dataesr/scientific-paragraphs-categorization huggingface dataset:
S-adenosyl-methionine (SAM) was added at final concentration of 0, 0. 1, 0. 5 or 1mM and samples were incubated 15 min at 30°C. 9uL (corresponding to 5 pmoles) were aliquoted and 2uL of 50 mM 1M7 (1-Methyl-7nitoisatoic anhydre) or DMSO (Mock reaction) was added and allowed to react for 6 min at 30°C. RNAs were then reverse transcribed with the Superscript III reverse transcriptase (Invitrogen®), a NGS library was prepared using NEBNext Ultra II DNA Library Prep Kit (New England Biolabs®) and final products were sequenced (NextSeq 500/500 Mid 2x150 flowcell). Sequencing data were analyzed and reactivity maps were derived using shapemapper. IPANEMAP  (Saaidi et al. 2020 ) was used to generate RNA structure models for each sequences. In the end, the 306 selected sequences were probed in the following conditions: • 30°C, without Mg 2+ and without SAM (30°C) • 30°C, with magnesium (Mg 2+ ).
Then, we can produce a Monte-Carlo estimate of P ni (r|r ni ) by sampling of the posterior Gamma distributions of m,u,d, and then computing the reactivity through  (12). If the sampled reactivities fall predominantly far in the tails of the histograms P (r|bp) or P (r|np), respectively, the reactivity measurement is discarded as an outlier. In practice, we find that 1000 samples for each site are more than sufficient. These samples can then be used to approximate the numerator and denominator of the right-hand side of  (14). In this way, we produce estimates of the ratios P ni (r ni |bp)/P ni (r ni |np), quantifying the odds that a site is paired. Figure  3B  shows a scatter plot of reactivities in our dataset, with the standarderror estimated by the standard SHAPE-Mapper pipeline  (62)  (which does a first-order error propagation through the Poisson count statistics), with each point colored according to the value of the log-odds-ratio  (14).
We also define a group of high-RBM effective energy sequences, having E eff > -300, and containing 31 sequences. Setting energetic thresholds is similar to sampling the model at a temperature different from 1, also found to be helpful in DCA models used for design  (56). The chosen threshold value E eff = -300 approximately separates the bulk of natural and RBM sampled sequences, from the CM samples (Figure  4B ). Infernal sequences. We also sampled sequences from the CM covariance model of the RF00162 family, downloaded from Rfam. We used the Infernal cmemit program (see Methods) to sample a large batch of sequences. Then, we selected 30 sequences uniformly spanning the range of Infernal bit-scores observed during sampling. The covariance model can model correlations along the sequence comprised in the consensus secondary structure, but fails to account for other correlations arising from tertiary contacts or pseudoknots.
SHAPE reactivies are in broad agreement with consensus secondary structure Sequence homologs in the RF00162 family are collected based on similarity to a group of manually curated sequences in the seed. Overall, for many of these sequences (both in the seed and in the full alignment), direct experimental evidence of their actual behavior and structure is limited, except for specific cases, such as the Thermoanaerobacter tengcongensis and the Bacillus sub-tilis yitJ SAM riboswitches, which have been extensively studied in the literature fueled by detailed knowledge of their published crystalized structures  (36, 43). For many other sequences in the MSA, their actual behavior is at most hypothesized based on indirect evidence. We have here obtained detailed SHAPE data of 151 sequences of the seed alignment.
Sequences are colored according to their origin: Natural sequences (seed + full) are in green, Rfam CM generated sequences in red, and RBM sequences in blue. The pink dashed line marks the energetic threshold E eff = -300. For the different groups of sequences (rows), the columns show: the total number of sequences probed in that group (with the number for which the experimental measurement was conclusive in parenthesis), the number of sequences which were responsive to SAM by significant reactivity changes in the Hallmark sites identified in Table  1 , and the number that were identified as not responsive. to be compatible with the two competing folds and be able to switch from one to the other. We computed the binding energies of our probed sequences across the P1 helix and the pseudoknot sites, using the Turner energy model as implemented in the ViennaRNA package  (35).
Learning of the parameter of the RBM model is done in an unsupervised way through contrastive divergence (see Method Section )  (27)  which includes both a positive and negative design term. We have first verified that the RBM model learned from sequence data encode structural constraints by predicting nucleotide-nucleotide contacts in the secondary structure and in the pseudoknot, performing at the same level of pairwise Potts models previously introduced to this aim (  (12, 79). Deep network learning approaches recently introduced, goes further in the direction of structural predictions from sequence data aiming at a complete structure predictions and following the impressive success of analogous approaches in the protein field  (29, 49, 51, 71). We have then verified through SHAPE-probing that the design of synthetic bistable aptamers was successful for the RBM model but not for the CM model taking only into account secondary-structure constraints: 50% of artificial sequences with an average 20% distance with any natural one were switching conformation in response to SAM, while Infernal generated sequences were not.
Details on range of calibration and percentage of analyte collected on the backup tubes are given in the Supplementary material: Appendix F, Sections F. 2 and F. 3. The adsorbent cartridges, calibration standards and pumped sorbent tubes were analysed by thermal desorption (TD) with an automatic thermal desorber TD100-xr from Markes International Ltd. coupled with an Agilent Gas chromatograph (GC) 6890C with a Deans Switching system and two detectors, FID and MSD 5973 Network from Agilent. The TD-GC-MSD/FID analytical conditions used for continuous online monitoring of the standard atmosphere chamber and for offline analysis of the sorbent tubes and POD cartridges are detailed in Table  1  for ozone precursors and air toxic compounds, respectively. Quantification was performed by Agilent Mass Hunter software with external standard calibration carried out by calibration tubes previously loaded with the certified reference cylinders.
Conditions during field tests To evaluate the suitability of extrapolated sampling rates for longterm exposure, the performance of the POD sampler was compared with that of pumped tubes and online GC monitoring under field conditions. Measurements were conducted at the Joint Research Centre (European Monitoring and Evaluation Programme, EMEP, station) in Ispra (VA), Italy during three distinct periods. Table  2  provides the range and average values of the meteorological data for the three different sampling periods. In the first period from July 13th to 16th, 2020, measurements were taken using 5 POD samplers with Carbopack X, and the online measurements were carried out using an Agilent GC 6890 with a Deans Switch System and double FID detector coupled with a Perkin Elmer Turbomatrix thermal desorption system with Air Monitoring Trap (M041-3628) and Nafion drier.
Acknowledgements We thank  Prof. Andreas Ernst  and  Prof. Felix Campelo  for useful discussion. This work was supported by the  LiquOrg synergy  grant,  ERC-2020-SyG-951146 , awarded to V. M. and F. P. V. M. acknowledges the support of the  Spanish Ministry of Science and Innovation  to the EMBL partnership, the  Centro de Excelencia Severo Ochoa  and the  CERCA Programme / Generalitat de Catalunya. I. R. acknowledges the support of  Fondation pour la Recherche Médicale  (grant  AJE202210016216 ).
Data Refining Data refining is an essential process when analysing a large volume of text. This extensive procedure consists mainly of converting unstructured text data into structured forms  (Ban et al. , 2019). The whole data refining process can be divided into five steps as follows: Step 1: Segment text reviews by language in an Excel spreadsheet to facilitate further analysis. Step 2: Change the case of the entire feedback to lower case and removing special characters, stop words (words that do not add meaning, such as he, have, etc. ) and the new line character in Python (see Fig. 1 ). Step 3: Exclude from the concerned subject all reviews, filled in by the users, that were not valid. Step 4: With the help of an online translator, all reviews covered by this study were translated into English no matter in what language they were originally written.
Step 5: Finally, with the assistance of an online spell checker, all reviews were revised in order to be correctly interpreted by further analysis software. This work is part of a research programme to investigate the learners' sentiments regarding the retention factor in MOOCs. The challenge was to develop a better and deeper understanding from the data collected, to improve the student experience of joining and learning through a MOOC in the SWAYAM portal. In order to accomplish the tasks online, a feedback form and reviews were collected from the enrolled course learners. A mixed-methods approach, i. e. , using quantitative and qualitative data, was used and it collected data from Google Forms that were constituted in two parts. The first part was a 5-point Likert-scale survey, with 25 statements about expected factors influencing retention or dropout in MOOCs.
The second part consisted of open-ended questions. The approach used for qualitative analysis was a systematic and novel multi-methodological procedure that combined word cloud visualisation (see Fig. 2 ); automated thematic analysis and sentiment analysis in NVivo 12 and Python. This integration of visualisations enabled us to identify five themes in order to analyse the factors of retention or dropout in MOOCs, and 21 sub-themes of relationships between MOOC motivation and retention. The main themes of the learners' feedback were identified by a manual study of each learner's feedback. Thereafter, an analysis of the text as such was performed. This was performed by using word frequency analysis (WFA) through NVivo, which gave us the number of times a word was used in the entire sample set of learner's feedback.
Frequency Analysis Frequency analysis was conducted in order to uncover the most mentioned words associated with motivation factors in MOOCs. This type of analysis is essential, especially at the beginning of the textual data analysis, since it can assist in identifying which factors are mostly discussed and assessed in the reviews. As shown in Table  4 , after conducting the word search query, unrelated words, verbs, adjectives, articles, conjunctions, prepositions, pronouns, and others were removed. Only nouns related to MOOC attributes were maintained. Also, the word MOOCs, which was the most frequent noun in the reviews, was removed since it is not a MOOC attribute but the MOOC unit itself. The outcomes of this query support the selection of MOOC retention or MOOC dropout issues or factors.
Arctic sea ice type (SIT) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SIT products are lacking. This study analyzed nine SIT products from five SIT retrieval approaches covering the winters from 1999 to 2018. These SIT products were inter-compared towards sea ice age product and evaluated with Synthetic Aperture Radar images. Among all, the largest daily Arctic multiyear ice (MYI) extent difference reaches 4. 5× 10 km 2 , while that in monthly data varies between 0. 6× 10 km 2 and 3. 6× 10 km 2. Overall speaking, the Zhang-and KNMI-SIT products based on Ku-band scatterometer perform the best. However, when using C-band scatterometer, KNMI-SIT shows overestimation of MYI in the early winter, and Zhang-SIT shows underestimation with anomalous fluctuations.
C3S-and OSISAF-SIT show large daily variability. IFREMER-SIT generally underestimates MYI. Factors that could impact their performances are analyzed and summarized: (1) Ku-band scatterometer generally performs better than C-band scatterometer on SIT discrimination, while the latter sometimes identifies first-year ice (FYI) more accurately, especially when FYI and MYI are highly mixed. (2) Simple combination of scatterometer and radiometer data is not always beneficial, e. g. under circumstances with strong atmospheric influence on microwave signatures. (  3 ) The representativeness of training data and efficiency of classification are crucial for SIT classification. Spatial and temporal variation of characteristic training dataset should be well accounted in the SIT method. Additionally, the change of separation pattern of microwave data could influence the adaptive classification method.
polarized channels of 37GHz and 19GHz ( ) is used for further ice type classification. The probability density functions (PDFs) of given ice classes used in the Bayesian classification are dynamically derived from training datasets, which are extracted from fixed target areas during 15-day period centered on the specific day  (Aaboe, Sørensen, et al. 2018). Within the C3S-SIT framework, the first major version (referred to as C3S-1) was produced as climate-consistent data records based on long-time satellite data input. In 2021, a second major version (referred to as C3S-2) is released, in which the main upgrades include the primary input satellite data, the reanalysis data used in atmospheric correction of Tbs and the implementation of a temperature-based correction scheme. OSISAF-SIT is a near-real time product which uses a similar method as C3S-SIT, with atmospheric correction and a Bayesian Before 2015 static PDFs were used instead.
Sea ice class-probabilities are estimated for and on swath projection at first. Then the fusion of radiometer and scatterometer is performed by gridding swath results with given weights. Both C3Sand OSISAF-SIT provide an ambiguous class (where the composition could be either pure FYI, pure MYI or the mixture, referred to as Amb), and the products are provided with processing flags and uncertainty fields. KNMI-SIT is purely based on scatterometer data, including QSCAT, OSCAT and ASCAT data (corresponding SIT products referred to as KNMI-Q, KNMI-O, and KNMI-A, respectively). QSCAT is a Ku-band (13. 4 GHz) conically scanning pencilbeam scatterometer. The inner beam is horizontally polarized (HH) at an incidence angle of 46°, whereas the outer beam is vertically polarized (VV) at an incidence angle of 54°.
OSCAT is similar to QSCAT, operating at the frequency of 13. 5 GHz with incidence angle for inner HH beam at 49° and outer VV beam at 57°. ASCAT is a C-band (5. 255 GHz) scatterometer with 3 vertically polarized (VV) antennas, whose incidence angle varies between 25° and 65°. KNMI-SIT uses a refined Bayesian algorithm based on the probabilistic distances to the Geophysical Model Functions of ocean wind and sea ice. Fixed thresholds extracted from stable wintertime (March) data are adopted for sea ice type classification. In KNMI-SIT, MYI is classified into Second Year ice (SYI) and older MYI, which could be regarded as qualitative proxy sea ice age/thickness  (Early and Long 1997, Belmonte Rivas, Otosaka, et al. 2018). In this study, backscatter data from OSCAT is not utilized by other series of SIT products.
Inter-comparison and evaluation mainly concern KNMI-Q and -A. Zhang-SIT employs adaptive classification method based on the thought of clustering, using radiometer and scatterometer data. The radiometer data are utilized in a way that AMSR-E/2 data comes prioritized whenever available and are replaced with SSMIS whenever missing. Scatterometer data is obtained from QSCAT and ASCAT successively since QSCAT stopped functioning. Meanwhile, scatterometer data was enhanced to the spatial resolution of 4. 45 km by the scatterometer image reconstruction technique  (Long, Hardin, et al. 1993, Early and Long 2001). Correction methods based on sea ice motion and spatial consistency are used for result improvement  (Zhang, Yu, et al. 2019 ). Among all the SIT products presented here, OSISAF-SIT is a near-real-time SIT product (5-hour latency) for operational applications.
Other data In this study, sea ice age (SIA) product and SAR images were used for inter-comparison and evaluation, respectively. In addition, sea ice drift product and atmosphere reanalysis data were utilized to provide additional information for ice type identification. The SIA product is from the National Snow and Ice Data Center, referred to as NSIDC-SIA. This product is derived by tracking ice trajectories, using passive and active microwave observations, as well as auxiliary data such as drifting buoys. It is generated from tracking of virtual Lagrangian ice parcels of each grid cell  (Fowler, Emery, et al. 2004) , limited by the simple drift model and the oldest ice age assignment of grids  (Korosov, Rampal, et al. 2018). NSIDC-SIA is available in standardized Equal-Area Scalable Earth (EASE) grid with 12.
Temporal analysis SIT products depict the distribution of MYI and FYI. Temporal analysis in this section focuses on the MYI extent. For SIT products, MYI extent is estimated as the integral of all pixels specified as multiyear sea ice within studied area. Note that both SYI and MYI (ice that is older than two years here) classes in KNMI-SIT are included in the calculation of MYI extent. The Amb class in C3S-and OSISAF-SIT could be regarded as MYI and FYI thus the MYI extent is calculated under both circumstances, resulting in a value for the classified MYI and an upper value including the ambiguous class as well for each product (shaded time series in Fig. 3 , 4 and 5). For NSIDC-SIA, extent is calculated by general extent of pixels with corresponding age. Data deficiency area around the North Pole of SIT products is excluded from extent calculation and analysis.
Combining with the information in Fig. 1 , we can find that KNMI-and IFREMER-and Zhang-SIT based on C-band scatterometer show larger discrepancies than using Ku-band scatterometer, especially in early winter months, and that OSISAF-SIT shows smaller discrepancies since using backscatter data (  2010 ). Among all, the largest difference in daily MYI extent reaches 4. 5 × 10 km2. In C3S-1, -2 and OSISAF-SIT, the average extent from the Amb class is 0. 21, 0. 26 and 0. 28 × 10 km2, respectively. The MYI extent temporal patterns of KNMI-Q and -O (Ku-band) are roughly consistent with IFREMER-Q (1999-2008) and Zhang-SIT, with differences varying between 0. 01 × 10 km2 and 0. 6 × 10 km2.
Cases in early winter A typical scene of early winter in MIZ is shown in SIT distribution patterns of C3S-SITs agree best with the SAR image. Slightly underestimation of MYI can be found in OSISAF-SIT in areas A and D (scatterometer data is used in this case). KNMI-A largely overestimates MYI, especially in the west part of SAR image. Zhang-SIT totally ignores the MYI pack (thin MYI tongue across ESS, similar as the case in Fig. 7  i-m), and this error lasts for the whole winter. The MYI underestimation in NSIDC-SIA can partly be resulted from its weekly temporal resolution.
Cases in mid-winter Cases in mid-winter focus on the transit zones between MYI and FYI, for better comparison of steady discrepancies among SIT products. Performances of SIT and SIA products in the above five cases are summarized in Table  2. In all the cases, NSIDC-SIA can generally capture the SIT distribution pattern meanwhile exhibits slight over-or underestimation of MYI because of the ice age assignment of the oldest ice and weekly temporal resolution. These results agree with previous studies  (Korosov, Rampal, et al. 2018 , Ye, Shokr, et al. 2019 ) and once again confirm the usage of SIA product as a cross-validation dataset. In the two cases of early winter (Case 1 and 2, Fig. 8   circumstances. Between the two products of C3S-SITs, C3S-2 performs slightly better than C3S-1 with more alike SIT distributions with the SAR image in cases 3 and 5 (Fig.
10  and 12 ). However, the improvement is in these five cases insignificant. As the only near-real-time operational product, OSISAF-SIT tends to underestimate MYI in most of the cases especially for the early period before inclusion of scatterometer data and dynamically updated PDFs (Case 1, 4 and 5). OSISAF-SIT shows better performance with more recent upgrades of the algorithm. This can also be found in the MYI extent time series (Fig. 3  and 4), where the MYI extent from OSISAF-SIT is smaller than that from most of the SIT products but less so after 2010. KNMI-SITs show consistent or overestimated MYI distribution in the five cases. Such overestimation of KNMI-SITs, especially KNMI-A, can be found in almost all the winter months. This is well reflected in the extraordinary large MYI extent of KNMI-A in November (Fig.
Conclusion Arctic sea ice cover has decreased dramatically over the past few decades, especially MYI. The change of SIT distribution impacts the Arctic and global climate. However, systematical inter-comparison and analysis for SIT products are still lacking. In this paper, nine SIT products based on five retrieval approaches were inter-compared through temporal and spatial analysis. Performances of them are evaluated qualitatively using SAR images. Differences among the SIT products in daily MYI extent within Arctic Basin can reach 4. 5 × 10 km 2 , while that in monthly data varies between 0. 6× 10 km 2 and 3. 6× 10 km 2. Large discrepancies occur in October, November and April, mainly in the Beaufort Sea and East Siberia Sea. Overall, performances of the SIT products can be summarized as follows: 1) C3S-SIT has the longest and most complete temporal record however large temporal variability and anomalous (flat or increasing) trends within certain years.
The fluctuation and misclassification are likely attributed to the single classification parameter from dynamic training datasets, which are vulnerable to environment changes and may not be representative for the entire Arctic. C3S-2 performs slightly better than C3S-1 with less misclassification and smaller temporal variability, which most likely is resulted from the upgrades of auxiliary data in atmospheric correction and temperature-based correction; 2) OSISAF-SIT is a near-real-time product and the archive data has discontinuities due to regular upgrades.
Acknowledgement This work is supported by the  National Natural Science Foundation of China  (Grant No. 42106225 ), the  Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)  (Grant No. 311021008 ), the  National Key Research and Development Program of China  (Grant No. 2019YFC1509104 ) and the  Natural Science Foundation of Guangdong Province, China  (Grant No. 2022A1515011545 ). We greatly thank C3S (https://doi. org/10. 24381/cds. 29c46d83, accessed on 1 April 2022), OSI SAF (https://osi-saf. eumetsat. int/products/osi-403-d， accessed on 1 April 2022),  KNMI (https://dataplatform. knmi. nl/dataset/, accessed on 1 April 2022),  Fanny Girard-Ardhuin  and  Zhilun Zhang  for providing SIT products. We also thank  NSIDC  for providing SIA product (https://nsidc.
org/data/NSIDC-0611/versions/4, accessed on 1 April 2022), ECWMF for providing ERA5 reanalysis (https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era5, accessed on 1 April 2022),  IFREMER  for providing ice drift product (ftp://ftp. ifremer. fr/ifremer/cersat/products/gridded/psidrift/data/arctic, accessed on 1 April 2022), as well as the ASF and MDA for providing Radarsat-1 and Sentinel-1 images (https://search. asf. alaska. edu/, accessed on 1 April 2022).