dataesr/scientific-paragraphs-categorization huggingface dataset:
Collagen deformation analysis Collagen pseudospeckle movement is analyzed with in-house developed tracking software simiar to PIV  [54, 55]. The software is written in the Matlab programming environment (The MathWorks). Bright field images of collagen and spheroids are used in a png format to analyze collagen movement. The spheroid and the invading cells' area are removed from the contraction analysis, using the edge detection function of the program. The analysis of collagen movement is based on a cross-correlation between the initial and the following image. Two subsequent images are separated into small sub-images, and a maximum correlation algorithm is used to determine the displacement of prominent parts from one image to the next one. The method is visualized in S7 Fig. As  the cells contract the collagen, the dark particle (marked by the white arrow) moves towards the growing spheroid.
Acknowledgments This work has received support under the program «Investissements d'Avenir» launched by the  French Government  and implemented by  ANR  with the references  ANR-JCJC SVSE 5 2011 (T. B. )  and  Labex CelTisPhyBio   ANR-10-LBX-0038 ANR-10-IDEX-0001-02 PSL (T. B, K. K. and D. V),  Cells-in-Motion Cluster of Excellence  ( EXC 1003 -CiM ),  University of Münster, Germany  (T. B. ),  Institut Thématique Multi-organismes Cancer-Plan Cancer   2014-2019  and  Ecole Doctorale Frontières du Vivant (FdV)-Programme Bettencourt (RS). The intravital imaging was supported by the  Fondation pour la Recherche Médicale  ( FRM N°D GE20111123020 ), the  Cancerople-IdF  ( n°2012-2-EML-04-IC-1 ),  InCA (Cancer National Institute , n°2011-1-LABEL-IC-4), and  SiRIC (INCa-DGOS-4654 ),  PICT-IBiSA and Nikon Imaging Center@ CNRS-Institut Curie.
This work has received support under the program «Investissements d'Avenir» launched by the  French Government  and implemented by  ANR  with the references  ANR-JCJC SVSE 5 2011 (T. B. )  and  Labex CelTisPhyBio   ANR-10-LBX-0038 ANR-10-IDEX-0001-02 PSL (T. B, K. K. and D. V. ),  Cells-in-Motion Cluster of Excellence  ( EXC 1003 -CiM ),  University of Münster, Germany  (T. B. ),  Institut Thématique Multi-organismes Cancer -Plan Cancer   2014-2019  and  Ecole Doctorale Frontières du Vivant (FdV) -Programme Bettencourt  (R. S. ). The intravital imaging was supported by the  Fondation pour la Recherche Médicale  ( FRM N°DGE20111123020 ), the  Cancerople-IdF  ( n°2012-2-EML-04-IC-1 ),  InCA (Cancer National Institute , n°2011-1-LABEL-IC-4), and  SiRIC (INCa-DGOS-4654 ),  PICT-IBiSA and Nikon Imaging Center@ CNRS-Institut Curie.
Patient-derived xenograft models We included 21 PDX models of triple negative breast cancer (TNBC) characterized previously at the Institut Curie (Table  1 )  (25, 26). Transcriptome data was acquired using the Affymetrix Human Gene 1. 1 ST Array applying standard procedures from the Institut Curie genomics platform. We use the tumor growth inhibition (TGI) value determined by the equation below as a measure of docetaxel response  (25, 26). We classified PDX models as a high-responder having a TGI > 60% and a poor-responder having a TGI < 30%. RTV is relative tumor volume.
Organoids derived from colorectal cancer patients We included 15 organoids derived from colorectal cancer patients previously characterized by Clevers and colleagues  (27). The Affymetrix gene expression data and drug response are publically available (GEO accession: GSE64392). For the docetaxel response, we classified 7 organoids as poor-responders (IC50 ≥. 362 µM) and 8 organoids as high-responders (IC50 ≤. 151 µM); for Irinotecan, we classified 6 poor-responders (IC50 ≥ 70. 0 µM) and 9 highresponders (IC50 ≤ 25 µM).
Analysis of gene expression data For Affymetrix microarray datasets with multiple probe sets, we used collapseRows function from the WGCNA R package using the maxMean model to merge multiple probes into one. For NanoString nCounter analysis, total RNA (50 ng per sample) was assayed on a Gen2 nCounter platform at the Institut Curie Genomics Platform according to the manufacturer's instructions. We normalized the data following published  (29)  and NanoString guidelines (NanoString, WA USA). Briefly, we first normalized using the geometric mean of the spiked-in positive controls (NanoString, WA USA), we then subtracted background using the negative controls provided by NanoString, and finally we normalized by scaling with the geometric mean of the housekeeping genes ACTB, CLTC, and TUBB and we used the Log2 values for all computational analyses.
Random Forest classification We exploited Random Forest classification using the randomForest R package  (30)  to identify gene panels whose expression pattern most accurately predict the tumor growth inhibition (TGI) for the PDX models and the extent of response for the patients. We chose Random Forest classification based on four factors: (i) It takes into account the relationships between genes, offering an improved analytical technique over differential expression alone, (ii) It does not over fit the data, making it easier to train a classifier on one cohort and validate it on an independent cohort, thus generating general conclusions as to the importance of different genes in the classification; (iii) The method is fast and does not require parameter fitting; and (iv) It calculates the importance of each feature in the classification, simplifying the feature selection process  (30, 31).
Importantly, this method is particularly well suited for cases having small sample sizes with a large number of observations, such as patient cohorts with gene expression data  (32). Moreover, Random Forests have been shown to be the most effective amongst nearly 200 classifiers recently tested in real world problems  (33). We built forests comprising 10,001 trees (an odd number ensures a deterministic model) and we kept all other settings at default. To identify the minimum effective gene panel, we ranked each gene's ability to classify samples using their Mean Decrease in Gini (MDG) applying a leave-one-out (LOO) approach and averaging the MDG across all permutations. We performed feature selection by sequentially adding genes in order of average MDG and calculated the internal out-of-bag (OOB) error rate across three repeats.
Random generalized linear model (RGLM) We exploit random generalized linear model using the randomGLM R package  (34). Briefly, RGLM is based on bootstrap aggregation of generalized linear models where the features (covariates) are selected using forward regression. For identifying important genes, we used the default values for the parameters nBags, nFeaturesInBag, and nCandidateCovariates. Following the same leave-one-out approach as used for random forest, we analysed the 21 TNBC PDX models using the TGI as a measure of docetaxel response (continuous response). For predictions using the distinct gene panels shown in Supplementary Figure  S2A , we set nCandidateCovariates = nFeaturesInBag.
NCI-60 database and pharmacogenomics analysis The NCI-60 database, available using CellMiner or the NCI Developmental Therapeutics Program (DTP), contains previously normalized and filtered whole exome sequencing (WOS), mRNA expression, promoter methylation, and drug activity, which are publically available from CellMiner (http://discover. nci. nih. gov/cellminer/) or the NCI Develop-mental Therapeutics Program (DTP) (http://dtp. cancer. gov). Details regarding acquisition of drug activity data, promoter methylation, mRNA expression and mutation profiles, together with their normalization and analysis have been previously reported  (37, 38). Briefly, we analyzed all chromatin regulator genes in the NCI-60 database to identify mutations, including homozygous deletions, heterozygous deletions, or not expressed.
925 (Figure  2C ). Moreover, the gene panel outperforms 100 random, 19-member chromatin regulator panels (Figure  2D , Supplementary Figure  S2B ), further validating the robustness of the method. Importantly, the chromatin regulator gene panel fails to segregate the PDX data according to their response to a combination of Adriamycin (doxorubicin), a topoisomerase II inhibitor, and cyclophosphamide (AUC = 0. 397) (Figure  2E , Supplementary S2C). This suggests that the gene panel is selective for the response to docetaxel. We next asked whether the ability of these chromatin regulators to classify docetaxel responders was valid in a cancer model for a different organ. We analyzed published Affymetrix gene expression data from organoids derived from colorectal cancer patients and the corresponding responses to treatment with either docetaxel or Irinotecan, a DNA topoisomerase I inhibitor (GEO accession: GSE64392)  (27).
The NanoString nCounter system may provide higher resolution predictive information The recent application of the NanoString nCounter system to the clinical setting  (11)  prompted us to test whether the ability of chromatin regulators to classify high-responders and poor-responders is validated using this approach. We isolated RNA from 9 of our previously characterized TNBC PDX models encompassing a range of TGI values and measured the gene expression of all chromatin regulators by NanoString (see Methods). We first compared the chromatin regulator gene expression data between Affymetrix and NanoString (Figure  3A , Supplementary Figure  S3 ). We found that among the 19 chromatin regulators, 15 correlate well. We also found 4 outliers common across most samples (SOX18, RBBP4, HIST1H2BM, and MED13), for which the gene expression values are consistently higher when generated via Affymetrix.
The SWI/SNF remodeler complex is associated with the docetaxel response Key SWI/SNF chromatin remodeler components are common features to the two chromatin regulator gene panels that were derived from different cancer types (SMARCA4, SMARCAL1, ARID2 in the TNBC PDX; HLTF, BCL7C, ACTL6A in the HER2+ patients), suggesting a common pathway. We then analyzed the two panels using the STRING tool  (41) , a database of known and predicted protein-protein interactions. We determine that many proteins participate in a large interaction network and that the SWI/SNF component SMARCA4 is a key node (Figure  5A ). We thus further explored the link between SWI/SNF gene expression and the docetaxel response in a cellular context first using the comprehensive NCI-60 database. We find that the SWI/SNF chromatin remodeler is the most altered chromatin regulator in the NCI-60 database, of which SMARCA4 and HLTF are the most altered components.
This suggests that targeting the SMARCA4 bromodomain is not sufficient to disrupt the functional node. We therefore decided to exploit genetic interference. We treated the HAP1 cell lines wildtype for SWI/SNF (HAP1-WT) and knockout for SMARCA4 (SMARCA4-KO) and SMARCA2 (SMARCA2-KO), a SWI/SNF component not appearing in our gene panels (Supplementary Figure  S5C ), with docetaxel and assessed cell viability. We observe a 4-fold decrease in the IC 50 for docetaxel in the SMARCA4-KO cell line relative to wildtype (Figure  5C ), consistent with the expression pattern in the PDX. Taken together, our results show a functional association between the SWI/SNF chromatin remodeler and the docetaxel response in the NCI-60 database and in the HAP1 cell line. These data warrant further exploration of pharmacological intervention to exploit this actionable type of target in combination with docetaxel to increase sensitivity and potentially combat inherent and acquired resistance.
Importantly, the ability to distinguish docetaxel responders is observed in both PDX TNBC models and organoids derived from colorectal cancer patients (Supplementary Figures  S2D, S2E ). This suggests a mechanistic link between these distinct chromatin regulators and the docetaxel response. Our data from the NCI-60 dataset further suggest a potential broad clinical applicability. To determine how to best translate chromatin regulator gene panels into valuable clinical tools, we compared the methods used to generate gene expression data: Affymetrix microarrays, which rely on an enzymatic amplification step, and the count-based, quantitative NanoString technology. Gene expression values for all chromatin regulators largely correlate between the two techniques, with some reproducible differences (Figure  3A , Supplementary Figure  S3 ).
Acknowledgments We thank  Sergio Roman-Roman ,  Nicolas Dorvault  ( U981   INSERM ),  Christopher Mueller ,  Jean Gautier , and  Bruno Amati  ( Mayent-Rothschild Sabbatical Scientists at Institut Curie ) for critical comments on the manuscript and the  Almouzni lab  for insightful discussions. We apologize to our colleagues whose work we could not cite due to space constraints.
Introduction Ecopath with Ecosim (EwE) is a modelling complex that has been used to create mass balanced models of marine and aquatic ecosystems since the 1980s, when the first Ecopath model of the French Frigate Shoals was created by Jeff  Polovina (1984). The software and techniques have been improved to include methods of comparing ecosystems using Ecological Network Analysis  (Christensen and Pauly, 1992) , to model dynamic changes using Ecosim  (Walters et al. , 1997) , to model spatial changes using Ecospace  (Walters et al. , 1999)  and to model both temporal and spatial dynamics using the spatial-temporal-framework and the habitat foraging capacity model  (Christensen et al. , 2014). EwE is the most applied tool for modelling marine and aquatic ecosystems globally, with over 400 models published to date  (Colléter et al.
, 2013). Ecopath was recognized as one of NOAA's top ten scientific breakthroughs (http://celebrating200years. noaa. gov/breakthroughs/ecopath/welcome. html) in its 200 year history, and the 30th anniversary of the approach was recently celebrated with an international conference  (Steenbeek et al. , 2014; Coll et al. , 2015). The EwE approach is able to address many of the questions asked by managers on marine policy issues such as natural variability and monitoring, management measures, ecosystem goods and services, "Good Environmental Status" targets for the EU Marine Strategy Framework Directive, and environmental change and climate adaptation  (Steenbeek et al. , 2014; Hyder et al. , 2015). It has also been partially re-coded in different programming languages  (Steenbeek et al. , 2015)  including Fortran  (Akoglu et al.
, 2015) , Matlab  (Kearney et al. , 2013) , and R  (Lucey et al. , 2014). However, of the 105 models used for comparative analyses by  Heymans et al. (2014)  few have been fitted to historical data using Ecosim (27/105) and even fewer (13/105) were actually calibrated by fitting to time series data (Table  S1  in  Heymans et al. , 2014). Additionally, very few models have actually been used in a management context. A noteworthy exception is the model of the North Sea  (Mackinson et al. , 2009b) , which has recently been used to establish a "key run" (see Section 8 below for a description of key runs) for the ICES Working Group on Multispecies Assessment Methods, WGSAM http://www. ices. dk/community/groups/Pages/WGSAM. aspx (ICES, 2013) , and used to evaluate EU Commission proposals for multi-annual plans in the North Sea  (STECF, 2015).
Balancing Ecopath models Once the Ecopath parameters have been entered into the software and the underlying assumptions tested with the PREBAL approach, the model requires balancing to maintain the laws of thermodynamics. When balancing the model it is imperative that thermodynamic and ecological rules are followed. Darwall et al. (2010)  describes the ecological and thermodynamic rules in an easy to read box (Box 1  Darwall et al. , 2010). Once the model has been balanced it might be useful to recheck the PREBAL estimations, to check for incompatible vital rates (P/B, Q/B, etc). It is important to annotate Ecopath input data with appropriate references and to describe the origin of the data used to create the model in the model pedigree  (Christensen and Walters, 2004; Morissette, 2007) , which describes the precision of the data and sets confidence intervals to be used with this data if undertaking Monte-Carlo simulations.
) be K -1. Thus, for example in the West Coast of Scotland model  (Alexander et al. , 2015) , there were 48 time series of catch or biomass used for estimating the SS, and therefore 47 parameters were estimated, either as vulnerabilities or primary production spline points (up to the number of years in the time series). Thus in this case 2-23 spline points or 47 vulnerabilities could be tested, for a possible 1756 combinations. Alexander et al. (2015)  tested each 5th combination (i. e. 5, 10, 15 vulnerabilities, 5, 10 15 spline points, etc. ) to reduce the repetitive nature of this task. However, an automated routine to run through these options would be preferable and is currently being programmed as a plug-in to the EwE desktop software  (Steenbeek et al. , 2015, Scott et al. , submitted).
It is expected that this routine will be released with the next version of the software. A full description of the methodology for fitting a model is given in the Ecopath wiki: Fit to time series (http:// sources. ecopath. org/trac/Ecopath/wiki/EwEugFitToTimeSeries). 6. 2. Special note: the model fits the data, but is its behaviour credible? It's not uncommon that the estimated vulnerability parameters that provide the best fit of model predictions to observation data can lead to unrealistically high estimates of F msy. When Ecosim estimates low vulnerabilities, this implies high productivity at low biomass and potentially high estimates of F msy that would be not be considered either feasible, or credible based on other assessments and history of F rates that have led to stock depletions in the past.
4 ). The CV of input parameters can be set in the Monte Carlo routine or can be obtained from the quality of the input data given in the pedigree routine. The Monte Carlo routine also runs in a new "ecological indicators" plug in that will be release in the new version of software, enabling to assess the impact of input uncertainty in some output results of Ecopath and of Ecosim  (Coll and Steenbeek, 2014). EwE users have recently developed several sophisticated routines for considering model parameter uncertainty, enabling representation of multiple possible representations of the modelled ecosystem and assessment of the impact of this performance model simulation (e. g. Gaichas et al. , 2012; Prato et al. , 2014; Guesnet et al. , 2015; Platts and Mackinson, 2015). A new (soon to be released) plugin enables users to create alternative Ecopath models with associated Ecosim parameters set, and evaluate the performance of management strategies when this uncertainty is accounted for  (Platts and Mackinson, 2015).
Acknowledgements The authors wish to thank  Dr Karen Alexander  and  Dr Natalia Serpetti  for their thoughtful comments on this manuscript. JJH was supported by the  Natural Environment Research Council and Department for Environment, Food and Rural Affairs  under the project MERP: grant number  NE/L003279/1 ,  Marine Ecosystems Research Programme. SM acknowledges support from  Defra  contract  M1228  ' Fizzyfish ' and  EU FP7 GAP2 , Grant agreement 266544. MC was partially funded by the  European Commission  through the  Marie Curie Career Integration Grant Fellowships  to the  BIOWEB  project and by the  Spanish National Program Ramon y Cajal. VC acknowledges support through an  NSERC   Discovery Grant   RGPIN-2014-05782.
Recent theoretical and technological advances in neuroimaging techniques now allow brain electrical activity to be recorded using affordable and user-friendly equipment for nonscientist end-users. An increasing number of educators and artists have begun using electroencephalogram (EEG) to control multimedia and live artistic contents. In this paper, we introduce a new concept based on brain computer interface (BCI) technologies: the Brainarium. The Brainarium is a new pedagogical and artistic tool, which can deliver and illustrate scientific knowledge, as well as a new framework for scientific exploration. The Brainarium consists of a portable planetarium device that is being used as brain metaphor. This is done by projecting multimedia content on the planetarium dome and displaying EEG data recorded from a subject in real time using Brain Machine Interface (BMI) technologies.
Among developed applications, it allows projecting EEG topographic activity directly on the dome surface of a planetarium as if viewers were standing at the centre of the brain, looking up at electrical brain activity projected on the scalp. The "Brainarium" (originally "Cerveaurium" in French) was initially designed to present neuroscience concepts in a fun, attractive, and interactive way for educational and entertainment purposes, by mixing art and science. In the method section of this paper, we will first present the general concept and architecture of the system in order to outline and illustrate its general functioning, describe a first performance that was designed for the Brainarium, and detail the specific implementation of the performance using open source software platforms. In the Result and Critical Reception section, we then mention the context in which our device has been used during exhibitions at museums and during "The Brain's Awareness Week" and present data on audience's experience in the Brainarium.
In Discussion, we finally introduce potential extension and further development of our system in the fields of education, entertainment, arts, and more specifically its possible benefits in clinical applications such as neurotherapy. 1  and 2  summarize the architecture of the system and the different hardware it is comprised of. EEG signal is acquired on a person present in the dome. As shown in these figures, we used the Emotiv Epoc headset (Emotiv, Inc. ), which includes 14 metal electrodes recording electrical brainwaves on the surface of the scalp at a frequency of 240 Hz (240 samples per second), but any EEG system compatible with BCI software can potentially be used. The signal is then transmitted, using a wireless connection, to a computer. This processing unit handles the signal processing part and calculates the control signals, which will be used to drive multimedia contents.
Even if the sound of the fan is not covering the sounds played inside the dome, it still creates a distracting background noise. The control console was composed of a classic personal computer equipped with a dual screen graphic card powerful enough to handle HD projection and two LCD monitors. One of the LCD monitors was used to control the demonstration. On the second graphical output, a video splitter was used to send the display signal to both a control LCD monitor and the video projector. For the EEG signal acquisition, the research edition package of Emotiv Epoc headset was used  [27]. Emotiv Epoc is a wearable EEG "headset" composed of 14 gold-plated electrodes. In order to record electrical signals generated by the brain, each electrode is covered by a small felt-based pellet that acts as a bridge between the electrode and the scalp.
Except from the Emotiv software suite (the basic software package provided with the Epoc headset by Emotiv), the software used to do signal processing and visualization is all part of the open source community. For the fractal application, the software package "Mind Your OSC" was used to collect data from Emotiv Control Panel software and send it as an Open Sound Control (OSC)  [28]  stream to visualization software. The interactive fractal video was displayed using the vvvv software (https://vvvv. org/), a graphical programming environment for easy prototyping and development. The vvvv software application is designed to facilitate handling of large media environments with physical interfaces, real time motion graphics, audio, and video that can simultaneously interact with many users. The freely available OpenVibe software  [29]  was used for signal acquisition, signal processing, and visualization of the EEG data in the context of the EEG topography application.