dataesr/scientific-paragraphs-categorization huggingface dataset:
8 x 10 -11 cm 3 molec -1 s -1 (Figure  6b ). Formulas also containing nitrogen (primarily nitrates and peroxynitrates in this dataset) have an OH reaction rate constant of approximately half this, with a tight distribution centered around an average of 1. 4 x 10 -11 cm 3 molec -1 s -1. These distributions indicate that for any given formula, assuming a constant kOH within a formula class is almost always accurate to within a factor of 2. It is important to note that, given the dataset used in this work to calculate these distributions and averages, these results apply only to atmospheric oxidation products and are not directly applicable to directly-emitted compounds or other atmospheric constituents. In contrast to the low variability observed for the formula-based estimation method developed by Donahue spans a larger range and typically overestimates kOH (Figure  6c ).
No correlation is observed between reactivity, , and vapor pressure, ̅ (Figure  6d , R 2 = 0. 15 within a formula class, R 2 = 0. 02 in the combined dataset), consistent with results reported by  Lannuque et al. (2018)  also showing no clear trend between kOH and p. This is in contrast to the Donahue method, which does predict a strong correlation between these properties (Figure  S5 , R 2 = 0. 80). However, Figure  6d  does demonstrate some trends that are in rough agreement with the broad conclusions  Donahue et al. (2011)  put forth in the manuscript developing their method: higher volatility compounds react somewhat slower, moderate volatility compounds have rate constants around 3x10 -11 cm 3 molec -1 s -1 , and lower volatility compounds have slightly higher reaction rates but are likely to partition to the particle phase and therefore not react quickly with OH.
All but the full-length construct yielded reasonably pure proteins, but only constructs with the GH5 and all three CBMs showed activity on xanthan gum, suggesting a critical role in catalysis for these CBMs. (Extended Data 6). The alternate GH5 (RuGH5b) was also expressed in a variety of forms but did not display any activity on XG (Extended Data 6). Analysis of the reaction products showed that RuGH5a releases pentasaccharide repeating units of XG, with various acetylation and pyruvylation (including di-acetylation as previously described  34  ), and larger decasaccharide structures (Figure  3a ). While isolation of homogenous pentameric oligosaccharides proved difficult, coincubation of XG with RuGH5a and a Bacillus sp. PL8 facilitated the isolation of pure tetrasaccharide followed by in-depth 1D and 2D NMR structural characterization (Extended Data 7), which was useful in determining RuGH5a regiospecificity in the XG backbone.
0. 7 with default parameters. Contigs were submitted to the Integrated Microbial Genomes and Microbiomes system for open reading frames (ORFs) prediction and annotation  57. Additionally, the resulting ORF were annotated for CAZymes using the CAZy annotation pipeline  58. This MAG collection was used as a reference database for mapping of the metatranscriptome data, as described below. Taxonomic classifications of MAGs were determined using both MiGA  59  and GTDB-Tk  60. Human fecal samples (20) from a second enrichment experiment (unbiased towards the cultivation of Bacteroides) as well as two enrichments with mouse fecal samples were processed for gDNA extraction and library preparation exactly as described above. Metagenomic shotgun sequencing was conducted on two lanes of both Illumina HiSeq 4000 and Illumina HiSeq X Ten platforms (Illumina, Inc.
MS1 scans were acquired at 60K resolution (AGC target=3x10  6  ; max IT=50 ms). Data-dependent collision induced dissociation MS/MS spectra were acquired using Top speed method (3 seconds) following each MS1 scan (NCE ~28%; 15K resolution; AGC target 1x10  5  ; max IT 45 ms). Proteins were identified by searching the MS/MS data against a database of all proteins identified in the original culture metagenomes using Proteome Discoverer (v2. 1, Thermo Scientific). Search parameters included MS1 mass tolerance of 10 ppm and fragment tolerance of 0. 2 Da; two missed cleavages were allowed; carbamidimethylation of cysteine was considered fixed modification and oxidation of methionine, deamidation of asparagine and glutamine were considered as potential modifications. False discovery rate (FDR) was determined using Percolator and proteins/peptides with an FDR of ≤1% were retained for further analysis.
Fractions were dried, weighed, and analyzed by LC-MS and fractions that contained the most significant yield of desired products were combined. Highly pure products were obtained by reconstituting samples in 50% water:acetonitrile and applying to a Luna® 5 µm HILIC 200 Å LC column (250 x 10 mm) (00G-4450-N0, Phenomenex). A gradient was run from 90-20% acetonitrile, with peaks determined through a combination of evaporative light scattering, UV, and post-run analytical LC-MS (Agilent qToF 6545) of resulting fractions. NMR spectra were collected using an Agilent 600 NMR spectrometer ( 1 H: 600 MHz,  13  C: 150 MHz) equipped with a 5 mm DB AUTOX PFG broadband probe and a Varian NMR System console. All data analysis was performed using MestReNova NMR software. All chemical shifts were referenced to residual solvent peaks [ 1 H (D2O): 4.
Looking for R. UCG13 and B. intestinalis. XG Loci in Metagenomes Available cohorts of human gut metagenomic sequence data (National Center for Biotechnology Information projects: PRJNA422434 81 , PRJEB10878 82 , PRJEB12123  83  , PRJEB12124 84 , PRJEB15371  85  , PRJEB6997 86 , PRJDB3601 87 , PRJNA48479 88 , PRJEB4336 89 , PRJEB2054  90  , PRJNA392180  91  , and PRJNA527208 92 ) were searched for the presence of xanthan locus nucleotide sequences from R. UCG13 (92. 7 kb) and B. intestinalis (17. 9kb) using the following workflow: Each xanthan locus nucleotide sequence was used separately as a template and then magic-blast v1. 5. 0  93  was used to recruit raw Illumina reads from the available metagenomic datasets with an identity cutoff of 97%. Next, the alignment files were used to generate a coverage map using bedtools v2.
29. 0  94  to calculate the percentage coverage of each sample against each individual reference. We considered a metagenomic data sample to be positive for a particular xanthan locus if it had at least 70% of the corresponding xanthan locus nucleotide sequence covered. The R. UCG13 locus and B. intestinalis XG locus were used as the query in a large-scale search against the assembled scaffolds of isolates, metagenome assembled genomes (bins), and metagenomes included into the Integrated Microbial Genomes & Microbiomes (IMG/M) comparative analysis system  41. Within the LAST software package, version 1066, the 'lastal' tool was used with default thresholds to search the 2 loci against 72,491 public high-quality isolate genomes, and 102,860 bins from 13,415 public metagenomes, and 21,762 public metagenomes in IMG/M.
0 Hz, consistent with a β-configured Glc(n. r) monomer. This confirmed connectivity between the α-Man and β-Glc(n. r) residues, suggesting a disparate structure to the previously reported degradation product. Recombinant enzymes were purified and analyzed for expression and purity by SDS-PAGE. Proteins generally expressed well with a single dense band indicating overexpression of the enzyme at its predicted molecular weight as compared to a size ladder. Exceptions included the R. UCG13 GH88 and CE-A, both of which had bands at the predicted enzyme size but also showed bands of comparable density at other sizes resulting from either proteolysis or copurification of undesired E. coli proteins. Extended Data 8 Figure 2 α-1,3 β-1,4 β-1,2 β-1,4 +6Ac α-1,3 β-1,4 β-1,2 β-1,4 +4,6Py α-1,3 β-1,4 β-1,2 β-1,4 α-1,3 β-1,4 β-1,2 β-1,4 α-1,3 β-1,4 β-1,2 β-1,4 α-1,3 β-1,4 β-1,2 β-1,4 +6Ac α-1,3 β-1,4 β-1,2 β-1,4 α-1,3 β-1,4 β-1,2 β-1,4 +4 Figure 4 Figure 5 α-1,3 β-1,4 β-1,4 β-1,4 β-1,2 β-1,4 ±4,6Py ±6Ac α-1,3 β-1,4 β-1,4 β-1,2 β-1,4 ±4,6Py ±6Ac α-1,3 β-1,4 β-1,2 β-1,4 β-1,4 n ±4,6Py ±6Ac α-1,3 β-1,4 β-1,4 β-1,2 β-1,4 ±4,6Py ±6Ac α-1,3 β-1,4 β-1,2 β-1,4 R.
Acknowledgments We gratefully acknowledge  Stephanie Theide  for growth curve analysis suggestions and  Tina Johannessen  and  Alexsander Lysberg  for help with Nanopore metagenomics. We thank the  University of Michigan Proteomics Resource Facility, Microbiome Core, and Natural Products Discovery Core  for their support in completion of this project. We are grateful for support from the  US National Institutes of Health  ( DK118024 ,  DK125445  to ECM and  UL1TR002240  in support of MPO) and the  Research Council of Norway  ( FRIPRO program , PBP and SLLR: 250479, LHH: 302639). We thank the  University of Michigan Center for Gastrointestinal Research (UMCGR) , ( NIDDK 5P30DK034933 ) for financial support with proteomics. The work.
The maximum cone beam half-angle is approximately 21. 5°and the limits of the beam are evident in the radiographs, as dark zones. φ for the middle of the sample is 2. 5°. The following datasets are acquired, each time allowing a 5 min stabilisation of the x-ray source:   A few of the acquired radiographs, as well as the experimental setup are illustrated in Figure  12. The radiograph of the 7 mm calibration sphere is normalised by I 0 , whereas the radiograph of the column of 2 mm spheres in the holder is directly normalised by the empty sample holder at the same rotation angle. The natural log of the normalised images is computed, and for the calibration sphere the path length L inside the sphere vs. these image values is also computed (see Appendix). This fitted function (L in mm vs log(I/I 0 )) is then applied to the natural ‡ from ballandrollerstore.
3D particle centres are compared by subtracting the mean position from all three datasets (labelled, tomopack, optimised) and relabelling the centres in the labelled image to their closest corresponding particle from the tomopack scan. The rigid-body motion of the labelled centres that minimises distances with the tomopack-positions and the optimised positions are both computed (which erases systematic errors) and applied to labelled centres, after which the absolute deviation and standard deviation of errors can be computed. Results are shown for all averaging levels (and thus SNR levels) in Figure  13 , revealing that the tomopack 3D guess already offers a good estimation of 3D positions with respect to the centres obtained from tomography. As expected, errors increase with noise level, and the direction normal to the detector, X, is the most error-prone.
The software used in this paper, as well as both experimental and synthetic data are hosted on: https://gricad-gitlab. univ-grenoble-alpes. fr/ ttk/radioSphere and will continue to be developed. The perspectives for development of this technique are manifold. First of all, it must be mentioned that the requirement of single-sized spheres is a strong limitation that can be lifted in a number of different ways: in the case of a two-(or more) source and detector imaging system, the limitation of monodispersity can likely be removed with ease. Otherwise, if the initialisation of 3D positions from a tomopack scan is abandoned and replaced with particles labelled in an initial tomography, the sensitivity field optimisation process should be able to successfully converge for displacement unknowns for spheres of different sizes, and may be able to yield good results also optimising three ro-tation unknowns for sufficiently unique shapes, in the style of  [15].
Online assisted reproductive technology training: participant registration The first 3 sessions of the virtual ART training program focused on manual semen analysis (Module 1A), sperm morphology (Module 1B), and ancillary semen tests (Module 1). Participants registered free of cost for each module by completing a short Google Form, where they were asked to fill in their personal details as well as a certified identification document to verify participant credentials. Besides being a user-friendly method, Google Forms was the most suitable approach as it automatically generates graphs and a comma-separated value (CSV) file containing all the responses collected. The link for registration via Google Form was posted on different platforms including LinkedIn, Facebook, Instagram, and WhatsApp.
Following registration for each module, participants were included into Whats App groups, formed to serve the respective number of participants. WhatsApp was used as the main communication platform to inform participants about the training and for direct communication with the course coordinators in potential queries or further guidance on the course. Moreover, all documents pertaining to course organization were posted on WhatsApp in English and translated in Spanish, Persian, Portuguese, French, and Arabic by a team of 9 international coordinators to provide information that was easily understood, particularly for candidates who were not native English speakers. However, as the course content was entirely in English, having the English language as a first or second language was a pre-requisite for all participants.
Reading material Scientific literature, including reviews, original studies, guidelines, and protocols, were made available in advance to all registered participants in a Dropbox folder. Participants were highly encouraged to read the shared documents before attending the corresponding training session. The access link to the relevant folder was shared in the description box of each WhatsApp group. For Module 1A, the reading material covered topics such as spermatogenesis, the importance of basic semen analysis in the diagnostics of male infertility, the impact of COVID-19 on performing laboratory procedures, and the importance of quality control in an andrology laboratory. For Module 1B, topics included the importance of sperm morphology evaluation in clinical practice, the technical challenges associated with sperm morphology evaluation, the staining techniques used, the importance of a quality control program in morphology evaluation, and the role of sperm morphology evaluation in ART setting.
Training sessions Online training sessions were conducted using the Cisco WebEx platform. Detailed and step-by-step instructions about how to connect through this platform were shared with all registered participants via Whats App. Before each training session, 2 "practice sessions" were organized to allow participants to familiarize themselves with using the WebEx platform and to solve any possible technical issue that arose. During these practice sessions, coordinators were available to answer any of the participants' questions about the use of this platform and to provide technical support. The link to the training session was shared in the Whats App group one day before the official commencement of the course. The training sessions for Modules 1A, 1B, and 1 were conducted on November 10, 2020, December 8, 2020, and January 12, 2021, respectively.
Multiple choice question test At the end of the two-hour training session, a link for an MCQ test was shared on each of the WhatsApp groups. The questions in the MCQ test were formulated by the relevant expert speaker in each section, collated and populated on the Google Forms platform. These questions were then moderated and revised independently by two or more international expert collaborators who were not involved in the management of the course. The aim of having the questions reviewed by independent experts was to help 1) maintain the standard of the questions and 2) ensure that the questions were clear, and easily understandable to a large international audience. The test included 40 to 60 questions based on the contents of each online module. The time allotted for each test ranged between 30 and 40 minutes, depending on the total number of questions in the test.
Assessment of the module delivery surveys At the end of the MCQ test, participants were asked to complete an online survey created on Google Forms. Completing this survey was a requirement for participants in order to obtain the certificate, provided that they scored a passing mark in the post-course MCQ test. The link for the survey was also shared on each of the WhatsApp groups. The survey was constructed for collecting demographic data of the participants and to evaluate 5 components of the online ART training modules: 1) participants' evaluation of the application process and organization/handling of the course; 2) participants' evaluation of the technological tools (Dropbox, WhatsApp, WebEx) used during the course; 3) participants' evaluation of the faculty and speakers; 4) selfrating of knowledge pre-and post-online courses; and 5) self-reported benefits of attending the lectures.
Statistical analysis Statistical analysis was performed using MedCalc statistical software version 19. 7. 4 (MedCalc Software bv, Ostend, Belgium). The answers were analyzed all together when the questions were common across all three modules, while answers for the self-rating of knowledge pre-and post-online courses were analyzed individually for each module. Some questions were optional, hence numbers of participants in the modules might vary slightly. Descriptive statistics of the participant cohort were represented as mean±standard deviation and median (interquartile range). Kolmogorov-Smirnov test for normal distribution was used to understand the distribution of data variables. Wilcoxon test for comparison was used to compare pre and post self-reporting of the participants' own performance based on the non-parametric distribution of data.
2 ). Although sharing of information using various electronic platforms was generally rated as very good or excellent by more than 80% of the participants, the preferred platforms appeared to be WhatsApp with a rating of 86%, Dropbox (85%), and WebEx (81%) (Fig. 3 ). Overall, analysis of the responses related to the technological tools used in the online ART course showed that 51. 7% of participants found this program to be very useful and the digital delivery methods had completely met their purpose. Moreover, 33. 0% had found these tools to be helpful and very good, although 11. 9% found them neutral and good. More than 70% of the participants regarded the fol-  www. wjmh. org lowing aspects of program as either very good or excellent: 1) participants' level of interest in participating in the modules, 2) experience preparing for the courses, 3) course coordinators support, 4) ability to adjust to a completely online course, 5) timing and duration of the course, 6) choice of course faculty, 7) opportunity to ask questions, 8) level of interest to participate in future courses, 9) short videos about the lecture topics, 10) lectures given by the ACRM faculty/guest speakers, 11) discussion of updates in current clinical practice, and 12) MCQ assessments to improve learning (Fig.
Moreover, although each lecture in itself was relatively short, the total length of time taken for the entire series of lectures within each module was adequate enough to require the participants to remain online for an extended amount of time. This presented a higher probability for the participants to become disengaged and get distracted than they would during a conventional in-person meeting  [21]. The ability to attend the online sessions may have also been challenging for some participants due to either poor or unstable internet connectivity, or other reasons beyond the course management's control. For example, access to the platform used to host the online training platform (WebEx) is restricted in some countries (such as Iran, Algeria, and Nigeria amongst others) and therefore the participants from those regions had to rely on virtual private network (VPN) to ensure connectivity.
www. wjmh. org Taking these challenges into consideration, from the second session onwards, several measures were implemented to address the aforementioned issues when plausible. To navigate the connectivity issues due to restriction in certain regions, the team of coordinators was pro-active in providing VPN access to those participants who faced difficulties in joining the training sessions via WebEx. Additionally, the time allotted for completing the MCQ test was also lengthened. The minimum score needed to pass the module was set at 65%, although this score is considered akin to a grade D or grade point average (GPA) of 1. 0 based on conventional American standards  [22]. Each module was run as a 2 hours training session followed by an hour of the MCQ test. In Modules 1A and 1, there were six 15-minute lectures followed by one 15 minute Q&A session, while Module 1B had four lectures of 15 to 30 minutes, with one 10 minute Q&A session.
CONCLUSIONS In conclusion, we report our experience in utilizing a web-based delivery model as an alternative to the conventional in-person training which had enabled the ACRM to provide continuous education on a global platform. We have shown that the online training for certain ART-related modules can be conducted by means of providing expert-led video demonstrations, power point presentations, clinical lectures, and online interactive e-learning activities. Moreover, complex issues in reproductive biology could be covered by interplay of both laboratory and clinical professionals. The application of several online-based communication tools such as WhatsApp, Dropbox and Cisco WebEx were instrumental in implementing the web-based delivery model. Leading into the future, the success of this online training creates an emerging avenue for a blended form of learning with both in-person and online education platforms for the Andrology and embryology specialties involved in ART training.
In order to correctly assess the nutritional quality of a raw or manufactured food product, the first step is to obtain the associated nutritional values. Food composition databases (FCDBs) managed at national level provide values for nutrients of foods. Unfortunately, values associated with some nutrients of interest may be lacking in the FCDB of the country in which the nutritional quality must be assessed, and finding values associated with nutrients for similar foods in other FCDBs is a way to deal with incompleteness. An additional issue arises because the vocabulary used to denote a given food in a given FCDB is usually different from the one used in others. In this paper, the authors address the problem of retrieving the nutritional value of foods by querying different FCDBs through FoodOn used as pivot ontology.
INTROdUCTION For national and international food trading, it is a challenge to automatically generate the nutrition information panel required by regulation for raw or manufactured food products in many countries. The first challenge is to identify the nutritional values of the raw or manufactured food product either by its identification in an appropriate Food Composition DataBase (FCDB)  (Pehrsson & Haytowitz, 2016) , or by designing a specific experimental analysis procedure which may require high expertise, 2 performant analysis tools and time. Unfortunately, values associated with nutrients of interest for a food may be lacking in the FCDB of the country in which the nutritional quality must be assessed. Finding values associated with nutrients for similar foods in other FCDBs is a way commonly used by nutritionists to deal with incompleteness.
This paper addresses the problem of semi-automatically identifying the nutritional value of raw or manufactured food products by querying different Food Composition Databases through a pivot vocabulary (named a master-code approach in  Ireland & Moller, 2016)  in order to deal with the lack of nutrient values. An additional issue arises because the vocabulary used to describe the ingredients of a food or a recipe in a given FCDB is usually different from the vocabulary used in others. A lot of efforts have been done during the 30 last years in order to harmonize food nutritional data sources through world wide networks like INFOODS  (INFOODS FAO, 2020)  or EUROFIR  (2020). A lot of standards exist concerning food classification and description systems, as reviewed and compared in  Ireland and Moller (2016).