dataesr/scientific-paragraphs-categorization huggingface dataset:
| Simulations of admixture Results indicated that over all scenarios, N = 600 (closer to estimated N e in the SLR) systematically displayed lower RMSE (root mean squared errors) than those with higher N e (Supporting Information Table  S4 ). For instance, only N = 600 resulted in a similar level of polymorphism (i. e. , polymorphic SNPs at the 1% level) as to empirical data, while all other datasets were significantly different in number of polymorphic sites. Therefore, for simplicity, we present results for the N = 600 scenario (the 20 best model related to N = 600 are presented in Table  2 ); other scenarios for models with the lowest RMSE are presented in Supporting Information Table  S3 ). The best scenarios in terms of RMSE were those where migration rate from the stocking source (SRC) to the St.
We also thank  Nicolas Auclair ,  Florent Archambault ,  Rémi Bacon ,  Christian Beaudoin ,  Anabel Carrier ,  Chantal Côté ,  Julie Deschesnes ,  François Girard ,  Guillaume Lemieux ,  Louise Nadon ,  Yves Paradis ,  Geneviève Richard , and  Éliane Valiquette  for logistic, field, and laboratory assistance. Funding was provided by the  Ministère des Forêts, de la Faune et des Parcs du Québec , and the  Canadian Research Chair in Genomics and Conservation of Aquatic Resources.
Implemantation of the 2D multiphysic element in Abaqus/standard FEM software To facilitate the writing of this element in the subroutine UEL, we decided to substitute the DOF initially used for the temperature (noted NT11 in Abaqus) by the DOF of concentration. The numerical scheme for the resolution of the thermomechanical equations is very close to the numerical scheme of the mechanical/diffusion proposed in this article. So, a classical "Coupled temp-displacement" solver option has been selected. UEL objective is to compute the vector RHS and the matrix AMATRX, which correspond, for our coupled mechanical-diffusion element, respectively to the residue R (right hand of Eq. 46) and the element matrix [K e ] (left hand of Eq. 46, without the increment vector). We give in Fig. 3  the flowchart of the implementation in Abaqus, with the UEL expansion in Fig.
The 2D multiphysic element is then implemented in Abaqus/standard FEM software using the subroutine UEL. The numerical scheme for the resolution of the thermomechanical equations being very close to the numerical scheme of the mechanical/diffusion proposed in this article, a classical "Coupled temp-displacement" solver option has been selected then adapted to our resolution. The coupled model leads to an increase in equivalent plastic strain and damage variable evolution due to diffusion. The study of diffusion in trapping sites leads to the conclusion that flux in trapping sites can be neglected. The evolution of equivalent plastic strain and damage variable obtained with the coupled model clearly enhances the effects of diffusion with mechanical behaviour. Therefore, it seems very important to take into account diffusion phenomenon in the case of lifetime studying of a material in contact with the environment, especially for prediction at long times.
An alternative approach, GloVe  [18] , learns representations of words by factoring a matrix of counts of co-occurrences of the words of a corpus. Its objective is to minimize the error of reconstruction of the matrix, considering only the non-zero values of co-occurrence counts. Even though the distributional hypothesis originated in linguistics and is naturally leveraged for word embedding, Perozzi et al. establish the connection with network embedding. To do so, they show that the frequency at which nodes appear in short random walks follows a power-law distribution, like the frequency of words in language  [19]. They propose DeepWalk, that consists in applying skip-gram with hierarchical softmax on a corpus of node sequences, deemed equivalent to sentences, generated with truncated random walks  [19].
Natural Language Processing in Networks of Documents As a special case of attributed networks, graphs of documents bring together the fields of natural language processing (NLP) and network embedding. A wide variety of unsupervised learning algorithms to represent words and documents have been proposed, from the well-known bag-of-word model  [10]  to the recently introduced attention-based Transformer  [24]  adapted for unsupervised pretraining  [4]. However, few works have fully explored the interplay between NLP techniques and network embedding. NetPLSA  [16]  adapts a topic modelling algorithm by regularizing a statistical topic model with a harmonic regularizer based on a graph structure. It generates topics that reflect the underlying communities of the network, providing cleaner topics than regular statistical models.
(  2 ) improvement of the model by focusing on natural language processing. The model would be able to predict the similarity of documents in a network, based on their textual content only. It would deal with more advanced NLP techniques and further take advantage of the interplay between word and document representations and the network topology. Compared to CANE, the representations should be produced only from text information (still using the network as training supervision) and a strong emphasis on link prediction for unseen documents should be put. (3) integration of the heterogeneity to handle different types of nodes and links. This would allow to apply the model to a wider variety of tasks, such as user-item recommendation and expert finding. Handling the diversity of node and link types shouldn't rely on hand-crafted meta-path, as proposed in metapath2vec, but should be learned during the process similarly to GraphSAGE.
At this point, the data provided by DSRT from Peerus would serve as a strong online evaluation of the proposed model. Step  (1)  has been achieved and is detailed in Section 4. 2. The results are presented in Section 5. 1. More work on its theoretical background will be done in a near future. Step (2) is ongoing research, that I briefly present in Section 4. 3 and for which I provide some preliminary results in Section 5. 3 motivating the research direction. For all evaluations, I detail the datasets used and the experimental setups in Section 4. 1.
Evaluation I first detail some datasets commonly used in the literature. Then I briefly present traditional experiments conducted for evaluating network embedding. 4. 1. 1 Datasets. I present below two small datasets, Cora and Cite-Seer, according to the treatments applied in  [21]  as well as a larger dataset, DBLP, widely used by the scientific community: • Cora  [15]  is a network of scientific articles in the field of artificial learning, grouping 7 classes (scientific subdomains) with 2708 documents, 1433 distinct words in the vocabulary and 5429 citations links. • CiteSeer  [6]  is a network of scientific articles grouping 6 classes on 3312 documents, 3703 distinct words in the vocabulary and 4732 citations links. • DBLP  [13]  is a database of several million scientific articles in the field of computer science, started in 1993.
Motivation for a Stronger NLP Component To gain more insight into the textual representations that are learned, Table  1  shows the accuracies obtained by TADW and GVNR-t with their respective text components only. We see that GVNR-t has significantly higher accuracies than TADW, but it is unclear if this is due to an underlying better natural language understanding. Table  2  shows the results of classification when predicting on unseen documents. We observe that if GVNR-t is capable of generalizing on the text attributes of the nodes, TADW relatively fails. However, the results achieved by GVNR-t are still lower than expected and motivates the use of more advanced NLP techniques to achieve better generalization.
We shall describe how climate change topics and concerns are transforming forest science organisations, collaborations and material infrastructures of knowledge, including practices of data production, and how this process of 'climatisation'  (Aykut et al. , 2017)  involves increasing exchanges and collaborations between forest science, ecology and climate science and the models that were previously developed in isolation from each of those fields. Then we shall document the co-existence of three microregimes of anticipation in the case of French forest science and management and eventually account for their tensions and relations. Each of these micro-regimes combines a vision of the future with an approach to the forest, including a certain type of scientific predictive approach associated with modelling practices.
Sociologists Aykut, Foyer and Morena (2017) proposed the notion of 'climatisation' to depict this multi-level and highly contextual process of re-alignment of a range of agendas and practices with climate issues. We argue that 'climatisation' importantly involves the realignment of knowledge practices toward anticipatory objectives and agendas, including predicting but also monitoring, preparing and adapting to projected futures. We shall describe the 'climatisation' of forest science organisations, collaborations and material infrastructures of knowledge, including practices of data production and modelling, and how this involves increasing exchanges and collaborations between forest science, ecology and climate science-as well as between the models that were previously developed in isolation in each of those fields.
Forest scientists and managers also started implementing new climate-related data collection and new collaborative arrangements around climate data sharing and use, for instance within the network of forest observation sites called RENECOFOR, initially created in relation to concerns over the effects of acid rain on forests. In 2012, RENECOFOR was also integrated into a Long-term Environmental Research Monitoring and Testing System (SOERE), whose goal is to produce data on the "System Earth" dynamic, illustrating how the collection of forest and climate related data becomes embedded within new collaboration arrangements between a broad range of disciplines such as ecology, forest science, genetics and population biology. The 'climatisation' of forest science and management also fostered the development of new methods for modelling forest growth and making decisions regarding which tree species to choose and when to fell trees for timber production.
Type of scientific predictive practices 3-i/ Accordingly, the models used in the first 'microregimes' must be user-friendly-the calculation speed is a key element in this: "If it takes too much time to set up the data and run the simulation, it is off-putting. It is a beautiful theoretical tool but in practice it is useless" (Forests modeller). When we asked modellers and managers about potential future improvements of the models, all of them referred to an increase in the calculation's speed. The development of remote-sensing using LIDAR (Laser Imaging Detection and Ranging) is also expected to help collect ever larger amounts of data to fuel the models with the idea that scientific progress meant both more and more past and present data for more and more anticipative models. Accordingly, the first micro-regime does not focus on knowing the ecological processes but relies on statistical methods applied to big datasets to find correlations between climatic, biological and socio-economic information.
Conclusion Overall, our results show that 'climatisation'  (Aykut et al. , 2017)  occurred in forest science and management not only at a discursive level-meaning the integration of the topic of climate change into forest science agendas and discourses-but also within organisations, collaborations and material infrastructures of knowledge, especially practices of data collecting and modelling (i. e. the network of forest observation sites called RENECOFOR, and the platform of models CAPSIS). The 'climatisation' of forest sciences also involves increasing exchanges and collaborations between forest science, ecology and climate science and the models that were previously developed in isolation in each of those fields. Yet, our fieldwork also points to the co-existence of a plurality of research agendas dedicated to anticipating the future of forests under a changing climate.
After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure-function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.
Results We collected both dMRI and awake resting-state fMRI data (7 sessions per animal) from 19 hybrid B6/129P mice. We extracted SC from dMRI data to build individual virtual brains, which were imported into The Virtual Mouse Brain (TVMB), the extension of the open source neuroinformatic platform TVB (9) designed for accommodating large-scale simulations and analysis in the mouse, to generate in silico BOLD activity (21) using the reduced Wong Wang model  (14, 23). We then compared simulated and empirical FC for each mouse in order to assess the power that an individual SC has to predict individual empirical FC derived from resting-state fMRI data (SI Appendix, Fig. S1 ). Further, SC was also obtained from the Allen connectome (our gold standard) in TVMB  (21)  to determine the contribution of information not available in dMRI-based SC.
In keeping with human data  (6, 27) , we found a high intersession correlation for each of the 19 mice, demonstrating stability across different recording sessions in a given mouse (Fig. 1A ). Intersession correlations within the same animal were greater than intersubject correlations, indicating that there is an individual functional organization per mouse, which may act as a functional fingerprint. Next, we sought to examine the extent to which individual functional connectomes correspond to individual structural connectomes. SC Obtained with a Deterministic Algorithm Is a Better Predictor of FC. Here we considered probabilistic (Fig. 1B ) and deterministic (Fig. 1C ) dMRI-based SCs, using iFOD2  (28)  and SD_Stream (29) within MRtrix3 software (29) tractography algorithms, respectively.
As the Allen SC was built from C57BL/6 mice, we verified the generality of our results in this strain (SI Appendix, Fig. S4A ). Global signal regression, which improves structure-function relations and averaging recording sessions within each mouse  (31) , which reduces noise, increased the PP but did not alter the results (SI Appendix, Fig. S4 B  and C ). Finally, splitting the recording sessions of each mouse and submitting the data to a test-retest analysis revealed a close agreement between datasets (SI Appendix, Fig. S4D ). Thus, our conclusions are strain and preprocessing independent, and reproducible. Importance of Long-Range Connections and Directionality. To identify the source of the systematic superior performance of the Allen SC, we focused on the major limitations of dMRI: 1) difficulty in resolving long axonal tracts, 2) lack of information on fiber directionality and synaptic transmission, and 3) imprecise estimation of connection weights caused also by the impossibility to detect synaptic connections.
Materials and Methods Additional details on materials and methods are provided in SI Appendix. All procedures were conducted in accordance with the ethical guidelines of the National Institutes of Health and were approved by the Institutional Animal Care and Use Committees (IACUC) at Technion and Allen Institute for Brain Science. Mice were group housed to prevent stress  (61). Structural and Functional Experimental Data. Nineteen male hybrid nonanesthesized mice were scanned using a 9. 4 Tesla MRI to obtain structural information and resting state functional data, 6. 31 ± 0. 82 (mean ± SD) sessions of 15. 7 ± 4. 4 min length (mean ± SD) per mice. The structural data have been processed using using MRtrx3 software  (29). To obtain the tract streamlines, we integrated the field of orientation probability density using both deterministic (SD_Stream, ref.
29 ) and probabilistic (iFOD2, ref. 28)  algorithms in order to obtain, respectively, deterministic and probabilistic processed dMRI-based connectome. The tracer-based connectome was built through the Allen Connectivity Builder pipeline  (21) , implemented in The Virtual Brain software  (9). The pipeline allowed the manipulation of the anterograde tracer experiments performed at the Allen Institute  (18)  to reconstruct a tracer-based mouse connectome and related brain volume. Surrogate Connectomes. In order to test different hypotheses about what could be the connectivity properties that give rise to the observed discrepancies in the simulated dynamics, we built different kinds of surrogate connectomes: a dMRI-based averaged connectome (averaging the 19 dMRI based connectomes), a filtered tracer-based connectome (filtering out the connections not detected in the dMRI connectomes), a symmetrized tracerbased connectome, asymmetrized dMRI-based connectomes, and hybrid connectomes (dMRI-based connectomes whose connections of 1 area are replaced with the tracer-based connections of that area).
Simulate Resting State Dynamics. We simulate resting state dynamics using the connectome-based model approach as implemented in The Virtual Brain software  (9, 62). In particular, we used the reduced Wong Wang model  (13, 23)  in the bistable configuration in order to reproduce the dynamical switching of the functional connections  (14). We transformed the simulated synaptic activity in BOLD signal using the Balloon-Windekessel method  (56, 57). Analysis. The emergence of the functional organization in the experimental and simulated brains was analyzed through the static FC, the FCD, and the FMC. We quantified the PP of a given connectome c as the Pearson correlation between the simulated FC, obtained using that connectome c, and the FC arranged during resting state experimental recordings.
ACKNOWLEDGMENTS. This work was supported by  ANR  Connectome  ANR-17-CE37-0001  (to F. M. , V. J. , and C. B. ); European Union's  Horizon 2020 Framework Programme for Research and Innovation ,  Award ID :  785907  ( HBP SGA2 ) to V. J. ;  Israel Science Foundation  ( 770/17 ; to I. K. );  National Institutes of Health  ( 1R01NS091037 ; to I. K. ); the  Adelis Foundation  (to I. K. ); and the  Prince Center  (to I. K. ).
These methods should effectively cope with high variance of cell shapes and sizes, and with their tendency to form dense clusters which makes cell counting and segmentation extremely difficult. Besides, such methods, especially those involving machine learning techniques, must be data-efficient since manual labelling of the datasets is a very tedious task. The endoplasmic reticulum (ER) is a multifunctional cellular compartment responsible for the protein synthesis, folding and processing. Protein folding in the ER may be impaired under different pathological conditions or stimuli. This condition is known as endoplasmic reticulum stress  (1). ER stress activates a complex signalling network, referred as the unfolded protein response (UPR). It is known that ER stress is observed in many neurodegenerative diseases (such as amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease etc.
It is made available The copyright holder for this preprint (which was this version posted December 23, 2019. ; https://doi. org/10. 1101/867218 doi: bioRxiv preprint each object requires only one-pixel mark. Cell counts predicted with our model were very close to expert estimates. We have also developed a web-application CellCountCV (Cell Counting with Computer Vision) for counting the cells on phase-contrast microscopy images. CellCountCV was used to analyze large series of microscopy images obtained in experimental studies (3880 images from experiment #1 and 5208 images from experiment #2) and it was able to demonstrate endoplasmic reticulum stress development and to catch the dose-dependent effect of tunicamycin. Thus, this approach can be used for automation of both qualitative and quantitative data analysis in biosensor studies and their applications.
Dataset preparation We have used 20 manually labelled images, split into training and validation set with 8:2 ratio. Each image was split into quadrants to reduce GPU memory usage. Simple augmentation, namely rotations on 90, 180 and 270 degrees, was performed to increase the available amount of training data. Finally, the model was tested on 24 images which were not seen by the model during training and that were not used for hyperparameter tuning. The training and validation image sets were preprocessed by human experts who manually labelled the cells with one-pixel marks.
Cells counting performance To assess the performance of produced FCNN model we used the images from two series that were never seen by the model during the training. 26 test images were randomly selected, and then manually labelled by human experts. The total amount of the cells on the test images exceeded 9000. The average error of predicted cell counts for testing images was about 3. 12%, which is enough for the method to be considered reliable (Table  1 ). On 80% of testing images the difference between the manual and predicted cell counts was less than 5%. The highest error percentage of 7. 3% was obtained for an image with several clusters of the cells where even the manual counting becomes error-prone. Finally, of all 9247 cells present in the testing dataset, the model counted 9037 of them, thus the overall difference was 2.
Web-application CellCountCV for cell counting The produced model was used to develop a simple web-application CellCountCV for counting the cells on microscopy images and to count the cells with fluorescence intensity above the user selected threshold. Fluorescing cells counting was implemented using simple binarization mask in image red channel and its application as a filter to a grayscale image, then the image is processed by counting model as usual. Web-service API is implemented with JSON-RPC protocol and we also created simple graphical user interface using React JavaScript library. It accepts a microscopy image and red intensity threshold and returns the cells count and the number of cells with red fluorescence. It was hosted on our institution (IIS SB RAS) server and can be accessed at http://cellcounter.
GEE is a general statistical approach to fit a marginal model for longitudinal/clustered data analysis  (9). We used Poisson distribution to model count outcomes -red cells count per 1000 cellswith autoregressive covariance structure. GEE models were built with statsmodels package (v. 0. 10. 1) for Python. The model demonstrated statistically significant distinctions in all but negative control groups (Int and DMSO) and also demonstrated the significant changes of red cells counts with time (Table  3 ). The source code used to produce the plots and to perform the analysis can be found https://github. com/denatns/CellCountCV.