dataesr/scientific-paragraphs-categorization huggingface dataset:
15 µL of MACSPlex Exosome Capture Beads were added. Samples were incubated on an orbital shaker overnight at room temperature protected from light. After washing twice with the MACSPlex buffer, 5 µl of each detection antibody were added (APC conjugated anti-CD9, anti-CD63, and anti-CD81) and samples were incubated for 1h at RT. Samples were washed with 200 µl of MACSPlex Buffer. Flow cytometric analysis was performed with a Cytek Aurora with the following configuration: FSC Threshold 50000, APC Detector Voltage 1100, FITC Detector Voltage 60, PE Detector Voltage 60). FlowJo software (v10, FlowJo LLC) was used to analyse flow cytometric data. The 39 single bead populations were gated to allow the determination of the APC signal intensity on the respective bead population. Median fluorescence intensity (MFI) for each capture bead was background corrected by dividing respective MFI values with matched non-EV controls that were treated exactly like EV-containing samples.
.  Breast cancer classifier based on the 'outlier' analysis For each marker and each breast cancer patient, a distribution was created composed by all the healthy donors' samples together with the patient's sample. For each of these distributions, an outliers detection analysis was performed using the R package 'extremevalues' (van der Loo, 2010) as follows: the distributions were fitted to a theoretical exponential one, deemed closest to our data points distribution, and the function getOutliersI was used with default parameters (rho = c(1, 1), FLim = c(0. 1, 0. 9), distribution = "exponential") to detect potential outliers. Samples thus flagged as outliers were then translated to true positives (patients' samples) and false positives (healthy donors' samples). Only results originating from distributions fitting with an R 2 ≥ 0.
.  Isolation of plasma EVs does not improve the quantification of mitochondrial DNA (mtDNA) for disease detection or monitoring Mitochondrial DNA (mtDNA) has been shown to be present in EVs recovered by ultracentrifugation and density gradient from plasma of therapy-resistant breast cancer patients with similar clinical features to those of our cohort  (Sansone et al. , 2017). Using a qPCR assay targeting the mitochondrial gene ND5, we quantified mtDNA in EV-rich and EV-poor SEC fractions in comparison to total plasma from the same breast cancer patients and healthy donors. MtDNA quantification from the total plasmas of six patients came back inconclusive and the complete data of these patients were eliminated for this analysis (N BCP = 21, N HD = 15). We observed a significant higher abundance of mtDNA in the EV-containing fractions (F7-11) and the total plasma of healthy donors compared to their EV-poor fractions (F18-24) (Figure  3a ; Table  S2 ; p HD(F7-11vsF18-24) < 0.
001) in breast cancer patients EVs compared to healthy donors (Figure  4a ). We further developed a pipeline to automatically call markers with statistically different distribution in patients as compared to healthy donors (R package 'extremevalues' for the detection of outliers -see methods and Table  S4 ) in order to identify cancer-specific signatures and classify patients with cancer. We observed that, using the four markers which were highly enriched in breast cancer patients cited above, with a threshold of ≥ 3 positive markers to classify a sample as positive for cancer, we were able to reach a 78% sensitivity (7/9 patients detected) with a 100% specificity for the identification of breast cancer patients (Figure  4b , Table  1 ). Restricting the analysis to the more significantly different probes together with the threshold of ≥ 3 positive markers ensure a 100% specificity with no positivity detected for any healthy donors (Table  S4 ).
A C K N O W L E D G E M E N T S We thank Drs. Joshua Welsh and Jennifer Jones, NCI, USA, for advice and feedback on MacsPlexExo analysis. This work was supported by Institut Curie, including through NCI. NIH-PIC3i-2018 program, INSERM, grants from INCa (11548) , ANR (ANR-18-CE13-0017-03; ANR-18-CE15-0008-01; ANR-18-CE16-0022-02), Fondation ARC (PGA1 RF20180206962), French IdEx and LabEx DCBIOL (ANR-10-INSB-04, ANR-10-IDEX-0001-02 PSL, ANR-10-LABX-0038, ANR-11-LABX-0043 and ANR-18-IDEX-0001 Université de Paris).
words) Intracranial EEG (iEEG) performed during the pre-surgical evaluation of refractory epilepsy provides a great opportunity to investigate the neurophysiology of human cognitive functions with exceptional spatial and temporal precisions. A difficulty of the iEEG approach for cognitive neuroscience, however, is the potential variability across patients in the anatomical location of implantations and in the functional responses that are recorded. In this context, we designed, implemented, and tested a userfriendly and efficient open-source toolbox for Multi-Patient Intracranial data Analysis (MIA), which can be used as standalone program or as a Brainstorm plugin. MIA helps analyzing iEEG signals while following good scientific practice recommendations, such as building reproducible analysis pipelines and applying robust statistics.
, 2007; Schalk et al. , 2008). These so-called "localizers" or functional mapping procedures are developed in a concerted effort between the clinical staff and researchers/engineers to reach the clinicians' needs. The intended target is each individual patient (i. e. , single patient analysis). In cognitive neuroscience, the major software suites have been initially developed for non-invasive data (MEG or surface EEG): Brainstorm  (Tadel et al. , 2011) , Fieldtrip  (Oostenveld et al. , 2011) , MNE-Python  (Gramfort et al. , 2013) , EEGLAB  (Delorme and Makeig, 2004). More recently, some efforts have been deployed to make these tools compatible with iEEG data, notably the registration and labelling of contacts within the anatomy, or the automatic computation of various offline re-referencing montages (e.
g. , average, bipolar, local). However, no explicit procedure has been developed that would consider the difficulties induced by patient idiosyncrasies for group analysis. Accordingly, it is not uncommon for iEEG empirical studies to implement the analysis of multiple patients using idiosyncratic methods (e. g. Anders et al. , 2019; Barbeau et al. , 2008; Dubarry et al. , 2017; Nakai et al. , 2017; Riès et al. , 2017; Sahin et al. , 2009) , but this is done without introducing a systematic approach. To our knowledge, only two studies specifically addressed this issue:  (Kadipasaoglu et al. , 2014)  presented a methodology based on surface-based co-registration and the use of mixed-effects multilevel analysis. More recently,  (Magnotti, 2020)  developed an analysis and visualization tool using R code that addresses multi patient iEEG data integration and which is fully standalone.
In this context, our goal was to design and implement an efficient open-source software solution for the analysis of multi-patient iEEG in the temporal and time-frequency domains. We developed an analysis pipeline that aims at capturing the steps and intuitions that an expert neurophysiologist would follow when combining single case patterns into a qualitative group description. At the same time, the approach had to go beyond intuitions to allow systematic and reproducible analysis. The MIA tool was developed and tested in the course of conducting various research projects  (Anders et al. , 2019; Dubarry et al. , 2017; Trébuchon et al. , 2020). We illustrate the use of the resulting toolbox using an SEEG dataset from a group of patients performing a language task (previously reported in  Dubarry et al.
Toolbox architecture MIA is an open-source toolbox developed under MATLAB (versions 2012a to 2017a ; The Mathworks, Natick, MA), and therefore MIA can be installed on all hardware platforms and operating systems that MATLAB supports. Freely available for download under the GNU General Public License version 3 and accessible from our public Git repository (https://github. com/MIA-iEEG/mia), MIA is also available as a plugin for Brainstorm (release September 2021 ;  Tadel et al. 2011). This open distribution and version control provides fully transparent content to motivate future collaborative work, such as developments of specific aspects of the tool to suit specific needs (see "Future developments" for examples). The general workflow within MIA consists in four five stages as illustrated on Figure  1  and detailed below.
Following a standard clinical terminology (which may vary among sites, cf. Holdgraf et al. , 2018) , we will use the term electrode to refer to each of the brain probes, strips, or grids implanted in the patient's brain. In turn, each electrode typically includes between 5 and 15 distinct recording contacts on a probe or strip, or up to 64 recording contacts (8 x 8) on a grid. The contacts are referenced using one of various montages, such as a bipolar montage (the difference between pairs of neighboring contacts) or a monopolar montage (the difference between each contact and a common reference). The signals recorded from these pairs of contacts are referred to as channels, and we note that channels of data can be arbitrarily re-montaged after the initial acquisition of the data.
Optional pre-processing steps include: • filtering (continuous data filtering is recommended), • artifact rejection (marking segments on continuous data is recommended), • bad channel rejection. Irrespective of these or other steps, MIA being dedicated to the analysis of evoked neural activity, it is mandatory to perform an epoch segmentation around the stimulus of interest. These epochs should include enough temporal points to disregard edge effects (see section "Normalization and timefrequency extraction" for details). All these pre-processing steps should be documented as they are performed. It seems particularly relevant to highlight this point in the context of clinical data, which may be diverse across patients and sometimes collected from different research centers, etc. Performing the pre-processing steps should ensure that the dataset fed to MIA has a consistent structure across patients.
After data preparation, from Brainstorm, in processes menu ("Run" button), the custom process "File -> Export data to MIA database" automatically imports the selected data into MIA and launches MIA main GUI on the newly generated database. As an alternative to Brainstorm, a legacy option is available if the data have been processed with BrainVision Analyzer  (Brain Products GmbH, Munich, Germany). In this case the appropriate result to be used as input for MIA is composed of three files per patient (. eeg,. vmrk,. vhdr) containing all trials and which are read using external functions from EEGLAB  (Delorme and Makeig, 2004). In this configuration, MIA can import multiple dataset at once, each containing the data from one patient and a default patient name is suggested for each. For scripting purposes (importing several patients at once), there is the option to exclude one or several contacts from each patient import by creating, in the patient directory, a bad_chan.
csv file containing the labels of the undesired channels (i. e. , "bad channels"). The data from these channels will not be imported. With the steps described above a MIA database is created which contains one folder per patient. Each of these folders will contain all the files that will be loaded at the later steps of the analyses. At the import stage (see Figure  1 ), one structured file is created (<patientID>_signal_LFP. mat ; see Table  1 ) per patient, containing the three fields: "F", the imported signals in the form of a 3D matrix (Channels x Samples x Trials), "Labels": the channel labels and, "Time": a time vector. In summary, these processing steps organize the sometimes-variable clinical data in a systematic data structure specifically designed for the follow-up analyses.
• Correlations (channels/patients) To assess the consistency of the activity recorded within a region, MIA computes two correlation measures, the inter-channel correlation (across the time courses of all relevant channels within the region), and the inter-patient correlation (across the time-courses averaged per patient within the region). Both correlation metrics are defined as the mean of all pair-wise Pearson correlations within a region, either among contacts or among patient average time-courses. The MIA interface does not provide a test of statistical significance for these correlations. An additional standalone script is provided to perform a bootstrap test which is implemented as follows (mia_get_roi_permute. m). For every region of interest, the structure of patient and channel sampling available to compute the observed correlation is preserved to draw random samples of data from among all available regions.
Additional features • Grouped raster plots To explore the single trial data within a group analysis, we developed a raster plot viewer which allows the user to display, for each region, the single-trial data in a stacked layout  (Delorme et al. , 2015). The stack is organized by electrodes and patients. When needed, trials can be ordered by a parameter chosen by the user (e. g. , response times, trial order, etc. ) • Export results by region(s) From the "Group analysis" GUI the user can export one or several region(s) to the Matlab workspace. This gives the user the opportunity to analyze the data from regions with his/her own custom code/software. This export creates a variable inside the Matlab workspace called "roi" (region of interest ; a cell array which dimension is 1 x number of selected regions) containing various variables concerning the region(s) (e.
• Contrasting conditions This feature is not included in the main graphic interface, but a script version is provided (mia_script_example_contrast_conditions. m). To evaluate the difference between two experimental conditions, MIA implements a two-step statistical test. First, within each region of interest, the differences between the two conditions are tested, at each time point, through a two-tailed unpaired t-test with trials as the random factor where patient and channel structure are disregarded. Then, to circumvent the multiple comparisons problem in the time domain, we implemented a non-parametric permutation method  (Maris and Oostenveld, 2007). In the spirit of the bootstrap approach used above to assess significance of each signal against baseline, this procedure aims at determining a minimum duration threshold for consecutive significant t-values in a between-condition context.
In this procedure, the surrogate dataset is created by randomly reassigning the two condition labels to the trials using a within-participant within-contact trial permutation. The hierarchical structure of the data is preserved since the same channels/patients are used within regions and only the two condition labels are randomly reassigned. A two tailed unpaired t-tests are performed on the surrogate data and the result is smoothed using a zero-phase FIR filter (Matlab filtfilt function). The maximum number of contiguous points passing the significance threshold (default alpha is 0. 001) at each iteration is pooled into a surrogate distribution of durations. The procedure is repeated N times (default = 500), and the duration threshold is defined as the 95% quantile of the surrogate distribution.
Results The processing options in MIA are illustrated using the data reported in  (Dubarry et al. , 2017). The original goal of the study was to assess the degree to which word retrieval involves parallel (simultaneous) processing across brain regions and hence, presumably, cognitive processes. To address this question, 15 patients with epilepsy implanted with SEEG for pre-surgical diagnosis purposes were asked to perform a picture-naming task. Intracerebral EEG data were recorded during the task. The signal was time locked to stimulus onset. All data are publicly shared for transparency and reproducibility on the Zenodo open-access repository (DOI: 10. 5281/zenodo. 4767855).
Import data and sanity check For the standalone version of MIA, the user has to add the package to Matlab path (by using the command addpath). Then entering the 'mia' command in Matlab prompt opens the main interface (Figure  5 ). ). At the first execution, the user will be prompted to set a database folder. This path can be changed later (e. g. for subsequent analyses of other datasets) by pressing the "Database" button. For the Brainstorm plugin MIA version, the user exports the data into a MIA database and the main interface automatically opens and sets the database folder to the newly generated one. The left panel (Patients) contains a list of the patients' labels in the current database. Each entry of the list corresponds to a new patient dataset which has been imported (button "import").
If no statistics have been computed on the current dataset, the user will receive a warning pop-up message before the window opens. Channels can be selected from the "Select Channel" button. This selection applies online for the three panels at the same time. It also applies for the plot of the z-scored time courses which is accessible from the button located underneath the left (z-scores) panel ("Plot Time Series" button). The selection of a set of channels sticks at the re-execution of the display: the same subset of channels will be displayed if this dataset is displayed again, even if the program has been closed in the meantime. Amplitude scales can be adjusted manually, separately for the z-scored values (lower left corner) and for the t-values (lower right corner). By default, these scales are set to the maximal value of the data.
, 2009). The time-frequency analysis has been programmed with a lot of flexibility in the choice of parameters such as frequency bands, steps, time-windows etc. The choice of these parameters must be guided by the research questions. For example, collapsing frequencies between 10 and 120 Hz may have little interpretative value, as that may collapse different neural processes  (Buzsáki, 2006). As another example, choosing narrow time-windows may compromise the analysis of lower frequency bands because of border effects. In addition, we highlight the use of the labeling table to code for the anatomical classification of contacts; however, users are free to adopt other classification criteria (e. g. functional responses in another data set) to group the contacts in the group analysis. The current initiatives in developing large databases (e.
g. OpenNeuro at openneuro. org) opens up perspectives for processing large amounts of data and call for the possibility to test other types of classifications. MIA was designed and developed with the idea of improving current practice in the group analysis of iEEG data. The program can input a wide variety of data formats through its connection with two wellestablished tools: Brainstorm and BrainVision. It includes rigorous descriptive and inferential signal processing and statistical methods that are explicitly described. Furthermore, the version-controlled open source format allows these methods to be edited, adapted or upgraded as needed in future developments, for example, alternative time-frequency decomposition methods such as Hilbert transform  (Hamamé et al. , 2014; Vidal et al. , 2012 Vidal et al.
Future developments We plan to continue developing the integration of the Brainstorm plugin. In the current version of MIA, the connection with Brainstorm is merely one of compatibility (see section on data preparation), which allows using the BIDS format  (Holdgraf et al. , 2018)  with MIA after data import in Brainstorm. MIA supports both SEEG and ECOG data, although it was not tested with ECOG data ; we plan to thoroughly test the toolbox with an ECOG dataset. We also plan to integrate MIA with tools designed for the automatic labeling of contacts (e. g. Groppe et al. , 2017; Medina Villalon et al. , 2018)  within existing atlases (e. g "MarsAtlas"  Auzias et al. , 2016). These two developments should help increase the usability of the pipeline. In the pipeline itself, we plan to develop modules for the processing of single trial features, such as the estimated onset time of a contact or region's significant activity  (Coon and Schalk, 2016).
Acknowledgements We would like to thank the following people:  Anais Llorens  for sharing the dataset we used to illustrate MIA's functions;  Vitoria Pia ,  Chotiga Pattamadilok ,  Manuel Mercier , and  John Mosher  for providing valuable feedback on previous versions of the manuscript;  Joey Hsu ,  Shuai Wang , and  Matteo Mascelloni  for their contributions to the MIA online repository; and  Francois Tadel  for his guidance and recommendations regarding the toolbox.
Study funding: This work has been funded by the  European Research Council   FP7/2007-2013  Grant  263575 , as well as  Agence Nationale de la Recherche  Grant Multimodel  2010 BLAN 0309 01. This research, carried out within the  Institut Convergence ILCB  ( ANR-16-CONV-0002 ), has benefited from support from the  French government , managed by the  French National Agency for Research (ANR)  and the  Excellence Initiative of Aix-Marseille University  (A*MIDEX) Highlights: • Software package for the analysis and visualization of.
The ExtremeX experiments also allow a general assessment of the respective contributions of circulation anomalies vs. soil moisture conditions for warm spells. The results are very similar for the three ESMs, showing that the models generally agree on the representation of extreme events and the driving processes behind these events. Warm spells of at least 3 d are assessed grid-point-wise and show that soil moisture is responsible for around one-third to half of the temperature anomalies in transitional and tropical climate zones (Fig. 8 ). The regions identified resemble the regions of strong soil moisture-temperature coupling highlighted by  Miralles et al. (2012)  for observational data and  Seneviratne et al. (2006)  for global climate models. Both studies additionally identify southern Europe and Eurasia as regions of strong soil moisture-temperature coupling, which is, however, not confirmed by the results presented here.
French Research Programs in Biomimicry and the Building Sector Research programs for system development. In this context, few architecture studios have carried out long-term, and applied research beyond the construction of a single building. Focusing on biomimicry, some research remains an exception, as presented in Table  2. For instance the SymBiO 2 program by XTU Architects has allowed for the development of a photobioreactors façade system inspired by an autotroph marine slug (Elysia chlorotica), in partnership with the laboratory GEPEA of the University of Nantes  [51]. Likewise, Tangram Lab developed bioluminescent facades systems in partnership with the Mediterranean Institute of Oceanography of Marseille  [53, 54]. The architecture studio ArtBuild developed an adaptive shading system that mimic passive mechanisms observed in plants.
Acknowledgments: We would like to thank the  Biomim'City Lab  and its external contributors for making the data analysed in this study publicly available. We would also like to thank  Natasha Chaayamor-Heil ,  Tessa Hubert ,  Delphine Mathou ,  Raphaël Prost ,  Darja Dubravcic ,  Benoit Boldron  and  Guillian Grave  for providing documents and information. We also would like to thank  Olivier Raffaelli ,  Olivier Bocquet ,  Steven Ware ,  Clémence Béchu ,  Patrick Arotcharen  and  Ronan Audebert  for providing the images credits.