dataesr/scientific-paragraphs-categorization huggingface dataset:
The low S/N of the binned visibilities from our PdBI dataset for L1521F make it difficult to robustly model them in detail, but it is possible to extrapolate the outer envelope properties to establish constraints on the maximum disk-like component (i. e. , maximum radius and flux) that can be added while reproducing the dust continuum interferometric fluxes obtained in L1521F. The 231 GHZ profile is best modeled using a PG model that includes an unresolved Gaussian component (FWHM 0. 13 , see Fig. C. 10): this suggests that a candidate disk of radius ∼< 20 au and flux ∼ 1. 3 ± 0. 4 mJy may be detected in L1521F. However, as in the case of IRAM04191, -Multiplicity In our dust continuum maps, L1521F is a single source (see Fig. B. 7). We detect a southeast extension in the 231 GHz map (see Fig.
Appropriate Staffing Because staffing is about more than just numbers and ratios, everyone must understand the needs of their patients, including spiritual and psychosocial needs. The AACN Synergy Model for Patient Care outlines principles to align patient needs with nurse competencies. 6 This model is used by some hospitals to make patient assignments by identifying patients' needs and aligning assignments with nurses who have the skills and competencies to address those needs. There are now computer programs that can help match data in an electronic format to do the same thing. These programs pull data from the electronic health record to determine staffing needs for patients based on where the patients are on the health and wellness continuum. 7 Nurses should talk to their leaders about providing an electronic system that uses data to assist Nancy Blake is Assistant Adjunct Professor, University of California Los Angeles School of Nursing, 700 Tiverton Ave, Los Angeles, CA 90024 (ntblake@aol.
Implementation and Results A FEM-based kinematic and dynamic model of the GEPRO 502 machine has been done using SAMCEF Mecano software. The model, represented in Fig. 15. 4, covers machine structure, feed drives and integrates control. Specific validation of this simulation model was performed by using hammer tests and positioning movements. The results are presented in Sect. 2. 1 of this book. The machining module has been configured for the three machines of the aerospace use case. For that, characteristics of the cutting tools applied in the validation use case have been collected and stored in a table shape database, reproduced in Table  15. 1, to be used by the simulation tool. The integrated simulation tool developed in the Twin-Control project is validated by comparing simulated data with data measured during the real process shown in Fig.
15. 3. Once the CAD model of the validation workpiece, shown in Fig. 15. 2, is positioned properly on the CAD model of the machine, it is translated to STL format in order to be used in the VMT model depicted in Fig. 15. 5. Validation consists of simulating four machining sequences as shown in Fig. 15. 6: a roughing operation of the external groove (blue path), a roughing operation of one triangular pocket (red path), a finishing operation of the vertex of the pocket (black path) and finishing operation of the walls of the pocket (green path). Position target functions replicated from the ISO code executed in the machine are used to feed the Virtual Machine Tool model. Figures 15. 9 and 15. 10 show the simulation results for the pocket roughing and wall finishing operations, respectively, comparing them to the monitored data during real machining at MASA.
NC Simulation The CNC simulation capabilities of ModuleWorks have been integrated in the ARTIS GEM-Visu HMI during Twin-Control project as depicted in Fig. 15. 21. An implementation of the aerospace use case has been done by developing a simplified design of the GEPRO 502 machine. The virtual model has been created starting from STL files provided by GEPRO. By using current axis position data recorded by the OPR of the GEPRO 502 machine, the virtual representation of the machine replicates the movements done by the real machine. In machines where the process is not visible for operators, like the GEPRO 502 machine where an opaque curtain blocks the access to the machining region, this feature provides the chance to the operator to control the process.
Implementation and Results Data from three machines located at MASA is uploaded to the cloud platform for machine tools hosted on a dedicated server (https://twincontrol. kasem. fr/). As machine operating data is one main input of the platform, reliability of machine tool connection is important. Every night, for 1 year and a half, behaviours indicators are extracted from raw data, operating and characterization test reports are generated. MASA production cycle time is long, and parts are not produced in series. It means that to have comparable indicators between parts specific moves should be identified or requested, as done in the characterization tests. Additionally, laws between move indicator and for instance move length and distance can be characterized. In this scenario, data transfer has been lost just before the drift of power.
Imagine that QUBIC observes a point source in the far field located directly along the line-of-sight with all 400 antennas open to the sky. The image formed on each of the focal planes (see the right panel of Figure  2 ) is an interference pattern formed by peaks and lobes. This pattern works like a beam pattern that convolves the sky signal. Therefore, if X is the signal from the sky (either in intensity, I, or polarization, Q, U), then the measured signal on the pixel p is S X (p) = X(n)B p synth (n)dn. This means that QUBIC data can be analyzed similarly to the data obtained from a normal imager, provided that we build a window function of the synthetic pattern for each pixel.
Statistical analysis All analyses were conducted using the R Studio 3. 1. 1 software (2014-07-10) and an alpha value for rejection of null hypotheses was set at p < 0. 05 for all analyses. The normality of distributions and homogeneity of variances were evaluated by applying the Shapiro-Wilk (W), and the Bartlett tests (Bartlett's K-squared), respectively. Data are not normally distributed, consequently, further analyses were conducted using nonparametric tests by applying the Kruskal-Wallis test (K test ) and the Spearman (rho) correlation coefficient. Assessment of differences and associations were analyzed first: i) between Li in water, brine, and vicuna calcified tissues (bones, bones plus teeth, and teeth) samples, and ii) between B in water, brine, and vicuna calcified tissues (bones, bones plus teeth, and teeth) samples, according to the data we have obtained in our study (Tables  3  and 4 ), which are summarized in Table A of the Electronic Supporting Information; and then between Li in water and body tissues (of vicuna and human) categorized in calcified, soft, keratinized and fluids, by considering the dataset presented in Table  5.
Li in water and body tissues. The assessment of the dataset shown in Table  5  reveal that there are no significant differences between the concentrations of Li in the four categories of human and vicuna tissues (K test = 3. 75, df = 3, p-value = 0. 29). Significant differences are found between the concentrations of Li in water and those in body tissues (K test = 6. 45, df = 2, p-value = 0. 04). The analysis of associations has shown that there is a significant, and positive.
Acknowledgment -This work was supported by grant  2018-2019  of the  SECTER -UNJu. Special thanks to  C. Meuric  for large field logistical support, and to the Secretary of Environment and  Sustainable Development  for authorizing wildlife bone collection. The authors declare that there is no conflict of interest regarding the publication of this article. Data accessibility -Data, associated metadata, and calculation tools are available from the corresponding author (lucrecialopezsteinmetz@hotmail. com).
The library was made starting from 5 ng of DNA using the Nextera XT library preparation kit (Illumina, San Diego, CA, USA), and the mean insert size of the final library was 450 bp. The library was then sequenced with a NextSeq 550 system using sequencing by synthesis (SBS) chemistry (Illumina). Read quality was controlled using Illumina's cloud service BaseSpace. A total of 37 million reads passed the quality filter; reads averaged 150 bp in length and showed an average quality score above Q30 in more than 90% of the bases. De novo assembly was performed with all quality reads using SPAdes  (16). Prior to assembly, the overlapping reads were merged with Fast Length Adjustment of SHort reads (FLASH)  (17). The contigs were aligned with Mauve  (18)  to the E. minasensis UFMG-EV T genome (GenBank accession number CDGH00000000).
Mosquito species estimation Metagenomic sequencing of long and small fractions of RNA was carried out for four biological replicate pools of mosquitoes from Ratanakiri and Kampong Chhnang provinces in central and northern Cambodia near the border with Laos, and four replicate pools from Kedougou in eastern Senegal near the border with the Republic of Guinea (Conakry). Mosquito species composition of sample pools was estimated using sequences of transcripts from the mitochondrial cytochrome c oxidase subunit 1 (COI) gene, which were compared with Anopheles sequences from the Barcode of Life COI-5P database (Fig. 1 , Additional file 1: Table  S1 ). In the Senegal samples, the most frequent mosquito species were Anopheles rufipes, Anopheles funestus, Anopheles gambiae group sp. , and Anopheles coustani, which are all human malaria vectors, including the recently incriminated An.
rufipes  [8]. In the Cambodia samples, the most frequent species were Anopheles hyrcanus group sp. , Anopheles maculatus group sp. , Anopheles karwari, Anopheles jeyporeisis, Anopheles aconitus and Anopheles dirus. All are considered human malaria vectors  [9] [10] [11] [12]. Elevated rates of human blood-feeding by a mosquito species is a prerequisite for malaria vectorial capacity  [13] , and therefore the main Anopheles species sampled for virome discovery in this study display consistently high levels of human contact in nature. In addition, a number of rare mosquito species calls represent species supported by less than 100 sequence reads and 1% frequency in the sample pool (Additional file 1: Table  S1 ). These rare calls could result from sequencing technical artifacts, mutations of COI, errors in the COI-5P database, and/or undescribed mosquito taxa not in the database.
Virus discovery by de novo assembly and classification by sequence similarity Small and long RNAseq reads were de novo assembled after removal of mosquito sequences. Non-redundant contigs longer than 500 nucleotides from assemblies of both sampling sites, Cambodia and Senegal, were translated into predicted peptide sequences and used to search the GenBank protein sequence database using BLASTX with an e-value threshold of 1e-10. BLASTX translates a DNA sequence in all six putative reading frames and searches against a protein database to identify peptide homology matches. This analysis pipeline allowed identification of 115 novel assembled virus sequences, 37 from the Senegal samples (virus ID suffix "Dak", Table  1 ), and 78 from the Cambodia samples (virus ID suffix "Camb", Table  2 ), possibly pointing to higher viral diversity in mosquitoes from Cambodia.
Some of the 115 virus sequences showed remote similarity by BLASTX to 24 reference viruses in GenBank that include single-stranded RNA (ssRNA)-negative strand viruses of the families Orthomyxoviridae, Rhabdoviridae and Phenuiviridae, ssRNA positive-strand viruses of the families Virgaviridae, Flaviviridae and Bromoviridae, dsRNA viruses of the family Reoviridae and multiple unclassified viruses of both ssRNA and dsRNA types (Table  3 ). Most of these remote similarities were with viruses characterized in a recent virus survey of 70 different arthropod species collected in China  [14] , which emphasizes the importance of high throughput surveys of the arthropod virosphere in the identification of viruses associated with different arthropod species. In order to place these 115 novel virus assemblies in an evolutionary context, phylogenetic trees were constructed from predicted peptide sequences of conserved regions of the RNA-dependent RNA polymerase (RdRP) gene annotated in the 115 virus sequences, along with RdRP peptide sequences of related virus sequences from GenBank.
Many of the viral contigs are too diverged from each other and from reference viruses in the phylogenetic tree to align informatively at the nucleotide level, and reliable sequence comparisons were only possible at the peptide sequence level. We translated nucleotide sequences for the novel viral contigs and compared them to the translated nucleotide database using TBLASTX. An example of this analysis is shown for viral contigs homologous to Xincheng virus (Fig. 3b ). Closely-related viral contigs (for example RdRP1. 7_XinchengVirDak and RdRP1. 3_XinchengVirDak), with on average 95% nucleotide identity over the full contig length, can thus be compared with the more divergent viral contigs such as RdRP1. 7_XinchengVirDak, which does not align at the nucleotide level with the first two, but does align when translated to peptide sequences.
This combined nucleotide and peptide-based analysis was applied to the 115 novel viral contigs. A total of 195 open reading frames (ORFs) were annotated among the 115 viral contigs, an average of 1. 7 ORFs per viral contig (Additional file 2: Table  S2  and Additional file 3: Table  S3 ). Based on TBLASTX alignments with the closest reference viral genomes, 56 of the 195 ORFs, found in 25 of the viral contigs, appear to be fragmented or frameshifted ORF sequences potentially associated with     S3 ). Thus, peptide comparisons with reference viral genomes provided evidence supporting a functional status for 86 of the 115 novel viral contigs, while 29 of the Table  3  Similarity of Senegal and Cambodia virus assemblies by BLASTX to 24 reference viruses in GenBank. Ten targets are shared, nine are Senegal-specific, and five are Cambodia-specific contigs displayed a potential non-functional status.
Viral origin of unclassified transcripts by small RNA size profiling A major drawback of sequence similarity-based identification of novel viruses in de novo sequence assemblies is the dependence of detection upon existing records of close relatives in public databases. Aguiar et al. proposed that the small RNA size profiles of arthropod-derived viruses detected by sequence similarity could be used as signature to recruit unclassified contigs from de novo sequence assemblies of potential viral origin  [19]. We implemented this strategy in order to identify additional sequences of putative viral origin in the set of 2114 contigs left unclassified by sequence similarity searching but meeting the same quality criteria as the 115 contigs (non-redundant and > 500 nucleotides), and with at least 100 small RNA sequence reads.
Node robustness is indicated by bootstrap values (number of replicates supporting the node), indicated by color of the dot at the branch base, see key. Protein lengths and functional status of RdRP peptide sequences from novel viruses in the current study are included to distinguish between complete and partial and/or non-functional pseudogenes (indicated by label "pseudo", functional status indicated in Additional file 2: Table  S2  and Additional file 3: Table  S3 ). Average protein size of reference virus RdRP genes is 2098 amino acids. b Genome comparison of novel and reference Xincheng Mosquito Viruses, which are too diverged to align at the nucleic acid sequence level. Grey blocks represent peptide sequence homology regions between compared sequences. The nucleotide sequences of the entire viral contigs, and not only of the RdRP gene as in (a), were translated and used to search the translated nucleotide database with TBLASTX.
In order to filter sequences originating in the mosquito host, sequences passing the above quality filter step were mapped against a custom database consisting of 24 Anopheles genomes available in Vectorbase in February 2016  [42]. Bowtie 1. 2. 0  [43]  was used to map small RNA libraries with two mismatches allowed, whereas the BWA-MEM algorithm from BWA-0. 7. 12  [44]  with default parameters was used to map long RNA libraries. Sequence reads that did not map against Anopheles genomes, herein referred to as non-host processed reads, were retained and used for de novo assembly and subsequent binning of virus transcripts.
Estimation of Anopheles species composition of mosquito sample pools Quality-filtered long RNA read pairs were mapped with SortMeRNA  [45]  against a custom database of Anopheles sequences of the mitochondrial cytochrome c oxidase subunit 1 gene (COI-5P database) extracted from the Barcode of Life database  [46]. 98% identity and 98% alignment coverage thresholds were fixed for the operational taxonomic unit calling step of SortMeRNA. Operational taxonomic unit counts were collapsed at species level and relative abundances of Anopheles species with at least 100 reads and 1% frequency in the sample pool were represented as pie charts using the ggplots2 R package.
Non-redundant contigs longer than 500 nucleotides were compared against the GenBank protein sequence reference database using BLASTX  [50]  with an e-value threshold of 1e-10, and the results were imported into MEGAN6 in order to classify contigs taxonomically using the LCA algorithm  [51]. Contigs of viral origin were further subjected to manual curation by pairwise sequence alignments of nucleotide sequences using BLASTN, and of translated query sequences searched against the translated nucleotide database using TBLASTX and the Easyfig genome comparison tool  [52]  in order to remove redundancies not detected in previous steps. Sequence assemblies and annotations are available in Additional file 7: Classified Virus Sequences and Additional file 8: Unclassified Virus Sequences.
Structural and functional annotation of virus assemblies Assembled contigs of viral origin were annotated as follows: ORFs were predicted with MetaGeneMark  [53] , and functionally annotated using Prokka  [54]  with Virus kingdom as primary core reference database for initial BLASTP searches and including also as reference Hidden Markov Models of virus protein families defined in vFam database  [55]. Also, protein sequences of predicted ORFs were processed with the Blast2GO pipeline  [56] , which generates functional annotation of proteins from BLASTP results against the virus subdivision of GenBank as well as Gene Ontology annotations from top BLASTP results. Prediction of InterPro signatures over viral proteins was also carried out with the InterProScan tool integrated in Blast2GO. The results of the different strategies of structural and functional annotation were integrated and manually curated with Artemis  [57].
Prediction of unclassified contigs of viral origin by small RNA size profiling In order to recruit contigs of potential viral origin from the pool of unclassified transcripts, we use the approach of Aguiar  [19]. This approach uses the size profiles of small RNA reads that maps over positive and negative strands of viruses detected by sequence similarity as a signature to identify unclassified transcripts by sequence similarity of potential viral origin. For this purpose, processed small RNA reads were re-mapped over virus contigs and unclassified contigs by sequence similarity using bowtie 1. 2. 0  [43]  allowing at most one mismatch and retaining only those contigs with at least 100 small RNA reads mapped. From the mapped small RNA reads over each contig, the small RNA size profiles were defined as the frequency of each small RNA read of size from 15 to 35 nucleotides that map over the positive and negative strand of the reference sequence.
To compute these small RNA size profiles, reads mapped over positive and negative strands of each reference sequence were extracted with Samtools  [62] , and the size of small RNA reads were computed with the Infoseq program of the EMBOSS package  [63]. Custom shell scripts were used to parse Infoseq output to a matrix representing the frequency of reads of different sizes and polarity across virus/unclassified contigs. This matrix was further processed in R (version 3. 3. 2). In order to normalize the small RNA size profiles, a z-score transformation is applied over the read frequencies of each contig (virus/ unclassified). The similarity between small RNA size profiles of virus and unclassified contigs is computed as the Pearson correlation coefficient of the corresponding z-score profiles, and the relationship between small RNA size profiles of virus/unclassified contigs was defined from this similarity values using UPGMA as linkage criterion with the R package Phangorn  [64].
Funding This work received financial support to KDV from the  European Commission ,  Horizon 2020 Infrastructures # 731060 Infravec2 ;  European Research Council , Support for frontier research, Advanced Grant # 323173  AnoPath;  French National Research Agency  # ANR-PRC2019-ArboVec , and  French Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID , and support to IVS from  USDA National Institute of Food and Agriculture Hatch  project # 223822. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials All sequence files are available from the EBI European Nucleotide Archive database (http://www. ebi. ac. uk/ena/) under project accession PRJEB29993 (datasets ERR3275139-ERR3275154 for small RNA libraries; datasets ERR2939203-ERR2939210 for long RNA libraries). Sequence assemblies and annotations for the 115 novel virus contigs are available in Additional file 7: Classified Virus Sequences. Sequence assemblies of 39 unclassified viruses are available in Additional file 8: Unclassified Virus Sequences.
In experiments aiming to analyze the E/I ratio of layer IV glutamatergic neurons we performed 350-μm-thick tangential thalamocortical slices. An Olympus BX51 microscope equipped with a ×40 fluorescent water-immersion objective allowed us to visualize YFP and DsRed fluorescent proteins by means of excitation beams supplied by Blue and Green Optoleds (Optoled Light Sources, Cairn Research, UK). Two sets of excitation/emission filters were used (470 and 525 nm filters for YFP, and 560 and 620 nm filters for DsRed) and images were collected and acquired with an iXon+ 14-bit digital camera (Andor Technology, UK) and with Imaging Workbench 6. 0 software (Indec Biosystems, USA), respectively. Whole-cell recordings were performed with a Multiclamp 700B and signals filtered at digitized at 4 kHz and 20 kHz respectively.
Off-line analysis of digitized data was performed using pClamp10. 1 software (Molecular Devices) and Neuromatic package within IGOR Pro 6. 0 environment (Wavemetrics, USA)  44. A paired recording was considered as connected when the average of PSCs recorded in the postsynaptic cells was 2-fold larger than the standard deviation of the noise. Paired-pulse ratios were calculated as PSC2/PSC1  15. The E/I of ratio of layer IV glutamatergic neurons was calculated as EPSCs/IPSCs. The spatial x-y coordinates of connected and unconnected YFP + interneurons and OPCs were extracted from DIC images. The recorded slice was oriented to fix the y axis as the shorter imaginary line from the soma of the YFP + interneuron, considered at position 0, to the cortical surface (Supplementary Fig. 4a ). The x axis was parallel to the cortical surface.