dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018; Teanby, 2015). Improved understanding of seismic moment and seismic efficiency would enhance impact detectability predictions on Mars and provide more robust initial conditions for seismic wave propagation modeling, and perhaps help explain the current lack of definitive impact detections from InSight. Here we use the iSALE (impact Simplified Arbitrary Lagrangian Eulerian) shock physics code  (Amsden et al. , 1980; Collins et al. , 2004; Wünnemann et al. , 2006)  to simulate vertical impacts onto Martian regolith in the size range of events expected during the InSight mission. For each simulation, we characterize the impact as a seismic source by calculating the scalar seismic moment and seismic efficiency. We also examine the anisotropy of the seismic moment tensor for vertical impacts and hence probe the analogy between impacts and underground explosions.
Approach We used the iSALE shock physics code  (Wünnemann et al. , 2006)  to simulate seismic wave generation by small impacts on Mars. iSALE is a finite-difference shock physics code based on the SALE hydrocode  (Amsden et al. , 1980)  and adapted to simulate hypervelocity impact processes in planetary materials. Modifications to SALE include an elastoplastic constitutive model, fragmentation and strength models for dilatant geological materials  (Collins, 2014; Collins et al. , 2004; Ivanov et al. , 1997; Melosh et al. , 1992) , various equations of state (EoS), and a porosity compaction model  (Collins et al. , 2011; Wünnemann et al. , 2006). In this first investigation of seismic signals generated by impacts on Mars we considered a range of typical impact scenarios likely to be detected by InSight, including the size range of the newly occurring nearby crater.
Supporting Information S1 details further how residual displacement was derived. In the second method, the seismic moment in the horizontal direction was calculated following  Walker (2003) : M rW (t) = 1 2t n ∑ i 𝜌[i]v r [i]V[i], ( 17 ) where t is time after impact, [i] is material density in cell i, V[i] is volume in cell i, and vr [i] is average (horizontal) radial velocity in cell i. Summation is over all n cells in the mesh, below the preimpact target surface level; cells above the preimpact surface were excluded, removing ejected material from the seismic moment calculation. M rW (t) was evaluated at the regular time intervals, at which mesh data were saved. In the third approach, we calculated the vertical seismic moment as M zGL = c P p z = c P n ∑ i 𝜌[i]v z [i]V[i], (18) where this summation is over the same target cells as above; vz [i] is the average vertical velocity in cell i.
Implications for Detectability of Impacts by InSight Our simulation results show a linear relationship between impact momentum and seismic moment (and therefore ground motion amplitude). We now use this result to re-evaluate InSight prelanding impact detectability estimates. The expected number of detectable impacts has been estimated in several ways  (Daubar et al. , 2015 (Daubar et al. , , 2018;; Lognonné & Johnson, 2015; Teanby & Wookey, 2011; Teanby, 2015). Teanby and Wookey (2011) estimated detectable impact numbers by combining seismic wave form modeling, observed cratering rates and an empirical relationship between crater size and seismic moment (Figure  8a ). This empirical scaling relationship relies on three elements: an empirical relationship between crater size and impact energy, based largely on terrestrial explosion data (Figure  8b ); the seismic efficiency of the impact; and a relationship between radiated seismic energy and seismic moment, based on small earthquake and nuclear explosion data (Figure  8c ).
For a nominal noise model, Teanby and Wookey (2011) estimated a total impact-generated seismicity of 10 13 to 10 14 Nm per Earth year, and one detectable teleseismic range impact every 10 years. Based on the much lower seismic moments predicted by this work, the TW2011 predictions at the lower end of their estimates seem the most likely. These imply a total impact-generated seismicity of 10 11 to 10 13 Nm and an average recurrence interval of over 100 years for a detectable teleseismic range impact. To better constrain the detectability of smaller impacts, more proximal to InSight, Teanby (2015) adopted an empirical approach to directly relate impact energy to ground velocity amplitude, based on data from impact and explosions experiments. This approach also employed the crater size to impact energy relationship from Teanby and Wookey (2011) so also likely represents an overly optimistic estimate of impact detections.
1970) : v(x, D) = 1. 59 × 10 -9 𝜉 2 x -1. 6 D 3 𝜉 2 = ( v i 1,000 m s -1 ) -0. 23( g 3. 71 m s -2 ) 0. 61 ( 𝛿 2,000 kg m -3 ) -0. 19 ( 𝜌 1,500 kg m -3 ) 1. 19 ( 21 ) where g is gravity in m s -2. This scaling relationship is based on data from impact experiments in a similar target material to that employed in our simulations and that showed a similarly low seismic efficiency and therefore may be most appropriate for surfaces on Mars similar to the InSight landing site. For a cohesive soil, using a fit to seismic data from terrestrial missile impacts  (Teanby, 2015)  v(x, D) = 1. 64 × 10 -7  1 x -1. 6 D 3. 6 𝜉 1 = ( v i 1,000 m s -1 ) -0. 23( Y 100 kPa ) 0. 62 ( 𝛿 2,000 kg m -3 ) -0. 2 ( 𝜌 1,500 kg m -3 ) 0. 59 ( 22 ) where x is the range in km, D is crater rim diameter in m, v ⟂ is vertical impact velocity in m s -1 , Y is the cohesion of the soil in kPa, and  and  are the impactor and target density, respectively, in SI units.
Statistical analysis Statistical analyses were performed with the Mann-Whitney test using StatEL software (www. adsciences. edu). p values were adjusted for multiple comparisons, using the Holm-Sidak method, and the statistical significance was set as p value less than 0. 01. Sample size calculation was performed to guarantee a desired power of 0. 8 (www. stat. ubc. ca/~rollin/stats/ssize/n2. html). Influences of sex and severity of the disease (Hoehn and Yahr score) were evaluated with the Mann-Whitney test and the Kruskal-Wallis test, respectively. After checking of the normal distribution of GAPDH expression in samples using a Shapiro-Wilks test, a Student's t test was applied to analyse the statistical differences between samples.
Predictions with machine learning algorithms (classifiers) Predictions were performed with the software Scikit-learn, a Python module integrating state-of-the-art machine learning engines  (Pedegrosa et al. , 2011). All appropriate features were processed with three standard classification methods: support vector machine (SVM with linear Kernel), logistic regression (LR) and random forests (RF). Stratified five-fold cross-validations were performed to assess classification performance. Briefly, datasets were randomly divided into five equal parts. Four of them were used as training sets and the last one was used in turn as the test set. The accuracy is computed on this remaining fifth part. This process is repeated 5 times on each part to calculate the mean accuracy and the 95% confidence interval.
Classification prediction with machine learning algorithms These discrepancies in transcript levels of the autophagy genes between PD patients and controls prompted us to consider a prognostic study using mRNA quantification. Is it possible to predict with good accuracy whether a blood sample arises from a sick or a non-sick individual? To answer this question, we have used different standard classifiers (i. e. , SVM, LR and RF) with the datasets from each individual gene. The three classifiers produced similar data. Consequently, only the results issued from the SVM classifier are reported in Table  2. The complete data issued from the three classifiers are reported in Supplementary Table  S7. These results confirmed the box plot data (Fig. 1 ) and the most discriminant characters of the MAP1LC3B and GABARAP genes with a predictive performance of 0.
842 ± 0. 218 and 0. 832 ± 0. 213, respectively (Table  2 ). However, taking into account the individual data for these two genes was not sufficient enough to allow accurate clinical prediction. In order to improve these predictive performances, we challenged, using the same classifiers, models combining data from MAP1LC3B and GABARAP gene expression with those of each one of the other genes. This led to substantially enhanced predictive accuracy and confidence (Table  2 ). The most informative prediction occurred while taking into account the dataset from the pair of genes MAP1LC3B and GAPDH and the pair of genes GABARAP and GAPDH (Table  2 ). For instance, using the SVM classifier, accuracy was increased to 94. 8% (± 0. 099) for the pair MAP1LC3B-GAPDH and to 94. 7% (± 0. 100) for pair GABARAP-GAPDH (Table  2 ).
Surprisingly, although both MAP1LC3B and GABARAP transcripts increased in patients, the combination of their datasets did not significantly increase prediction accuracy. This is probably linked to a significant individual variability generated by the dysregulation of the expression of each of these two genes. From a functional point of view, it is noteworthy that the increase of MAP1LC3B and GABARAP gene expression is paralleled by a reduction of GAPDH gene expression. Are these observations linked or, on the contrary, do they represent independent pathways? The decrease of GAPDH expression in patients could impede the elimination of damaged mitochondria via mitophagy thus increasing cellular cytotoxicity  (Klettner et al. , 2017). Then, activation of MAP1LC3B or GABARAP gene expression could be a pathway to promote autophagy and favour cellular protection to counteract the deleterious effect of impaired mitophagy.
Study population This multicentre cohort study included all consecutive patients with early-stage HCC occurring on a background of cirrhosis who underwent LR between 2010 and 2018 at 5 French expert hepato-pancreatico-biliary (HPB) centres. All 5 centres had performed at least 100 LLRs before 2010. Inclusion criteria were as follows: age > -18 years, diagnosis of HCC on definitive pathological examination, HCC preoperatively meeting the Milan criteria (no macrovascular invasion, no extrahepatic lesions, and 3 lesions <3 cm or a single lesion <5 cm), presence of cirrhosis (F4 according to the Meta-analysis of Histological Data in Viral Hepatitis score), and HCC qualifying for curative-intent LR as decided by the local multidisciplinary team. Exclusion criteria included the presence of additional cholangiocarcinoma or mixed hepatocellular and cholangiocellular carcinoma on the resected specimen, and previously treated lesions.
The comparison of the 2 models was performed using the likelihood ratio test through the anova function in R language (R Foundation for Statistical Computing, Vienna, Austria). Proportional hazard assumption of Cox models was assessed using Schoenfeld residuals through cox. zph function in R language. As described previously, statistical cure was modelled using a non-mixture cure model fitting a Weibull distribution, using the flexsurvcure function in R language (https://github. com/ jrdnmdhl/flexsurvcure). A Weibull non-mixture regression was performed to assess the association between co-variables and statistical cure. The statistical cure fraction was expressed as percentage (with 95% CI) for the population of interest. 2, 3 The calibration of the non-mixture model was assessed using calibration plots with estimated DFS using non-mixture models on the y-axis and observed DFS using Kaplan-Meier estimator on the x-axis.
Background: Carbapenem-resistant Enterobacteriaceae are considered by WHO as "critical" priority pathogens for which novel antibiotics are urgently needed. The dissemination of carbapenemase-producing Escherichia coli (CP-Ec) in the community is a major public health concern. However, the global molecular epidemiology of CP-Ec isolates remains largely unknown as well as factors contributing to the acquisition of carbapenemase genes. Methods: We first analyzed the whole-genome sequence and the evolution of the E. coli sequence type (ST) 410 and its disseminated clade expressing the carbapenemase OXA-181. We reconstructed the phylogeny of 19 E. coli ST enriched in CP-Ec and corresponding to a total of 2026 non-redundant isolates. Using the EpiCs software, we determined the significance of the association between specific mutations and the acquisition of a carbapenemase gene and the most probable order of events.
For W3110 ΔompC ΔompF pOXA-232 strains carrying pACYC184 derivatives, disk diffusion assays were performed on Mueller Hinton (MH) agar plates supplemented with 2 mg/l chloramphenicol. Fitness was determined by growth curve analysis with an automatic spectrophotometer Tecan Infinite M200 during 24 h in LB, MH, or M9 media supplemented with 0. 4% glucose. Growth metrics were estimated with the R package "growthcurver"  [17]. The area under the curve which includes contributions of the most important growth parameters (log phase, growth rate, and carrying capacity) was used as a growth metric.
Genome sequencing and genome sequences retrieved from sequence databases Ec genomes were sequenced by using the Illumina HiSeq2500 platform, with 100 nucleotide (nt) single-end reads for the four isolates from Lebanon and 100 nt paired-end reads for the other isolates. Libraries were constructed by using the Nextera XT kit (Illumina) following the manufacturer's instructions. The OXA-181producing Ec-MAD ST410 isolate was selected as a reference strain and sequenced to completion by using the long-read PacBio technology; 10,947 E. coli and 1451 Shigella genome sequences deposited in the NCBI database (19 June 2018) were retrieved for a global analysis of the specificity of CP-Ec (Additional file 2: Table  S2  for STs with at least 1 CP-Ec isolate or 1 isolate with a 4-AA insertion in FtsI).
Ninety-six additional Ec ST167 isolates were retrieved from Enterobase (https://enterobase. warwick. ac. uk/). Raw reads from 62 Ec ST410 and 21 Ec ST38 isolates identified in Enterobase were retrieved from the NCBI database. (Additional file 2: Table  S2 ). Redundancy in the genome collection was removed by filtering for isolates from the same study, diverging by less than 7 SNPs. We kept 1 randomly selected isolate. In case of differences in resistome, assuming that antibiotic resistance gene (ARG) loss was more likely than ARG gain, we kept an isolate with the largest number of ARG. Raw reads from the isolates of the major STs were retrieved from the NCBI database. When raw data were not available, raw reads were simulated from the genome assembly with ART  [18].
Sequence assembly, genome annotation, and mutation identification The PacBio reads were assembled with the RS_HGAP_ Assembly. 3 protocol from the SMRT analysis toolkit v2. 3  [19]  and with Canu  [20]. The consensus sequence was polished with Quiver  [19]  and manually corrected by mapping Illumina reads by using breseq  [21]. Illumina-sequenced isolates were assembled with SPAdes  [22] , and the quality of the assemblies was assessed with Quast  [23]. Contigs shorter than 500 bp were filtered out. All assemblies and downloaded genomes were annotated with Prokka  [24]. The presence of antibiotic resistance genes and plasmid replicons was assessed with ResFinder  [25]  and PlasmidFinder  [26] , respectively. ResFinder and PlasmidFinder were run in local from scripts and databases downloaded from the repositories of the Center for Genomic Epidemiology (https://bitbucket.
Mapping, variant calling, and identification of SNPs of interest Sequence reads were mapped to reference genomes with BWA  [31]. For each of the analyzed STs, an isolate with a complete chromosome sequence was chosen. For ST361 and ST206, no strain with a complete genome sequence was available from public databases, and we selected the isolate with the smallest number of contigs. A pseudochromosome was generated after sorting the contigs with mauve  [32]  and used as a reference. Variant calling was performed with the Genome Analysis Toolkit v 3. 6. 0 [33]. The criteria for variants were occurrence of the alternative base in more than 90% of the reads covering the position, a depth coverage of at least 10 (DP > 10), a quality by depth (QD) > 2, a Fisher strand bias (FS) < 60, a mapping quality (MQ) > 40, a mapping quality rank-sum test (MQRank-Sum) > -12.
5, and a read position rank-sum test (ReadPos-RankSum) > -8. For Ec ST410 isolates, variants associated with different clades of the phylogeny were extracted with VCFtools  [34]  and annotated with snpEff  [35]. The effect of the non-synonymous mutations was assessed with the sorting intolerant from tolerant (SIFT) algorithm  [36]. The algorithm searches for protein homologs in the refseq database using mutated proteins as query and assigns a score to each position. This score is weighted by the properties of the AA changed. If this score is below a threshold (0. 05), the change is predicted to be functional.
OmpC protein sequences were aligned with the mafft L-INS-i approach  [30]. Gblocks  [40]  was used to refine the alignment, and the best fit model (WAG, with a gamma distribution) was estimated with protest 3  [41]. The visual display of phylogenetic trees was done with FigTree (http://tree. bio. ed. ac. uk/software/figtree/) and annotated trees with the script plotTree, R (https://github. com/katholt/plotTree).
Testing the independence between mutations in ftsI, ompF, and ompC and the acquisition of carbapenemase genes To assess the association between the different genetic events, we used the method developed by Behdenna et al. [42]  implemented in the software EpiCs. The described events were mapped on the tree by parsimony, and the probability distribution of the number of paired events occurring in the tree was computed under the null model of independence. Two types of paired events are described in the following methods: inseparable pairs, when both events occur in the same branch, and genealogically ordered pairs, when the second event is found in a node more recent than the first one. We have considered the following genetic events: (i) "ompC mutations" encompassing acquisition of ompC alleles from phylogroup D strains by recombination, ompC mutations changing the charge of AA in the pore lumen, and ompC inactivation; (ii) "ompF mutations" including ompF gene inactivation and point mutations in the OmpR binding sites of its promoter; (iii) "ftsI mutations" including the four different four-codon insertions (YRIN, YRIK, TYPI, and YTIP) in ftsI; and (iv) the acquisition of a carbapenemase gene.
Mutated colonies were grown on plates containing 10 g/l tryptone, 5 g/l yeast extract, 15 g/l agar, and 5% w/v sucrose for plasmid curing. Mutant strains were sequenced by using Illumina MiSeq platform, with 150-nt paired-end reads and Nextera XT kit (Illumina) for library preparation. Reads were mapped onto the MG1655 genome (Genbank NC_ 000913. 3) to confirm that mutations in ftsI gene have been correctly introduced and to check that the rare off-target mutations are not predicted to interfere with the β-lactam susceptibility phenotype (Additional file 3: Table  S4 ).
Results Most CP-Ec ST410 isolates received by the French NRC belong to a single lineage In order to determine the genetic bases for the dissemination of CP-Ec lineages, we first analyzed ST410 CP-Ec isolates, which show a high prevalence among isolates collected by the Fr-NRC  [8]. We sequenced the genomes of 54 CP-Ec isolates, 50 collected by the Fr-NRC (including 22 from patients repatriated from 15 different countries), 4 from Lebanon, and 3 non-CP isolates of animal origin (Additional file 1: Table  S1 ). We reconstructed their phylogeny together with 148 Ec ST410 genome sequences retrieved from public databases (Additional file 2: Table  S2 ). We filtered for redundancy in this collection by removing 50 clonal isolates differing by less than 7 SNPs in the core genome  [47]  and keeping the isolate with the largest number of ARG.
To determine whether the association between the acquisition of a carbapenemase gene and a mutated PBP3 characterized by a 4-AA insertion is specific to ST410 isolates or if it is also observed in other E. coli lineages, we analyzed E. coli and Shigella genomes from the NCBI database. None of the Shigella isolates encoded a carbapenemase gene or carried an insertion in PBP3; 487 E. coli isolates (4. 4%) encoded a carbapenemase gene, and 248 (2. 3%) carried a 4-AA insertion in PBP3: 163 YRIN, 49 YRIK, 3 YTIP, and 33 YRIP (Additional file 4: Figure  S3 , Additional file 5: Table  S9 ). After removing redundancy for almost identical isolates of the same origin, 80% (146 out of 182) of nr isolates mutated in ftsI were CP-Ec (Additional file 5: Table  S9 ). All the 123 nr isolates showing the YRIN insertion were also mutated at position 532 (I/L) and 112 at position 349 (E/K).
Acknowledgements The authors thank  Alexandre Almeida ,  Eduardo Rocha , and  Adriana Chiarelli  for their discussion and critical reading of the manuscript;  Guillaume Achaz  for the advices for the EpiCs analyses; and  Sylvie Letoffe  and  Laurence Ma  for their help in performing the  TM-MAGE  and the Illumina sequencing, respectively. They also thank  Jean-Marie Pages  for the gift of strain W3110 ΔompC and ΔompF. This work used the computational and storage services (TARS cluster) provided by the  IT Department at Institut Pasteur, Paris.
Funding This work was supported by grants from the  French National Research Agency (ANR LabEx IBEID  and  ANR-10-LABX-33 ), from the  Joint Program Initiative on Antimicrobial Resistance  ( ANR-14-JAMR-0002 ), and from the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant Agreement No. 773830  (Project ARDIG, EJP One Health). Funding bodies had no role in the design of the study; collection, analysis, and interpretation of data; and in writing the manuscript.
Study Populations. ree data sets were considered. e learning set is composed of 170 pairs of rest ECGs of 10 s duration, a standard 12-lead ECG and a 3D ECG, recorded on 121 patients admitted in the ICU of the Lyon Cardiovascular Hospital, France. All pairs were recorded the same day, except for one pair which was recorded at 6 days interval. e acquisition time difference of the 169 pairs of ECGs is in average 40 min 28 s with a standard deviation of 30 min 50 s (Min: 2 min 9 s, Max: 1 hour 16 min 29 s). 97 pairs of ECGs have been recorded by one technician and the other 73 pairs by another technician. e 121 patients are composed of 83 men and 38 women, aged of 61 years in average with a standard deviation of 15 years (Min: 18, Max: 87), and were admitted for coronary heart diseases and/or arrhythmias.
e 12-lead ECGs were acquired with a legacy electrocardiograph, and the 3D ECGs with our PEM device, both at a sampling rate of 500 samples/s with an amplitude resolution of 5 µV. Written informed consent was obtained from each patient. e two test sets respectively called C and I are continuous 12-lead ECG recordings performed in 90 coronary patients who underwent elective percutaneous transluminal coronary angioplasty (PTCA) at the Charleston Area Medical Center, WV. e ECGs were recorded during balloon inflation that mimics myocardial ischemia (test set I), and before PTCA in the catheterization laboratory for control (test set C), within the framework of the STAFF Studies Investigations approved by the Investigational Review Board. Informed consent was obtained from each subject. We already described this database  [5, 6]  which includes four locations of balloon inflation: le anterior descending coronary artery (LAD) for 31 patients, le circumflex coronary artery (LCX) for 15 patients, le main coronary artery (LM) for 2 patients and right coronary artery (RCA) for 42 patients.