dataesr/scientific-paragraphs-categorization huggingface dataset:
It is also evident that there remains a clear performance gap between the IID and OOD-Extra 0. 063 ± 0. 005 0. 070 ± 0. 006 0. 112 ± 0. 004 Inv + Mod (ours) 0. 065 ± 0. 007 0. 069 ± 0. 007 0. 107 ± 0. 007 Table  1. Quantitative comparison of different methods under style shifts. The performance is measured by ADE (lower is better) over 5 seeds. Both the vanilla baseline and our invariant approach alone suffer from large errors, since they either average the domainvarying styles or ignore them. Our modular network incorporates distinctive style features into prediction and hence yields much better results. In particular, enforcing the causal invariance of the first encoder ψ leads to the best OOD robustness, while being highly competitive in the training environments. Our modular adaptation strategy (updating the style modulator f ) yields higher sample efficiency than the conventional counterpart in the low-data regime.
g. , pruning the connections between inertia features and left or right-hand traffic rules, given their presumably minute significance. Another limitation of our work is tied to the scale and diversity of experiments. Thus far, we have demonstrated the strengths of our method on two human motion datasets and two base models as proofs of concept. Nevertheless, our method is highly generic and we hypothesize that it can also bring similar benefits to other types of motion problems and datasets, e. g. , vehicles [11], sports [81] and driving simulations  [50]. Extending the current empirical findings to more contexts can be another valuable avenue for future work. Societal Impact. Out-of-distribution robustness remains a salient weakness of motion forecasting models while having a crucial impact on the safety of autonomous systems, especially in the context of autonomous driving.
Background: In the phase III ASCLEPIOS I and II trials, participants with relapsing multiple sclerosis receiving ofatumumab had significantly better clinical and magnetic resonance imaging (MRI) outcomes than those receiving teriflunomide. Objectives: To assess the efficacy and safety of ofatumumab versus teriflunomide in recently diagnosed, treatment-naive (RDTN) participants from ASCLEPIOS. Methods: Participants were randomized to receive ofatumumab (20 mg subcutaneously every 4 weeks) or teriflunomide (14 mg orally once daily) for up to 30 months. Endpoints analysed post hoc in the protocol-defined RDTN population included annualized relapse rate (ARR), confirmed disability worsening (CDW), progression independent of relapse activity (PIRA) and adverse events. Results: Data were analysed from 615 RDTN participants (ofatumumab: n = 314; teriflunomide: n = 301).
20, 21 In the ASCLEPIOS I and II phase III trials, ofatumumab (20 mg subcutaneously every 4 weeks) was superior to teriflunomide (14 mg orally once daily) in participants with RMS, significantly reducing relapse rate, disability worsening and MRI lesion activity with a favourable safety and tolerability profile that allowed for home administration without premedication. 22 he ASCLEPIOS trials and comparison across trials via network meta-analyses have shown that ofatumumab is among the most highly efficacious treatments for MS. 22, 23  this study, we assessed the benefit-risk profile of ofatumumab versus that of teriflunomide, analysing clinical and MRI data in a subpopulation of participants with early RMS (recently diagnosed and treatment-naive (RDTN)) from the combined ASCLEPIOS I and II trial populations.
Trial design and participants Details of the ASCLEPIOS I (ClinicalTrials. gov identifier: NCT02792218) and II (NCT02792231) trials have been reported. 22 ASCLEPIOS I and II were randomized, double-blind, double-dummy, active-controlled, multicentre trials of identical design conducted concurrently in participants with RMS. Participants were randomized (1:1) to ofatumumab 20 mg subcutaneously every 4 weeks (starting at week 4, after initial dosing of 20 mg on days 1, 7 and 14) or teriflunomide 14 mg orally once daily for up to 30 months. 22 Protocols were approved by the relevant institutional review board or ethics committee at each trial site, and all participants provided written informed consent.
e. percentage reduction in NfL concentration) was the ratio of geometric means (ofatumumab vs. teriflunomide) × 100. Safety data were collected during the treatment period (screening to the end of the trial) and the safety follow-up period until a participant's last visit. After the last treatment dose, participants were followed up for at least 9 months. Data collected on or before 100 days after the last dose of study medication were included in the analysis except for SAEs, for which all data collected until the end of the trial were included. Analyses of individual endpoints in RDTN participants were undertaken post hoc.
63 (0. 28, 1. 40); p = 0. 254; 6mPIRA, HR (95% CI): 0. 57 (0. 24, 1. 33); p = 0. 194). a RDTN were those who had not received a prior MS disease-modifying therapy and who had received a diagnosis in the 36 months before screening. b Age at baseline was calculated from the date of the first administration of trial drug and the birth year (no exact birth date was captured for data privacy reasons). Eligibility for trial entry was assessed at the screening visit. c EDSS score at baseline was defined as the EDSS score at the last assessment before administration of the first dose of trial drug. EDSS scores range from 0 to 10. 0, with higher scores indicating a higher degree of disability. MRI-related outcomes. Ofatumumab reduced the mean number of Gd+T1 lesions per scan from baseline to the end of the trial by 95% versus teriflunomide (0.
We chose to study the FLGamide family of neuropeptides, first identified by Zatylny-Gaudin et al. [21] , and possibly related to arthropod orcokinins. We identified an incomplete N-terminal precursor, as well as two C-terminal ends from the assembly performed in the BioProject ID: PRJNA242869 (Figure  1A ) and a new assembly realized from the SNC of juveniles. Six neuropeptides are cleaved (Figure  1B ), as shown by the detection of these peptides by mass spectrometry in nerve endings, in the hemolymph, and in the CNS in the work of Zatylny-Gaudin et al. [21]. Figure  1C  shows that the expression is only located in the CNS. The presence of three neuropeptides, with a C-terminal FLGamide end, initially led to the name "FLGamide neuropeptides". These neuropeptides were therefore named based on their order of appearance on the precursors: FLGa 1 and FLGa 2 have a common PYY C-terminal end; FLGa 3, FLGa 4, and FLGa 5 are amidated in their C-terminal end; and FLGa 6 does not have any C-terminal amidation.
Highlighted in blue: female genital tract; in green: CNS female; in red: CNS male. This study, therefore, aims to elucidate the possible role of the FLGamide in the regulation of the egg-laying mechanisms in cuttlefish. From this perspective, we studied the tissue expression of the FLGamides, using data from the transcriptome studies by Zatylny-Gaudin et al. [21]. We localized the neuropeptides by immunocytochemistry, using specific polyclonal antibodies, and by mass spectrometry, using a peptidomic approach. We evaluated the biological activity through a myotropic test performed on the different contractile organs of the female genital tract involved in egg-laying.
Structure Determination The complete NMR analysis of the peptide FLGa 1 was performed, in the presence of 150 mM of deuterated dodecylphosphocholine, using the sequential assignment strategy  [22]. The complete chemical shift assignment is presented in Table  1. The pattern of the NOESY cross-peaks was characteristic of a helical structure of the peptide with numerous inter residue i-i + 3 connectivities. The structure was solved using AMBER software, and the superposition of the 18 lowest energy conformers of the peptide FLGa 1 in the DPC micelles, fitted on segment 4-14 backbone atoms, is presented in Figure  5. The summary of the refinement statistics is given in Table  2. An alpha helix is clearly defined from the residues 4 to 14; the first residues, as well as the C-terminal units, are unstructured.
Discussion The results of the present study provide insights into the role of a family of neuropeptides found in the whole S. officinalis female genital tract in the regulation of spawning. The expression profile, determined from the 16 transcriptomes sequenced by Zatylny-Gaudin et al. [21]  (BioProject PRJNA242869), made it possible to highlight that the transcripts were localized in the three main regions of the CNS (OL, SupEM, and SubEM). The SubEM is the region of the CNS that innervates the visceral mass and the genital tract, as well as the only neurohemal area described to date in cuttlefish  [23] [24] [25] [26]. Moreover, lower expression levels were observed in the glands involved in the secretion of the inner and outer capsules of the egg (Figure  1C ). The occurrence of the neuropeptide transcripts in these glands allows us to speculate about the mechanisms responsible for the rapid response induced by external stimuli.
In Silico Analyses The transcriptome sequencing, assembly and annotation are described by Zatylny-Gaudin et al. [21]. The expression was quantified using fragments per kilobase of exon per million fragments mapped (FPKM) values from the 16 tissue-specific transcriptomes. The FPKM values of a given transcript from several tissues were compared to establish an expression pattern. In this case, the FPKM values represented the pooled expression of five animals used to determine each transcriptome. The multiple sequence alignments of FLGamide, orcokinins, feeding-circuit peptides, FLGamide precursors from mollusks and arthropods were performed using CLC Main Workbench 6. 7. 1 and SIM-Alignment Tool for protein sequences (Expasy). The Loligo pealei transcriptomic data were retrieved from the website http://ivory.
idyll. org/blog/ 2014-loligo-transcriptome-data. html (accessed on 17 February 2014), where the L. pealeii genome and five transcriptomes are available, as well as a public Blast server. The protein precursors of FLGamide came from a first assembly by Zatylny-Gaudin et al. [21]  (BioProject PRJNA242869, https://zenodo. org/record/3647818#. YI-8emYzZqs, accessed on 11 December 2019) and a second supplemental one corresponding to the transcriptomes of juvenile CNS (296520 transcripts).
Peptide Sequencing and Protein Precursor Identification The fragmentation pattern based on the occurrence of y, b, and a ions was used to determine peptide sequences. The database searching was performed using the Mascot 2. 5. 1 program (Matrix Science, Boston, MA, USA). A database corresponding to a homemade S. officinalis transcript database (including 356,644 entries) was used (BioProject PRJNA242869). The variable modifications allowed were as follows: methionine oxidation and dioxidation; C-terminal amidation; and N-terminal pyroglutamate. "No enzyme" was selected. Mass accuracy was set to 200 ppm and 0. 6 Da for the MS and MS/MS modes, respectively.
Structure Calculations The 1 H chemical shifts were assigned according to classical sequential assignment procedure. The NOESY cross-peaks were integrated and assigned within the Ccp-Nmr [41] and NMRView software  [42]. The volumes of the NOESY peaks between the methylene pair protons were used as the reference of 1. 8 Å. The structure calculations were performed with AMBER 17  [43]  in two stages: cooking, and simulated annealing in an explicit solvent. The cooking stage was performed at 1000 K to generate 100 initial random structures. The simulated annealing calculations were carried out during 20 ps (20,000 steps, 1 fs long). First, the temperature was raised quickly and was maintained at 1000 K for the first 5000 steps, then the system was cooled gradually from 1000 K to 100 K from step 5001 to 18,000, and finally the temperature was brought to 0 K during the 2000 remaining steps.
Acknowledgments: We thank  Jean-Luc Blaie  and  Maxime Marie , captains of the professional fishing boats " Père Daniel " and his crew for their valuable help in providing cuttlefish. We thank  Beatrice Adeline  for technical support in histological analyses, and  Christophe Roger  for technical support in the construction of specific facilities. We thank  Didier Goux  from the technical platform CMAbio for his help in optical microscopy. We thank  Baptiste Legrand  and  Matthieu Simon  for their help with the AMBER software program. Part of this work was performed using the  PRISM core facility (Biogenouest ,  Univ Rennes ,  Univ Angers ,  INRAE ,  CNRS FRANCE). This work was financed by the  ANR "NEMO"  and the  Conseil regional of BASSE-NORMANDIE.
V. DATA AVAILABILITY All experimental data presented in the figures that support the findings of this study are available from the Zenodo repository  [49]. Raw SWSANS data from D33 are also available from the ILL repository  [50]. Intensity (arb. units) Intensity (arb. units) Intensity (arb. units) FIG. 4. Sketch of the scattering intensity as a function of θ rel (distance from the Bragg angle) in different regimes; θ B = 0. 1, θ 0 = 0. 05 everywhere. The blue curves are for θ C = 0. 047, the green curves are for θ C = 0. 044, the red curves are for θ C = 0. 036, and the orange curves are for θ C = 0. 02. (a) SWSANS spectra without magnon damping. One can see the divergence at the cutoff angle, cf. Eq. (A6). (b) SWSANS spectra for relatively small magnon damping γ 0 ≈ 0. 0002. Solid curves here stand for the spectrum calculated numerically using Eq.
ACKNOWLEDGMENTS The authors thank  T. Arima ,  T. Nakajima ,  R. Takagi , and  H. M. Rønnow  for helpful discussions. This research was supported in part by  JST CREST  (Grants No. JPMJCR20T1  and No. JPMJCR1874 ). V. U. and J. S. W. acknowledge funding from the  SNSF  Projects No. 200021_188707  and  Sinergia CRSII5_171003 NanoSkyrmionics. V. U. acknowledges financial support from the  SNSF National Center of Competence in Research, Molecular Ultrafast Science and Technology (NCCR MUST ). The authors thank the  Heinz Maier-Leibnitz Zentrum  and  Institut Laue-Langevin  for provision of neutron beamtime according to the proposals No. 15362  and No. 5-42-535 [50 ], respectively. The contribution to the study by O. U. was funded by the  Russian Federation President  Grant No. MK-1366. 2021. 1.
Anthropogenic and natural emissions contribute to enhanced concentrations of aerosols, so-called Arctic Haze in the Arctic winter and early spring. Models still have difficulties reproducing available observations. Whilst most attention has focused on the contribution of anthropogenic aerosols, there has been less focus on natural components such as sea-spray aerosols (SSA), including sea-salt sulphate and marine organics, which can make an important contribution to fine and coarse mode aerosols, particularly in coastal areas. Models tend to underestimate sub-micron and overestimate super-micron SSA in polar regions, including in the Arctic region. Quasi-hemispheric runs of the Weather Research Forecast model, coupled with chemistry model (WRF-Chem) are compared to aerosol composition data at remote Arctic sites to evaluate the model performance simulating wintertime Arctic Haze.
, 2021) , observations for Na + , Cl -, NH + 4 , NO - 3 and SO 2- 4 measured with ion chromatography are used  (Sharma et al. , 2019). At Villum, the same observations are collected using a filter-pack over a week and analysed using by ion-chromatography (Cl -, SO 2- 4 ), cat-ion ionchromatograph (Na + ) and segmented flow analysis (NH + 4 ). For all the EBAS stations, the units of inorganic aerosols (NH + 4 , SO 2- 4 , NO - 3 ) are converted to model units (µgm -3 ), using the ratio of molar weights of NH + 4 , NO - 3 , SO EBAS documentation notes, in the case of Alert only, that there are uncertainties of around 33% and 36% in Na + , SO 2- 4 , NO - 3 and Cl -, respectively, and higher uncertainties (43%) for NH + 4. Surface mass concentration data, diameter less than 2. 5 µm (r d ≤ 2. 5 µm), from the Interagency Monitoring for Protected Visual Environments (IMPROVE) database is also used for model evaluation for Simeonof (55.
3N, -160. 5W), a sub-Arctic site on the Aleutians islands, south of Alaska and an inland site, Gates of the Arctic (66. 9N, -151. 5W) which is located south-east of Utqiaġvik. The samples are collected on-site (e. g. Simeonof site) over 24 hours every three days (http://views. cira. colostate. edu/fed/QueryWizard/Default. aspx, Malm et al. (1994)). At these two sites observations of Na + , Cl -, OC, NO - 3 and SO 2- 4 are used. To compare with the OC observations at the two Alaskan sites modelled OA is divided by 1. 8, the reported ratio of OM/OC in the documentation for these two stations  (Malm et al. , 1994). In this study, mass concentration data with diameter ≤ 2. 5 µm are defined as fine mode aerosols, while diameter < 10 µm then are defined as coarse mode aerosols. Sub-micron (d a < 1.
Campaign data Details about the field campaign (January 23-27 and February 24-28, 2014) measurements near Utqiaġvik, Alaska can be found in KRP18 and KRP19. Briefly, atmospheric particles were collected using a rotating micro-orifice uniform deposition impactor located 2 m above the snow surface at a site located 5 km across the tundra from the NOAA Barrow Observatory and inland from the Arctic Ocean. The sampled particles were analysed by computer-controlled scanning electron microscopy with energy scattering X-ray spectroscopy (CCSEM-EDX) to determine the individual particle morphology and elemental composition. The analysed samples were collected either during daytime or nighttime and only when wind directions were  This section focuses on evaluating the capability of the model to simulate Arctic Haze aerosols during wintertime and improving model treatments of SSA.
Marine organics Recent data-analysis studies  (Saliba et al. , 2019; Kirpes et al. , 2019) , have suggested that marine organics contribute significantly to natural aerosol composition as ocean biomass can influence SSA number concentrations and diameter. In the CONTROL run, marine organics are not activated; however a source code is included in the model by  Archer-Nicholls et al. (2014). For this reason, the parameterization, based on  Fuentes et al. (2010, 2011) (F10 and F11 from now on, respectively)  is activated in the MOSAIC scheme to include a source flux for marine organics with dry diameters up to 0. 45 µm. The scheme is based on an analysis of data from a cruise in mid-latitudes investigating the influence of dissolved organic matter on the production of sub-micron SSA. The F10 SSA source function also depends on MO80 whitecap coverage.
, 2013) , surfactant (amphiphilic organic material) activity  (Callaghan, 2013)  and fetch-dependent threshold for breaking waves  (Revell et al. , 2019; Hartery et al. , 2020) , have also been shown to affect whitecap lifetime, with implications for SSA production. Goddijn-Murphy et al. (2011)  analysed Marine Aerosol Production (MAP) whitecap data, in combination with analysis of in-situ and satellite data (from Quick Scatterometer, QuikSCAT) for winds and waves. The satellite data were used to derive an expression with a lower wind speed dependence compared to MO80  (Salisbury et al. (2014) , SALI14 from now on). Here, the SALI14 parametrization is implemented instead of the MO80 whitecap fraction expression: W(U) = 4. 60 × 10 -3 × U 10 2. 26 (3) Based on Figure  2  in SALI14, the seasonal mean of W(U 10 ) using Eq.
Wind-speed sensitivity to sub-micron SSA emissions In the regional runs presented so far, the lower wind speed dependence based on satellite data is used (SALI14) since it improves modelled SSA compared to observations in the 100 km runs (see in section 4. 2). However, RUS10 found evidence for higher wind speed dependence during the ICEALOT cruise in the Arctic. They found that wind speed is a good predictor of a marine factor, calculated using positive matrix factorization, for sub-micron organic mass (OM1 sea ). Their analysis showed a high correlation between OM1 sea , sub-micron sodium (Na + 1) concentration and wind speed at 18 m (correlation r equal to 0. 90 for the North Atlantic and Arctic region, see Table  S3  Supplementary Material in RUS10). Average OM1 sea concentrations (0. 2 µ g m -3 ) reported by RUS10 for the eastern Arctic Ocean are about half those reported at Utqiaġvik by  Shaw et al.
Also, the variations in modelled NH + 4 between the ALASKA_CONTROL and ALASKA_NEW for January and February simulations are small. The model underestimates observed NH + 4 , which peaks at 0. 2 µ g m -3 during February. Calculated biases and RMSEs for all aerosol species and for January and February campaigns are given in APPENDIX F. Comparison with data from Gates of the Arctic (see APPENDIX E) shows that there are not significant differences between the control run and including all the updates in February 2014. The model still underestimates observed tOC due to missing local anthropogenic sources and overestimates SO 2- 4 , NO - 3 , Na + and Cl -. However, due to short period of the simulation only two observations are available, thus more detailed observations are needed to examine further the reason why the model differs from the observations at this site.
Results for NO - 3 are also improved overall due to less formation via heterogeneous uptake of HNO 3. Inclusion of the SST dependence only has a small effect on sub-micron SSA in the Arctic. In the future, other SST dependencies could be considered such as that proposed by  Sofiev et al. (2011)  which could increase sub-micron SSA at low temperatures  (Salter et al. , 2014 (Salter et al. , , 2015;; Barthel et al. , 2019). However, further field data studies are needed to confirm such dependencies in the Arctic. A source of ss-SO 2- 4 is also added to the model leading to improved modeled SO 2- 4 in the high Arctic (e. g. Alert) and Alaskan (e. g. Gates of the Arctic, Simeonof) sites. However, at sites such as Utqiaġvik, which may be influenced by the Prudhoe Bay oilfields, the model still underestimates sub-micron SO 2- 4 possibly due to missing anthropogenic emissions.
The results of this study also highlight that SSA dry removal is less important than the role of open leads in the Arctic during wintertime. The role of wet deposition on SSA is also examined. In that case, the precipitation flux is doubled and as result super-micron SSA decreased, but the sensitivity did not affect sub-micron SSA. Wet deposition is not addressed further in this study, because according to NOAA climate data recorded precipitation and snowfall was the lowest during February 2014. Wet deposition is addressed in details in the companion paper for BC. Our results suggest that further investigation is needed to determine more realistic dry deposition velocities over snow, ice and ocean in the Arctic and to derive more realistic sea-ice fractions, including the presence of open leads, which can vary over periods of days.
13 https://doi. org/10. 5194/egusphere-2022-310 Preprint. Discussion started: 11 May 2022 c Author(s) 2022. CC BY 4. 0 License. that higher particle organic fractions are expected in algal bloom regions with high C:Chl-a ratios and Chl-a varying between 0. 4-10 µg/L. The use of the F11 high biogenic activity option in our simulations is justified since MODIS-Aqua satellite data (https://neo. sci. gsfc. nasa. gov/view. php?datasetId=MY1DMW_CHLORA&date=2014-12-01) for January-February 2014 show that Chl-a south of Alaska and along the west coast of the United States varied between 0. 3 and 3. 0 µg/L. Fujiki et al.
Data availability. All data used in the present paper for Zeppelin and Alert are open access and are available at the EBAS database infrastructure at NILU -Norwegian Institute for Air research: http://ebas. nilu. no/. Observations for Villum are obtained after personal communication with Henrik Skov. Observations from IMPROVE database can be obtained from: http://views. cira. colostate. edu/fed/QueryWizard/. Suband super-micron aerosol mass concentrations at Utqiaġvik, Alaska can be obtained from the follow link: https://saga. pmel. noaa. gov/data/ stations/.