dataesr/scientific-paragraphs-categorization huggingface dataset:
However, mutations leading to changes in the amino acid sequence of the C44-P52 loop, although not affecting the folding of the protein, may result in the putative inhibitors' inability to access the binding pocket and provide a probable development of drug resistance. To avoid this situation in which the future evolution of the Mpros can undermine all our efforts, we should focus on key functional residues or those whose further mutation will destabilise the protein (e. g. , P39, R40, P52, G143, G146, or L167). Alternatively, we would suggest targeting the region between II and III domains, which contributes to the dimer formation. Our results provide the basis for drug design efforts aimed at this important protein target as part of the multifaceted global effort to eradicate COVID-19. In view of the presented challenges to finding a potent drug targeting Mpro, in our opinion, the most successful strategy would be to screen a large database of compounds with diverse structures involving or designing inhibitors de novo using a fragment-based approach.
First, their standard deviation of expression was not correlated with maximal expression level. This is expected if change in expression is dependent on SET-2 loss and not on background variation. Second, standard deviation was low for the majority of these genes and high for a small subset. This observation is expected if SET-2 acts only on a subset of genes. We therefore chose an arbitrary cutoff of minimal expression that discarded the first subpopulation (red line in Supplementary Figure  S1C ), leaving a reduced set of 7,238 genes having at least one value greater than 5 (Supplementary Table  S1 ). We then processed the resulting matrix with the dudi. pca() function of the ade4 package [63] (version 1. 7-6) 3  using parameters scan = FALSE, scale = FALSE, and nf = 4. We then processed the resulting object with the ade4:wca() function using lineages as classes and parameters scan = FALSE, nf = 2.
0 list of transcription factors (TF), CEBP-1 targets (CEBP-1), LIN-15B activated genes (LIN-15B), X-linked genes (X-linked), mes-2; mes-4 upregulated genes (mes up) and GLP-1 activated genes (GLP-1). Soma specific genes (p = 2e-4) and LIN-15B activated genes (p = 5e-4) are significantly over-represented in this dataset. * p-value < 0. 05, ** p-value < 0. 01, *** p-value < 0. 001.
6e-32 for over-enrichment in sma-2 L4 upregulated genes, p = 0. 03 for under-enrichment in sma-2 L4 downregulated genes. (C) Overlap between upregulated contributors and genes upregulated in sma-2 oocytes or sma-2 L4 stage animals. (D) Comparison of the list of 137 genes upregulated in sma-2 oocytes or sma-2 L4 stage animals and set-2 mutant germlines with ubiquitous genes, soma specific genes (soma), germline enriched genes (germline), the wTF3. 0 list of transcription factors (TF), CEBP-1 targets (CEBP-1), LIN-15B activated genes (LIN-15B), X-linked genes (X-linked), mes-2; mes-4 upregulated genes (mes up) and GLP-1 activated genes (GLP-1). Soma specific genes (p = 2e-4) and LIN-15B activated genes (p = 5e-4) are significantly over-represented in this dataset. * p-value < 0. 05, ** p-value < 0.
ACKNOWLEDGMENTS We thank  Dieu Huong Hoang ,  Sonia Grzeskowiak , and  Chloé Exbrayat-Héritier  for technical help with RNAi experiments,  Cécile Bedet  for help with bioinformatic analysis,  Laurent Modolo  ( Pôle Bioinformatique, LBMC ) for suggesting multivariate class analysis, developers of R, git, ade4 package, and  Ubuntu  for their software. Thanks to  Paola Fabrizio  for critical reading of the manuscript.
Statistical inference and data analysis with the BCG-HC model The BCG-HC distribution with parameters λ, δ and θ, characterized by the cdf (17) (and having the pdf (18)), can be considered as a parametric model. Statistical inference and applications of the BCG-HC model are explored in this section. Table  2 : Some moments, skewness and kurtosis of X for BCG-HC distribution for the following selected parameters values in order (λ, δ, θ): (i): (5, 5, 2. 5), (ii): (0. 8, 12, 15), (iii): (1, 8, 8), (iv): (0. 2, 7, 10), (v): (9, 10, 0. 3) and (vi):  (6, 6, 8). (i) (ii) (iii) (iv) (v) (vi) E(X) 2.
Data analysis In this subsection, we prove empirically the flexibility of the BCG-HC distribution by means of two practical data sets. The BCG-HC distribution will be compared with some competitive models listed in Table  3. We compare the fitted distributions by using the following usual goodness of fit measures: -(where the maximized log-likelihood), AIC (Akaike information criterion), BIC (Bayesian information criterion), CVM (Cramér-Von Mises), AD (Anderson-Darling) and KS (Kolmogorov Smirnov with its p-value (PV)) statistics. These  Jacob and Jayakumar (2012) The gamma-exponentiated exponential distribution (GEE)  Ristić and Balakrishnan (2012)  Kumaraswamy half-Cauchy (KHC) Ghosh (2014) The gamma half-Cauchy distribution (GHC)  Alzaatreh et al. (2016)  measures typically summarize the discrepancy between the data and the expected values under the considered model.
22 The uniqueness of the obtained MLEs for the BCG-HC model is observed in Figure  8. Table  9  gives the confidence intervals of the parameters of the BCG-HC model for Data set 1 at the levels 95% and 99%. Table  10  provides the values of goodness of fit measures for the BCG-HC model and other fitted models. Based on these numerical results, we see that the BCG-HC model provide better fit to Data set 2 than the competitors.
Seoul Orthohantavirus in Wild system selective for the partial large segment protein gene  (9). We sequenced amplicons using GENEWIZ (https://www. genewiz. com), assembled them using EMBOSS Merger software (http://www. bioinformatics. nl/cgi-bin/emboss/merger), and analyzed them with BLAST (http://blast. ncbi. nlm. nih. gov/Blast. cgi). We performed sequence alignment with Mafft  (10)  and built a maximum-likelihood phylogenetic tree with iQ-TREE (11), using 1,000 replicates for bootstrapping. Of the 1,414 mammals, 13 black rats tested positive for hantavirus RNA. We detected RNA in 14 samples: 9 brain homogenates, 4 multiorgan homogenates, and 1 serum sample. We confirmed the positive samples using PCR with highly conserved hantavirus small segment primers  (12). Sequence analysis of partial large (deposited under GenBank accession nos.
MT276868-81) and small (deposited under Gen-Bank accession nos. MT276854-67) segments revealed 99. 42% identity with SEOV strain Rn-HD27 from China Seoul Orthohantavirus in Wild Black Rats, Senegal (GenBank accession no. HM748799) and 99. 64% identity with SEOV strain Hu02-529 from South Korea (GenBank accession no. MF149956) (Figure  1 ). We detected SEOV RNA in 13 black rats caught in 3 villages: Goumbayel (7 rodents), Soutouta (4 rodents), and Dianke Makha (2 rodents). These villages were located ≈1 hour's drive from the main road between Tambacounda and Kidira (Figure  2 ). Frequent movement of goods and humans between these 3 villages might explain the low genetic diversity among the new SEOV strains from black rats. We did not observe signs of disease in the infected animals at the time of capture.
Acknowledgments We thank  Coly Bâ ,  Ambroise Dalecky ,  Christophe Amidi Diagne ,  Philippe Gauthier ,  Laëtitia Husse ,  Mamadou Kane ,  Youssou Niang , and  Aliou Sow  for their outstanding work during trapping sessions. We thank  Oumar Ndiaye  and  Magueye Ndiaye  for their technical laboratory support. We also thank  Pierre Rollin  for helping to obtain ELISA reagents. This work was supported by grant nos. ANR-11-CE-PL-0010  and  ANR-11-JSV7-0006  (A. A. S. , O. F. , M. M. D. , A. G. , Y. B. , M. D. ) from the  Agence Nationale de la Recherche.
DNA Ploidy Analysis Frozen tumor tissue samples were quickly thawed and dissociated mechanically, and the DNA was stained with the BD CycleTEST PLUS DNA Reagent Kit (Beckton Dickinson, San Jose, CA, USA). DNA flow cytometry was performed on a Beckman Coulter Epics XL MCL flow cytometer equipped with a 488-nm argon laser (Beckman Coulter, Villepinte, France). The DNA diploid peak was located on DNA histograms according to an external standardization procedure using normal human lymphocytes. When a slight DNA abnormality was suspected, thawed normal human lymphocytes were added to the cell suspensions before DNA staining. The resulting DNA histograms were interpreted using MultiCycle AV software (Phoenix Flow Systems, San Diego, CA, USA). DNA indices (DIs) were computed as previously described  [42].
Hybridization was followed by the appropriate washing steps. Scanning was performed with an Agilent G2505C DNA Microarray scanner at 100% PMT with 5 µm resolution at 20 • C in a low ozone concentration environment. Quantitation of the Cy5 and Cy3 signals from the scans was performed with Feature Extraction v10. 1 (Agilent) using the default parameters. Raw aCGH signals and log2 (ratio) profiles were normalized and centered using an as-yet unpublished in-house method according to the dye composition (Cy5/Cy3) and local GC% composition. These profiles were segmented with the circular binary segmentation algorithm  [43]  through its implementation in the DNAcopy v1. 30 package for R v2. 15. 1 using default parameters. DNA copy number imbalances were detected by considering a minimum of three consecutive probes and a minimal absolute amplitude threshold that was specific for each profile according to its internal noise.
Gene Mutation Analyses Exons 2 and 3 of KRAS (NM_033360. 2), HRAS (NM_005343. 2) and NRAS (NM_005343. 2), exons 11 and 15 of BRAF (NM_004333. 4), exon 3 of CTNNB1 (NM_001098209. 1) and exons 1 and 21 of PIK3CA (NM_006218. 2) were analyzed by direct Sanger sequencing after specific amplification by PCR, as previously described  [44]. Sense and antisense sequences were screened for exonic alterations using SeqScape v2. 5 software (Applied Biosystems, Foster City, CA, USA). All detected mutations were confirmed by at least one independent PCR.
Clinical, Histopathological and Biological Data The clinicopathological and biological characteristics of the 126 pts included in the training (66 pts) and validation (60 pts) sets are summarized in Table  1. Histopathological subtypes  [16]  of cFA (n = 59) showed a microfollicular, macrofollicular and mixed architectural pattern of growth in 19, 13 and 27 pts, respectively (without fetal/solid type), whereas cFTCs (n = 67) with various growth patterns were minimally invasive (capsular invasion only) and widely invasive in 24 and 33 pts, respectively. DNA flow cytometry was performed in the training and validation sets. As a whole, 37 (63%) out of 59 cFAs and 30 (58%) out of 52 cFTCs (15 of which were unsatisfactory for interpretation) were DNA diploid tumors. The other cFAs were DNA aneuploid, tetraploid and multiploid tumors in 10 (17%), four (7%) and eight (13%) cases, respectively (none were DNA haploid).
Evaluation The primary endpoint was time between handling of the bag or the suitcase containing respectively the Enk™ and the Manujet™, and the first effective insufflation that was observed and verbalised aloud by the nurse. The secondary evaluation criteria were time between decision-making and handling, handling and puncture, puncture and insufflation and decision-making and insufflation, the frequency of difficulties of use and noncompliance with the recommendations felt by the learners and observed by the investigator. The scenario data was collected on live by one of the investigators. An audio-video recording was also made for the control and prevention of data loss. In addition, at the end of the scenario, each participant fulfilled a questionnaire specifying his demographic data, the nature of his previous learning before the present course and experience of the two devices under study, their role and the difficulties encountered during the simulation training.
Statistical analysis The statistical analysis was performed using the Statview® 5 (Abacus Concept, Inc. Berkzley CA) software. Data were medians (percentiles 25-75%) and number (proportion, %) of participants or team according data. The number of team or participants to include in this study was determined using the primary endpoint. The mean value of the time handlingfirst insufflation with the Manujet™, was 180 + 50 s in adult difficult airway previous workshops with the same scenario (unpublished personal data, 2012). Fifteen teams were needed by group to objective a 30% difference (i. e. 60 s) with a 5% α risk, a β risk of 10% and a two-tailed test. However, as the program and context of the three-day and one-day trainings were different, we choose to include 30 teams in each group with a stratification of the course.
Results Finally, the study included 161 participants, i. e. 76 in the E group (29 teams) and 85 in the M group (31 teams). Demographic data of the participants (table 1) were similar in both groups. The majority of participants (55. 3%) were graduate anaesthetist physicians with a median duration of experience of 12 (5 -25) years. In each group, eleven teams participated to a three-day course and the others (18 in the E group and 20 in the M group) to a one-day course. Half of the participants, i. e. 36 (50. 7%) in the E group and 39 (51. 3%) in the M group (P > 0. 9999), had already followed a training with a high-fidelity patient simulator. Most participants (93. 7%) had available in their structure a difficult intubation trolley, but 76 (47. 5%) participants had no available tracheal oxygenation device.
Statistical analysis The statistical analysis was performed using software SPSS version 21 (IBM, USA). Because the data for mRNA levels were not normally distributed, non-parametric tests were used. The Mann-Whitney (U) test was used for pair-wise comparison, Kruskal-Wallis test was performed when comparing three indicators. The comparison between repeated measures at the longitudinal trajectory was performed using Friedman's test. Data are presented as median with interquartile range (25th to 75th percentilies). P < 0. 05 was considered to indicate a statistically significant difference.
Understanding the driving forces of an epidemic is key to inform intervention strategies against it. Correlating measures of the epidemic success of a pathogen with ancillary parameters such as its drug resistance profile provides a flexible tool to identify such driving forces. The recently described time-scaled haplotypic density (THD) method facilitates the inference of a pathogen's epidemic success from genetic data. Contrary to demogenetic approaches that define success in an aggregated fashion, the THD computes an independent index of success for each isolate in a collection. Modeling this index using multivariate regression, thus, allows us to control for various sources of bias and to identify independent predictors of success. We illustrate the use of THD to address key questions regarding three exemplary epidemics of multidrug-resistant (MDR) bacterial lineages, namely Mycobacterium tuberculosis Beijing, Salmonella Typhi H58, and Staphylococcus aureus ST8 (including ST8-USA300 MRSA), based on previously published, international genetic datasets.
In each case, THD analysis allowed to identify the impact, or lack thereof, of various factors on the epidemic success, independent of confounding by population structure and geographic distribution. Our results suggest that rifampicin resistance drives the MDR Beijing epidemic and that fluoroquinolone resistance drives the S. aureus ST8/ USA300 epidemic, in line with previous evidence of a lack of resistance-associated fitness cost in these pathogens. Conversely, fluoroquinolone resistance measurably hampered the success of S. Typhi H58 and non-H58. These findings illustrate how THD can help leverage the massive genomic datasets generated by molecular epidemiology studies to address new questions.
Although historical data on transmission dynamics are frequently lacking, the genotypes of extant pathogens are increasingly available. Therefore, using these genetic data to infer the epidemic success of a pathogen or group of pathogens can greatly improve our understanding of the drivers of pathogen transmission and spread. Here, we illustrate applications of the time-scaled haplotypic density (THD), a recently developed measure of epidemic success  (Barbier et al. , 2018; Rasigade et al. , 2017) , to model the driving forces behind major pathogen epidemics, using rich genetic datasets on three exemplary human pathogens. After discussing the motivation for the THD method and its mathematical foundations, we first revisit the Mycobacterium tuberculosis Beijing epidemic in Eurasia  (Merker et al.
Because of their sophistication and computational demand, however, state-dependent diversification models can be difficult to use with large datasets (typically, n > 1,000) and the multiple-feature hypotheses that are increasingly common in pathogen epidemic studies. To circumvent the statistical drawbacks of group-based correlates of epidemic success and the complexity of state-dependent diversification models, our group proposed a simpler, alternative approach to epidemicity analysis that provides individual-based correlates. The expected benefit of defining correlates of epidemic success at the individual level is to facilitate the detection of factors associated with epidemic success using regression models that can control for multiple confounders (Box 1).
The IAM assumes that the m haplotype markers lie on a non-recombining DNA segment, that they evolve independently with a common evolutionary rate µ, and that at most one change per marker has occurred in both lineages since their MRCA. Assuming that µ is known, the IAM allows us to replace the bandwidth with a more intuitive timescale parameter t 50 , which is the TMRCA such that haplotypes with shorter TMRCAs account for 50% of the density. Practically, we solve the IAM relation From a computational complexity standpoint, the THD method scales linearly with the size of the input matrix of genetic distances, hence quadratically with the sample size. The single-threaded computation time is ~0. 1 s for a sample size of 1,000 isolates on a standard computer (Intel I7 CPU at 3. 4 GHz). As a comparison point, this computation is two orders of magnitude faster than a typical, fast phylogeny reconstruction method (BIONJ) applied on the same dataset (Figure  S1 ).
| G ROUP-LE VEL CORREL ATIONS B E T WEEN THD, THE MALTH US IAN PAR AME TER , AND D IVER S IT Y IND I CE S Several aggregate measures can be interpreted as proxies of epidemic success at the level of a group of isolates: (a) the group's prevalence, which reflects possible dominance but provides no information on population dynamics; (b) the genetic diversity of the group, which is comparatively lower in recently expanding lineages; and (c) the Malthusian parameter r. We examined the correlation patterns of THD with these aggregate measures in a well-documented dataset of M. tuberculosis genotypes from a collection of 4,987 isolates of the Beijing lineage F I G U R E 1 Overview of the THD method's inputs, estimation, and modeling steps. THD estimation is a special case of kernel density estimation where the observations are haplotypes and the kernel bandwidth b is calibrated relative to a timescale parameter t 50 defined as the median of the kernel distribution.
Typhi mutants outcompeted their susceptible progenitors in FQ-free medium, suggesting that FQ-R associated mutations do not necessarily entail a fitness cost and might even benefit their carrier in the absence of antibiotic pressure  (Baker et al. , 2013 ). Yet, our knowledge of the impact of fluoroquinolone resistance on fitness derives from laboratory experiments. This impact has not yet been examined in an epidemiological setting. In this context, we illustrate the use of THD to address the question of whether fluoroquinolone resistance has a measurable impact on pathogen success at population scale. We used a genomic dataset generated from an international study of H58 and non-H58 S. Typhi  (Wong et al. , 2015). The dataset is comprised of 1,832 whole-genome sequences of isolates from 68 countries, mainly in Asia and Africa.
t 50 (timescale); (b) associate the timescale with a median distance h 50 ; (c) determine the corresponding bandwidth b * ; and (d) for each haplotype of interest, compute THD as the average kernel density under the truncated geometric distribution with bandwidth b * and truncation limit m. The resulting vector of THD values is typically used as the response variable of linear regression models using potential predictors of epidemic success as the explanatory variables. A summary of the model estimation steps is shown in Figure 1. Practical guidelines on parameter selection and model interpretation are proposed in Box 2. A reference implementation of THD for the R platform is available at https://github. com/rasig adela b/thd.
Paglia et al. , 2014  [39]  compared 107 Ω N2 values of negatively charged ions predicted by N 2 -MOBCAL to the measured Ω N2 values determined by three independent Synapt G2 instruments from different laboratories. Among their dataset, we selected 13 carboxylic acids (glyceric acid, fumaric acid, succinic acid, ketoleucine, malic acid, salicylic acid, oxoglutaric acid, mevalonic acid, aconitic acid, ascorbic acid, citric acid, glucuronic acid, and gluconic acid) and eight saccharides (fructose, galactose, glucose, mannose, mannitol, lactose, sucrose, and trehalose) that have the same functional groups (-OH or -COOH), elements (CHO), and similar m/z values as the compounds studied here for the assessment. In general, the N 2 -MOBCAL predicted Ω N2 values agreed well with the measured Ω N2 values from all three Synapt G2 instruments, with average relative difference (RD% = (measured Ωpredicted Ω)/(measured Ω)) values ranging from -0.
1 ± 4. 0% to -1. 3 ± 4. 3% (µ ± 1σ). Worse RD% values among all data were found from glyceric acid, fructose, and glucose with RD% of 8. 5-9. 2, 6. 8-10. 8, and 6. 0-10. 1, respectively. These highly oxygenated compounds (a C/O ratio ≥ 1) may need further molecular dynamic simulation to accurately represent their molecular conformations, and caution is warranted for the application of N 2 -MOBCAL for this class of compounds. Based on this, we concluded that N 2 -MOBCAL was suitable for the purpose of this study. In addition to a published dataset, we measured Ω N2 values of cis-norpinic acid, terpenylic acid, and cis-pinic acid, then compared them with N 2 -MOBCAL predicted Ω N2 values. The RD% values were 2. 4% for terpenylic acid (IMS: 137. 7 Å 2 , N 2 -MOBCAL: 134. 4 Å 2 ), 1. 0% for cis-norpinic acid (IMS: 136.
6 Å 2 , N 2 -MOBCAL: 135. 3 Å 2 ), and 4. 5% for cis-pinic acid (IMS: 140. 6 Å 2 , N 2 -MOBCAL: 147. 0 Å 2 ). These RD% values fall within the range of RD% values obtained from the dataset of Paglia et al. , 2014  [39] , except for cis-pinic acid. This discrepancy is discussed in a later section. The N 2 driftgrams of the major product ions detected in the MS 2 experiment of m/z 357 ion are shown in Figure  3. Both  Müller et al. , 2009 [43]  and  Kahnt et al. , 2018 [19]  attributed the m/z 185 product ion to cis-pinic acid though they assigned the m/z 171 product ion to a different product ion;  Müller et al. , 2009 [43]  assigned it to cis-norpinic acid, whereas  Kahnt et al. , 2018 [19]  assigned it to a diaterpenylic acid. To assign the structures of product ions unambiguously, N 2 drift times of cis-pinic acid, cis-norpinic acid, and terpenylic acid was compared to those of product ions (Figure  4 ).