximize prediction accuracy. While the primary goals of this study were methodo-logical, it is worth noting that biomarkers identified were largely consistent with the current understanding of the neural basis of ASD. The most informative features to predict ASD concentrated in the intrinsic functional connectivity of three main functional systems: the defaultmode, parieto-insular, and language networks, previously involved in ASD  [69, 70, 71].
As a psychiatric study, the present work has a number of limitations. First, although composed of samples from geographically distinct sites, the representativeness of the ABIDE sample has not been established. As such, the features identified may be biased and necessitate further replication; though their consistency with prior findings alleviates this concern to some degree. Further work calls for characterizing this prediction pipeline on more participants (e. g. ABIDE II) and different pathologies (e. g. ADHD-200 dataset). Second, while the present work primarily relied on prediction accuracy to assess the classifiers derived, a variety of alternative measures exist (e. g. sensitivity, specificity, J-statistic). This decision was largely motivated to facilitate comparison of our findings with those from prior studies using ABIDE, which most frequently used prediction accuracy.
Depending on the specific application for a classifier, the prediction metric to optimize may vary. For example screening tools would need to favor sensitivity over accuracy or specificity; in contrast with medical diagnostic tests that require specificity over sensitivity or accuracy  [4]. The versatility of the pipeline studied allows us to maximize either of these scores depending on the experimenter's needs. Finally, while the present work focused on the ABIDE dataset, it is likely that its findings regarding optimal pipeline decisions will carry over to other aggregate samples, as well as more homogeneous samples. With that said, future applications will be required to verify this point. In sum, we experimented with a large number of different pipelines and parametrizations on the task of predicting ASD diagnosis on the ABIDE dataset.
Acknowledgments. We acknowledge funding from the  NiConnect  project and the  SUBSample  project from the  DIGITEO Institute, France. The effort of  Adriana Di Martino  was partially supported by the  NIMH  grant  1R21MH107045-01A1. Finally, we thank the anonymous reviewers for their feedback, as well as all sites and investigators who have worked to share their data through ABIDE.
subsection 3. 7 we inspect classifier weights to identify which connections are the most important for prediction. 2. 6. Exploring dataset heterogeneityPrevious studies of prediction using the ABIDE sample [13, 54]  have included less than 25% of the available data, most likely to limit heterogeneity -not only due to differences in imaging, but also to factors such as age, sex, and handedness. We explored the effect of such choice on the results, by examining data subsets of different heterogeneity (see Table1). These included: subsample #1 -5 For the sake of simplicity, we report only the 3 main methods here. However, we also investigated random forests and Gaussian naive Bayes, that gave low prediction performance. Feature selection also led to worse results, both with univariate ANOVA screening and with sparse models such as Lasso or SVC-1 -see FigureA4for more details.
Generating microgrid scenarios with PEVs Load time series from two different small microgrid residential areas and one commercial building, in a city of Zhejiang Province of China, provided by Nian Liu et al. [8] , were considered here, as well as a microgrid house with high load fluctuation, extracted from the REDD dataset. A scenario regarding the microgrid residential area was already tackled by the BBPSA dataset. The cases of study described here comprise a small Wind Power Turbine (WPT) and Solar PV array. The last two have been adapted from time series of a WPT with 160 kW capacity and PV array with a total capacity of 80 kW. We design three scenarios: the case 1 is composed only with the REDD microgrid house (Figure  2 ); the second one combined two different residential areas and loads from the house (right side of Figure  2 ); finally, case 3 combines the demand profiles from the commercial building and the microgrid household.
Introduction Breast cancer is the third common cancer all over the world with 1. 677 million cases per year  [1]. Both its detection and treatment are important concern for public health. E. g, pathologists of the Paris hospital La Pitié Salpétrière have to analyze more than 2000 stained biopsies per day. They evaluate cancer gradation on a scale from 1 to 3 according to 6 criteria. One of the most important criteria is the nuclei size atypia. Along with the cancer progression, nuclei sizes are growing until becoming abnormally big. In 2012, the International Conference on Pattern Recognition organized a contest  [2]. Pathologists had to analyze and grade images. A free database of commented slides results from this contest. This is a great opportunity for designing and testing tools that could offer pathologists a second opinion.
Handling the data ORWL data is held in abstract objects called locations. These are uniquely identifiable entities represented by a global location ID (orwl_myloc). Access to the locations is handled through a queue. To gain control in read or write mode to a location, a handle must insert a request in the queue. Locations have to be declared and an amount of memory has to be allocated as follows: orwl_handle2 for iterative access are initialized and defined as in Listing 4. The macro ORWL HANDLE2 INITIALIZER initializes the handle object. A memory size is allocated by a call to orwl_scale. Functions orwl_write_insert and orwl_read_insert are used, respectively, to connect an handle with a location in write or read mode. The last parameter of the function provides the initial position of the request in the queue.