dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and Future Work We presented an analysis of benchmark datasets for the claim retrieval task and an evaluation of the performance of different supervised and unsupervised approaches on different data to compare their robustness and scalability and extract insights on which methods work best and why. We showed that an unsupervised system that exploits the complementarity of different embeddings achieves state-of-the-art performance on par with the best supervised method and outperforms all others while being highly efficient, scalable and robust. Our study has several limitations: we focused on the retrieval performance for the English language. In future work, it would be interesting to compare the performance when applied to different languages and in multilingual settings. Moreover, the usefulness of different language models was shown to vary considerably.
It remains unclear which properties exactly render a model useful for this task and how biases introduced by language models (cf. [15] ) affect claim retrieval, e. g. yielding higher performance for certain topics or claims expressing stances uttered with preference by advocates of particular political leanings. Thus, as part of our future work, we plan to investigate in more detail the different semantics that are encoded in different language models and their informative value and biases for this task. Despite the fact that the datasets belonging to the same discourse type, tweets vs. political debates, are continuous extensions of their previous versions, we found relatively large fluctuations in systems' performance scores when using either of them. At the same time, the debate data differs from the tweets data in more aspects than the genre and features input queries that do not match the task definition entirely.
Regarding characteristics such as the importance of word overlaps, the tweets and debates datasets are similar. Thus, our comparative analysis provides insights on the performance of the different methods on different datasets and their robustness, but cannot provide insights on the generalizability of methods regarding application on different discourse types or out-of-distribution data more generally. Relating to this, our data analysis investigated several characteristics that have been recognized as important factors in previous works and in a top-down fashion drawing on domain insights. Yet, the data may contain additional biases introduced by the methodology of dataset creation, which may not be captured by our analysis  [8]  and which may artificially reduce task difficulty  [53]. Finally, the data diverges from data that can be expected in real-world claim retrieval systems in the sense that it does not feature ranked lists with multiple similar claims verifying an input query, as can be expected when drawing on data from multiple fact-checking portals.
Section 4) and thus share the same numbers. The length of verified target claim titles (cf. Subfigures (c) and (f) of Figure5and Table5) fluctuates more strongly across datasets (mean #tokens: 9. 0 for 2020-tweets, 10. 4 for 2021-tweets and 2022-tweets, standard deviation 4. 4 -4. 8 for 2020-tweets) and they are longer in the debates data (mean #tokens: 13. 6, standard deviation 4. 0). Verified claim titles matching training queries are longer than all others for the tweets datasets and for 2021-tweets and 2022tweets, the titles of claims matching test queries are longer than the average,.
claims from political debates). More precisely, we train supervised approaches on each of the 2020-tweets, 2021-tweets, 2022-tweets, and 2021/2-debates training datasets and apply each of the trained models on all of the test datasets. Since 2021-debates and 2022- debates share the same training sets and verified claims, we only train one model per approach on the debates data. We train three models for each neural network-based approach on each dataset to investigate their robustness/sta- bility and report the average scores. With this, we compare performances of the supervised approaches for in-distribution vs. out-distribution training and relate these scores with the performance of unsupervised approaches on each test dataset.
: Short vclaim in the set of 2022-tweets target verified claims due to an extraction error. Dataset Targets Threshold Count 2020-tweets all 33. 8 328 (3. 161%) 2020-tweets train 34. 9 25 (3. 805%) 2020-tweets test 31. 3 8 (5. 333%) 2021-tweets all 34. 2 486 (3. 515%) 2021-tweets train 35. 3 25 (3. 197%) 2021-tweets test 43. 5 5 (3. 497%) 2022-tweets all 34. 6 543 (3. 816%) 2022-tweets train 35. 3 25 (3. 197%) 2022-tweets test 44. 5 10 (5. 556%) 2021/2-debates all 41. 8 877 (4. 556%) 2021/2-debates train 39. 9 19 (5. 367%) 2021/2-debates test 40. 6 3 (5. 769%).
This yields the best results on all datasets. The same can be observed for RIET Lab with its models for 2021-tweets and 2022-tweets. Check square learnt its least useful model on 2021-debates which scores worse than all models learnt on any other dataset for all test data, including 2022-debates which contains data of the same discourse type. Together with the insights from the previous subsection which showed that individual training runs led to great variation in performance, this finding highlights that the supervised approaches are vulnerable to overfitting, making their performance hard to predict. However, when a model is learnt successfully for this task, it generalizes well across data of different discourse types. Training on 2020-tweets generally yields the best results, with the exception of RIET Lab and Check square, for which the model trained on 2022-tweets achieves better scores on the 2020-tweets test set and the models learnt on 2021-tweets and on 2020-tweets perform on par on the 2021-debates test set.
As outlined in Section 5, tweet datasets have a higher number of positive training examples than the debates datasets and a smaller number of verified claims to match against. At the same time, retrieval of matching pairs depends largely on token overlaps for both the tweets and the debates data. These characteristics explain why models learnt on the tweets data perform better than models learnt on the debates datasets, even when applied to data of the other discourse type. 2020-tweets has a smaller training set than the other tweet datasets, but also a smaller number of non-matching verified claims, i. e. negative training examples, and the ratio of positive vs. negative examples has a lesser bias towards negative examples. Unless a model overfits the data, training on 2020-tweets thus yields the best models.
Electrophysiology Two days after transfection, CHO cells were mounted on the stage of an inverted microscope and bathed with a Tyrode solution (in mmol/L: NaCl 145, KCl 4, MgCl 2 1, CaCl 2 1, HEPES 5, glucose 5, pH adjusted to 7. 4 with NaOH) maintained at 22. 0 ± 2. 0°C. Patch pipettes (tip resistance: 2. 0 to 2. 5 MΩ) were pulled from soda-lime glass capillaries (Kimble-Chase, USA) with a Sutter P-30 puller (USA). A fluorescent cell was selected by epifluorecence. The pipette was filled with intracellular medium containing (in mmol/L): KCl, 100; Kgluconate, 45; MgCl 2 , 1; EGTA, 5; HEPES, 10; pH adjusted to 7. 2 with KOH. Stimulation and data recording were performed with pClamp 10, an A/D converter (Digidata 1440A) and a Multiclamp 700B (all Molecular Devices) or a VE-2 patch-clamp amplifier (Alembic Instruments).
Acknowledgements We thank the  Agence Nationale de la Recherche  for its financial support to the  Région Pays de la Loire  ( ANR FLASH  Covid-19 -CoV2-E-TARGET) and the  laboratory of excellence 'Ion Channels, Science and Therapeutics'  (grant No. ANR-11-LABX-0015 ). 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 ). We thank the  Cytometry Facility Cytocell from Nantes  for expert technical assistance. We thank  Hugues Abriel  and  Jean-Sebastien Rougier  ( Institute of Biochemistry and Molecular Medicine, university of Bern ) for fruitful discussions and support and critical reading of the manuscript.
Flow cytometry and antibodies Supplementary Table  1  lists the antibodies utilized for FACS analyses in this study. Cells were run on a FACS Celesta (BD Biosciences) or Cytek Aurora (Cytek Biosciences). Flow cytometry data were analyzed utilizing FlowJo v10 (FlowJo). Intracellular and surface cytokine staining gating was based on the examination of cytokine staining for unstimulated cells and isotype control antibody staining.
Antibody-coated bead-based cytokine assay Bead-based Luminex kits assessing human IFNa and TNFa (ThermoFisher) were utilized for measuring cytokine concentrations in supernatants from overnight stimulated pDCs. Beads were assessed on a Luminex 200 machine and the Xponent 3. 1 software (Luminex) was utilized to analyze the data.
Equivalent numbers of HEK Blue hTLR7 cells were stimulated with 10 mM 3F and 3F-AF488, and vehicle for 15 hours in HEK Blue detection media (Invivogen). NF-kB activity was measured by assessing the optical density at 620-655 nm using a SpectraMax 12 (Molecular Device) and analyzed with SoftMaxPro V5 software (Molecular Device).
Image processing Cell images were processed via Airyscan in the Zen software module (Zeiss). Images were further processed in the open-source software Fiji  (26). Baseline intensity thresholds for analysis were defined by cellular autofluorescence and isotype antibody intensity staining for each experiment. Manders' coefficients were determined utilizing an in-house customized plugin coded for thresholding of 3F signal and region of interest selection  (27).
Therefore, we conclude that EGA can modulate cytokine expression by R837-treated human blood pDC by decreasing the expression of IFNa and TNFa as well as the amount of TNFa secreted by pDCs stimulated with R837. EGA was also capable of decreasing IFNa secretion by pDCs stimulated with ssRNA40. EGA diminishes IFNa and TNFa secretion by R837-stimulated pDCs. FACS-sorted pDCs were pre-incubated for 30 mins with 20 mM EGA or vehicle. pDCs were then stimulated with 5 mg/mL R837 for 5 hours and stained for intracellular IFNa expression. Representative FACS plots (A). Summarized data of the frequency of intracellular IFNa + pDCs (B). After overnight culture, the amount of IFNa in culture supernatants was measured by beadbased cytokine assays (C). Representative FACS plots of intracellular TNFa expression (D) and summarized data (E).
The amount of TNFa in culture supernatants (F). Both IFNa (G) and TNFa (H) transcripts were measured by qRT-PCR. In (B-G, E-H), individual lines indicate data generated with pDCs from different subjects. Data analyzed by paired t-test. *p < 0. 05, **p < 0. 01, and n. s. , not significant, for the comparison between groups.
However, these experiments were performed on pDCs rested overnight in IL-3  (17)  which do not exhibit bifurcated cytokine responses  (17, 60). Therefore, data generated in this study extend our knowledge on how pDCs respond to small molecule TLR7 agonists, and further support additional studies testing the effectiveness of EGA, an endosomal trafficking inhibitor, in the regulation of TLR7-mediated cytokine response in the near future. EGA, a late endosome trafficking inhibitor, decreased IFNa expression and secretion by pDCs stimulated via TLR7. This was demonstrated by measuring IFNa transcript, intracellular IFNa expression, and the amount of IFNa secreted in the culture supernatants. EGA also decreased PR8-influenza virus-induced IFNa expression by inhibiting their trafficking to late endosome/ lysosome, resulting in the inhibition of viral uncoating, as previously described  (22).
Acknowledgements. G. P. and B. B. thank the  European Research Council  ( ERC Starting Grant   757448-PAMDORA ) for their financial support. We acknowledge HPC resources from "Mesocentre SIGAMM" hosted by  Observatoire de la Côte d'Azur. L. E. thanks  Alain Miniussi  for maintenance and re-factorisation of the code FARGOCA. G. P. thanks  Frédéric Masset  for helpful discussions. The authors wish to thank the anonymous referee for helpful comments and suggestions.
Optical photometry The optical light curve is compiled using photometry from publicly available ZTF g -and r -band photometry, 1 which we obtained via the 1 https:// lasair-ztf. lsst. ac. uk/ object/ZTF20abrbeie/ ZTF forced photometry service  (Masci et al. 2019  ). In addition, we obtained ATLAS c ('cyan', 4200-6500 Å) and o ('orange', 5600-8200 Å) imaging o v er the course of the light curve. The region was also co v ered infrequently as part of the ongoing Pan-STARRS Near Earth Object search that provides grizy coverage. Data from both ATLAS and Pan-STARRS were obtained as forced photometry 2   (Smith et al. 2020 ). To search for a host galaxy, we use the stacked grizy images from the P an-STARRS 3 π surv e y  (Waters et al. 2020 ). We use the PHOTUTILS package and perform photometry in a circular aperture with a 1 arcsec radius centred on the location of AT2021lwx, and use the PS1 weight image to estimate the photometric uncertainties.
2023 ). and v (5468 Å) filters with the Ultraviolet-Optical Telescope (UV O T;  Roming et al. 2005 ) , respectively. The XRT data were reduced with the tasks XRTPIPELINE and XSELECT. The source and background events were extracted using a circular region of 40 arcsec and an annular ring with inner and outer radii of 60 and 110 arcsec, respectively, both centred at the position of the source. The Ancillary Response Files were created with the task XRTMKARF and the Response Matrix File (RMF), swxpc0to12s6 20130101v014. rmf, w as tak en from the Calibration Data base. 4 Due to the low count rates, the XRT spectra were grouped to have a minimum of three counts per bin using the FTOOL GRPPHA. In terms of the UV O T data, we used the task UV O TIMSUM to sum all the exposures when more than one snapshot was included in each individual filter data and the task UV O TSOURCE to 4 https:// heasarc.
AC K N OW L E D G E M E N T S We thank the anonymous referee for their helpful comments. We thank Nick Stone for helpful discussions on the interpretation of the transient. We are grateful for the rapid turnaround of the DDT proposal by the time allocation committee of the GTC. PW acknowledges support from the Science and Technology Facilities Council (STFC) grant ST/R000506/1. YW acknowledges support from the Royal Society Newton Fund. MP and GL were supported by a research grant (19054) from Villum Fonden. TEMB and LG acknowledge financial support from the Spanish Ministerio de Ciencia e Innovaci ón (MCIN), the Agencia Estatal de Investigaci ón Downloaded from https://academic. oup. com/mnras/article/522/3/3992/7115325 by Technical Information Center Denmark -DTU user on 29 June 2023.
DATA AVA I L A B I L I T Y Swift X-ray and UV data are publicly available from the UK Swift Science Data Centre ( https:// www. swift. ac. uk/ swift portal ). ZTF data can be retrieved from the ZTF forced photometry service ( http s://web. ipac. caltech. edu/staff /f masci/zt f/forcedphot. pdf). The GTC spectra will be made public upon acceptance via the GTC archive.
Study population We retrospectively analyzed all patients undergoing MRg-FUS Vim unilateral ablation for the treatment of disabling tremor from February 2018 to July 2020. The study population consisted of 90 patients (68 M, 22F), with a median age of 70 years (95% CI 67. 8-72, range 36-90) and median disease duration of 15. 5 (95%CI 11-19. 67, range 2-47). Demographic data are reported in Table  1. All patients presented with disabling tremor, refractory to drug therapy (38 patients with essential tremor (ET), 52 patients with tremorgenic Parkinson's (PD) and were clinically selected by a neurologist expert in movement disorders (PS, FP). Sixty patients underwent left thalamotomy (right hand tremor), and 30 patients right thalamotomy (left hand tremor). Based on the time of treatment, patients were divided into three groups of 30 units each: the first group included patients enrolled from February 2018 to January 2019 (Group A), the second group from January 2019 to September 2019 (Group B), the third from September 2019 to July 2020 (Group C).
Clinical parameters The parameters evaluated before treatment and at followups were demographic data (age, sex, pathology), the Fahn-Tolosa-Marin scale for tremor (FTM), the skull density ratio (SDR) between cortical bone and cancellous bone (values > 0. 3 were considered suitable for treatment), days of hospitalization, and thalamotomy-related complications (paresthesia, postural instability, and transient hemiparesis, ataxia, dysarthria, motor, and facial deficits, painful dystonia and strength deficit on the treated side). We evaluated the part A of the FTM, rating resting, postural and action tremor of the arm treated/to be treated (0-12 points); tremor relapses were extrapolated by comparing the last value of the Fahn-Tolosa-Marin scale with the follow-up values at 24 h, considering relapse as an increase of more than 5 points.
Statistical analysis Data were collected, organized, and analyzed through XLSTAT 2017: Data Analysis and Statistical Solution for Microsoft Excel (Addinsoft, Paris, France 2017). Descriptive statistics were calculated for all variables. All numerical data were expressed as mean ± standard error of the mean. The Student's t-test was used to determine group differences between variables, and the Wilcoxon test was used if the assumptions of normality for the t-tests were not fulfilled. The assumption of normality was evaluated using the Shapiro-Wilk and Kolmogorov-Smirnov tests. The Chi-square test was used to compare the distribution of categorical variables. A multiple comparisons with repeated measures test was used to asses variance of continuous variables. Statistical significance was set with a p values ≤ 0.
Tentative identification of metabolites by HPLC-ESI-MS/MS and HPLC-HRMS In order to gain information about the nature of anthocyanins and the presence of other compounds, we performed untargeted metabolomics for potato extracts. Table  3  shows the HPLC-ESI-MS/MS data for 30 compounds organized by retention time (RT) and divided into six families. We confirmed the annotation by comparison with databases and literature  (Lewis et al. , 1998; Fossen et al. , 2003; Ieri et al. , 2011; Eichhorn et al. , 2011; Navarre et al. , 2013; López-Cobo et al. , 2014) , by using authentic standards for HCCs, and by HPLC-HRMS accurate mass for six compounds (Table  3 ). We propose the identification of two miscellaneous (sinapine, salicylic acid glucoside, one remains unknown), four polyamines (caffeoyl putrescine, feruloyl putrescine, bis dihydrocaffeoyl spermidine, tris dihydrocaffeoyl spermine), four HCCs (3-CGA, 4-CGA, 5-CGA, cis-5-CGA), ten anthocyanins (four peonidin derivatives, one cyanidin derivative, five pelargonidin derivatives, plus two pelargonidin hexoses without identification), two non-anthocyanin flavonoids (eriodictyol hexose, rutin, plus three putative flavonoids with phenolic acids), and two glycoalkaloids (solanine, chaconine).
We thank Dr. Fernando Villarreal (Instituto de Investigaciones Biológicas, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CONICET) for suggesting substantial improvements to the manuscript. This work was financially supported by grants from Agencia Nacional de Promoción Científica y Tecnológica (PICT 2018 3815, PICT 2018 0221), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) (PIP 2015 0762) and Universidad Nacional de Mar del Plata (UNMdP, EXA 921/19). This work has benefited from the support of IJPB's Plant Observatory technological platforms. The IJPB is financially supported by Saclay Plant Sciences (ANR-17-EUR-0007). M. L. L. , M. X. S. , and A. B. A. are career members from CONICET. M. M. M. was an advanced undergraduate student, directed by M.
On the one hand, the geometrical efficiency of the silicon detector was estimated by numerical simulations. The angular distribution was considered isotropic for the emitted =0 protons. On the other hand, the AGATA efficiency was measured at low energies with a radioactive 152 Eu source, and simulated with the AGATA Geant4 code library  46, 47  to determine the efficiency at high energies after scaling the simulations to the measured efficiencies at low energies. Determination of the  22  Na flux in novae. The amount of  22  Na ejected in a nova outburst was obtained with the simulation codes MESA  [31] [32] [33] [34] [35]  and SHIVA 1,37 from its abundance in the ejected layers. MESA and SHIVA use several criteria for the ejection of a specific layer, based either on achieving escape (velocity) or a luminosity above the Eddington limit where the radiative pressure exceeds the gravitational force.
Acknowledgments. The authors thank the  GANIL accelerator  staff for their beam delivery and support as well as the AGATA collaboration. This work was supported by the  Normandie Region , the  European  project  ChETEC-INFRA  ( 101008324 ) and SPIRAL2-CZ. Y. H. K. would like to acknowledge the support of the  Institute for Basic Science  ( IBS-R031-D1 ).
Acknowledgements This work was financially supported by the  European Commission  through the  Marie Skłodowska-Curie  projects  HORATES  ( GA-955837 ), by  MCIN/AEI/10. 13039/501100011033  under grant  PID2020-119777GB-I00 , and the  Severo Ochoa Centres of Excellence Program  under grant  CEX2019-000917-S , and by the  Generalitat de Catalunya  under grant no. 2021 SGR 01519. Calculations were performed at the  Centro de Supercomputacion de Galicia (CESGA)  within action  FI-2022-1-0018  of the  Red Espan ˜ola de Supercomputacio  ´n (RES). We thank  Antonio Cappai  and  Miguel Rubı  ´for useful discussions.
Module 2: Heat treatment The heat inactivation parameters (D-values) of G. stearothermophilus were obtained from the literature. More specifically, 566 D-values for several strains and matrixes were obtained from 37 studies (Supplementary Data Table  1 ). The obtained D-values were transformed using a log 10 transformation and plotted against temperature (Supplementary Data Fig. 1 ). A linear secondary model, was fitted into the log 10 D values to estimate the effect of heat temperature on the kinetic parameter D, as following: log 10 D = log 10 D ref + ( T ref -T ) z + ε 1 (8) ε 1 Normal(0, sd 1 ) (9) Where: T ref is the reference temperature equals to 121 • C, T is the applied temperature during thermal treatment, D ref (min) is the decimal reduction time at the reference temperature T ref and z ( • C) is the temperature increase required to reduce D-value by 90%.
The error (ε 1 ) incorporated in the model was considered as mainly representative of variability due to the different strains included in the D-values dataset and possibly of some uncertainty due to the data collection process (although unlikely considering the significant amount of data and sources) (see Supplementary Data Table  1 ). The number of spores surviving the UHT treatment (N HT ) was estimated using a Poisson distribution, as suggested by  Nauta (2001)  and recently used by  Santos et al. , 2020 : N HT Poisson(N 0t × Pr sur ) ( 10 ) Pr sur = 10 ( -t HT D ) (11) Where: N 0t is the number of spores present in the raw material per tank, as defined in section 2. 1. 1 and Pr sur is the probability for one spore to survive the heat treatment  (Nauta, 2001)  deduced from Eq. (8).