dataesr/scientific-paragraphs-categorization huggingface dataset:
Lm overnight cultures were diluted 1:100 in fresh BHI and were grown at 30 • C on glass coverslips (22 × 22 cm) submerged in a six-well plate. Three days post-incubation, the coverslips were washed three times with distilled water, fixed with 4% glutaraldehyde during 2 h and stained with 0. 1% acridine orange for 15 min. Coverslips were mounted onto microscope slides with Aqua-Poly/Mount (Polysciences) and examined under a confocal laser-scanning microscope (Leica SP5II). Sections were taken every 0. 5 m and processed using Fiji-ImageJ  (54, 55). Biofilm thickness was calculated using Leica SP5II software.
lmo0651 encodes a putative transcription regulator In a previous study, we reported the transcriptome profiling of Lm while infecting the mouse spleen and identified the gene lmo0651 as highly expressed during spleen infection when compared to growth in BHI medium ( ∼ =100-fold)  (7). In addition, lmo0651 was also previously shown to be upregulated when Lm was infecting the mouse intestinal lumen or incubated in human blood  (57). Through bioinformatics and database analysis, the Lmo0651 protein was predicted as a transcriptional regulator containing a putative typical GntR winged HTH DNA-binding domain  (19)  (Figure  1A ). Protein sequence alignments showed that Lmo0651 appears to be conserved across the Listeria genus, found in the genome of almost all pathogenic and nonpathogenic species and serogroups (Figure  1B  and S3A ).
We selected a subset of up-and down-regulated genes and performed qPCR on cDNA from WT and lmo0651 bac-teria grown to exponential phase. RT-qPCR results and RNA-seq data exhibited a very strong Pearson correlation coefficient (R 2 = 0. 93) (Figure  2B ), therefore validating the differential expression levels detected by transcriptomics. Together, these results pointed Lmo0651, renamed MouR, as the transcriptional activator of the Agr system.
MouR is a dimeric GntR protein To further explore the function of MouR, we purified MouR and resolved its three-dimensional structure (Figure  3 ). The crystal structure of MouR was determined by the SAD method using SeMet-substituted protein. The atomic model was refined against a native data set to 2. 2 Å resolution (Supplementary Table  S3 ). The final model of MouR shows a homodimer that includes residues Asn3 to Arg217 for both subunits and three histidines from the His-Tag for one of the subunits (Figure  3A ). The subunits in the homodimer display a RMSD of 1. 5 Å (http://zhanglab. ccmb. med. umich. edu/TM-align/) (65). The protein presents the fold of the GntR superfamily of dimeric transcription factors with each subunit composed by two domains (Figure  3B ). The N-terminal domain of MouR (Asn3-Cys73) is a wingedhelix dsDNA-binding domain, characteristic of the GntR family, and includes the canonical HTH DNA-binding motif (␣1-␣2-␣3) followed by a ␤-hairpin (␤1-␤2).
MouR-dsDNA complex model The GntR superfamily of dimeric transcription factors is characterized by the presence of an N-terminal wingedhelix DNA-binding domain. Three structures of FadR-dsDNA complex are available on PDB: two from E. coli (ID: 1HW2 and 1H9T)  (22, 69)  and one from Vibrio cholera (ID: 4P9U) (70). These FadR proteins in complex with DNA present an identical structural arrangement of their wingedhelix domains and conserved DNA-interacting amino acid residues. The superimposition of MouR winged-helix domain onto FadR-dsDNA complexes (ID: 1HW2, 1H9T and 4P9U) shows that it has a similar structure. Therefore, we modelled the MouR-dsDNA complex using the FadR-dsDNA complex from E. coli (ID: 1HW2) (Figure  4A ). Analysis of the model strongly shows that Arg44 and Arg48 on the ␣3-helix appear ready to interact with DNA (Figure  4B ).
To evaluate if the residues oriented towards the putative C-terminal ligand-binding cavity play a role in the MouRdependent transcriptional activation of agr genes, we engineered Lm mouR strains to express MouR with the residue substitutions: Y80F (Lm mouR+mouR(Y80F)), H133F (Lm mouR+mouR(H133F)) and H134F (Lm mouR+mouR(H134F)). These substitutions alter the physical-chemical properties of the residues lining the cavity by decreasing the polar character of the cavity and reducing the availability of hydrogen-bond partners. We confirmed that mouR is expressed in these strains at levels comparable to the WT (Supplementary Figure  S2 ). The expression of agrB was analyzed by RT-qPCR in these strains as compared to the WT. Our data showed that agrB expression in the mutant strains was not significantly different from the WT strain, indicating that none of the mutations impaired the ability of MouR to activate agrB expression (Figure  5C ).
MouR regulates chitinase activity and biofilm formation While the Agr system has not been deeply characterized in Lm, it was consistently associated with Lm chitinase activity and capacity to develop a biofilm  (28, 51). We thus an-  alyzed the potential connection between MouR and these specific processes. We first confirmed that the in vitro growth of the WT, mouR and mouR+mouR strains was similar (Figure  6A ). To test Lm chitinase activity, we spotted overnight cultures of the different strains on LB agar plates supplemented with chitin. The formation of a translucent halo on the opaque medium directly correlates with the bacterial chitinase activity  (51). Our data showed that the mutant mouR had a striking reduction in chitin hydrolysis when compared to both the WT and mouR+mouR strains which, in turn, were comparable to each other (Figure  6B ).
As initially predicted by bioinformatics, the crystal structure of MouR revealed that a monomer is composed of a typically conserved GntR N terminus with a winged-HTH DNA-binding domain and a variable C terminus with an E-O domain. Deeper analysis of the Cterminal structure led us to classify MouR as a member of the FadR sub-family, the largest sub-group of the GntR superfamily  (19). The presence of exactly six ␣-helices within the C terminus allowed us to further classify it as a member of the VanR sub-class  (79). Despite the large size of the FadR sub-family, currently making up close to 50% of all GntR proteins sequences on Pfam, only five VanR regulators are presently available in the PDB database  (67, 68, 79). To our knowledge, this is the second GntR member  (80)  and the first VanR type regulator described in Listeria.
ACKNOWLEDGEMENTS RT-qPCR technique was performed at the ' CCGen -Cell Culture and Genotyping ' i3S Scientific Platform, with the assistance of  Paula Magalhães  and  Tânia Meireles , and RNA-seq was performed at the  'GenCore -Genomics Core Facility' i3S Scientific Platform , with the assistance of  Ana Mafalda Rocha. We are grateful for access to Proxima 2A at  SOLEIL (Gif-sur-Yvette, France ) and ID29 at  ESRF (Grenoble, France ) and thank the respective support staff. We are also very grateful to  Prof. Rui Appelberg  for PhD co-supervision of J. P. , R. P. and F. C.
FUNDING Norte-01-0145- FEDER-000012 -Structured program  on bioengineered therapies for infectious diseases and tissue regeneration, supported by  Norte Portugal Regional Operational Programme  [NORTE 2020], under the  PORTUGAL Partnership Agreement , through the  European Regional Development Fund (FEDER) ;  FCT Doctoral Fellowship  [ SFRH/BD/86871/2012 ,  SFRH/BD/89542/2012 ,  SFRH/BD/61825/2009  and  SFRH/BD/112217/2015  to J. P. , R. P. , F. C. and C. B. ] through  FCT/MEC  co-funded by  QREN and POPH (Programa Operacional Potencial Humano) ;  FCT Investigator program  ( COMPETE, POPH and FCT)  (to S. S. ). Funding for open access charge:  Norte-01-0145-FEDER-000012 -Structured program  on bioengineered therapies for infectious diseases and tissue regeneration, supported by  Norte Portugal Regional Operational Programme  [NORTE 2020].
In this study, we used MMSMR to estimate survival, progression, and recovery of healthy and diseased striped bass age three and older. Using a 9-year mark-recapture dataset from the Rappahannock River in the Chesapeake Bay in VA, USA, we evaluated whether these epidemiological variables were correlated with changes in environmental conditions, specifically hypoxia and temperature at the mouth of the river and flow within the river. In order to better understand the consequences of environmental variation and disease on population viability, we used our model output to simulate the effects of different environmental conditions on the population dynamics of a cohort of striped bass across 6 years.
, 2009)  as well as in situ data from the CBP Chesapeake Monitoring Program  (Irby et al. , 2016). Analyses show that ChesROMS-ECB has significant success  (Stow et al. , 2009)  in reproducing variability in physical and biogeochemical fields and performs as well or better than other Chesapeake Bay models in terms of reproducing observed temperatures and oxygen concentrations. Monthly average water flow estimates for the Rappahannock were obtained from the USGS at the most downstream nontidal site on the Rappahannock River (1,668,000 at 38°18′30″, 77°31′46″, USGS 2017). Summer average flow data were estimated by averaging data from July, August, and September.
e. , removed from the study). We fitted numerous MMSMR models that estimated the effect of the class variables, age, time, and disease state, on yearly rates of survival, state transition, capture, and health assessments. In these models, we tested for all combinations of additive and interactive effects of time, age, and disease state on survival, transitions among disease states and capture rates. We always included an effect of time on capture rates because we had a priori knowledge that sampling efforts differed across years. Full details of the model structure and models that were tested are in the supporting information Table  S1. Models were run using the software E-SURGE  (v 2. 1. 2, Choquet, Rouan, & Pradel, 2009).
| Goodness of fit We ran goodness-of-fit tests to check whether there is statistically significant heterogeneity among individuals in the same state and group (i. e. , at any point in time). For multistate data, the model typically tested is the JollyMove (JMV) model. It consists of two main components, the 3G test and the M test  (Pradel, Wintrebert, & Gimenez, 2003). The JollyMove model allows for time-and statedependency in the data conditional on the first capture  (Choquet, Lebreton, Gimenez, Reboulet, & Pradel, 2009). The 3G test evaluates the hypothesis that "there is no difference in the probability of being reencountered between newly tagged and previously tagged individuals at occasion i in state j. " Our dataset had no instances of multiple recaptures, as fish were lethally sampled at recapture, so the 3G test was not possible.
The M test evaluates the null hypothesis that whether or not they are recaptured at time t, individuals in the same state at time t will not differ in the probability that they are encountered in different states at time t + 1 and beyond. For our dataset, the test of the JMV model was identical to the M test, because there were no cases of multiple recaptures. Goodness-offit tests were run using the U-CARE software  (Choquet, Lebreton et al. , 2009).
| Disease prevalence We were interested in quantifying changes in disease prevalence over time. Estimates from mark-recapture data can be erroneous if capture rates differ between healthy and diseased striped bass. To adjust for this, we first calculated yearly disease prevalence in the fish that were caught in the fall (i. e. , the catch prevalence or apparent prevalence). For this calculation, we limited our dataset to fish that were being captured for the first time. Disease prevalence in recaptured fish, which were mostly reported by fishermen, could be additionally biased if fish in one disease state are more likely to survive to recapture than fish in another state. We also calculated disease prevalence for the river (i. e. , river prevalence). To do this, we had to correct for the bias in capture rates across disease states.
, 2014) , other physiological impacts are less well understood and may contribute to the poor prognosis of this disease. Our model showed that a very small proportion of mildly or moderately diseased fish (<2% per year) can revert to a healthy state. In cases of tag returns, some fish exhibited re-epithelialization of pigmented foci and/or ulcers, which in some cases would be recorded as a regression of disease states. Histological data generally showed active granulomatous inflammation with acid fast bacteria underlying these lesions  (Lapointe et al. , 2014) , so rather than a "latent" state as found for human tuberculosis  (Flynn & Chan, 2001 ), this appears to represent a slowed or arrested disease state. To date, no records of spontaneous clearance of mycobacterial infections in the laboratory have been published, and piscine mycobacteriosis is still considered to ultimately be a fatal disease  (Gauthier, Rhodes, Vogelbein, Kator, & Ottinger, 2003).
We did not find evidence of an effect of age on disease incidence, disease progression, or recovery from disease. This result appears to contrast with a previous study in the Chesapeake Bay in which the apparent disease prevalence (i. e. , prevalence in sampled fish) increased with age then began to decrease in female fish 7 years and older, likely due to mortality of sick fish in older age groups  (Gauthier et al. , 2008). Our dataset consisted of so few individuals over the age of four that we had to collapse our data into only three age classes. The lack of mark-recapture data on older fish reduced our power to detect an age effect and this may account for the discrepancy in results. Previous studies have found complex associations between hypoxia and striped bass survival  (Costantini et al.
Results from goodness-of-fit test for the JollyMove model all fish were lethally sampled at the first recapture. The goodness-of-fit tests indicated that there was marginally nonsignificant heterogeneity among individuals in the 3-year age class and significant heterogeneity among individuals in the 4-year age class (Table1). There were insufficient data in the 5+ age class (which was comprised of only 14 tagged individuals) to test goodness of fit for this group. In order to account for heterogeneity among individuals in the dataset, we used an overdispersion coef-.
Results from goodness-of-fit test for the JollyMove model all fish were lethally sampled at the first recapture. The goodness-of-fit tests indicated that there was marginally nonsignificant heterogeneity among individuals in the 3-year age class and significant heterogeneity among individuals in the 4-year age class (Table1). There were insufficient data in the 5+ age class (which was comprised of only 14 tagged individuals) to test goodness of fit for this group. In order to account for heterogeneity among individuals in the dataset, we used an overdispersion coef- ficient (ĉ) of 2. 096. Degrees of Age class χ 2 p-value freedom 3 year olds 21. 2 0. 058 13 4 year olds 18. 9 0. 026 9 5+ year olds Not enough data nearly.
RNN Based Learning and Other Methods Since we need publicly available and agreed upon data in order to train and test the ML methods, including the RNN, we call upon the data released by the US Environmental Protection Agency (EPA) in the ToxCast database (https://www. epa. gov/chemical-research/exploring-toxcast-data, October 2015 release) which contains bioactivity data obtained for around 10,000 of compounds tested in more than several hundreds in vitro assays  [44] , and the Toxicity Reference database (ToxRefDB) with results from several types of in vivo studies for several hundreds of chemicals  [45]. These data sets do not fully cover each other so that not all compounds tested in ToxCast are present in ToxRefDB. We consider a subset of these data including compounds for which both in vitro and in vivo results are available.
The subset selection follows three steps. First, we look for the overlap of compounds present both in ToxCast and ToxRefDB and having results for in vivo studies performed in rats during two years. We obtain a matrix with 418 compounds and 821 assays, with a lot of missing values. Secondly, we look for a large complete sub-matrix and we obtain a matrix of 404 compounds and 60 in vitro assays. Finally, in order to be sure to get a minimum of active compounds in the datasets, i. e. , compounds for which an AC50 (half maximal activity concentration) could be measured, we remove assays with less than 5% of them and obtain a final matrix of 404 compounds and 37 assays. For each of the 37 assays, we build a QSAR classification model to predict the bioactivity of a compound. These models use structural descriptors computed from the compound's structure described in Structured Data Files.
Two types of descriptors are used: (i) 74 physico-chemical properties (e. g. , molecular weight, logP, etc. ), which are continuous variables calculated using the RDKit Open-Source software  [46]  and normalized into the interval [0; 1] and (ii) 4870 fingerprints which are binary vectors representing the presence or absence of a chemical sub-structure in a compound  [47]. The different types of fingerprints were generated using the pybel package in Python  [48]  and the PaDEL sofware  [49]  and are the following: FP3, Estate, KlekotaRoth, MACCS and PubChem fingerprints. Fingerprints being present in less than 5% of compounds are removed, leading to a final set of 731 fingerprints. Therefore, the obtained dataset is composed of 805 structural descriptors for the 404 compounds. The property that we wish to predict, is the activity in each in vitro assay in a binarised form.
Gradient descent learning is often used for the RNN, but in this work we determine weights of the RNN using the cross-validation approach in  [51]. The Multi Layer RNN (MLRNN) uses the original simpler structure of the RNN and investigates the power of single cells for deep learning  [20]. It achieves comparable or better classification at much lower computation cost than conventional deep learning methods in some applications. A cross-validation approach is used to determine the structure and the weights and 20 trials are conducted to average the results. The structure of the MLRNN used here is fixed as having 20 inputs and 100 intermediate nodes. Boosted Trees (called XGBoost in the sequel) is a popular tree ensemble method. The open-source software library XGBoost  [52]  provides an easy-to-use tool for implementing boosted trees with gradient boosting  [53]  and regression trees.
Classification Settings and Performance Metrics For each of the 37 assays, we randomly subdivide the corresponding dataset D into a training set D T and a testing set D t. From D, we randomly create 50 instances of D T and its complementary test set D t so that, for each instance, D = D T ∪ D t. Each of the ML techniques listed above are first trained on each D T and then tested on D t. The results we present below are therefore averages over the 50 randomly selected training and testing sets. Since the output of the datasets is either 0 or 1, this is a binary classification problem. Let TP, FP, TN and FN denote the number of true positives, false positives, true negatives and false negatives, respectively. Then, the performance metrics that we use to evaluate the results are the Sensitivity (TP/(TP + FN)), the Speci f icity (TN/(TN + FP)) and the BalancedAccuracy, denoted for short BA ((Sensitivity + Speci f icity)/2).
Results on Unbalanced Datasets The MLRNN, RNN and XGBoost algorithms are exploited to classify the 50 × 37 pairs of training and testing datasets and results are summarized into Figure  1. Since these are unbalanced datasets, the BA may be a better metric to demonstrate the classification accuracy. In addition, the situation of misclassifying positive as negative may be less desirable than that of misclassifying negative as positive. Therefore, the metric of Sensitivity is also important. When looking at the BA obtained on the training data set in Figure  1a , we observe that the RNN method is not good at learning from these unbalanced datasets, while the MLRNN and XGBoost techniques learn much better. Compared to the training accuracy, the performance on the testing dataset is more important since it demonstrates whether the model generalises accurately with regard to classifying previously unseen chemical compounds.
The testing results are presented in Figure  1d-f. Here, we see that RNN performs the worst in identifying true positives (Sensitivity) and tends to classify most unseen chemical compounds as inactive, except for some assays. It can be explained by the overall number of inactive compounds much larger than the number of active compounds in the training dataset. The MLRNN and XGBoost perform a bit better in identifying the TPs, and the MLRNN performs the best. However, Sensitivity is still low and really depends on the assays and probably on the balance between active and inactive compounds in the corresponding datasets. Among all assays, the highest testing BA achieved by these classification tools is 66. 19% attained by the XGBoost for assay number 17, with the corresponding Sensitivity being 46.
Results on Balanced Datasets From the previous results, it appears that most of the classification techniques used are not good at learning unbalanced datasets. Therefore, we try balancing the 50 × 37 training datasets with data augmentation, while the corresponding testing datasets remain unchanged. Here, the MLRNN, RNN and XGBoost are used to learn from the 50 × 37 datasets which are augmented for balanced training using the SMOTE method  [54]  as implemented in the Python toolbox unbalanced_learn  [55]. Specifically, we plot two descriptors (Descriptors 732 and 733) of the training dataset after data augmentation in Figure  2. We can see that new samples are generated based on the original ones, and added to the dataset. Since the new points are correlated with the existing original points, this could be called "oversampling" (because of the correlation) or "augmentation" because the added points do not exist in the original dataset.
The resulting Sensitivity, Speci f icity and BA are summarised in Figure  3. Compared to the training balanced accuracies given in Figure  1a , Figure  3a  shows that it is now evident that all the classification techniques we have discussed are capable of learning the training datasets after data augmentation. The training BA of the RNN method is still the lowest, but its testing BA is the highest for most of the assays. Among all assays, the highest testing BA is 68. 88% which is obtained with the RNN for the assay 17, with the corresponding testing Sensitivity being 66% and which is also the highest testing Sensitivity observed. Note that these values are higher than those reported in Figure  1. Finally, for a better illustration, Figure  4  compares the highest testing results obtained among all classification tools for classifying the datasets before and after data augmentation.
Conclusions and Perspectives From the results presented here, we can draw several conclusions. First, the methods we have proposed can correctly predict bioactivity from the physico-chemical descriptors of compounds. However, some methods appear to be significantly better than others. In addition, the capacity to build good models seems to depend strongly on the assays themselves and their corresponding datasets. Moreover, we see that data augmentation techniques can play an important role in classification performance for the unbalanced datasets. This work on ML applied to toxicology data raises further interesting issues. Since there is no absolute winner among the classification techniques that we have used, we may need to test other methods such as Support Vector Machines (SVM)  [56]  or Dense Random Neural Networks (DenseRNN)  [57].