dataesr/scientific-paragraphs-categorization huggingface dataset:
For the ten starting arrangements, the RMSD to the WT complex was calculated. During the period of the planned simulation, this starting target RMSD value was reduced linearly to zero (Aci-Seche et al. 2011 ) and these values were used as target RMSD values at the respective current time. A current RMSD value is calculated from the instantaneous arrangement of HasA and different substructures of HasR, onto which the guiding potential acts (called target masks in the following), the optimal selection of the target mask was determined in short test runs (data not shown). The backbone non-hydrogen atoms, as well as Cβ and Cγ (if available) of the loops building the contact areas of HasA, as well as HasR were found to yield simulations in which all important interactions were formed without overconstraining the simulation and were selected for the production runs.
The DM complex structure The final model (PDB code 5C58) contains one HasA, one HasR, and one heme molecule. The conformation of the HasR barrel and of the plug is the same as in the WT complex. However, the extracellular loops L2, L3, L10, L11 forming CA2 and loop L4, as well as parts of L9 from CA1 could not be modeled due to missing electron density. L4 by itself plays no role in either HasA or heme binding and displays the highest B-factors in the WT complex 3CSL structure. Those long loops are likely to be quite flexible as long as they are not trapped in one conformation upon HasA binding. Additionally, the electron density of the two loops of HasR bearing the heme-coordinating histidines (L7 of the barrel bearing His603, and apex C of the plug bearing His189) are much less well defined than in the WT complex.
The structure of the DM complex is in accord with the much weaker interaction of holoHasA as compared with Ile671Gly-HasR reflected in the respective K a values obtained from ITC data (see Table  2 ). Three interpretations of the DM complex are possible: It is a necessary transient arrangement of holoHasA with HasR during the progression of arrangements leading to the WT complex formation. It might occur in only one out of several alternative reaction paths leading to the WT complex. It could be absent from all of these alternative reaction paths, because of the non-native interactions between holo-HasA and HasR caused by the mutations. Because experimental techniques to track the native reaction paths are unavailable, we used molecular dynamics simulations to discriminate between these interpretations.
At approximately half of the initial RMSD, HasA adopts more stable poses, which can be seen by groups of points close in space. This is caused by local potential energy minima, i. e. transient interactions with HasR, which have to be broken again for further approach of HasA toward the end structure. Which intermediates are visited by a simulation depends strongly on the starting arrangement. Only in the final stages of the complex formation do the points in Fig. 7  restrict to narrower regions in space and finally they all converge toward the WT complex. The arrangements of the simulations seem to cluster and converge. The clusters are putative intermediate metastable arrangements. To quantify these visual impressions, snapshots of all simulations were combined and clustered using the PTRAJ tool from the AMBER software  (Roe and Cheatham 2013).
Acknowledgements We acknowledge the  Baden-Württemberg grid (bwGRiD) , which is part of the D-Grid system, and the  High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung of the University of Tübingen , the state of Baden-Württemberg 1 3 through bwHPC and the  German Research Foundation (DFG)  through grant no  INST   37/935-1 FUGG  for providing the computer resources making the computations possible. This project has been supported by a  DFG  Grant  WE 962/11-1. ABG was supported by a post-doctoral Grant from  Agence Nationale de la Recherche   ANR-08-BLAN-0160  (HEMETRANS); This work has been supported by grants from  ANR (HEMETRANS)  and  HEMESTOCKEXCHANGE (ANR Blanc   ANR-12-BSV3-0022-01  Work in PD's lab is now supported in part by the  DYNAMO Labex  grant from  ANR  ( ANR-11-LABEX-0011-01 ).
Context and Data Set Used The dataset we considered is comprised of questions asked in 2012-2013 by first-year medicine/pharmacy students at a major public French university (Université Joseph-Fourier). Not all of them asked questions, but 1608 students were enrolled that year. The Faculty of Medicine and Pharmacy has a specific hybrid training system (part of the work is done remotely and the other part in the classroom) for their first-year students. The first year (called PACES) takes place over a year divided into two semesters. Each semester ends (in January and May) with a competitive exam on the content studied during the period. A proportion of the students who fail the competitive exam are allowed to retake the year only once. Each course is composed of four to six 4-week sequences on the PACES 1 platform, as illustrated in Figure  1.
In the example shown in Figure  2 , the introduction of weights helps the annotator choose between "example" and "schema" for the value associated to dimension 2. The automatic annotation is thus sensitive to weight variations, nonetheless after the weight adjustments, only 1% of the questions were still considered ambiguous (cf. Figure  3 , the words "definition" and "functions" had the same weight within dimension 3). Finally, we used the sample of 200 questions from the evaluation step (cf. section 4. 1), which were not used for the weight adjustment phase, to evaluate the automatic annotator and to calculate kappa values per dimension. Practically, we implemented the annotator using the Natural Language Toolkit (NLTK), a major library in Python to handle natural language data. In particular we used the "Punkt Sentence tokenizer" module for French to divide each sentence into a list of segments, and each segment into a list of words.
Results and discussion The automatic keyword extraction method led us to a set of 268 keywords (to compare with 149 keywords selected by manual identification). Among these keywords, 50 were common to both datasets (e. g. , "role," "why," "can we"), but the majority were found only in the automatic keywords list (e. g. , "method," "also," "enzyme"). The kappa values found with the Decision Tree and K-NN algorithms (the two giving the best results) are provided in Table  6 , for each dimension, and compared with the results obtained with the previous expert rule-based annotator. We note that the expert rule-based annotator outperforms both machine learning-based annotators on dimensions 1, 3, and 4, whereas the fully automatic annotator performed slightly better on dimension 2. The fully automatic annotator also performs better than the mixed one (manual keywords extraction + ML) for the four dimensions.
05). For FinGrd, only ANT had a statistically significant difference (U = 66393, p =. 020, η 2 =. 003), the other two courses with data available did not. In terms of attendance, for GlbAtt there was only a statistically significant difference for BCE (U = 54755, p <. 001, η 2 =. 024). For CouAtt however, there were statistically significant results for all courses: BCH (U = 213974, p <. 001, η 2 =. 035), HBDD (U = 71554, p <. 001, η 2 =. 097), BEH (U = 110005, p <. 001, η 2 =. 021), and ANT (U = 88238, p <. 001, η 2 =. 042). In terms of number votes done on other students' questions (NbVotDone), the results are statistically significant for all courses: BCH (U = 270020, p <. 001, η 2 =. 246), HBDD (U = 225962, p <. 001, η 2 =. 197), BCE (U = 134731, p <. 001, η 2 =. 215), and ANT (U = 84866, p <.
Clustering students based on their questions Method: First, we performed four separate clustering analyses using the k-means algorithm (with k between 2 and 10) and the agglomerative hierarchical clustering algorithm over four datasets: students who asked questions in the BCH (1227 questions by N1 = 244 students), HBDD (979 questions by N2 = 201 students), BCE (685 questions by N3 = 114 students), and ANT courses (649 questions by N4 = 75 students). We performed the clustering using as features for each student the proportion of questions asked in each dimension (e. g. , the proportion of questions with value 1 in dimension 1) asked (a) overall, (b) during the first half of the course and (c) during the second half of the course. We also considered the overall proportion of questions asked in the first half and second half of the course (44 features overall as mentioned in Figure  7 ).
059) and for the final grade (χ 2 (3) = 20. 71, p <. 001, η 2 =. 074). This was also the case for HBDD (AvgGrd: χ 2 (3) = 25. 11, p <. 001, η 2 =. 112, and FinGrd: χ 2 (3) = 28. 95, p <. 001, η 2 =. 132). For BCE, there was no statistically significant difference between clusters for AvgGrd and no FinGrd data available. For ANT, there was a statistically significant difference between clusters for the average grade (χ 2 (2) = 7. 84, p =. 020, η 2 =. 081) and for the final grade (χ 2 (2) = 13. 50, p =. 001, η 2 =. 160). In terms of attendance, for BCH there was a statistically significant difference for CouAtt (χ 2 (3) = 10. 51, p =. 015, η 2 =. 031), but not for GlbAtt. For HBDD, the test showed a statistically significant difference for GlbAtt (χ 2 (3) = 9. 33, p =. 025, η 2 =. 041) but not for CouAtt.
More specifically, our goal is to answer two questions: RQ4. 1: When performing two separate clustering exercises on two separate datasets from two different years, is the intrinsic quality of clustering similar or does it vary? In other words, is it necessary to see if applying the predictive model of clustering from year N to the data from year N + 1 gives clusters of comparable quality to clustering done directly on year N + 1 (cf. Figure  9 )? RQ4. 2: Are the clusters of student characteristics similar from one year to the next? To answer this question, we used the silhouette coefficient to compare the quality of three clusterings: the previously computed clustering made on data from 2012 (cf. Figure  9  -arrow 1), a clustering obtained in a similar manner with k-means on data from 2013 (cf.
Therefore, it seems that in our context using the predictive model resulting from the clustering performed over a given year to the data from the next year is as efficient, in terms of cluster quality, as performing clustering directly on the next year's dataset. This result is necessary but not sufficient, however, as we do not yet know whether the characteristics of those clusters (in terms of grades, attendance, etc. ) are identical, which is mandatory if we want to reliably provide more information to the instructors about students asking those questions. The results of the silhouette coefficient showed that the predictive model of 2012 applied to the 2013 question dataset (in orange) and the clustering made directly on the 2013 dataset (in green) are similar in terms of cluster quality (but are of course different in terms of clusters obtained, although a comparison for each course revealed some similarities).
Therefore, it seems that in our context using the predictive model resulting from the clustering performed over a given year to the data from the next year is as efficient, in terms of cluster quality, as performing clustering directly on the next year's dataset. This result is necessary but not sufficient, however, as we do not yet know whether the characteristics of those clusters (in terms of grades, attendance, etc. ) are identical, which is mandatory if we want to reliably provide more information to the instructors about students asking those questions.
Do Student Characteristics Vary from Year to Year? To answer this question, we characterized the clusters of the 2012 model on data from 2013 according to the nine variables used for 2012 clustering. Then, we tried to see if there was a match between the clusters obtained on the 2013 dataset (in orange) with those identified on the 2012 dataset. RetStu share the same patterns of questions, i. e. , the nature of questions asked by students in each cluster remains the same (even though centroids may vary). We have therefore answered RQ. 4. 1 and RQ4. 2 positively.
In addition, we have presented an approach that will allow us to predict online student profiles from their questions using a predictive model derived from clustering performed on data from the year before. This last result opens the possibility of helping teachers by providing them with more information about the students asking questions online, thanks to similarities to students who asked the same types of questions the year before. A limit of this work lies in the fact that automatic annotation depends on weights that are empirically chosen and manually attributed to keywords. It is likely that with larger and more diverse datasets of questions, the automatic approach would outperform the manual approach proposed here, which could have an impact on the quality of clusters. The fact that we used a single source of data (even if we considered several courses across different years) could also raise questions regarding the applicability of our work in different contexts.
Experimental Setup The scheme of the setup is presented on Figure  3. The driving signal for the excitation was provided by a function generator (Tektronix AFG 1062), at a given frequency f exc. A 12 V DPDT relay (Hongfa HDF31, Radiospares) was used to switch between the excitation source and the ground. The relay was controlled by a data acquisition card (DAQ) (Labjack T7, Lakewood, CA, USA). The QTF, a common NC32LF, oscillating around 32,756 Hz, was followed by a homemade transimpedance amplifier with a 10 MΩ feedback resistor and a lock-in amplifier (EG&G 7260). Shielded cables were necessary in order to prevent electromagnetic radiation during QTF relaxation, that would have otherwise caused a large signal background. The QTF frequency was first estimated by a quick frequency scan. The excitation frequency was then set 20 Hz below this value.
The excitation voltage V exc was set to obtain a signal amplitude of about 1 volt after the transimpedance, ensuring a high signal-to-noise ratio and thus improving the accuracy of the measurement. In order to get the BF signal, the time constant and the filter slope of the lock-in amplifier were respectively 640 µs and 12 dB/oct. A Labview (National Instruments) program collected the data from the LIA, directly followed by a signal treatment algorithm, thus obtaining f 0 and Q. The algorithm had to fulfill the criteria we had set before: 1% signal error and 1 s measurement time. For our labview program, we decided to avoid using a nonlinear fitting, but rather a combination of a numerical low pass filtering and peak detection. It proved to be faster and as accurate. For testing the algorithm, a function generator was used to generate an exponentially decaying sine wave, similar to the QTF relaxation profile.
This continuous current operation favors the laser emission stability, so it is profitable for QEPAS. The synchronization of all the instruments is crucial for RT-QEPAS, it was done through a Labview program with a state machine architecture. The program also conveniently allowed the user to quickly switch between the QTF characterization and gas sensing, enabling fast troubleshooting. During the BF measurement, the LIA constant and the reference signal were set to 1 ms and to 32,731 Hz whereas during QEPAS the were changed to 100 ms and 32,751 Hz (or measured f 0 for RT QEPAS). The duration of the overall measurements was mostly limited by the QTF relaxation time after the electrical excitation. Due to the high Q value and the large excitation voltage, it takes not less than one second for the QTF to go under an energy threshold that could be considered low enough compared to the acoustic energy.
Since then, we have developed standardized collection and sequencing protocols to process 4,728 samples across 60 cities worldwide (Table  S1 ). Sampling took place at three major time points: a pilot study in 2015-16 and two global city sampling days (gCSD, June 21st) in 2016 and 2017. Each sample was sequenced with 5-7M 125bp paired-end reads using Illumina NGS sequencers (see Methods). To deal with the challenging analysis of our large dataset, we generated an open-source analysis pipeline (MetaSUB Core Analysis Pipeline, CAP), which includes a comprehensive set of state-of-the-art, peer-reviewed, metagenomic tools for taxonomic identification, k-mer analysis, AMR gene prediction, functional profiling, de novo assembly, annotation of particular microbial species, and geospatial mapping.
g. , climate, surface type, latitude, etc. ) and samples show greater diversity near the equator. Moreover, sequences associated with AMR markers are widespread, though not necessarily abundant, and show geographic specificity. Here, we present the results of our global analyses and a set of tools developed to access and analyze this extensive atlas, including: two interactive map-based visualizations for samples (metasub. org/map) and AMRs (resistanceopen. org), an indexed search tool over raw sequence data (dnaloc. ethz. ch/), a Git repository for all analytical pipelines and figures, and application programming interfaces (APIs) for computationally accessing results (github. com/metasub/ metasub_utils).
A Core Urban Microbiome Centers Global Diversity We first investigated the distribution of microbial species across the global urban environment. Specifically, we asked whether the urban environment represents a singular type of microbial ecosystem or a set of related, but distinct, communities, especially in terms of biodiversity. We observed a bi-modal distribution of taxa prevalence across our dataset, which we used to define two separate sets of taxa based on the inflection points of the distribution: the putative "sub-core" set of urban microbial species that are consistently observed (>70% of samples) and the less common "peripheral" (<25% of samples) species. We also defined a set of true "core" taxa which occur in essentially all samples (>97% of samples). Applying these thresholds, we identified 1,145 microbial species (Figure  2C ) that make up the sub-core urban microbiome with 31 species in the true core microbiome (Figure  2A ).
Core and sub-core taxa classifications were further evaluated for sequence complexity and genome coverage on a subset of samples. Of the 1,206 taxa with prevalence greater than 70%, 69 were flagged as being low quality classifications (see methods). The sub-core microbiome was principally bacterial, with just one eukaryotic taxon identified and not flagged: Saccharomyces cerevisiae. Notably, no archaea or viruses were identified in the group of sub-core microorganisms (note that this analysis did not include viruses newly discovered in this study). For viruses in particular, this may be affected by the sampling or DNA extraction methods used, by limitations in sequencing depth, or by missing annotations in the reference databases used for taxonomic RNA viruses are omitted with our DNA-based sampling.
For clarity we note that this analysis only considers taxa already present in reference databases, not newly discovered taxa (below). Despite the remaining unidentified taxa, we estimate that most (80%) of the classifiable taxa in the urban microbiome could be identified with roughly 1,000 samples. However, as noted below, this new diversity is likely not evenly distributed across regions. As humans are a major part of the urban environment, the DNA in our samples could be expected to resemble commensal human microbiomes. To investigate this, we compared non-human DNA fragments from our samples to a randomized set of 50 samples from 5 commensal microbiome sites in the Human Microbiome Project (HMP)  (Consortium et al. , 2012) (stool, skin, airway, gastrointestinal tract, urogenital tract).
We processed 28 metagenomic soil samples  (Bahram et al. , 2018)  using the same pipeline as the rest of the data and compared soil samples to our samples using MASH. Our samples were very dissimilar from the soil samples (Figure  2F ) even in comparison to human skin microbiomes. This suggests that the unclassified DNA may represent heretofore uncharacterized taxa that are not known commensals being shed into the environment. We next estimated the fraction of sequences in our data that did not resemble sequences in known reference databases. We took a subset of 10,000 reads from each sample and aligned these reads to a number of reference databases using BLASTn  (Altschul et al. , 1990). We then identified reads that mapped to sequences in the reference databases at 80%, 90%, and 95% Average Nucleotide Identity (ANI) (Figure  2G ).
We used a broad set of databases for reference: RefSeq, NCBI's NT Environmental, a large database of Metagenome Assembled Genomes (MAGs) from  Pasolli et al. (2019) , and MAGs from MetaSUB itself (Section 2. 4). At 80% ANI, the most permissive threshold, 34. 6% of reads did not map to any database while 47. 3% of reads did not map or only mapped to MAGs from MetaSUB itself. This mirrors results seen by previous urban microbiome works  (Afshinnekoo et al. , 2015; Hsu et al. , 2016). Next, we analyzed the fraction of sequences that aligned to these same databases by region. Sur-prisingly, samples from Europe had the highest fraction of unaligned reads, followed by the middle east, while samples from Sub Saharan Africa had the smallest fraction of unaligned reads (Supp. Figure  S1C ). The proportion of reads aligned to each database did not vary significantly by region.
We further investigated the relationship between geography and sample composition. In ecology, an increasing distance from the equator is associated with a decrease in taxonomic diversity  (O'Hara et al. , 2017). The MetaSUB data recapitulates this result and identifies a significant decrease in taxonomic diversity (though with significant noise, p < 2e16, R 2 = 0. 06915) as a function of absolute latitude; samples are estimated to lose 6. 9672 species for each degree of latitude away from the equator (Supp. Figure  S1A ). The effect of latitude on species diversity is not purely monotonic, since several cities have higher species diversity then their latitude would predict. This is expected as latitude is only a rough predictor of a city's climate. While this is an observation consistent with ecological theory, we note that our samples are heavily skewed by the location of the target cities, as well as the prevalence of those cities in specific latitude zones of the northern hemisphere.
Global Diversity Varies According to Covariates Despite the core urban microbiome present in almost all samples, there was also geographic variation in taxonomy and localization. We calculated the Jaccard distance between samples measured by the presence and absence of species (which is robust to noise from relative abundance) and performed a dimensionality reduction of the data using UMAP (Uniform Manifold Approximation and Projection, McInnes et al. (  2018 )) for visualization (Figure  2A ). Jaccard distance was correlated with distance based on Jensen-Shannon Divergence (which accounts for relative abundance) and k-mer distance calculated by MASH (which is based on the k-mer distribution in a sample, so cannot be biased by a database) (Supp. Figure  S10A, B, C ). In principle, Jaccard distance could be influenced by read depth as low abundance species drop below detection thresholds.