dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by the  European Commission  through the  Partnership for Research and Innovation in the Mediterranean Area ( PRIMA) program  under  Horizon 2020  ( KARMA  project, Grant agreement number  01DH19022A ). X. W. is supported by the  National Key Research and Development Program of China  (no. 2020YFC1808300 ) and  National Natural Science Foundation of China  (no. 42102300 ).
Data collection At admission, we collected characteristics: age, gender, weight, height, comorbidities and reason for admission. We collected liver disease. The first electrocardiographic evaluation was used for the QT measurement. The QT interval was corrected (QTc) with Bazett formula. We defined QTc time prolonged if > 440 milliseconds, as mentioned in the 2005 Montreal criteria of CCM  [2]. At the time of the echocardiography, we collected the clinical status: heart rate, blood pressure, respiratory rate. The organ failure was assessed using the sequential organ failure assessment (SOFA) score. We collected circulatory support as catecholamine, the respiratory support, and need for extra renal purification. We collected the large biological report, including liver test, ionogram, blood count, cardiac test, lactatemia.
Strain acquisition For each subject, we analysed the LV longitudinal deformation as recommended  [11]. We assessed seven segments of the apical four-chamber view, three-chamber view and two-chamber view. The offline analysis was performed using the QLAB Philips software (Philips Medical System). The apical, lateral-basal, and septo-basal points were positioned on the LV wall for the software to delineate the ventricular myocardium during a cardiac cycle and determine the regional strain and Global longitudinal Strain (GLS). SR at systolic and diastolic peak was measured by analysing the curves: SR at systole (SRS) and SR at diastole (SRD). SRS was the most negative value of the SR curve after the opening of the aortic valve. SRD was the peak strain rate during early diastole. Segmental data were not analysed.
Conventional and two-dimensional strain echocardiography Data are shown in Table  2. 2D-strain echocardiography wasn't performed in 2 patients due to low echogenicity. All variables (GLS, SRS, and SRD) were obtained in 42 patients (95%). The GLS was impaired (> -18%) in 28 patients (64%), in 75% of patients who died and in 54% of patients who survived. Myocardial relaxation was impaired in 67% of patients with E/A<1, E' s <10 cm/s and TDE>200 ms.
Moreover, measures such as global and nodal efficiency could also be implemented to test a plausible hypothesis that learning favors faster communication and less effort in transferring information. A final consideration must be made with respect to the scale resolution of analysis presented in this work, as this study focused on dynamic variations occurring at a larger time scale corresponding to the time period of task execution. However, the available dataset would in principle allow adapting existing analysis procedures to enable a finer scale analysis of the dynamics occurring at the scale of the individual trial. This possibility might uncover additional features that would enhance understanding of functional network-based predictors of learning phenomena. Third, with respect to the computation of functional connectivity to build multilayer modularity tensors, it is important to note that the dynamic connectivity matrices computed in each trial (windowed phase locking values, PLVs) were not used in our work other than to calculate the values of the static PLVs in each trial.
It is in fact the latter that were used as inputs for the construction of the multilayer network tensors for community detection. An equivalent and computationally more advantageous alternative would have been to calculate the static PLVs directly using the entire signal within each trial, thus avoiding the intermediate step involving the calculation of the wind owed PLVs. Nevertheless, our choice to go through the additional step of calculating the windowed PLVs has the advantage that the database that we created could be used as a basis for more detailed analyses (e. g. , for questions specific to temporal dynamics at the level of the individual trial) in future studies, with the possibility of comparing in a more consistent manner future findings with the results obtained in this work. Fourth, with respect to the algorithm used to calculate global and regional flexibility, we replicated in this work the methodological steps described in previous works that used the same or similar protocols to the one used in  Bassett et al.
In this sense, a recent study has proposed the use of a generative modeling approach called weighted stochastic clock models as an effective tool to describe different types of community structure in brain networks that goes beyond simple modularity  (Faskowitz et al. , 2018). Consensus partitions and flexibility measures can be estimated using this method. This method was successfully applied to study changes in the human connectome that occur across the life-span, and has indeed great potential to be implemented on longitudinal datasets like ours to identify how community structure regimes relate to aspects of behavior and cognition, such as learning. Fifth, our sample of young adults has, out of 30 participants, 25 female volunteers. Testing mainly females makes the generalization of our results slightly less robust, although there are premises in the literature suggesting no advantage for either female or male adults.
Participants Thirty healthy volunteers (25 females; mean age = 22. 2 years, SD = 6. 2 years, range 18 to 50 years) gave written informed consent to participate in the experiment and were compensated with credits for their participation. All participants reported normal or corrected-tonormal vision acuity and a right-hand preference (84 ± 22)  (Oldfield, 1971). Research was approved by the Cantonal Ethics Committee for Human Research ( Vaud, Switzerland; protocol N°2019/02345) and was in accordance with the code of ethics of the World Medical Association (Declaration of Helsinki) for experiments involving human subjects in research.
Trials were randomized within each block and blocks randomized within each epoch. The DSP task practiced during home sessions was coded with PsychoPy  (Peirce et al. , 2019)  in JavaScript and was made available in the form of a Web-based task that participants could access from their personal computer. Participants were asked not to perform close sessions and to keep the same daily schedule and environment as much as possible for home practice. The DSP task practiced during laboratory sessions was coded with Psychopy and run on the laboratory computers. Stimuli were synchronized with the EEG hardware by means of TTLtriggers marking the EEG signal with task-related events (e. g. , visual displays onsets). During these sessions, participants were seated in a quiet room sheltered from electromagnetic disturbances (Faraday cage).
, 2013b (Bassett et al. , , 2015;; Mattar et al. , 2018; Wymbs & Grafton, 2015). This choice was motivated by the vastly superior number of practiced trials in EXT sequences. First, MTs were calculated for each sequence performed in the home sessions during the 42 days of practice. The MT was computed from the first to the last button press of the 10 displays. In case of error, the time spent visualizing the error display was not included into the computation of MT. The MTs of the two EXT sequences were then averaged together for each of the 30 home-based sessions, resulting in 30 MT values for each participant. These data were then fitted with a double exponential function of the form (Equation  1 ): MT ¼ ae tK þ be tλ ; ( 1 ) where t is time, K 2 (-∞, 0] is the exponential drop-off parameter describing the fast rate of improvement (typically occurring during the first days of practice), λ 2 (-∞, 0] is the exponential drop-off parameter describing the slow, sustained rate of improvement, and a and b 2 [0, +∞) are constants.
EEG Recording, Preprocessing, and Functional Connectivity Estimation Continuous EEG was obtained with a Biosemi ActiveTwo system using 128 active electrodes placed at the international radial ABC system location and two pairs of bipolar electrodes recorded electrooculography in both vertical and horizontal directions. Two additional electrodes (active CMS: common mode sense; and passive DRL: driven right leg) were used as reference and ground to compose a feedback loop for amplifier reference. Data sampling was set at 1024 Hz with 24-bit A/D conversion. EEG data were acquired at each EEG session during performance on the DSP task, and during two 5-min resting-state periods occurring at the beginning and at the end of the experimental session (the reader should refer to Figure  2  for a visual representation of the methodological steps described in the following paragraphs).
The regularization parameter is set to 0. 3 in our study. The noise covariance matrix was computed from a 500-ms prestimulus baseline. In this work, we used the MATLAB function implemented in Brainstorm toolbox  (Tadel et al. , 2019)  to compute wMNE, with the signal to noise ratio set to 3 and depth weighting value to 0. 5 (default values). The regional time series of the 68 cortical regions of interest of the Desikan-Killiany atlas were computed by averaging the activity of sources included in each region of interest (Figure  2A ). Afterward, we filtered the regional time series in different EEG frequency bands: theta: 4-8 Hz; alpha: 8-12 Hz; beta: 12-28 Hz; gamma: 28-45 Hz. For each frequency band, we computed functional connectivity using the phase locking value (PLV), a measure for assessing phase synchrony between two signals in a particular frequency band, formed from estimates of the instantaneous phase of the signal.
Here, we analysed retrospectively viral load data obtained in 78 animals that were included as control arms of these studies and that were infected with different strains of SARS-CoV-2 (historical, beta, gamma, delta and omicron (BA. 1)). In addition, we performed longitudinal measures of viral culture to evaluate a potential effect of VoC on viral infectivity. Using the techniques of mathematical modelling, we characterize the viral kinetics in these animals and we discuss their biological insights.
Experimental procedure Data comes from studies performed on cynomolgus macaques to evaluate the viral dynamics of SARS-CoV-2 variants. Our study includes 78 cynomolgus macaques (Macaca fascicularis) coming from control arms of several studies and have received no pharmacological interventions besides placebo. All animals were infected with doses ranging from 7×10 4 to 10 6 PFU of different SARS-CoV-2 strains. Animals are infected via both nasopharyngeal and intratracheal route with 10% of the initial volume administered in the nose and 90% in the trachea. The study is composed of 5 groups, each infected with a different SARS-CoV-2 strains: 44 Historical (hCoV-19/France/lDF0372/2020 strain; GISAID EpiCoV platform under accession number EPI_ISL_406596), 9 Bêta (B. For each group both genomic RNA and subgenomic RNA swab samples were quantified using real time PCR in both the nasopharynx and in the trachea.
size of 144 nm over a 295x295 μm 2 FOV and pixel dwell-times between 15-20 μs, corresponding to an acquisition time of 62 s per image. Image acquisition was controlled by the Prairie View software. For local dye injections, we used patch pipettes. The borosilicate glass capillaries were pulled with a vertical puller (Narishige, PC-10, Japan) to a tip resistance between 3-5 MΩ and lled with an ACSF solution containing 500 µM Dextran-Alexa Fluor 488 dye (10,000 MW, anionic, xable, Invitrogen). The pipette tip was placed some 50 to 80 µm below the slice surface to inject the dye below the damaged and cut open cells on the surface of the acute slice. 8 to 10 psi of pressure was applied via a Picospritzer III (Parker, Germany) for several minutes at a time, which yielded an even distribution of the dye within the FOV, which persisted long enough to acquire images with high contrast between ECS and cellular structures.
3 Olympus) mounted on a z-focusing piezo-actuator (Pifoc 725. 2CD, Physik Instrumente). The epiuorescence signal was descanned separated from incident beam using a long-pass dichroic mirror (580 DCXRUV, AHF) and detected by an avalanche photodiode (SPCM-AQRH-14-FC, Excelitas) with a bandpass lter (680SP-25, 520-50, Semrock) along the emission path. Signal detection and hardware control were performed with the Imspector scanning software (Abberior Instruments) via a data acquisition card (PCIe-6259, National Instruments).
Assessing spatial resolution with uorescent beads Green uorescent beads 170 nm, Thermo sher) were dried on a coverslip and then mounted to a slide. 5 x 5 μm 2 images were acquired on the respective microscopes under comparable imaging conditions. 5 μm thick z-stacks were acquired with 0. 2 mm steps and the section in focus was selected to determine xy resolution. In-focus beads were imaged in xz format to determine axial resolution. Full width at the half maximum measurements were determined using the FWHM plug-in for FIJI. Image processing and data analysis LISHI images were deconvolved in 3D with an open source Richardson Lucy (RL) algorithm (https://github. com/dmilkie/cudaDecon), using an experimentally measured PSF. The RL software is bundled into LLSpy, a python LLSM data processing and visualization toolbox (https://github.
com/tlambert03/LLSpy). The deconvolution was run on a CUDA compatible GPU graphics card. A 3x3 median lter (noisy pixel removal) followed by background subtraction was applied before RL deconvolution with 20 iterations. Spurious horizontal and vertical stripes due to artifacts were removed with the ImageJ FFT lter. The deconvolved data was rotated to coverslip coordinates and resampled to obtain isotropic voxel dimensions (102 3 nm 3 ). TUSHI images were visualized using ImageJ. Volume fraction of ECS was calculated from binarized images obtained either by wavelet analysis in SpineJ plugin  23.
Acknowledgements The study was supported by grants from the  European Research Council (ERC-SyG  ENSEMBLE # 951294 ),  Human Frontiers Science Program  (# RGP0036/2020 ),  ERA-NET NEURON  ( ANR-17-NEU3-0005 ) and  Agence Nationale de la Recherche  ( ANR-17-CE37-0011 ) to UVN. AKJB was funded by an  Alberta   Innovates Postdoctoral Fellowship  ( 2+1 ), a  EuroBio Imaging User Access Fund Award , and a  Rebecca Hotchkiss International Scholar Exchange Award. AI was funded by a fellowship from  Bordeaux Neurocampus Graduate Program. SKK was supported by a  National Research Foundation of Korea  grant ( NRF-2019R1A2C2086052 ). We thank the animal facilities at IINS and in Talence for their professional support.
X-ray diffraction (XRD) analyses XRD analyses were carried out on whole rock and clay fractions in the Biogeosciences Laboratory of Dijon (UMR CNRS 6282) at the University of Burgundy, France. Ninety-nine samples (Supplementary table  S1 ) were analysed on a Bruker D8 Endeavor diffractometer with Cu-Kα radiations, a LynxEye detector coupled to a Ni filter, and under a 40-kV voltage with 25-mA intensity. Semi-quantitative estimations from the resulting diffractograms were calculated using the MacDiff software 4. 2. 5  (Petschick, 2001)  to determine the main intensity peaks. Oriented glass slides were measured three times following the protocol set by  Moore and Reynolds (1989) , including both ethylene glycol and heating (490 • C) preparation stages. Using the MacDiff software 4. 2. 5, clay mineral proportions were determined by calculating the area of the basal reflections (d 001 ).
8 ml of Ln Spec resin (300 mg of dry resin -100-150 μm) in a custom 1 ml PFA PrepFAST® column (50 mm-long x 6. 4 mm-diametersee Table  1 ). To verify consistency between conventional column chromatography and automated column chromatography, five samples were processed through both techniques and analysed afterwards. We also experienced the impact of using different digestion methods on the chemistry procedures and on the measured isotopic data. After the leaching procedure, 50 mg of the leached sample's powder were acid digested instead of treated by alkaline fusion and then processed by automated column chromatography. After verifying consistency among the results, an additional set of 8 samples was treated by alkaline fusion and then processed with automated column chromatography to enlarge the data set.
Evolution of silicate chemical weathering: Hf -Nd isotopic compositions of the clay fractions The depicted changes in clay mineralogical assemblages suggest an increase in physical erosion during the Campanian-Danian interval. Physical erosion can enhance silicate chemical weathering by disaggregation of exposed rocks and continual exposure of fresh material to weathering processes. There is however a threshold to this correlation Fig. 8. Illite + Chlorite / Smectite (I + C/Smc) ratio, Chemical Index of Alteration (CIA), ε Nd , Δε Hf , from units 5 and 6 of DSDP site 361. Palaeoprecipitations of the South African plateau are based on palynology data set compiled in  Braun et al. (2014). Dataset: Bamford and de Wit (1993);  Bamford and Stevenson (2002) ;  Rayner et al. (1997) ;  Sandersen (2006) ;  and Smith (1988 and Smith ( , 1986)).
, 2017) , especially during the Turonian-Campanian interval. Paleoclimatic data in the studied area are quite scarce and relies mainly on palaeoflora data sets. Based on the compilation by  Braun et al. (2014) , the paleoflora data from onshore sites; although localized and from sites relatively far apart from each other, suggest an evolution from humid to semi-arid climate on inner areas of the South African Plateau during the Turonian-Danian interval (ca. 93-66 Ma) (Fig. 8 ;  Braun et al. , 2014). This inland aridification is further supported by the increase of around 10% in palygorskite observed in clay mineralogical assemblages at site 361 during the Campanian-Danian interval (ca. 77-62 Ma), which is a paleoenvironmental indicator of arid to semiarid conditions  (Chamley, 1989; Knidiri et al.
Although additional studies are needed to better constrain the regional controls of the uplift and to sediment routing, we favor the late stage of the tectonic uplift (80-70 Ma) in the southwestern part of the South African Plateau as the main forcing mechanisms in the enhancement in denudation processes recorded in the Cape Basin during the Campanian-Danian interval. Relief formation on the southwestern African margin may explain the contrasting regional climate change offshore and onshore that seems to appear from palynological studies during the late Cretaceous  (Braun et al. , 2014). This data suggested an evolution from arid to humid conditions offshore, and from humid to arid conditions onshore, further supported by the increase in palygorskite proportions identified in our dataset. If confirmed by additional data, this apparent divergence between offshore and onshore climate data may be reconciled by relief   Bamford and de Wit, 1993; Bamford and Stevenson, 2002; Rayner et al.
Acknowledgments We thank  IODP  for providing access to the material. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  860383  and from the  ANR RISE. We thank  Rémi Chassagnon  ( Laboratory ICB  and analytical  platform ARCEN, University of Burgundy ) for TEM observations realized at  INRAe of Dijon ,  Ludovic Bruneau  of the  Biogéosciences Laboratory and GISMO analytical Platform (University of Burgundy ) for the XRD analyses,  Marie-Laure Rouget  and  Bleuenn Gueguen  of the  Pôle Spectrométrie Océan (Brest ),  Olivier Reubi  of the  Institut des Sciences de la Terre (ISTE-Lausanne ), and  Philippe Telouk  of the  ENS Lyon  for their analytical support. We thank the reviewers for their constructive comments improving the discussion and the manuscript in general.
In previous studies on ochre use at Porc-Epic Cave 20,28,29 , ochre types recovered at the site were only distinguished based on macroscopic features, which prevents exploring connections between technology and ochre composition. Furthermore, these first analyses did not provide data regarding ochre availability in the surrounding landscape and its exploitation by MSA Porc-Epic visitors, which complicated inferring how much ochre was brought to the site from distant sources. The first goal of the present study is to identify the elemental and OPEN.
A supplementary calibration  22, 39  , based on the Lucas-Tooth and Price methodology  40  was applied. This calibration, developed with X-LabPro software (Ametek, Berwyn, USA) adjusts the mass attenuation coefficient and calibration slopes for major and trace elements by using certified standards and reference samples analysed by ICP-OES and ICP-MS  41. Results of EDXRF measurements were treated with CoDaPack software  42. This software treats raw concentration data by replacing values below the detection limit  43  and performing centred log ratio transformation (clr)  44. We performed principal component analyses (PCA) using PAST v. 4. 08 software  45  for ten major, minor and trace elements (Si, K, Ca, Ti, Fe, V, As, Rb, Sr, Y). Elements with more than 10% of measurements below the limit of detection (Ba, Cr, Ga, Mn) were not taken into account.
Acknowledgements The authors thank  João Zilhão ,  Josep María Fullola Pericot ,  Valentín Villaverde  and  Yonas Beyene  for insightful discussions during the course of this work;  Lucinda Backwell  for her critical reading of the manuscript; Àfrica  Pitarch Martí ,  Renata García Moreno  and  Alain Queffelec  for their help with chemical analyses;  Eric Lebraud ,  Gabriel Monge  and  Michel Dubar  for help performing XRD analysis;  Isabelle Svahn  for help conducting SEM-EDS analyses,  Frédéric Santos  for suggesting sound statistical tests,  Lila Geis  for assistance with the EDXRF analyses of some natural ochre pieces,  Laure Dayet  for useful discussions on this work; and  Ana Rosso ,  Renzo Rosso  and  Ana Herrero  for editorial input. Scanning electron microscopy analyses were conducted at the  Bordeaux Imaging Center (UMS 3420 CNRS, University of Bordeaux/Inserm US4 , member of the national infrastructure  France BioImaging ).
Cells lines Cell lines were obtained from the American Type Culture Collection (Manassas, VA) and submitted to no more than 20 passages, which were routinely tested for mycoplasmas (MycoAlert Mycoplasma Detection Kit, Lonza) and cultured in a humidified environment at 37°C and 5% CO 2. Ovarian cancer cell lines OVCAR 3 (ATCC ® HTB-161 ™ ) were cultured in RPMI 1640 supplemented with 20% foetal bovine serum (FBS) respectively and 0. 1% bovine insulin. A1847 (Cellosaurus, CVCL_9724) cells and AsPc1 (ATCC ® CRL-1682 ™ ) were cultured in RPMI 1640 supplemented with 10% FBS. HEK 293T HEK 293T/17 (ATCC ® CRL-11268 ™ ), MDA-MB-231 (ATCC ® HTB-26 ™ ), and HeLa (ATCC ® CRM-CCL-2 ™ ) were maintained in Dulbecco modified Eagle medium supplemented with 10% FBS.
Biolayer interferometry Bio-layer interferometry (BLI) on the Octet R2 system (Pall ForteBio) was used to measure binding kinetics between nanobody and biotinylated rhMSLN-Fc. Streptavidin biosensor was rehydrated in binding buffer (PBS supplemented with 1% BSA and 0. 05% Tween 20) for 10 min at 25°C. Biotinylated rhMSLN (10µg/ml) in binding buffer was bound to streptavidin sensor for 120 s. After an equilibration step in binding buffer for 30 s at 25°C, the MSLN-bound sensor was exposed to various concentrations of nanobody (50, 12. 5, and 3. 13 nM) for 300 s (association step) and then to a nanobody-free binding buffer for 300 s for the dissociation step. Kinetic constants were determined by fitting data with a 1:1 stoichiometry using the Octet analysis studio software.