dataesr/scientific-paragraphs-categorization huggingface dataset:
OBSERVATIONS KMT-2020-BLG-0414 occurred at equatorial coordinates (α, δ) J2000 = (18:07:39. 60, -28:29:06. 8), corresponding to Galactic coordinates ( , b) = (2. 82, -3. 95). It was announced as a "probable microlensing" event by the KMTNet Alert-Finder system  (Kim et al. 2018 ) on 1 June 2020, about 40 days before peak, when the event was manifested as an I ∼ 18. 7 difference star. As mentioned in Section 1, by this date, KMTC and KMTS had been closed down for more than two months due to Covid-19. Hence, only KMTA data contributed to the alert and to subsequent monitoring of the event. The event lies in the KMNTet BLG32 field, which has a cadence of Γ = 0. 4 hr -1 , with every tenth I-band observation complemented by one in the V band for the source color measurements 3. The event was independently identified by the Microlensing Observations in Astrophysics (MOA,  Sumi et al.
6m (OPD16) telescopes with the I filter. At UT 05:58 on 10 July 2020 (HJD = 9040. 75), the LCO & µFUN Follow-up Team identified that this event was currently undergoing an anomaly and would peak at a very high magnification soon, based on the real-time LCO and MOA data. Noting that SSO was predicted to be rainy that night, the Team issued an alert to the MOA collaboration. MOA responded to the alert and densely observed this event 60 times over the peak. Due to the very high brightness, the MOA observer decreased the exposure time from 60s to 5. 2s over the peak. We carefully inspected these 60 MOA images and excluded 19 data points from the analysis due to saturation or bad seeing. At UT 13:53 on 10 July 2020 (HJD = 9041. 08), the LCO & µFUN Follow-up Team also issued an alert to all µFUN observers.
As a result, the 0. 3m Perth Exoplanet Survey Telescope (PEST) in Australia and the 0. 4m Possum Observatory (Possum) at New Zealand responded to the alert and took intensive observations without a filter. Finally, the event was also observed by the 3. 6m Canada-France-Hawaii Telescope (CFHT) with the SDSS-i filter. For the light curve analysis, the KMTNet, MOA, CFHT and LCO data were reduced using custom implementations of the difference image analysis technique  (Tomaney & Crotts 1996; Alard & Lupton 1998) : pySIS  (Albrow et al. 2009)  for the KMTNet data,  Bond et al. (2001)  for the MOA data and ISIS  (Alard & Lupton 1998; Alard 2000; Zang et al. 2018)  for the CFHT and LCO data. The OPD, PEST and Possum data were reduced using DoPHOT  (Schechter et al. 1993 ). On the OPD16 images, the target was affected by a bleed trail from a saturated star, resulting in some systematics.
We therefore do not include OPD16 data in the analysis. The I-band magnitude of the KMTA light curve has been calibrated to the standard I-band magnitude. For the source color measurements, we use the MOA V -band data, while KMTA V -band data are not used due to poor seeing. The errors from photometric measurements for each data set i were renormalized using the formula σ i = k i σ 2 i + e 2 i,min , where σ i and σ i are original errors from the photometry pipelines and renormalized error bars in magnitudes, and k i and e i,min are rescaling factors. We obtained the rescaling factors using the procedure of  Yee et al. (2012) , which enables χ 2 /dof for each data set to become unity. We derived the rescaling factors using the binary lens (2L1S) and triple lens (3L1S) models, respectively, in order to understand how the event would have been interpreted in the absence of MOA data on the peak.
This requires a fourth parameter ρ = θ * /θ E , where θ * is the angular radius of the source. As we will show in Section 4, the full light curve cannot be explained by a 2L1S model, and in fact requires 3L1S. However, if we exclude the MOA data on the peak (9040. 78 < HJD < 9041. 20, the only data set to cover the peak), then the remaining data are quite well fit by a 2L1S model. We therefore begin by analyzing this restricted (non-MOA-peak) data set. We are motivated by two considerations. First, and most importantly, 3L1S models often "factor" into two 2L1S models  (Han 2005; Gaudi et al. 2008; Gould et al. 2014; Han et al. 2013 Han et al. , 2017 Han et al. , 2019a)). In particular, the 3L1S caustic is often very nearly the superposition of the two 2L1S caustics. In such cases, one can often exclude the data from the neighborhood of the anomaly from the "second" 2L1S model in order to accurately determine the parameters of the "first" 2L1S model.
In these cases, the "first" 2L1S model provides a powerful basis for finding the full 3L1S model, by one of several techniques. This proves to be the case for KMT-2020-BLG-0414. Second, it is of independent scientific interest to understand how the event would have been interpreted and check the so-called "higher-order effects" in the absence of MOA data on the peak. For example, MOA could have been weathered out on the night of the peak (as was KMTA). The analysis would have led to a report of a single low-mass-ratio planet and a lens that is much brighter than the blended light. The comparison of this reconstructed "report" with the full model can inform our understanding of other 2L1S events with incomplete light-curve coverage. Therefore, for the remainder of this section, we will exclude the MOA data on the peak.
Static Binary Lens Model The 2L1S model requires three additional parameters (s, q, α), which are respectively the separation of the two lens bodies scaled to θ E , the mass ratio between these bodies, and the angle of the source trajectory relative to the binary axis. For modeling, we use the advanced contour integration code  (Bozza 2010; Bozza et al. 2018 ), VBBinaryLensing 5. We initially carry out a sparse grid searches for the parameters (log s, log q, α). The grid consists of 21 values equally spaced between -1. 0 ≤ log s ≤ 1. 0, 20 values equally spaced between 0 • ≤ α < 360 • , and 61 values equally spaced between -6. 0 ≤ log q ≤ 0. 0. For each set of (log s, log q, α), we find the minimum χ 2 by Markov chain Monte Carlo (MCMC) χ 2 minimization using the emcee ensemble sampler (Foreman-Mackey et al.
3L1S ANALYSIS While the 2L1S models described in Section 3 fit the non-MOA-peak data very well, they completely fail to explain the features of the MOA data in the peak region. Moreover, a 2L1S grid search that includes all the data fails to return any model that even approximately traces the data over the peak. See Figure  1. We therefore conduct a 3L1S grid search. Relative to static 2L1S models, 3L1S models have three additional parameters, (s 3 , q 3 , ψ). These are, respectively, the normalized separation of the third body from the primary, the mass ratio of the third body to the primary, and the angle of the second from the third body, as seen from the primary. Note that, to avoid confusion, we rename (s, q) → (s 2 , q 2 ).
The Blended Light For KMT-2020-BLG-0414, the baseline object was detected by the OGLE-III survey, (V, I) base = (20. 70±0. 08, 18. 46±0. 03). We further consider the statistical errors due to the mottled background from unresolved stars  (Park et al. 2004 ). We follow the approach of  Ryu et al. (2020b)  using the GalSim package  (Rowe et al. 2015)  with the readout noise of  Udalski et al. (2015). We find σ I = 0. 08 mag and σ V = 0. 012 mag, and thus the baseline object has (V, I) base = (20. 70 ± 0. 14, 18. 46 ± 0. 09), yielding the blended light of (V -I, I) B = (3. 1 +1. 0 -0. 6 , 19. 32 ± 0. 20). This value is consistent with the lens properties that are predicted by the microlensing light-curve and CMD analyses. For example, using the median θ E and π E values of the close u 0 < 0 solutions, the host mass M 1 = 0.
W. Z. thank Subo Dong for fruitful discussions. W. Z. , S. M. and X. Z. acknowledge support by the National Science Foundation of China (Grant No. 11821303 and 11761131004). Work by C. H. was supported by the grants of National Research Foundation of Korea (2019R1A2C2085965 and 2020R1A4A2002885). Work by JCY was supported by JPL grant 1571564. This research has made use of the KMTNet system operated by the Korea Astronomy and Space Science Institute (KASI) and the data were obtained at three host sites of CTIO in Chile, SAAO in South Africa, and SSO in Australia. This research uses data obtained through the Telescope Access Program (TAP), which has been funded by the TAP member institutes. The MOA project is supported by JSPS KAKENHI Grant Number JSPS24253004, JSPS26247023, JSPS23340064, JSPS15H00781, JP16H06287, and JP17H02871.
08, 0. 002) (0. 35, 0. 010) LCO SAAO i 9038. 3 -9055. 5 107 ISIS (1. 31, 0. 001) (1. 05, 0. 005) LCO McDonald i 9040. 7 -9046. 7 62 ISIS (1. 03, 0. 003) (1. 10, 0. 005) CFHT i 9041. 9 -9051. 9 23 ISIS (3. 25, 0. 000) (1. 10, 0. 020) µFUN OPD06 I 9040. 4 -9045. 7 105 DoPHOT 3 (1. 15, 0. 002) (1. 33, 0. 000) µFUN OPD16 I 9038. 5 -9049. 6 861 DoPHOT µFUN PEST unfiltered 9042. 2 -9051. 0 98 DoPHOT (0. 82, 0. 016) (0. 71, 0. 020) µFUN Possum unfiltered 9041. 8 -9052. 0 143 DoPHOT (0. 81, 0. 027) (0. 87. 0. 020) NOTE- Schechter et al. (1993) 2 MichaelDAlbrow/pyDIA: Initial Release on Github, doi:10. 5281/zenodo. 2680493Alard&Lupton(1998);Alard(2000);Zanget al. (2018)4Schechter et al. (1993).
Overall, most countries have no comprehensive appraisals of the economic costs of IAS, precluding e cient allocation of resources to mitigate ecological and economic impacts  (Lodge et al. 2016). Recently, however, the InvaCost database  (Diagne et al. 2020b ) was launched to tackle this critical knowledge gap and provide comparable means to assess costs of IAS. By systematically retrieving, collating and standardising reported economic costs of IAS, it provides the rst global assessment of invasion monetary impacts  (Diagne et al. 2021). With costs recorded against numerous environmental, typological, temporal and spatial descriptors, this database provides a critical step towards comprehensive assessments at continental (e. g. , Africa,  Diagne et   India, a rapidly emerging developing economy which is categorised as mega-biodiverse and is home to four of the global biodiversity hotspots  (Myers et al.
Materials And Methods Information on the economic cost of IAS in India was extracted from the InvaCost database (https://doi. org/10. 6084/m9. gshare. 12668570. v3; 9,823 entries using the latest version;  Diagne et al. 2020b, c; Angulo et al. 2021). The data in InvaCost were collected via (i) literature searches using the ISI Web of Science, Google Scholar and the Google search and (ii) targeted searches through contacting experts and stakeholders to request additional cost information. InvaCost records costs whilst explicitly considering their source material, reliability (i. e. classi cation of cost estimates based on the type of publication and method of estimation; low vs. high) and implementation approach (i. e. observed vs potential). All the retrieved costs were standardized to 2017 USD for comparability.
In order to obtain costs speci c to India, we ltered costs based on the "O cial_country" column of the database (see Online Resource 1), thereby excluding costs in other countries or at greater spatial scales (e. g. subcontinental or continental). Costs in InvaCost are reported over different durations (e. g. several months, single years or several years), therefore, we standardised costs such that each cost entry corresponded to a single year. This was done using the expandYearlyCosts function of the 'invacost' R package  (Leroy et al. 2021) , thereby 'expanding' the data based on the difference between the "Probable starting year adjusted" and "Probable ending year adjusted" columns for each cost entry (see Online Resource 1). We removed cost entries that occurred outside of the 1960-2020 time range.
Environmental distribution Among the environments invaded by IAS in India, only 8% of the total entries were from diverse or unspeci ed environments. However, they constituted 93% of the total economic costs (i. e. Diverse/Unspeci ed; US$ 118 billion, n = 8). Species from semi-aquatic environments constituted only 6. 7% of the total costs (US$ 8. 6 billion, n = 35). Aquatic and terrestrial species-related costs were one order of magnitude lower than semi-aquatic costs, and constituted less than 1% of total costs (aquatic, US$ 509 million, n = 16; terrestrial, US$ 207 million, n = 35). One out of the ten species in the dataset for which economic costs could be attributed occured in semi-aquatic environments (e. g. Aedes aegypti), whereas the other nine species occured in terrestrial environments.
Additionally, searching for costs was di cult because appropriate parallel regional language terms for 'invasive species' or 'biological invasions' do not exist. Accordingly, it has been shown that up to twice as many costs entries could be found in non-English sources in the InvaCost database  (Angulo et al. 2021). Lastly, InvaCost only considers those cost entries which were directly attributable to IAS and could be monetised. IAS may impact native ecosystems in ways that are di cult to monetise, for example through declines in ecosystem services or native species populations. Alternatively, they may cause quanti able, but indirect impacts, where attribution of species costs or invasion status may be secondary. The above-mentioned reasons suggest that the current cost projections re ect only a fraction of the actual costs and are thus grossly underestimated.
Taxonomic distribution More than 95% of India's invasive animals and plants did not have a single documented cost. Similar patterns have been observed in invasion-related costs in the UK  (Cuthbert et al. in press), Australia  (Bradshaw et al. in press), Russia,  (Kirichenko et al. in press), France  (Renault et al. in press) ,  Mexico (Rico-Sánchez et al. in press)  or Argentina,  (Duboscq-Carra et al. in press). Out of the 157 invasive animal species in India, 99 are reported from marine environments. Although the database has cost entries from marine environments, the species responsible for these costs were categorised as diverse or unspeci ed. Indeed, considering the entire database globally, marine species comprise only approximately 1% of total aquatic invasion costs  (Cuthbert et al.
Only three of the 21 high concern invasive species (HiCIS) from India  (Mungi et al. 2019)  had cost entries in our dataset. If low reliability and potential costs were considered, three more species from HiCIS would be included, viz. Achatina fulica, Eichhornia crassipes and Prosopis juli ora. The remaining 15 HiCIS did not have a single cost entry in our dataset, despite probably high actual costs. More focused searches on HiCIS may reveal costs that could not be included in these analyses.
Acknowledgements We are grateful to  Claire Assailly ,  Frederic Jourdain ,  Chunlong Liu ,  Lise Nuninger ,  David Roiz  and  Wen Xiong  for preliminary data collection and veri cation;  Liliana Ballesteros-Mejia  for data standardisation;  Boris Leroy  for the 'invacost' R package;  Anagha Pund  for helpful discussions; and  Ashok Bang  for translating the abstract in Hindi.
Funding The construction of the InvaCost database was funded by the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  through the  InvaCost  project. The work was conducted following a workshop funded by the  University Paris Saclay  and the  CNRS. Funds for FC came from the  AXA Research Fund Chair of Invasion Biology of University Paris Saclay. CD was funded through the  2017-2018 Belmont Forum and BiodivERsA joint call for research proposals , under the  BiodivScen ERA-Net COFUND programme with Project "Alien Scenarios " ( BMBF/PT DLR 01LC1807C ). DM was funded through grant no. 18-18495S ,  EXPRO  grant  19-28807X  ( Czech Science Foundation ) and the long-term research development project  RVO 67985939  ( Czech Academy of Sciences ).
Two panels, one formed of 150 regular wine consumers and one of trained assessors, evaluated the mouthfeel characteristics of Sangiovese wines using the CATA questionnaire relative to six attributes (silk, velvet, dry, aggressive, hard, mouthcoat). CATA profiles were compared by Multiple Factor Analysis (MFA). To assess the effect of astringency sensations on the liking of Sangiovese wines by consumers, two references maps (with data from consumers and trained assessors) were also produced.
Wine Evaluation by Consumers Participants were recruited by an external consumer association, applying three specific screening criteria: aged above 21; buying a bottle of wine at least once a month, and consuming wine at least once a week. 150 regular wine consumers evaluated the Sangiovese wines. Participants expressed their general liking for Sangiovese wines (a priori liking M = 4. 4). Individual wine involvement was measured (M = 5. 1) together with wine knowledge (M = 4) (for a detailed overview of the dataset see  [30] ). The final sample was composed of 62% male, 42% completed secondary school, and 36% graduated from university; the average age was 41. 6 years old (S. D. 13. 7), and the range age was between Foods 2021, 10, 218 4 of 10 21 and 75 years old; 57% of subjects consumed wine three or four times a week; 26% paid, on average, a bottle of wine for home consumption between 9 € and 15 €.
Attributes were presented in balanced order between and within participants following a Williams Latin square design. In order to minimize dumping, an option for 'Other' was included with an open-ended text box; these data were not used in any analysis. Before CATA evaluation, consumers expressed their liking according to a 9-point hedonic scale (1 = 'dislike extremely', 9 = 'like extremely'). Consumers were encouraged to wash their mouth between each tasting with still mineral water and unsalted crackers. The protocol used for data collection complied with national ethical requirements and was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. In particular, all subjects provided their informed consent to participate in the study, and all data were collected anonymously.
Data Analysis Cochran's Q test, which was followed by the multiple pairwise comparison test (p < 0. 05), was applied to the CATA data to identify significant differences in the frequency of use of the terms (Cf%) between consumers and trained assessors for each of the terms included on the CATA questionnaire. Analyses were carried out by the XLSTAT software package (Addinsoft, XLSTAT 2017). MFA was used to assess the configurational similarity of wine spaces obtained with CATA data by the consumer and trained panels. Principal Component Analysis (PCA) and MFA were used to model liking scores from consumers as a supplementary variable RATA data (from trained assessors) and with CATA data (for consumers), respectively. MFA was performed using FactoMineR  [31]  package.
Effect of Astringency Subqualities on Consumer Liking In order to identify the drivers of liking for Sangiovese wine, the subqualities data corresponding to consumers and trained assessors were used to analyze the relationships with hedonic ratings from consumers in two separate preference maps. A MFA was performed on the citation frequencies by CATA and the liking scores by consumers. In Figure  3 , the factor map for the contingency table (a), the groups representation (b), and the individual factor map (c) related to the six astringency terms and the twelve Sangiovese wines are shown. respectively. The RV range 0. 65-0. 71 can represent an indicator of good agreement between configurations from consumers and trained panels  [32]. In the individual factor map (Figure  2b ), the wine spaces, compared through the partial range representation, were close except for Sangiovese wines CRH, CCH, and TSH.
Effect of Astringency Subqualities on Consumer Liking In order to identify the drivers of liking for Sangiovese wine, the subqualities data corresponding to consumers and trained assessors were used to analyze the relationships with hedonic ratings from consumers in two separate preference maps. A MFA was performed on the citation frequencies by CATA and the liking scores by consumers. In Figure  3 , the factor map for the contingency table (a), the groups representation (b), and the individual factor map (c) related to the six astringency terms and the twelve Sangiovese wines are shown. In Figure  4 , the PCA showed the attributes of astringency obtained from trained assessors by RATA as active variables and the liking from consumers for the Sangiovese wines as the supplementary variable.
Although we do not have sufficient resolution to confirm all the assignments based on side chain information, we are confident in our assignments for the following reasons: First, the presence of all factors in the isolated intermediates was confirmed by mass spectrometry (MS) (Supplementary Data 1, 2). Second, our maps with density corresponding to DDX28, MRM3, and the NSUN4-mTERF4 regions have sufficient local resolution to assign the secondary structure. Thus, we can fit either homology models or crystal structures of the respective factors. NSUN4 and mTERF4, for example, form a heterodimer and here the X-ray structure of the entire complex fits the density after small adjustments (Supplementary Fig. 5 ). Third, the DDX28 density clearly adopts a DEAD box helicase fold and DDX28 is the only DEAD box helicase known to be involved and present in the MS analysis.
4, 150 mM KOAc, 5 mM Mg(OAc) 2 , 1 mM DTT). After washing, elution was performed by three times incubation with 200 μl elution buffer (0. 2 mg/ml 3×Flag peptide in washing buffer) for 15 min. The final elution was concentrated using 100 kDa cut-off Amicon concentrator, and the final concentration was measured using a NanoDrop photometer. Electron microscopy and image processing. Before freezing, a final concentration of 0. 05% Nikkol was added to improve the ice quality. 3. 5 μl of the sample (MALSU1 sample: OD 260 = 0. 8, GTPBP10 sample: OD 260 = 1. 5) was directly applied onto 2 nm carbon pre-coated R3/3 holey copper grids (Quantifoil), blotted for 3 s and plunge-frozen in liquid ethane using a Vitrobot Mark IV. Cryo-EM data was acquired on a Titan Krios transmission electron microscope (Thermo Fisher Scientific) operated at 300 kV under low-dose conditions (28 e -Å -2 in total) with a nominal pixel size of 1.
084 Å on the object scale using EPU 2 software. A total 12,430 micrographs (for MALSU1 sample) and 9834 micrographs (for GTPBP10 sample) were collected on a Falcon II direct electron detector. The original frames were aligned, summed and drift-corrected using MotionCor2  54. Contrast transfer function parameters and resolution were estimated for each micrograph using CTFFIND4  55  and Gctf  56  , respectively. Micrographs with an estimated resolution below 4 Å and astigmatism below 5% were manually screened for contamination or carbon rupture. As a result, a total 11,388 micrographs (for MALSU1 sample) and 9452 micrographs (for GTPBP10 sample) were selected. Particle picking was carried out using Gautomatch with a low pass filtered mature human 39S mitoribosome as a reference  4. After 2D classification, a total 785,354 particles (for MALSU1 sample) and 1,813,074 particles (for GTPBBP10 sample) were submitted to 3D refinement, 3D classification, CTF refinement as shown in Supplementary Fig.
1  using Relion 3. 1  57. During the revision of this paper, we collected another dataset using uL17 as bait. This dataset will be discussed somewhere else. However, a small portion of the dataset also contains the desired state 1. Together with data of state 1 from the other two datasets, we combined all of them together, and did another round of 3D classification focusing on MRM3/DDX28 region. The resulting class which shown the best resolved MRM3/DDX28 region was picked to get the final reconstruction of state 1 (Supplementary Fig. 2 ). All the final map was filtered according to the local resolution in Relion. Model building and refinement. In general, the models of human mature 39S mitoribosome (PDB:3J9M)  4  and two intermediates (PDB:5OOL, 5OOM)  31  were used for initial rigid body docking into the density.