dataesr/scientific-paragraphs-categorization huggingface dataset:
A. MLT and Invariant Latitude Distributions The distribution of MLT for the general dataset is shown in Fig. 2(a) , which shows that the data cover all MLT  sectors evenly. Even though the measurements are evenly distributed in MLT, spacecraft charging events are only found on the nightside, between 17h and 05h MLT, predominantly Meng et al. [5] , Frooninckx and Sojka  [7] , and Eriksson and Wahlund  [13]. The distribution of events is equally divided in the northern and southern hemispheres (55 events north and 55 events south), see Fig. 3. The invariant latitude distribution for the general dataset is shown in Fig. 4.
D. Charging Versus Magnetic Activity The dependence of spacecraft charging events on geomagnetic conditions (Kp and AE indices) is investigated by comparing the natural distribution in the general dataset and the distribution during the events [see Figs. 8  and 9 ]. The Kp and AE indices are used as a proxy for the energy input from the solar wind to the Earth, where higher indices correspond to larger disturbances. The averages of the Kp and AE indices were not centered at the time of the charging event, but the charging events were placed in the given time range. This is not expected to make a difference in the correlation since the indices give a general proxy of the disturbances in the geomagnetic field and do not provide local information. To obtain the Kp index, 13 selected subauroral ground-based magnetic observatories are used to derive the mean value of the disturbance levels in the two horizontal field components.
The Kp index is provided in a 3-h resolution and is available for all 109 events. Looking at the occurrence of Kp indices in the general dataset, it shows that lower indices are more common than the ones at the higher end [see Fig. 8(a) ]. The most frequent Kp indices at spacecraft charging events are between 1 and 3+ [see Fig. 8(b) ]. The normalized distribution is shown in Fig. 8(c ). It shows an increasing number of spacecraft charging events with increasing Kp index. The small percentage of Kp over 5 in the general dataset does not provide reliable statistics. However, even when excluding these specific events, a clear increase is detected. The AE index, developed by Davis and Sugiura  [15] , provides a measure of global electrojet activity. It shows the total range of deviation from quiet day values of the horizontal magnetic field around the auroral oval and is a good measure for substorms.
VI. DISCUSSION Spacecraft charging and possibly associated electric breakdown present a substantial risk to the health of spacecraft orbiting Earth, where the ambient plasma is relatively tenuous. During magnetospheric perturbations, high fluxes of energetic electrons can cause substantial spacecraft surface charging. In this report, we have investigated charging events detected in the Jason-3/Amber data. In total, 109 clear spacecraft charging events were found, with the largest spacecraft potential in excess of 2000 V negative. All spacecraft charging events show a well-defined peak structure that we expect from a charging event in the count rates' time evolution. These charging events were first automatically detected in the database and then confirmed visually. Finally, in our study, the majority of the events are found in 2017.
This is probably due to high-energy electrons enhancements in the midnight sector. Note that all the spacecraft charging events, but one, are found on the nightside. On Jason-3, 22 charging events (20%) were observed in sunlit conditions and 87 (80%) in shade. On Freja, sunlit events were rare and most charging was observed in eclipse  [8]. On DMSP, no charging in sunlit conditions was recorded  [2]. This may be due to a lower ambient ion density for the higher altitudes of Jason-3 in comparison to the other satellites. Both DMSP and Freja data are in general in good agreement with the above results deduced from AMBER onboard Jason-3. Furthermore, inspecting the latitude distribution of spacecraft charging events, we find that spacecraft charging events are detected in between 55 • and 80 • invariant latitude (shown in Fig.
VII. CONCLUSION This study provides an analysis of spacecraft charging events at low altitudes using AMBER data on the Jason-3 satellite. Plasma environment conditions leading to negative spacecraft charging were investigated and 109 spacecraft charging events were found. All spacecraft charging events are associated with electron inverted "V" structures. At the Jason-3 orbit, the ion signature predominately lasts under 30 s, while the electron signature lasts between 30 s and 1 min. The charging events generally begin only when the electron average energy exceeds 4-5 keV. 1) The ion signature predominately lasts under 30 s, while the electron signature lasts between 30 s and 1 min. 2) During most of the studied spacecraft charging events, Jason-3 charges to potentials between -30 V and -1 kV.
(a), which displays a nonuniform distribution. The dominant part of the dataset is between 15 • and 80 •. The spacecraft charging events are located in the latitude range of 55 • -80 • and predominately in the 70 • -80 • range [see Fig. 4(b)]. This latitude distribution is also in good agreement with the results from earlier studies by Gussenhoven et al. [2], Wahlund et al. [3], Anderson [4], Meng et al. [5], Frooninckx and Sojka [7], and Eriksson and Wahlund [13].
DATA AVAILABILITY The AMBER scientific data and Jason-3 ephemeris are downloaded from http://clweb. irap. omp. eu/. The hourly AE index is downloaded from http://wdc. kugi. kyoto-u. ac. jp/dstae/ index. html, and the 3-h Kp index from http://wdc. kugi. kyotou. ac. jp/kp/index. html. The magnetometer data from the RANK station of the CARISMA network are accessed from https://www. carisma. ca/carisma-data-repository.
5, 21, 27 ading the severity of DHRs can also be challenging, as the ideal severity classification is yet to be determined. Virtually every allergy society has published a different grading system, and these systems can often vary based upon the type of triggers, eg, allergen-specific immunotherapy vs contrast media. However, many authors call for simplification and a common classification. 28 The World Allergy Organization (WAO) has recently published a guidance document on anaphylaxis with a more encompassing severity classification, which could be helpful in the future. 29 wever, these classifications usually focus on general allergy and are based on data from allergic reactions to any trigger. This can be limiting when grading DHRs to chemotherapy or biologics, which tend to feature symptoms not contemplated on standard grading systems.
A penicillin allergy rule (PEN-FAST) was derived from prospective data from 622 patients that underwent penicillin allergy testing in Melbourne (Australia) and subsequently externally validated in cohorts from Sydney, Perth, and Nashville (United States) (n ¼ 945 patients). 177 The 4 features associated with a positive penicillin allergy test result on multivariate analysis were subsequently summarized in the mnemonic PEN-FAST: penicillin allergy, 5 or fewer years ago, anaphylaxis/angioedema, severe cutaneous adverse reaction (SCAR), and treatment required for allergy episode. The major criteria included an allergy event occurring 5 or fewer years ago (2 points) and anaphylaxis/ angioedema or SCAR (2 points); the minor criterion (1 point), treatment required for an allergy episode. A cutoff of fewer than 3 points for PEN-FAST was chosen to classify a low risk of penicillin allergy, for which only 17 of 460 patients (3.
2, 61, 298 Recent data by Isabwe et al  23  suggest that dose reduction and fluids as premedication could be helpful in patients with multiple recalcitrant reactions. It is beneficial to keep in mind that other drugs such as premedication or concomitant antineoplastics/ biologics can be possibly involved. 2, 10 e BWH seems to have reduced the number of reactive RDD procedures to biologics in the last years when comparing the data from Sloane et al  61  and Isabwe et al. 23  Describing different endophenotypes to better guiding the selection of the optimal RDD protocol and premedication (including fluids) might constitute the landmark that could potentially explain this remarkable improvement. However, more data are needed in different populations.
Acknowledging the previously mentioned differences, we believe there are 2 distinguishable successful organizational models for dealing with these high-risk and high-complexity procedures: On the one hand, the RCHU's Technical Area (supplementary text 1) considers the allergy department as a whole and does not segregate it into isolated "units" or "centers" dedicated to specific conditions. Consequently, it has access to a larger staff pool and flexible (can be adapted locally) multipurpose spaces with varying degrees of supervision and resources for different patients and procedures, depending on risk and complexity. On the other hand, the Danish Anaesthesia Allergy Centre, a highly specialized center focused on dealing with one specific high-risk and highcomplexity condition (perioperative anaphylaxis) at a national level.
3 W. The AMUX-driver is based on a lumped architecture for a minimal footprint. Its input-output transmission lines are broadband coplanar waveguides to ensure the minimum loss, even beyond 100 GHz. ON-chip low-ohmic R-C-damped decoupling networks were used for proper biasing and high operation stability. Single-ended input interfaces were designed to ease packaging efforts as the second step, while the differential output allows a direct drive of the E/O modulator. The AMUX-driver data path is composed of a two-stage input linear amplifier, a resistively degenerated Gilbert cell [see Fig. 2(a) ], which is followed by a two-stage linear output preamplifier and a linear driver. Data input amplifiers provide input impedance matching on a 50-source, single-ended-todifferential conversion, and linear gain to drive the interleaving cell with high-quality differential signals.
B. Large-Signal Digital Measurements The AMUX-driver large-signal digital measurements were conducted on the wafer at 100 GBd in PAM-4 and NRZ. To generate high-quality (2 15 -1)-bit 50-GBd PAM-4 input data, 50 Gb/s signals were fed to an InP DHBT 2-bit active combiner module. The AMUX-driver was connected to one of the combiners' outputs through a passive broadband balun, 20-cm V-connector cables, and 67-GHz V-connector probes. A passive adjustable 65-GHz V-connector delay line was connected to one of the balun's outputs to decorrelate the AMUX-driver input data signals. These 50-GBd PAM-4 input signals, D1 and D2, were measured at cables' output prior to connection with the 67-GHz input probe [see the insets in Fig. 4(a) ] with about 220-mV pp PAM-4 amplitude. Moreover, the AMUX-driver is clocked with a 50-GHz 300-mV pp lowjitter sine wave.
Eye diagrams were captured using a 122-GHz bandwidth sampling oscilloscope, whose remote heads were connected to 67-GHz V-connector output probes, through 65-GHz 10-dB attenuators. Fig. 4 (a) and (b), respectively, depicts the AMUX-driver 100-GSa/s 100-GBd (200-Gb/s) PAM-4 high-quality differential output eye diagrams with 2. 0-and 2. 4-V ppd output swings, both obtained with the same input signals. In Fig. 4 (a), the interleaving cell tail current was reduced to optimize the output signal quality to the gain's and output swing's detriment (see Section II). An 11-ps (198    4 (c) shows the 3. 2-V ppd -swing high-quality 100-GSa/s 100-Gb/s NRZ eye diagram measurements, while the AMUX-driver is operated in the saturated regime (320-mV pp inputs). Moreover, AMUX-driver's 160-GBd measurements were conducted using a similar setup, yet an InP DHBT 2:1 selector module was used instead of the combiner to generate 80-Gb/s 200-mV pp -amplitude input data in multiplexing two independent 40-Gb/s NRZ signals (see  [20] ).
Manuscript received February 25, 2022; accepted March 15, 2022. This work was supported by the European Commission through the Photonics Public Private Partnership Initiatives under Grant H2020-ICT-2016-2017 (QAMeleon Project) and Grant H2020-ICT-2019-2 (Twilight Project). (Corresponding author: R. Hersent. ) R. Hersent, F. Jorge, F. Blache, V. Nodjiadjim, M. Riet, and C. Mismer are with the III-V Lab, 91767 Palaiseau, France (e-mail: romain. hersent@3-5lab. fr).
When creating a flavor to elicit a specific odor object characterized by odor sensory attributes (OSA), expert perfumers or flavorists use mental combinations of odor qualities (OQ) such as Fruity, Green, Smoky. However, OSA and OQ are not directly related to the molecular composition in terms of odorants that constitute the chemical stimuli supporting odor object perception because of the complex non-linear integration of odor mixtures within the olfactory system. Indeed, single odorants are described with odor descriptors (OD), which can be found in various databases. Although classifications and aroma wheels studied the relationships between OD and OQ, the results are highly dependent of the studied products. Nevertheless, ontologies have proved to be very useful in sharing concepts across applications in a generic way but also to allow experts' knowledge integration implying non-linear cognitive processes.
Several databases are compiling OD of large sets of odorants: Arctander's handbook (  [11] ), Atlas of odor character profiles (  [12] ), Fenaroli's handbook (  [13] ), Flavor-Base (Leffingwell & Associates, http://www. leffingwell. com/flavbase. htm), Flavornet (  [14] ), Flavors and Fragrances of Sigma-Aldrich (http://www. sigmaaldrich. com/industries/flavors-and-fragrances. html), The good scents company (  [15] ). However, the vocabulary used in describing odor is extensive and ambiguous. As a matter of fact, are "citrus odor" and "odor of citrus" referring to the same odor descriptors? (  [16] ). Moreover, there is no agreement about the number of OD essential to cover the complete range of odor stimuli which varies from 4 to 146  ([17] ). Though several teams worked on the different relationships, associations, or similarities between OD, none of them had yet gained wide acceptance (  [18] ;  [19] ;  [20] ).
g. [22] ). In a sense, OQ could be considered as broad categories, related more to odor material than to molecules. Classifications and flavor wheels usually dedicated to a specific category of food products such as wine or coffee have been established and could help to make links between OD and OQ. However, these classifications are highly dependent of the studied databases and/or food product and are hardly reconcilable (  [12] ;  [19] ; caramel:  [23] ; honey: International Honey Commission (IHC) http://www. ihc-platform. net/reports. html; wine:  [24] ). For example, whereas the OD "Apple" is classified in the OQ "Fruity" in the five above cited sources, the OD "Vanilla" is classified in five different OQ "Spicy", "Balsamic", "Warm", "Wood/Phenolic" or "Caramel/Vanilla" depending on the source.
The good scents company (publicly available database, the odor descriptions from one to several sources are listed in the "Organoleptic Properties" section). We extracted manually the OD from these databases. The words describing the odorants were tokenized. Suffixes (e. g. "like", "note"), auxiliary verbs (e. g. "has") and some other words that did not rely on olfactory information (e. g. "powerful") were discarded. Unlike the analysis of the Arctander database proposed by  [17] , we kept all the OD into account and we did not combine very similar descriptors (like Leather/Leathery or Wine/Winey) For instance, the odor of Ethyl butanoate (CAS 105-54-4) was specified in Arctander as "Powerful, ethereal-fruity odor suggestive of Banana and Pineapple, and very diffusive" these annotations resulted in the set of OD: "ethereal-fruity", "banana" and "pineapple".
Then we created the OD database by aggregating the information of the three databases following For a given odorant, a description was thus provided by the OD database as a set of terms in which each term may be associated to an "intensity". We defined this intensity as the number of citation of the same OD for a given odorant across the databases: the higher the number of citation, the more "intense" the smell related to this OD was expected for the odorant. As an example, the odorant description of Ethyl.
Relationships between odor descriptors (OD) and odor qualities (OQ) The correspondence between an OD and one or several OQ was obtained thanks to the expertise of a junior flavorist. This expert was not one of the four flavorists previously interviewed for OQ elicitation. The methodology used to obtain the relationships was based on a check-all-that-apply (CATA) questionnaire (Dooley et al. , 2010). The CATA list consisted of the 20 OQ defined by the experts during the elicitation step (see 2. 2 above). For each OD of the OD database, the flavorist was asked if the OD supported none, one, or several OQ. For instance for the OD "Apple", the flavorist was asked to tick all the OQ that correspond e. g. "Fruity". We obtained a binary matrix with the OQ in columns and OD in rows. These results allowed us to translate each OD sets into OQ sets.
The Ontology for Odor Perceptual Space (OOPS) We formalized the Ontology for Odor Perceptual Space (OOPS) as a tuple {C, R, P}, where C corresponded to the three classes OD, OQ and OSA with respectively 175 sub-classes from the databases aggregation, 20 sub-classes from the expertise collection and 15 sub-classes from the sensory evaluation of the wines; R represented the hierarchical relations among the classes by "is-a" relations; and P, as properties, represented the non-hierarchical associative relations between classes as shown in Figure  1. Results from the data collection in table forms, were implemented in OWL using the software Protégé (open-source ontology editor, version 5. 2. 0;  [28] ). This allowed the visualization of the properties among the classes OD, OQ and OSA; an example is shown in Figure  2  for the OQ "Vanilla".
Equation 2 , 2 M being a given odorant, Arct[OD(M)], FlavorBase[OD(M)] and Goodscent[OD(M)] being the sets of OD of the odorant M by the Arctander, Flavor-Base and Goodscent databases. We ended up with 175 different OD for the 49 odorants. ∪    ∪    Equation 2.
Residual Tumors from G2-G3 Curcumin-Treated Rats Exhibited Common Changes in 120 Proteins All tissues were analyzed using the data independent acquisition (DIA) proteomic approach. The number of proteins detected in the two comparisons G2 vs. G1 and G3 vs. G1 were 3093 and 3096, respectively. In the first case, 693 proteins exhibited significant (p < 0. 05) abundance changes, with 290 showing an increase and 403 a decrease. In the G3 vs. G1 comparison, this number increased to 833, with 514 showing an increase and 319 a decrease. Cross-comparing the two files, G2 vs. G1 and G3 vs. G1, led to a list of 116 common proteins, with 50 exhibiting an increase and 66 a decrease. The main location of these proteins recorded on https://www. proteinatlas. org (accessed on 5 July 2022) are illustrated in Figure  2 , and their full names given in Supplementary Table  S1.
Sequencing-grade trypsin was added in a ratio ≥2 µg mm -3 tissue (incubated at 37 • C overnight). The reaction was stopped with formic acid (9% final concentration) and incubated at 37 • C for one hour, and the acid-treated samples were centrifuged at 16,000× g for ten minutes. Salts were removed from the supernatant and collected in new reaction microtubes using self-packed C18 STAGE tips. Peptide concentrations were finally determined with the Micro BCA™ Protein Assay Kit (Thermo Fisher Scientific, Saint-Herblain, France). Two hundred nanograms of each sample were analyzed by LC-MS/MS with a nanoHPLC (NanoElute, Bruker Daltonik GmbH, Bremen, Germany) coupled to a Tim-sTOF Pro2 (Bruker Daltonik GmbH, Bremen, Germany). Measurements were acquired in diaPASEF mode (data independent acquisition parallel accumulation serial fragmentation).
Mass spectrometry data were analyzed using Spectronaut TM v16. 0 (Biognosys AG, Schlieren, Switzerland). The direct DIA workflow in Spectronaut TM was used for analyzing the dia-PASEF dataset with no need to build a library from the DDA runs (library-free) with default analysis settings. Retention time prediction type was set to dynamic iRT, and calibration mode was set to automatic. These settings also included mutated decoy method, and cross run normalization was enabled. The FDR (faults discovery rate) was estimated with the mProphet approach, and Q-value cut-off for both precursor and protein was set to 1%. Interference correction was enabled for quantification which required a minimum of 2 precursor ions and 3 fragment ions. The term protein refers to protein groups as determined by the algorithm implemented in Spectronaut TM.
Acknowledgments: We are grateful to all the staff at the  Animal Facility (UTE-IRS UN) at the Nantes Université  for the excellent care of the animals during all the experiments. We acknowledge the  IBISA MicroPICell facility (Biogenouest) , member of the national infrastructure  France-Bioimaging  supported by the  French national research agency  ( ANR-10-INBS-04 ).
Mechanical and Geometrical Characterisation of the Stiffness Patterns Young's moduli of the patterned hydrogels were measured on a NanoWizard II AFM (JPK Instruments, Berlin, Germany) in the force mapping mode with MLCT C tip cantilevers (Bruker, Palaiseau, France) with a nominal spring constant of 0. 01 N/m. Young's moduli were fitted from the indentation curves using a Hertz-Sneddon model  [36]. Rigidity maps were sampled every 0. 3 to 1 µm, depending on the size of the pattern. , The sizes of the rigid dots were determined via comparison to the AFM data. A new image was generated, composed of the sliding average of the AFM data on squares of 3 pixel sides. The limits of the stiff dot were set at places where the gradient of the sliding average was larger than 500 Pa/µm. This region was then filled with the largest disk.
Characterization of the Surface Chemistry The uniformity of the surface density was characterized by immunostaining. The functionalized hydrogel was incubated first with an anti-fibronectin primary polyclonal antibody (Sigma-Aldrich, Saint Quentin Fallavier, F3648, 1 h, room temperature), and then with an Alexa-conjugated secondary antibody A488 (Molecular Probes, Thermo Fisher Scientific, Courtaboeuf, France) (1 h, room temperature). A stack of fluorescence images was acquired with a 40× water immersion objective using an upright Leica SP confocal microscope(Leica Microsystems, Weztlar, Germany). Fourier transform analysis was performed on the thickness-averaged intensity profile using ImageJ software (v 1. 53, NIH, USA).
Deformation Field Before calculating the displacement field, images were globally registered for global rotation and translation in x, y, z using autocorrelation of the image of the contractile cell with the reference image (i. e. , after trypsin) in four different regions taken as further away as possible from the cell in the corners of the image. From the displacements of these four areas, the rigid registration was calculated and allowed aligning almost perfectly the two images. Bead displacements were calculated using a MATLAB script based on the CR toolbox developed by J. Diener  [37, 38]. A Kanade-Lucas-Tomasi (KLT) particle tracking algorithm  [39]  was used to calculate the displacement of each bead. KLT is a pyramidal optical flow method, which was recently shown to be much more accurate and faster than traditional particle image velocimetry techniques (PIV) for TFM  [40].