dataesr/scientific-paragraphs-categorization huggingface dataset:
min -1. The effluent was split equally into two deactivated fused silica capillary columns (100 cm x 0. 25 mm), one leading to the FID (270 °C) and the other into a heated EAD port (200 °C) (transfer line: Syntech, Kirchzarten, Germany) and leaded to antenna. The GC effluent for antenna was mixed with charcoal-filtered, humidified airflow (300 ml. min -1 ). StarWorkstation 6. 41 software (Varian, Palo Alto, USA) was used for data processing. Different types of male swarm extracts were tested on several conspecific females' antennae. Antennae of 19 An. coluzzii females were tested with solvent extracts, Porapak-Q VCT tube extracts and Micro-trap extracts (8, 9 and 2 females, respectively) and 20 An. gambiae females were tested with solvent and Porapak-Q VCT tube extracts (10 females each). Before GC-EAD analysis of the VOC extracts, we checked the living antenna for good activity using a negative (solvent) and a positive (mix of many VOCs) stimulus solutions.
4 mg mL -1 DY96 (Sigma-Aldrich, St. Louis, MO, USA) in water placed inside a μ-dish (ibidi GmbH, Gräfelfing, Germany) were used. DY96 only emits a detectable fluorescence signal when the chitin fibers are parallel with the imaging plane. [18] he dye is also known as diphenyl Brilliant Flavine 7GFF and is typically applied to polysaccharides including cellulose or chitin. [41] The specificity was tested by recording the emission spectra of stained cuticle and chitin. Imaging data was acquired with a SP-8 laser scanning microscope (Leica) equipped with a 63× water objective (NA = 1. 2). A multiphoton laser (Spectra-Physics, Stahnsdorf, Germany) at 780 nm was used for excitation and the signal was recorded on a HyD detector between 540 and 580 nm. Micro Computer Tomography: μCT was performed on hydrated cornea portions stained with iodine vapor for 12 h.
The stained samples were placed in a plastic vial and kept in contact with water to allow full hydration of the sample during the measurement. Transmission scans were acquired using an EasyTom160/150 equipped with a microfocus X-ray source using a tungsten filament and a digital flat panel detector (RX Solutions, Chavanod, France). The acceleration voltage was set to 70 kV, with a current of 100 μA recording 1120 images at full rotation with a total measurement time of 3. 7 h at a sample-to-detector distance of 773. 193 mm yielding a voxel size of (2. 18 μm)  3. Reconstruction of the final volume was performed using the X-Act software (RX Solutions, Chavanod, France). Fourier Transform Infrared Spectroscopy: FTIR data was acquired using a Lumos II micro-spectrometer equipped with a nitrogen-cooled mercury cadmium telluride detector in transmission (Bruker, Billerica, MA, USA).
To reduce noise from water vapor and CO 2 , a chamber with continuous N 2 was placed around the microscope stage. Thin cornea sections of 5 μm were placed on CaF 2 windows (Korth Kristalle GmbH, Altenholz, Germany) for measurements. For background correction, spectra of the sample carrier were acquired by averaging 32 interferograms with a step size of 4 cm -1 each time before measuring the sample. Subsequently the trans-mittance spectrum of the sample was recorded in the same manner. For measurement control and data analysis Opus spectroscopy v8. 5. 29 software was used. The transmission spectra were converted into absorbance spectra and the ratio of the peaks between 1700 and 1600 cm -1 and 1180 and 1000 cm -1 (Figure  S3C , Supporting Information). Micro-Raman Spectroscopy: Micro-Raman spectroscopy was performed on 10 μm hydrated cryosections mounted between a glass slide and cover slip and sealed with nail polish.
An alpha300 confocal Raman microscope (WITec, Karlsruhe, Germany) equipped with a P-500 piezoscanner (Physik Instrumente, Karlsruhe, Germany) was used. The laser ( = 532 nm) was focused by a 40× objective lens (NA = 1. 2, Nikon, Tokyo, Japan) with a laser power of 15 mW on the sample. Spectra were acquired by a thermoelectrically cooled CCD detector (DU401A-BV, Andor, Belfast, North Ireland) behind a 600 g mm -1 grating spectrograph (UHTS 300, WITec, Ulm, Germany) with a spectral resolution of 3 cm -1. The WITec Control software (Version 5. 2, WITec, Ulm, Germany) was used for measurement setup and control. The chitin, protein, and hydration maps were generated using the WITec Project Five v5. 2 software by mapping the integrated intensity of the beta-sheet peaks between 1576 and 1635 cm -1 , the chitin amide I between 1640 and 1750 cm -1 and the CH band of aromatic and aliphatic side chains between 3035 and 3095 cm -1 , and the OH bands between 3.
The sample chamber and connections were sealed using parafilm. After obtaining the phase map at different ROI, the channel was flushed with 3 mL of the new solution to ensure complete exchange of medium before continuation of measurements. Data analysis was performed using in-house python scripts and CV2 libraries for alignment of the images and calculation of RI maps. In order to define the cornea RI gradients, datasets extracted from profile plots from cornea cross-sections obtained at various heights along the length of cones from subsequent sections of a single animal with Fiji were seleceted. [43] The profiles were ordered from distal to proximal depending on section number and cone diameter. The comparison of maximum RI of longitudinal and cross cornea sections (Figure  S3A , Supporting Information) was performed on sections from cones in close proximity from a single animal mounted on the same slide.
Quartz and silver behenate powders were used for detector calibration. A Vortex EM, silicon drift detector with 50 mm 2 sensitive area was used for recording the XRF signal. Maps were acquired with a step size of 1 μm in x and y and an acquisition time of 100 ms. XRF maps were generated from integrating the BrK peak intensity and then corrected by primary beam intensity. XRD data reduction was performed using DPDAK v1. 4. 1 to obtain XRD 1D profiles. [44] Background and primary beam correction were performed using in-house python scripts based on the numpy library. Fiber correlation peak fitting was performed using dpdak. Protein/chitin volume fractions and water content were calculated using Equation (1). [22] The profiles shown in Figure  2  were averaged over ≈100 diffraction patterns in the corresponding region using in-house python scripts using sympy library.
Model Generation for FDTD and Ray-Tracing Analyses: The μCT volume data was segmented using Amira (v5. 3) to label the extent of six cones, as well as the surrounding intercone, outer-cornea, and epicornea. A Matlab script was developed to calculate the radially averaged surface profile along each cone, as well as its outer-cornea protrusion, epicornea dent, and the bordering of internally exposed intercone. The angle between the central axis of the cone and corneal normal differed by 22. 2°± 1. 5°in the section of the cornea that was scanned. To remove the influence of off-axis optics from the analysis, the surfaces of the outer-cornea and epicornea to each cone were aligned by rotating the corneal normal to align with its central axis and then translated the tip of the cone-in-cone and epicornea cone so that they were on the axis.
Acknowledgements The authors thank the  HZB  and  ESRF  for beamtime allocation at the beamlines mySpot and ID13, respectively. Special thanks are given to  Chenghao Li  ( MPI-CI, Department of Biomaterials ),  Manfred Burghammer  ( ESRF, ID13 ) and  Peng Li  ( ESRF, ID13 ) for beamtime support. The authors are grateful to  Daniel Werner  ( MPI-CI, Department of Biomaterials ) for sup-port with the μCT setup,  Antje Völkel  and  Agata Baryzewska  ( MPI-CI, Department of Colloid Chemistry ) are acknowledged for help with finding and using the Abbe refractometer. Carolin Fischer  ( TUD, B CUBE ),  Susann Weiche ,  Susanne Kretschmar , and  Thomas Kuhn  ( TUD, Histology facility ) are being thanked for sectioning and Mallory staining. The authors thank  Alice Ludewig  ( TUD, B CUBE ) for support with FTIR measurements.
MRI data acquisition Functional image acquisitions were performed at the MRI facility of the Campus Biotech (Geneva, Switzerland), with a Siemens MAGNETON Prisma 3T scanner, and using a 64-channel head-and-neck coil. For the sensorimotor task, echo-planar sequences were used (EPI, TR = 2. 5 s, TE = 30 ms, with a flip angle of 90°, GRAPPA = 2), with a resolution of 2. 5 × 2. 5 mm, and a slice thickness of 2. 5 mm (no gap, 43 slices). Anatomical images were acquired with T1-weighted MPRAGE sequences (192 slices, FOV = 240 mm, TR = 2. 3 s, TE = 2. 32 ms).
Dynamic functional connectivity analysis through co-activation patterns CAPs analysis is based on point process analysis  ( Tagliazucchi et al. , 2012 )  and temporal clustering  ( Liu and Duyn, 2013 ). In particular, given the activity time course of one or more seed regions, different dynamically occurring network configurations that co-activate with these seeds are extracted. For the present implementation, we built upon the Tb-CAPs toolbox  ( Bolton et al. , 2020b ). The data was pre-processed using custom MATLAB (MATLAB 2019b) scripts and SPM12 functions (Wellcome Department of Cognitive Neurology, Institute of Neurology, UCL, London, UK). Volumes were realigned to the first scan, and spatially smoothed with a gaussian filter (FWHM = 6 mm), after being normalized to MNI space. Then, linear trends were removed from each voxel's time course, and such time courses were further temporally z-scored.
As consequence the number of measures of average duration, per CAP and per condition, was not always equal to the number of participants. These different sample sizes meant that average duration could not be tested with a Friedman's test. We hence, tested average duration with Linear Mixed Models (LMM), using the lmer function provided with the package lme4  ( Bates et al. , 2015 )  available for R (version 3. 6. 1). The average duration was modelled as a function of CAP, condition, interaction between CAP and condition, and a random-intercept accounting for inter-participant variability. 𝐴𝑣𝑔𝐷𝑢𝑟 ∼ ( 1 |𝑃 𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡 ) + 𝐶𝐴𝑃 + 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 + 𝐶𝐴𝑃 ∶ 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 The parameters of the model were tested for significance by sequential comparison of the simplest model with only the random-participant intercept against a model adding the CAP parameter, followed by comparing the latter model with one adding the condition parameter, and finally comparing this one with the full model that includes the interaction parameter.
The sensitivity to the induction of the PH can be interpreted as a group variable as it is a binary value. To assess the effect of sensitivity to PE induction (positive PE-strength score) and interaction with condition, another set of models was used (for F i and B i , Models II and III). This was due to convergence not being achieved with the current amount of data available if both PH, PE, and respective interactions with conditions, are analysed simultaneously. To compensate for this, we lowered the significance threshold of the subsequent Wald  2 tests to 0. 025, instead of 0. 05. As before, the effect of each model's parameters was assessed by sequentially comparing simpler models and more complex models, e. g. from Model B i 0 to Model B i III (PH), using the Wald  2 test. These results were corrected for FDR with the Benjamini-Hochberg procedure to correct for multiple comparisons (i.
Acknowledgments This work presented here was supported in by the  Bertarelli Foundation  , the  Swiss National Science Foundation  , the  National Center of Competence in Research (NCCR)  " Synapsy The Synaptic Bases of Mental Diseases  " grant number  51NF40-185897 , as well as by two generous donors advised by Carigest SA. The first of such donors wishes to remain anonymous, whilst the second one is the  Fondazione Teofilo Rossi di Montelera e di Premuda.
Recording of Ventilatory Variables In non-anesthetized and unrestrained mice, ventilatory variables were recorded by whole-body plethysmography (WT n = 25; Epo-TAg h n = 25)  (Bartlett and Tenney, 1970; Voituron et al. , 2014). The animal chamber (200 ml) was connected to a differential pressure transducer (model DP 45-18, Validyne Engineering Northridge, CA, United States), which measured pressure fluctuations within the chamber, relative to a reference chamber of the same volume. The differential pressure transduced signals were recorded by a Spike 2 data analysis system (CED, Cambridge United Kingdom). To avoid stress effects on ventilatory variables, mice were habituated in the recording chamber two or 3 days before the experiments. To evaluate the acute ventilatory response to hypercapnia, air was enriched with CO 2 gas for 5 min (CO 2 4%, O 2 21%, balanced N 2 ).
Analyses of Ventilatory Variables Values are presented as mean ± standard deviation (SD). The Kolmogorov-Smirnov test assessed the normality of distribution. The effects of hypercapnia on ventilatory variables in male WT and Epo-TAg h mice were evaluated by multivariate analyses of variance (MANOVA) with Greenhouse and Geisser adjustments. All analyses were performed with the Statistica software (Stat Soft, Tulsa, United States). Differences were considered significant when p < 0. 05.
A Kolmogorov-Smirnov test assessed the normality of distribution. Differences between mean numbers of c-FOS-positive cells per structure obtained under control or hypercapnic conditions in WT and Epo-TAg h mice were analysed by multivariate analyses of variance (MANOVA). Newman Keuls post-hoc tests were used to assess specific differences between groups. All analyses were performed with the Statistica software (Stat Soft, Tulsa, United States). Differences were considered significant when p < 0. 05.
FUNDING This work was supported by the  "Relations Internationales," the "Bonus Qualité Recherche"  and the  "Institut Fédératif de Recherche Biomédicale" programs of the University Paris 13. This work was also supported by a  Legs Poix grant  ( NV , n  °1022 ). FJ was supported by a  Laboratory of Excellence GR-Ex fellowship. The  Laboratory of Excellence GR-Ex  ( ANR-11-LABX-0051 ) was funded by the program " Investissement d'avenir " of the  French National Research agency  ( ANR-11-IDEX-0005-02 ).
Objective: Between-session non-stationarity is a major challenge of current Brain-Computer Interfaces (BCIs) that affects system performance. In this paper, we investigate the use of channel selection for reducing between-session non-stationarity with Riemannian BCI classifiers. We use the Riemannian geometry framework of covariance matrices due to its robustness and promising performances. Current Riemannian channel selection methods do not consider between-session non-stationarity and are usually tested on a single session. Here, we propose a new channel selection approach that specifically considers non-stationarity effects and is assessed on multi-session BCI data sets. Methods: We remove the least significant channels using a sequential floating backward selection search strategy. Our contributions include: 1) quantifying the non-stationarity effects on brain activity in multi-class problems by different criteria in a Riemannian framework and 2) a method to predict whether BCI performance can improve using channel selection.
Results: We evaluate the proposed approaches on three multisession and multi-class mental tasks (MT)-based BCI datasets. They could lead to significant improvements in performance as compared to using all channels for datasets affected by between-session non-stationarity and to significant superiority to the state-of-the-art Riemannian channel selection methods over all datasets, notably when selecting small channel set sizes. Conclusion: Reducing non-stationarity by channel selection could significantly improve Riemannian BCI classification accuracy. Significance: Our proposed channel selection approach contributes to make Riemannian BCI classifiers more robust to between-session non-stationarities.
I. INTRODUCTION A Brain-Computer Interface (BCI) is a system that translates users' cognitive states and intentions, measured through neurophysiological signals, into commands for an interactive application  [1]. Whereas BCIs have been mostly developed for interaction and communication of severely paralyzed people with the environment, they were also investigated for other purposes, for instance for driving and entertainment  [2, 3]. Most BCI applications use neurophysiological signals recorded noninvasively, typically using Electroencephalography (EEG)  [1]. A standard BCI system includes preprocessing (including "This work was supported The European Research Council with project BrainConquest under Grant ERC-2016-STG-714567". * Kh. S. is with Inria Bordeaux Sud-Ouest, Talence, France (e-mail: temporal and/or spatial filtering), then represents EEG signals in a compact and discriminative way, then train a model for predicting the class label of trials or generating feedback  [1, 8].
We focused here on Riemannian classifiers of EEG covariance matrices due to: 1) the intrinsic properties of the spatial covariance matrix leading to smoothing noise, 2) the robustness of this representation for analyzing data contaminated by artifacts  [10]  (both of these items being possible sources of non-stationarity  [8] ), 3) their good performances even with small training datasets  [9] , 4) its success in multiple BCI challenges  [9] , and benchmark comparisons  [26]. In particular, Riemannian classifiers demonstrated their superiority to other classifiers, notably Common Spatial Pattern (CSP) methods and/or Deep Learning classifiers, in the vast majority of comparative BCI studies  [26, 36, 37, 40, 41]  or in recent BCI competitions  [9, 38, 39, 42]. They are thus considered by many to be the current state-of-theart EEG classifiers for most BCI paradigms.
A. Existing channel selection methods for BCI Due to the sensitivity of CSP spatial filters to noise  [33]  and even to a small number of outliers  [30] , channel selection approaches were considered to robustify it. Robustifying CSP by selecting channels in the first session, and testing it on other sessions led to comparable BCI performance as compared with selecting channels in each session  [30, 33]. In that research, channels were selected based on EEG recorded from a single session, i. e. , without between-session variability  [30, 33]  and processed by a classifier which was calibrated in each session. These works focused on possible data distortion variations within sessions. Besides, they did not address how to find the optimal number of channels to select. A sparse CSP which selects the smallest number of channels within a constraint of classification accuracy was evaluated on datasets with a single training or test session  [6].
Selecting channels based on neurophysiological knowledge was also explored, although it could not necessarily lead to an improvement in BCI performance as compared with using all channels  [16]. Arvaneh et. al. , experimentally confirmed that channel selection based on sparse CSP  [6]  could lead to about 10% improvement in classification accuracy of motor imagery BCI as compared to using only C3, C4, and CZ channels. In Riemannian geometry-based BCI approaches, Barachant et. al. [18]  proposed a channel selection method without between-session nor within-session variation consideration, evaluated on a single session dataset. The subsets of channels were selected by considering the average distance between the means (M-M criterion) of each class pair: M-M = � � 𝑑𝑑 𝑅𝑅 (𝐶𝐶 ̅ (𝑐𝑐 𝑖𝑖 ) , 𝐶𝐶 ̅ (𝑐𝑐 𝑗𝑗 ) )/𝑁𝑁 𝑃𝑃 𝑁𝑁 𝑐𝑐 𝑗𝑗=𝑖𝑖+1 𝑁𝑁 𝑐𝑐 𝑖𝑖=1 (1) where  ̅ (  ) is the Riemannian mean of class   (see Eq.
3),   is the number of class pairs, dR the Riemannian distance (see Eq. 2), and   the number of classes. This approach suffers from some limitations: 1) Data distribution between sessions might be affected by different geometrical transformations resulting from different non-stationarity sources. Preserving the channels that reduce between-class mean distances the least may not capture all necessary information for removing nonstationarity effects (e. g. shift between data distribution). Whereas using the distance between means is consistent with the Minimum Distance to Mean (MDM) classifier  [25] , considering within-class variations and choosing channels leading to a more compact representation around each class mean may also positively affect MDM performance. 2) For choosing the optimal number of channels, this approach considered the evolution of the criterion against the number of selected sensors and kept the minimum number of channels which keeps a constant fraction of distance between class means, using a threshold, without considering the subject and dataset dependency.
e, a system calibrated on multiple sessions and tested on subsequent sessions  [7] , 2) lack of a Riemannian channel selection approaches considering non-stationarity. In this paper, we thus propose several (offline) channel selection methods for Riemannian BCI classifiers that specifically aim at reducing between-session non-stationarities. It should be noted that we presented preliminary research on two class problems, with evaluations on two data sets, as a short (4 pages) conference paper in  [22]. Contrary to this previous work, here we explore multiclass problems, on three data sets, we study the optimality of the channel subset search strategy, propose a new search stopping strategy, propose a measure to predict the benefits of the proposed channel selection method, and investigate differences between different criteria, including two new ones that were not presented in this preliminary study.
1) Selection criteria 1-1) Average of intra-class variation Non-stationary could lead to shifts in data distribution between sessions (Fig. 1 )  [22]. This would lead to large intraclass variation between different sessions as compared to within-session variations. We thus propose the Average of Intra-class Variation (AIV) to measure how much the data is affected by non-stationarity. AIV should thus be minimized. 𝐴𝐴𝐴𝐴𝐴𝐴 = ∑ 𝜎𝜎 (𝑐𝑐 𝑖𝑖 ) 𝑐𝑐 𝑖𝑖 𝑁𝑁 𝐶𝐶 (5).
4) Efficiency Predictor To predict possible performance improvement when using channel selection, we quantify the existence of between-session non-stationarity effects in the dataset by comparing the AIV at the run level to the AIV at the whole training set level, where the training set includes possible between-session variations. The size of the gap between run-level AIV and training set level AIV could be used as an efficiency predictor of channel selection. Similar AIV in run-level and training set level could reflect low between-session variability, and thus a low chance of improvement by using channel selection, whereas a large AIV gap could reflect an opportunity for improvement by removing the channels that are mostly affected by betweensession non-stationarity. Since we want to reveal the difference between within-session and between-session AIV, we use   , ⊂ �� , ,  � 0 �� as an indicator of run-level non-stationarity effects.
1) Data set 1: Mental Tasks (MT) data set Sixteen BCI-naive participants including eight females; aged 21. 5 ± 1. 2 with subject ID S12: S19 and eight males with subject ID S21, S23: S29 took part in this study, published in  [34]. The EEG signals were recorded at 512Hz from two g. USBamp amplifiers (g. tec, Austria), using 30 scalp electrodes (F3, Fz, F4, FT7, FC5, FC3, FCz, FC4, FC6, FT8, C5, C3, C1, Cz, C2, C4, C6, CP3, CPz, CP4, P5, P3, P1, Pz, P2, P4, P6, PO7, PO8, Oz, 10-20 system), referenced to the left ear and grounded to AFz. Each subject participated in 6 sessions; each session was on a separate day with irregular gaps between them. Each session was composed of five 7-minutes runs in which users had to learn to perform mental tasks including Mental Calculation (MC), Mental Rotation (MR), and Left Hand movement Imagination (LHI) tasks.