dataesr/scientific-paragraphs-categorization huggingface dataset:
Curation and Evolution of the Database The dedicated website of ROMA is hosted by the Mars System of Information (MarsSI) facility, which is part of the web portal PSUP (Planetary SUrface Portal). MarsSI has been certified as a national observation service by the French Institut National des Sciences de l'Univers (INSU), and is curated by a permanent data engineer at the Observatoire des Sciences de l'Univers de Lyon (OSUL). This will ensure a long-term community access to the ROMA database, which will be permanently accessed at https://roma. univ-lyon1. fr. In the long term, a global and homogenized database will be needed to simplify the search for reference spectra in planetary science. To this aim, the data in ROMA will be integrated in a wider library, the Solid Spectroscopy Hosting Architecture of Databases and Expertise (SSHADE;  Mandon, Beck, Quantin-Nataf, Dehouck, Thollot, et al.
, 2021; Schmitt et al. , 2014) , which gathers a larger panel of spectroscopic measurements (from gamma to radio wavelength) on various types of materials (ice, minerals, organics, etc. ). In this contribution, we described the current state of this database, which will likely be improved in the future by the authors or from other contributions with additional measurement types (e. g. , elemental data) and with new samples relevant for the exploration of Mars, as new rock analogs are available for curation and as our knowledge of the landing sites of the rovers evolves. In particular, the measurements on the following natural rock samples would be a great future addition to the database: marine, lacustrine, and fluvial sediments (possibly present in the landing regions of the Mars 2020 and ExoMars rover missions;  Horgan et al.
Conclusion The ROMA database, which includes jointed analyses of the reflectance in the VNIR range of rocks and their estimated mineralogical composition, is made publicly available for the community at https://roma. univ-lyon1. fr. The main specificity of this database is to include spectral measurements of unprocessed rock samples (in addition to particulate rock samples), which are more realistic analogs and often have different spectral features than the fine powders usually analyzed in reflectance spectroscopy. The reflectance spectra and band parameters of these rock samples will be valuable data to be compared with the measurements performed by VNIR spectrometers on planetary surfaces, and especially the SuperCam and MicrOmega reflectance spectrometers onboard the Perseverance and Rosalind Franklin Martian rovers.
Acknowledgments The authors thank  E. Robert ,  B. Reynard ,  J. -A. Barrat ,  F. Moynier , and the  Observatoire de Lyon  for providing a significant number of samples of this database, as well as  R. Vera  for dispensing some discussion on XRD analysis and interpretation. S. Potin  is greatly acknowledged for the efforts made in building the reflectance instrumental setup. The authors also thank  B. Schmitt  for integrating ROMA into the SSHADE spectral database. L. Mandon  and  C. Quantin-Nataf  have been supported by the  Agence Nationale de la Recherche  ( ANR ,  ANR-18-ERC1-0005 ). P. Beck  has been supported by the  H2020 European Research Council  ( ERC ,  SO-LARYS ERC-CoG2017_771691 ).
The samples were analyzed by means of a Perkin Elmer Spectrum 2000 FTIR spectrometer in transmittance mode. The liquid formulations were spread on silicon wafer with a thickness of 12 μm by means of film bar. The intensity of the UV light was set as 130 mW cm -2. The evaluation of the conversion was done according to the decrease of epoxy peak situated at 860 cm -1. All data were recorded as 32 scans with a spectral resolution of 4. 0 cm -1 and handled with the software Omnic from Thermo Fischer Scientific. Some FTIR analyses were also done on the thermal treated samples and in non-isothermal condition. In this case, a Thermo Fischer IS-20 spectrometer in equipped with a hot-plate GladiATR device (Pike Technologies) with a monolithic diamond ATR configuration. The spectra were collected from 30 to 180 °C (each step was 10 °C).
The spectrum of air was recorded as background. A total of 64 scans with a resolution of 4 cm -1 were recorded for each sample. The data were handle with the software Omnic from Thermo Fischer Scientific. Electrospray-Mass Spectrometry (ESI/MS)-The molecular weight of the linear polymer chains was evaluated by means of mass spectrometry. The irradiated samples were dissolved in CHCl 3 and then they were solubilized in acetonitrile, the solvent used for the test. The solution was introduced into a classic (LCQ) quadrupole ion trap (Thermo Scientific, San Jose, CA, USA) equipped with an electrospray source. The spectra were acquired in the range m/z 200-800 in the positive mode. The electrospray source and ion trap were operated under the following conditions: flow rate, 5 μL min -1 , electrospray ionization voltage, 4.
The measurements were done to detect the T g as maximum of tan curve and were stopped after the rubbery plateau. The samples had dimension of 12 mm x 4 mm x 0. 3 mm and they were UV-irradiated in a silicon mold. Thermogravimetric Analysis (TGA)-The thermal stability of the polymers was studied by means of a Mettler Toledo TGA1. The test was done imposing a heating ramp of 10 °C min -1 from room temperature to 700 °C under N 2 atmosphere with flow of 40 mL min -1. The analysis was done considering different features: T 5% , temperature at which the sample lost 5 wt%; T peak , temperature at peak of degradation, evaluated as peak of the first derivative and final char residue, analyzed as wt%. DSC-The DSC analysis was performed on a Mettler Toledo DSC-1 equipped with Gas Controller GC100. The data were analyzed with Mettler Toledo STARe software V9.
Introduction While the CERN Large Hadron Collider (LHC) already collected an unprecedented amount of data, no significant deviations from the Standard Model (SM) predictions have been observed so far. In the upcoming Runs, the LHC experiments will be able to look for physics Beyond the SM (BSM) in new ways, complementing the quest for patent signatures, such as new resonances, with searches for small deviations with respect to the SM expectations. This will be made possible by the increasing statistical precision granted by the available data sets, together with the improved understanding of the detector behaviour and the application of advanced data-analysis techniques based on state-of-the-art machine learning algorithms. The primary theoretical tool for BSM searches in precision measurements is the Standard Model Effective Field Theory (SMEFT) [1-3] (see  [4]  for a review).
Effective Field Theory framework We consider the SMEFT truncated at dimension-six: L SM EF T = L SM + 1 Λ 2 α c α Q α + O(Λ -4 ) (2. 1) where Λ is the cutoff scale of the EFT, c α are the Wilson coefficients, and the index α runs over the labels of a complete and non-redundant set of operators that are invariant under the SM gauge symmetries. Here we work in the so-called Warsaw basis  [2]  and the Lagrangian is defined as in the SMEFTsim package implementation  [39, 40]. In particular, we require the fermionic operators to be invariant (up to insertions of the Yukawa couplings) under a U (3) 5 flavour symmetry. We also assume CP conservation and take the Fermi constant (G F ) and the W and Z boson pole masses (m W , m Z ) as input quantities for the EW sector  [41, 42]. The SU (2) Higgs doublet is denoted by H, the gauge field strengths associated to the SU (2) and U (1) symmetries by W i µν and B µν respectively, the left-handed lepton and quark doublets by l, q and the right-handed quark and charged-lepton fields by u, d, e.
Event generation For each channel we simulate events at parton-and tree-level with Madgraph5_aMC@NLO (v. 2. 6. 5) [43] , interfaced to the SMEFTsim package (v. 3)  [39, 40]. The syntax used to generate each process is reported in Table  2  for the SM case. The simulations are produced for pp collisions at a centre-of-mass energy of 13 TeV, using the NNLO parton distribution functions provided by the NNPDF collaboration  [44] , with α S = 0. 118 and in the four-flavour scheme (LHAPDF identification code 325500). The renormalization and factorization scale choices are determined by the Monte Carlo generator as the transverse mass of the 2 → 2 scattering after k T clustering. For each process, the SM, interference and quadratic EFT components (Eq. (2. 4)) are extracted using two alternative techniques: a) performing an event simulation for each contribution.
ACKNOWLEDGMENTS. We thank  Susana Godinho  ( Queen Mary University of London, United Kingdom ) for gateway cloning plasmids;  Mathieu Coppey  ( Institut Curie, Paris, France ) for advice on optogenetics and sharing reagents; Ayad PNAS 2022 Vol. 119 No. 26 e2121868119 https://doi. org/10. 1073/pnas. 212186811911 of 12 Downloaded from https://www. pnas. orgby 217. 155. 96. 24 on July 28, 2022 from IP address  217. 155. 96. 24. Eddaoudi ( University College London, Institute for Child Health ) for flow cytometry analysis;  Jonathan Fouchard ,  Julia Duque ,  Kazunori Yamamoto , and  Diana Khoromskaia  for comments and discussions; and members of the G. C. and B. B. laboratories for discussions. M. K. was supported by a  Swiss National Science Foundation early postdoctoral fellowship  ( P2LAP3_164919 ) and by a  European Research Council   consolidator grant  ( CoG-647186 ) to G.
These extracts were used in a reverse transcription polymerase chain reaction (RT-PCR; SuperScript III One-Step RT-PCR System with Platinum Taq DNA Polymerase (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA)) using pan-flavivirus primers FU2 and cFD3 (melting temperature 45 • C and 50 s extension time)  [15]. Six samples were positive, all from Cq. xanthogaster mosquitoes. The RT-PCR products were gel purified using the Nucleospin gel and PCR clean up kit (Macherey Nagel, Düren, Germany) following the manufacturer's instructions and subjected to sequencing by the Sanger method at the Australian Genome Research Facility (Brisbane, Australia). All sequenced samples were identified as Palm Creek virus (PCV) (see Table  2 , Genbank accession numbers MW959131 to MW959136) an insect-specific flavivirus first isolated from a pool of Cq.
xanthogaster mosquitoes collected in Darwin in 2010  [16] , and subsequently isolated from two pools of Cq. xanthogaster mosquitoes collected from Kununurra, Western Australia, in 2010  [17]. The prevalence of PCV in our samples was 3. 03% (6 positives out of 198 pools), and more specifically, 37. 5% (6/16 pools) in Cq. xanthogaster, the only species in which PCV has been detected so far. The calculated minimum infection rate in this mosquito species in our samples was 6. 82% (6/88 Cq. xanthogaster). A MUSCLE alignment was performed on the 737 nucleotide and corresponding 252 amino acid sequences to compare the new isolates to each other and to the two published reference PCV genomes (Genbank accession numbers KC505248. 1 and KT192550. 1) using Geneious Prime (Biomatters, Auckland, New Zealand).
First strand cDNA was generated using Protoscript II (New England Biolabs, Ipswich, MA, USA), using the supplied random primer mix and reaction conditions as recommended by the manufacturer. The reaction product was then converted to double-stranded DNA (16 • C for 60 min) using an enzyme mixture consisting of E. coli DNA ligase, DNA polymerase I and RNase H (New England Biolabs, Ipswich, MA, USA), followed by heat inactivation (80 • C for 5 min). The double-stranded cDNA was used as template for library construction using the Nextera XT library kit (Illumina, San Diego, CA, USA) with barcoded primers. The library was sequenced on a NextSeq 500 generating 2× 150 bp paired reads. De novo assembly of the data yielded 10 million reads, 6. 5M of which were assembled in a 5914 nucleotide-long genome sequence, which was identified as a macula-like virus (Genbank accession number MW959137).
It was most closely related to the three published sequences for Hubei macula-like virus 3 (HMLV3) (Genbank accession numbers KX883799. 1, KX883800. 1 and MT240795. 1) with 77-79% nucleotide identity over 64-93% of the query cover (E-values 0. 0) using the blastn algorithm on NCBI BLAST  [15, 16]. HMLV3 was first identified from two spider samples as part of a large-scale metagenomics study in arthropods collected in China in 2016  [18]. Another strain was subsequently detected in soybean thrips collected in 2018 in the United States of America  [19]. Another putative virus sequence shared a high identity with our sequence (72. 35% identity over 90% of the sequence, E-value 0. 0): Pyongtaek Culex Macula-like virus  [20]. This sequence was discovered by metagenomics from Cx. bitaeniorhynchus (Giles, 1901) mosquitoes collected in South Korea in 2018.
3% identity), which translated into a single amino acid difference (99. 3% identity) (Genbank accession number MW959138). The close genetic relationship between isolates from different arthropods (two mosquito genera, spiders and thrips), and the fact that traditionally, maculaviruses replicate in plants, support the hypothesis that these viruses could be following a horizontal transmission cycle between plants and arthropods. This is also the working hypothesis for a number of other insect virus families, including viruses in the Tymoviridae family  [14, [22] [23] [24]. There are still 17 samples that remain MAVRIC positive and unidentified. These may contain new virus species within the families tested here, or novel viruses from insect virus families that have gone undetected in Australian mosquitoes so far.
Acknowledgments: The authors would like to thank  Neville Hunt  and  Jasmin Moran  for assisting in setting and dismantling the traps and the  Northern Territory Government Medical Entomology  technicians and professionals for sorting and identifying mosquito collections:  Jane Carter ,  Nadine Copley ,  Tomoko Okazaki ,  Megan Hoskins  and  Alexander Roberts. We would also like to acknowledge the crocodile farm managers and staff where the mosquitoes were collected.
For noncoherent uniform illumination, a quartz-tungsten halogen lamp was used. The same IR imaging system was used for lateral and top view imaging of the modifications created at the interfaces between stacked semiconductors. After the separation of samples, the top and bottom samples were also inspected by visible-light reflection microcopy (Nikon Lv-UEPI-N) so that the effects on surfaces separated from internal modifications can be differentiated. Additionally, to evidence material transfer between samples, confocal microscopy (Leica, DCM 3D) was used for surface profilometry with nanometric longitudinal resolution. A commercial software (Leica, LeicaMap) was used for the image data analyses. The welding strengths were measured using a commercial mechanical force tester (LF-Plus Lloyd Instruments).
STATISTICAL ANALYSES All analyses were carried out using R (version 3. 6. 3; R Core Team 2021). The raw datasets used and the corresponding R scripts are available online  (Rodrigues et al. 2021 ). The different statistical models built to analyze the data are described in Table  S4. The general procedure was as follows: Mate choice and mating propensity were computed as binary response variables and analyzed using generalized liner mixed-effect models (glmer function of the lme4 package;  Bates et al. 2015)  with a binomial error distribution. For the analyses of mate choice, the intercept of the models was forced to zero, which gives the estimate of the fixed factor as the difference to a probability of 0. 5 in a model with categorical factors and a binomial distribution  (Crawley 2007).
e. , due to possible confounding effects of abiotic conditions and/or plant quality despite controlled conditions for plant growing). Therefore, differences among populations were not tested. Instead, post hoc comparisons between estimated regression coefficients, obtained from the maximal models, were performed using the package emmeans  (Lenth et al. 2018 ) to determine, for each population, the difference to random mating (i. e. , the difference between the estimate and the zero-intercept using the function test) and the effect of Wolbachia infection in the chosen male on latency to copulation and copulation duration (i. e. , the difference between estimates obtained for infected and uninfected males within each population using the function pairs). As the data were used only once in each of these analyses, no P-value adjustment was performed.
ACKNOWLEDGMENTS We are grateful to  D. Godinho  and  J. Alpedrinha  for useful discussions and comments and all Mite Squad members for unconditional support in the laboratory and elsewhere. This work was funded by an  FCT-ANR  project  ( FCT-ANR//BIA-EVF/0013/2012 ) and an  ERC consolidating grant  ( COMPCON GA 725419 ) to SM, and a  FCT  project ( EXPL/BIA-EVL/0131/2021 ) to LRR. LRR was funded through  FCT PhD grant  ( SFRH/BD/87628/2012 ). This is publication ISEM-2022-027 of the  Institut des Sciences de l'Evolution.
Background: The adventure of the mRNA vaccine began thirty years ago in the context of influenza. This consisted in encapsulating the mRNA coding for a viral protein in a lipid particle. We show how the mRNA encoding S protein has been modified for that purpose in the context of the anti-SARS-CoV-2 vaccination. Results: by using data coming from genetic and epidemiologic databases, we show the theoretical possibility of fragmentation of this mRNA into small RNA sequences capable of inhibiting important bio-syntheses such as the production of beta-globin. Discussion: we discuss two aspects related to mRNA vaccine: (i) the plausibility of mRNA fragmentation, and (ii) the role of liposomal nanoparticles (LNPs) used in the vaccine and their impact on mRNA biodistribution. Conclusion: we insist on the need to develop lipid nanoparticles allowing personalized administration of vaccines and avoiding adverse effects due to mRNA fragmentation and inefficient biodistribution.
Hybridizing Power of SARS-CoV-2 Viral RNA Fragments In the following, we focus on viral RNA sequences of the miR type, playing the ro of possible germ for mRNA hybridization of proteins participating in fundamental phy iological mechanisms, such as the transport of oxygen by the hemoglobin or the inductio of immune function protein synthesis. The hybridization seed, if it comprises at least consecutive nucleotides complementary to those of a target mRNA, can cause the elong tion of the corresponding protein to be stopped in the ribosome, therefore the inhibitio of its biosynthesis  [78] [79] [80] [81] [82] [83] [84] [85] [86]  or as tRNA fragments, viral RNA fragments can compete wi mRNA for ribosome binding  [87]. In the search for hybridization seeds, we have selecte RNA sequences from different databases  [88] [89] [90]  using the classic BLAST software.
Hybridizing Power of SARS-CoV-2 Viral RNA Fragments In the following, we focus on viral RNA sequences of the miR type, playing the role of possible germ for mRNA hybridization of proteins participating in fundamental physiological mechanisms, such as the transport of oxygen by the hemoglobin or the induction of immune function protein synthesis. The hybridization seed, if it comprises at least 8 consecutive nucleotides complementary to those of a target mRNA, can cause the elongation of the corresponding protein to be stopped in the ribosome, therefore the inhibition of its biosynthesis  [78] [79] [80] [81] [82] [83] [84] [85] [86]  or as tRNA fragments, viral RNA fragments can compete with mRNA for ribosome binding  [87]. In the search for hybridization seeds, we have selected RNA sequences from different databases  [88] [89] [90]  using the classic BLAST software.
Data Availability Statement: All the data are coming from public databases: Nextstrain. Available online: https://nextstrain. org/ncov/Europe (accessed on 10 November 2022); Nuccore. Available online: https://www. ncbi. nlm. nih. gov/nuccore (accessed on 10 November 2022); Genecards. Available online: https://www. genecards. org/cgi-bin/carddisppl?gene=HBB (accessed on 10 November 2022); Mirbase. Available online: https://www. mirbase. org/cgi-bin/mirna_entrypl?acc=MI0 000093 (accessed on 10 November 2022); EMA. Available online: https:/www. ema. europa. eu/ en/documents/assessment-report/NCBI (accessed on 10 November 2022); NCBI. Available online: https://www. ncbi. nlm. nih. gov/nucleotide (accessed on 10 November 2022); ACS. https: //www. acs. org/content/acs/en. html (accessed on 10 November 2022).
Automatic detection of abusive online content such as hate speech, offensive language, threats, etc. has become prevalent in social media, with multiple efforts dedicated to detecting this phenomenon in English. However, detecting hatred and abuse in low-resource languages is a non-trivial challenge. The lack of sufficient labeled data in low-resource languages and inconsistent generalization ability of transformer-based multilingual pre-trained language models for typologically diverse languages make these models inefficient in some cases. We propose a meta learning-based approach to study the problem of few-shot hate speech and offensive language detection in low-resource languages that will allow hateful or offensive content to be predicted by only observing a few labeled data items in a specific target language.
We investigate the feasibility of applying a meta learning approach in cross-lingual few-shot hate speech detection by leveraging two meta learning models based on optimization-based and metric-based (MAML and Proto-MAML) methods. To the best of our knowledge, this is the first effort of this kind. To evaluate the performance of our approach, we consider hate speech and offensive language detection as two separate tasks and make two diverse collections of different publicly available datasets comprising 15 datasets across 8 languages for hate speech and 6 datasets across 6 languages for offensive language. Our experiments show that meta learning-based models outperform transfer learning-based models in a majority of cases, and that Proto-MAML is the best performing model, as it can quickly generalize and adapt to new languages with only a few labeled data points (generally, 16 samples per class yields an effective performance) to identify hateful or offensive content.
Although a major research has been dedicated to the automatic identification of hate speech and offensive language in English  [1] ,  [2] ,  [4] ,  [12] , creating annotated corpora and analyzing hateful and offensive content on other languages such as Italian  [13] , Spanish  [14] , Danish  [15] , Turkish  [16] , Arabic  [17] , etc. have raised many concerns recently. Since multilingual social media foster their users to interact in their primary languages, it is essential to develop automatic technologies including hate speech and offensive language detection tools for low-resource languages to reduce the vulnerability among users with different languages, other than English. However, the lack of sufficient annotated data containing hatred, offense, and abuse for low-resource languages has made this problem far from being solved at scale.
Indeed collecting and labeling data is a labor-and timeconsuming work, and the complex, subjective, and implicit nature of hate speech makes the reliability of annotation process more difficult. Regarding the aforementioned challenges, we investigate the problem of the limited availability of labeled training datasets in low-resource languages for hate speech detection by proposing a few-shot cross-lingual approach based on meta learning. Meta learning is an effective solution proposed for few-shot learning problems, in which we have a few labeled data for a target task, and it has shown a great performance in different computer vision tasks, such as classifying new image classes with a few available instances of that class  [18] ,  [19]. Recently, meta learning has raised attentions regarding few-shot learning problems in NLP tasks as well, where a diverse tasks with different numbers of labels across tasks were studied  [20] ,  [21].