dataesr/scientific-paragraphs-categorization huggingface dataset:
then periodical controls must be regularly performed, in order to ensure that the process is well controlled. The lognormal model can be used to provide indications on the optimal frequency of these controls. The higher the exceedance fraction, the more frequent the controls. In AltrexChimie, the value of the multiple exposure index is used like an exposure measurement value, thus allowing its statistical treatment. No other free software provides explicit multiple exposure assessment functionalities. Meanwhile, all of them can be used with multiple exposure indexes instead of measurements. The statistical computations offered by AltrexChimie are among the simpler ones, like the ones proposed by BWStat, IHStats, or the R libraries SAND and STAND. Hyginist offers a greater number of statistical indicators, allowing for a comprehensive assessment for experts.
This is an intrinsic contradiction of the proposed procedure that clearly highlights the lack of guidance when a substantial number of measurements is available. This explains the computational choices made in AltrexChimie, which are different to IHDA or Expostats. One major issue during the development of the new version of AltrexChimie as a web application was the protection of user data. While an online tool is less limited by operating systems and updates, an offline tool is more reassuring to the user because important data are not transmitted over the network. To guarantee data confidentiality, innovative software functionalities were implemented, so that users can keep their data on their own computers, and no data is stored on the INRS servers. Other online tools such as Expostats and BWStats V3 do not provide a fully structured data storage system: Only screenshots and copy/pasting are allowed.
The possibility of publishing an open-source software library derived from AltrexChimie was also discussed during the development phase. Some companies already have measurement management tools, either purpose-designed or included in laboratory information management software and they may therefore be interested in upgrading by adding only AltrexChimie's statistical computation engine. This possibility was rapidly excluded, because such a library would not provide significant advantages over already existing packages such as STAND, which can be used instead of AltrexChimie in this particular context. Finally, some important limitations of AltrexChimie are related to the choices made for the multiple exposure assessment. First of all, the toxicological information of the effects of substances on target organs is not included in AltrexChimie, and the effects are only considered additive.
AltrexChimie can be accessed from altrex. inrs. fr; it does not require users to log in, and user data is stored on the user's computer only. Several improvements of the exposure assessment methodology and the associated tools can be envisioned. Among these, a simple procedure for handling left-censored data (measurements below the LOD); guidelines for analyzing SEGs with large measurement datasets, using more specific models; recommendations on the use of the procedure for biometrology data. Finally, a shift towards the Bayesian methodology with its underlying assessment based on uncertainty would certainly allow both more realistic (yet less accurate) assessments and provide a greater use of the knowledge and expert judgment of industrial hygienists in the overall assessment, as opposed to simply applying statistical routines to analyze measurement data.
Image acquisition For all experiments, we recorded functional imaging data on a 3T SIEMENS Tim Trio System (Siemens, Erlangen, Germany), using a T2*weighted gradient multiband echo-planar imaging (M-EPI) pulse sequence with an acceleration factor of four, 3-mm isotropic resolution, 36 slices in a 64 Â 64 matrix, 20% distance factor, TR/TE ¼ 650/30 ms, and FA ¼ 50. Structural images had 1-mm isotropic resolution (192 contiguous 1-mm slices, TR/TE/TI ¼ 1900/2. 27/900 ms, FoV ¼ 296 mm, in-plane resolution of 1 Â 1mm). Additional physiological data (heart rate and respiration) were recorded with the MP150 BIOPAC acquisition system (Santa Barbara, CA).
Basic data analysis Data preprocessing. Preprocessing and statistical analyses of functional images were performed with Statistical Parametric Mapping software (SPM12, Welcome Department of Cognitive Neurology, London (fil. ion. ucl. ac. uk/spm/); Matlab (mathworks. com/products/matlab. html). Functional data were first manually realigned to the AC-PC axis, followed by motion correction of the functional images. Each participant's structural image was co-registered to the mean functional image and then segmented to allow estimation of normalization parameters. Using the resulting parameters, we spatially normalized the anatomical and functional images to the Montreal Neurological Institute (MNI) stereotactic space. The functional images for the main experiment were resampled into 2 mm 3 voxels.
For each GLM of experiments 1-2, six motion correction parameters and four physiological parameters were also included as regressors of no interest to account for signal changes not related to the conditions of interest. Physiological parameters were analyzed by using Retrospective Image Correction  (Glover et al. , 2000) , as implemented for Matlab (cbi. nyu. edu/software). All group results were thresholded at a combined voxel threshold of p < 0. 05 (FDR corrected) and a cluster extent threshold of k ¼ 10.
ICA component estimation based on experimental factors and brain lateralization The above-described ICA decomposition was applied to the data in four different ways. Since an ICA analysis cannot be easily accomplished in a hierarchical statistical manner (including the differentiation of experimental factors) and given non-linearity effects when combining or splitting data from different conditions, we performed each of these analyses separately by splitting the data into relevant subsections. This leads to independent ICA estimations across these approaches with no statistical dependencies across these approaches and with no issues potentially relating to double-dipping. Also, the number of relevant ICA components for each factor can only be estimated in separate analyses (see below). Here we fully describe the four different ways of the ICA in detail: first, we quantified BOLD oscillations to sound processing in general since we aimed at finding general dynamic BOLD oscillations to temporally extended sounds; second, we quantified differential BOLD oscillations for vocal and nonvocal trials since vocal signals have higher social relevance; third, we quantified BOLD oscillations separately in the left and right AC given some potential processing differences of sound; and, fourth, given that the human brain also shows endogenous BOLD oscillations in the absence of external stimulation, we quantified BOLD oscillations in the resting brain to compare them to task-related BOLD oscillations.
The ICA decomposition for these data should reveal the default oscillation activation in the auditory cortex during a resting-state condition. The time-frequency analysis and the ICA decomposition was applied to the same 4 0 262 voxels as part of the common area of activation determined from experiment 1 of the eventrelated and the block-design run. For each of these four different analyses, we determined the ICA components that best represent the single time-frequency signals. For each component, we determined the weights that each voxel contributed to each component.
Auditory cortical BOLD oscillations elicited by sounds Having looked at voxel-wise activity differences, we next investigated whether this prioritization of vocal signals also appears in neural infraslow BOLD oscillations in response to extended sound stimulation. For the functional data resulting from experiment 1 (block-design), oscillatory brain responses were quantified as measures of induced neural BOLD oscillations. The power of oscillations was baseline corrected to 3. 25s before stimulus onset and quantified in the frequency range 0. 05-0. 75 Hz, which was defined by stimulus duration (20s) and sampling rate (1. 5 Hz, corresponding to TR ¼ 0. 65s; Nyquist frequency 0. 75 Hz) in its lower and upper limit, respectively. To this end, we subjected the time course of each cortical voxel that commonly (i.
C1-2 might especially signify ongoing sound stimulation and analysis by continuous induced oscillations that are based on continuous neural firing  (Galazyuk and Feng, 1997; Wang et al. , 2005). Component C3 showed sustained LF oscillations starting at about 13s after stimulus onset, had large negative voxel weights in the posterior secondary AC, and had a resemblance to a resting-state brain oscillation rather than to stimulation-related oscillations (Fig. 3 ). This resemblance is demonstrated with data from experiment 2, which included a resting-state brain acquisition during a "task-free" recording of whole-brain data in an independent sample of participants. These resting-state brain data were analyzed similarly to the data of experiment 2 (block-design) by epoching the data into 20s segments of simulated and randomly assigned vocal and non-vocal events.
Under a broader definition, reinforcement is the enhancement of a reproductive barrier over time through the adaptive coupling of any barrier effects  [2]. Regardless of the sense in which the term is used, reinforcement necessarily increases total reproductive isolation over time. Extensive empirical and theoretical work  [5, 6]  has shown that reinforcement can and does push divergent populations 'further along' the speciation continuum, even leading to complete reproductive isolation under some conditions  [7]. Yet the genetics of reinforcement is an emerging field. A couple of studies have identified reinforcing loci through candidate gene or QTL mapping approaches  [8, 9] , though studies leveraging whole-genome datasets are still missing. Genomic approaches to the study of reinforcement have the potential to identify reinforcement at the genomic level that might otherwise be phenotypically cryptic, and to shed light on the identity, distribution and effect size of loci underlying reinforcing barrier traits.
7 Gb), we carried out a pool-sequencing approach. Pooled samples representing each population were obtained by mixing individual DNA extracts from each population in equimolar proportions. Library preparation and sequencing were carried out at the MGX-Montpellier GenomiX platform (Institut de Génomique Fonctionnelle -Institut de Génétique Humaine, Montpellier, France). Sequencing libraries (average insert size: 478 bp (Borum Choosy1), 497 bp (Låsby Choosy2), 478 bp (Hobro Non-Choosy1) and 445 bp (Randers Non-Choosy2)) were prepared using two methods to increase sequence diversity: Illumina TruSeq DNA Sample Prep Kit v2 (three quarters of samples), and Illumina Nextera DNA sample Prep kit. Overall, three-fourths of all sequence data were obtained using the TruSeq protocol. All libraries were sequenced on an Illumina HiSeq2000 machine using a paired-end 100-bp read-length protocol, targeting an average raw coverage per pool of 30X.
Bowtie2 v2. 2. 3  [72]  was used to map reads to the mouse reference genome assembly GRCm38  [73]  and SAMtools  [74]  was used to format binary sequence alignment/map (. bam) files and remove PCR-duplicate reads. We used the program BayPass, which provides an accurate estimation of the genome-wide covariance matrix (Ω) across population allele frequencies, to infer the genome-wide population structure of our dataset  [75]  (figure  1b ). (b) Identification of differentiated copy-number variants from pooled paired-end sequencing data We detected CNVs significantly and consistently differentiated in allele frequency between Choosy and Non-Choosy populations using a combination of read depth, read-pair orientation and insert size information, adapting the approach developed by Schrider et al.
(c) Genomic distribution and characteristics of differentiated CNVs On Choosiness-associated CNVs (identical and non-identical by state), we first performed a permutation test (accounting for 'masked regions' of low coverage that were excluded in upstream analyses) to assess whether mutations were distributed heterogeneously, i. e. whether the observed number of CNVs per 10 kb was larger or smaller than expected by chance. We also performed a permutation test of the duplications and deletions to test for enrichment in genes annotated in the UCSC mm10 known gene database while accounting for masked regions of the genome  [83] [84] [85]. Finally, in order to test whether Choosinessassociated CNVs were present in regions of reduced or enhanced recombination, we performed permutation tests to measure over-representation in 'cold-spots' of recombination reported by Morgan et al.
[39]  and in recombination 'hot-spots' reported by Smagulova  [86]. To perform these permutation tests, we used BEDtools  [87] , the RegioneR  [83]  package in R version 3. 5. 1  [88]  and custom bash scripts. The R package ChromPlot was used to visualize the genomic distribution of CNVs,  [89]  and Gviz was used to visualize gene models of Ensembl transcripts  [90].
(d) Functional enrichment analyses The Ensembl Variant Effect Predictor  [91]  was used to determine the predicted effect of Choosiness-associated CNVs (e. g. coding sequence variants) and to identify gene annotations (Ensembl and RefSeq IDs) in their vicinity (within 1 kb upstream and downstream of each gene). This allowed us to explicitly test for the presence of genes that belong to candidate multigene families (ORs and VRs) and to test for overlap of divergent CNVs with genes expressed in the M. m. musculus vomeronasal organ that show differential expression between behavioural classes  [58]. We used the program DAVID  [92]  to perform functional enrichment analysis for gene IDs nested within one or several annotation terms. In DAVID, annotation terms are derived from 14 public annotation sources (e.
(c) Several Choosiness-associated CNVs occur in candidate gene families We observed 480 Choosiness-associated deletions and four Choosiness-associated tandem duplications. A permutation test showed that Choosiness-associated deletions were significantly under-enriched in genic regions (198 occurred in genic regions; p < 0. 05, z = -2. 453, 10 000 permutations) (electronic supplementary material, table  S6 ). Applying the available Ensembl gene IDs for deletions and tandem royalsocietypublishing. org/journal/rstb Phil. Trans. R. Soc. B 375: 20190540 duplications combined in the DAVID software, 10 annotation terms showed significant enrichment (Benjamini-Hochberg adjusted p < 0. 01; electronic supplementary material, table  S7 ). The enriched sets of genes are associated with synapse function, plasma membrane function, phosphoprotein production, alternative splicing and protein localization to the cell surface.
Tens to hundreds of thousands of CNVs were identified in each of the four populations sampled (each with a mean individual sample size of 30, electronic supplementary material, table  S1 ). In a study of European populations of M. m. domesticus, Pezer et al. sampled eight individuals per population and identified approximately 4000-10 000 CNVs within populations using a sequencing depth and PCR-validation approach  [59]. By contrast, Morgan et al. [39]  observed 1749 CNVs in a sample of 6886 mice from the Diversity Outbred mouse stock using a readdepth approach different to ours, whereas Locke et al. [62]  observed 9634 autosomal CNVs in the genotyping array data of a more genetically diverse dataset of 290 individuals-both laboratory and wild-derived strains. Fundamental differences in the methodology and scope of CNV detection, the number of individuals sampled, and the effective population size make it difficult to compare our results to other studies.
Funding. H. L. N. was funded through an  Erasmus Mundus Joint Masters Degree scholarship  and an internship stipend from C. M. S. 's research grants. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  [FP7/2007-2013]- Marie Curie European Reintegration Grant  (ERG), under grant agreement no. PERG06-GA-2009-251008 , as well as from the  Agence Nationale pour la Recherche (ANR)  under grant agreement no. 2010BLAN171401-AssortMate.
A user can control breathing rates (breaths per minute or BPM), tidal volume (V T , air volume pushed into the lungs), inspiratory/expiration time ratio (I/E ratio). All the mechanical components (Fig. 1 : components 1, 4, 7) were developed in open-source CAD systems. The use of a parametric OpenSCAD generator of 3-D printable components (junction boxes for the feedback pressure sensors (Fig. 1 ) allows to fit any tubing system. A backup battery enables short-term patient mobility and safety protocols in software provide alarm signals when the monitored proximal pressure exceeds the permissible range, or the pressure sensors are disconnected. The electrical architecture is illustrated in Fig. 2. The development process of a medical device as an embedded real-time system can be divided into the main following steps: 1.
Thus, the signals from pressure sensors located at opposite ends of the airway can be interpreted as proximal pressure. Based on the above Eq. (  6 ), it is possible to determine the readings of the sensors corresponding to the minimum allowable PEEP pressure and the maximum critical pressure of 40 cmH 2 O (Fig. 8 ). The control system is based on the Arduino controller and a stepper motor setup (NEMA-23 motor). The Arduino Nano board was chosen as a controller due to low relative expense while having sufficient digital and analog pins. A significant number of medical software development standards contain information and requirements regarding software design, validation, and certification  [107] [108] [109] [110] [111] [112]. However, in the global pandemic, meeting all requirements can be difficult.
Control system assembly 9 Print and assemble the case for the control system according to Fig. 18. 10. Connect the components according to the schematics (Fig. 19 ) and build a breadboard (Fig. 20, A ). Note that the fuses in the schematic cannot be installed into the breadboard and must be omitted. Additionally, there are some sets of redundant or extra connections that are not needed for this specific implementation. Install the breadboard into the case (Fig. 20, B ). The complete system is then assembled as shown in Fig. 21. 10 Install the firmware by uploading the ''arduino_firmware. ino" file to the Arduino Nano controller via Arduino IDE (https://www. arduino. cc/en/main/software).
Operation instructions Using the control knobs on the top panel (Fig. 22 , component 1), a user must set the desired breathing mode and connect the patient to the mask. The LCD (Fig. 22 , components 3 and 4) displays the input parameters (V T , BPM, and I/E) and feedback (proximal airway pressure and estimated airflow). LEDs 6 and 7 (Fig. 22 ) reflect the motor operation mode. LED 5 signals an alarm when the proximal airway pressure exceeds the permissible range. When using the device, there may be a danger of electric shock. Performed incorrectly, BVM-based ventilation can accelerate hypoxia and aggravate airway obstruction  [116]. This can result in serious injury or death  [117] [118] [119] [120] [121] [122] [123] [124] [125] [126]. According to the international ''Medical Device Software" standard IEC 62,304  [127, 128] , ventilators are class C medical equipment that can lead to patient death.
The airway resistance was kept at a constant Rp5  [106]  with a compliance of 0. 05 to simulate a healthy adult lung. The PEEP valve was not touched to determine if it was consistent for all tests. The values for every measurement, excluding the flow, oxygen concentration, and FiO 2 percentage, were recorded through the PneuView3 (Fig. 25 ) by taking a screenshot of the software screen once it became constant. While waiting for the data to become constant, the maximum values for tidal volume, proximal pressure, and lung pressure were recorded in real time. Due to the data being recorded in real time, the values fluctuated as the tests went on. Statistical analysis was completed by calculating the standard error between each test using the built in STDEV. P function in Excel. Since the tests were run for three trials (N = 3) to determine repeatability, the standard error (SE) was found by the equation (  7 ).
The meaningful difference being, the blue box did not contain super glue to hold the pressure sensor to the ports on the underside of the lid, whereas the green box did. After completing these tests, it was found that adding a junction box could cause significant changes in the data if the box was not assembled correctly. It was also seen that at low tidal volumes the lung was unable to calculate the majority of values, thus half the data could not be collected. In some instances the lung struggled to maintain consistent data causing values to be estimated. However, the main significant difference for each test, and trial, was the tidal volume. For the majority of tests, the tidal volume recorded was different than the volume manually set on the user interface. The standard deviations were also incredibly unsafe with the majority being over 60 mL for all three trials.
This makes sense since there was a higher sample rate for calculations, and less time between breaths to let air escape through any leaks. The 10 BPM data could be a considered an outlier because the majority of values are above both the 5 and 15 BPM tests. This could have been caused by a shift in the valve bag, a slip of the motor, or inaccurate data gathered from the lung itself. The mechanical issues associated with this design should be addressed in future work to confidently confirm that the ventilator is consistent, accurate, and reliable. The standard error results (Fig. 28 ) indicate, notably, significant difference between the means of the targeted tidal volumes. This tidal volume was not what was manually input by the user, but the volume that was outputted by the PneuView software.
11. 0  [21]  and subsequently polished using Pilon v1. 18  [22]. The in silico MLST was determined using Seemann's MLST v2. 11 (https:// github. com/tseemann/mlst). The final assemblies were annotated by Prokka v. 1. 12 [23]  through ummidock/ prokka:1. 12 Docker image. Reads were deposited at the European Nucleotide Archive (ENA) with accession numbers summarised in Supplementary Table  S1.
Comparative analysis by whole genome sequencing Molecular antibiotic resistance mechanisms The in silico analysis of gene mutations involved in antibiotic resistance (pbp1a, pbp2x, pbp2b, parC, parE, gyrA, folA and folP) was done manually using Geneious R9 (Biomatters, Auckland, New Zealand) and the R6 genome (NC_003098) as reference. The acquired resistance mechanisms were screened using Abricate v0. 8. 0 (https://github. com/tseemann/abricate) through flowcraft/abricate:0. 8. 0-3 Docker image for the Comprehensive Antibiotic Resistance Database (CARD)  [24]  and ResFinder  [25]  databases.
Phylogenetic analysis To better explore the diversity and relationship with other pneumococci with related serotype and ST to the penicillin-resistant 11A isolates included in this study, 47,529 available S. pneumoniae genomes deposited in ENA were downloaded on 29 October 2018 using get-SeqENA v1. 3 (https://github. com/B-UMMI/getSeqENA) with Aspera Connect v3. 7. 2. 141527 (https://asperasoft. com/software/clients/connect/). In case of large fastq file sizes, they were first downsampled for an estimated depth of coverage of 100 × with the sam-ple_fastq script commit 01ac0db available at https:// github. com/jacarrico/sample_fastq and making use of Seqtk v1. 2-r94, (https://github. com/lh3/seqtk). The serotype was in silico deduced using Seroba v1. 0. 1  [26]. Among the 47,529 available genomes, we selected 238 with serotypes 9V or 11A and with one of the following MLST: ST156, ST166, ST838 or ST6521 (Supplementary Table  S2 ).