dataesr/scientific-paragraphs-categorization huggingface dataset:
The 48-h larval assay (ASTM 2004) adapted to 96 microwell plates  (Fabbri et al. 2014; Balbi et al. 2016 Balbi et al. , 2018) )  was performed. Briefly, at 30-min post-fertilization, aliquots of 20 μL of a 10 × solution of TCS (obtained from a 1 g/L stock solution in ethanol), suitably diluted in filter sterilized ASW, were added to fertilized eggs to reach the desired nominal final concentrations (from 0. 001 to 1,000 μg/L) in a 200-μL final volume. Control samples in ASW and samples in ASW added with vehicle (ethanol, maximal final concentration 0. 01% v/v) were run in parallel. Samples from 6 independent parental pairs were utilized. At 48 hpf, samples were fixed with 4% buffered formalin and examined by and inverted Olympus IX53 microscope (Olympus, Milano, Italy) at 40 × , equipped with a CCD UC30 camera and a digital image acquisition software (cellSens Entry) by an operator blind to the experimental conditions.
A larva was considered normal when the shell was D-shaped (straight hinge) and the mantle did not protrude out of the shell, and malformed if had not reached the stage typical for 48 hpf (trochophore or earlier stages) or when some developmental defects were observed (concave, malformed or damaged shell, protruding mantle). The recorded endpoint was the percentage of normal D-larvae (D-veligers) in each well respect to the total, including malformed larvae and pre-D stages (acceptability of test results based on controls with normal D-shell larvae > 75%). Even at the highest concentration, ethanol was ineffective (data not shown).
Shell size analysis Light microscopy images of D-veligers at 48 hpf in control larvae and larvae exposed to 10 and 100 μg/L TCS were analyzed for shell length (the maximum anterior-posterior dimension of the shell parallel to the hinge line) and height (the maximum dorsal-ventral dimension perpendicular to the hinge) using ImageJ software as previously described  (Balbi et al. 2018). Samples from 4 independent parental pairs were utilized.
Data analysis Data from the 48-h larval assay, expressed as mean ± SD of 6 experiments carried out in 3 replicate wells, were analyzed by ANOVA plus Dunnet's post hoc test. The EC 50 , as the concentration causing 50% reduction in the embryogenesis success (at 95% confidence intervals C. I. ), was calculated by PRISM 5 software (GraphPad Prism 5 software package, GraphPad Inc. ). LOEC (lowest effect concentration) was evaluated by the Mann-Whitney U test. Data on gene transcription, obtained from 4 independent RNA samples, are expressed as mean ± SD. Statistical differences were evaluated with respect to controls (p < 0. 05, Mann-Whitney U test).
Effects of TCS on gene expression The level of RNA transcripts for genes involved in different biological processes: shell biogenesis, neuroendocrine signaling, antioxidant/biotransformation processes, sphingolipid (ceramide) metabolism, and apoptosis/proliferation, was evaluated by qPCR (Tab. S1). Data on basal expression in larvae at 24 and 48 hpf with respect to eggs indicate progressive upregulation across development for most genes (Fig. S2 ). The effects of TCS (1 and 10 µg/L) on gene transcription at 24 and 48 hpf are reported in Fig. 2 (A  and B , respectively).
In mouse embryonic stem cell-derived osteoblasts, TCS affected skeletal differentiation through dysregulation of the BMP/ERK/Smad/Runx-2 signaling pathway  (Cheng et al. 2019 ). In the zebrafish embryo, TCS (from 62. 5 to 250 μg/L) induced developmental defect of craniofacial cartilage and impairment of skeletal mineralization, as well as changes in expression of marker genes of bone development; the effects were mediated by inhibition of the BMP signaling pathway  (Wang et al. 2022). Our data support the hypothesis that the signaling pathways involved in CaCO 3 deposition and mineralization can represent a significant target for TCS also in calcifying larvae of marine bivalves. Shell morphology in TCS-exposed samples was further investigated by SEM. The results indicate that although many shells of larvae exposed to a wide range of concentrations of TCS (0.
This fact limits the validity of the models, which become rapidly outdated when the Operating Conditions change or after running a maintenance task. Within the framework of Industry 4. 0, and thanks to the vast amount of data provided by IoT and Supervisory Control And Data Acquisition (SCADA) systems, it is possible to compute a digital replica of a physical entity. Such a model is a characterization of behaviors defined by the variables registered during the operation of the system. Models are used to identify and differentiate anomalous behaviors from normal ones. Anomalies are also known as outliers, abnormalities, or deviants; and could be classified in the following categories  (Chandola et al. , 2009) : • Contextual anomalies: anomalous data detected in a specific context only, meaning that, in all other situations they would be perceived as normal; • Collective anomalies: a group of related data points is anomalous compared to the reference dataset; the individual data points could represent normality, while it is their actual sequence that represents an anomaly.
Real case application A real case is proposed to assess the effectiveness of the methodology proposed in comparison to the other two traditional algorithms. The purpose is to perform an effective anomaly detection from a small training set. This case comprises 2639 samples, where 1173 are used for the training set. The sampling rate is 1 sample per hour grouped into sliding windows of 168 samples (one week of measurements) to reduce variations derived from different days of the week. Maintenance tasks are carried out every 2500 h. The main events of this dataset are the following: • A maintenance inspection task is registered between samples 1500 and 2000. There is no information about which components were repaired, cleaned, or replaced. • A short stop is registered around 2200. • From sample 2300 on, no more events are registered; the system runs under normal operating conditions.
The article search was carried out in 2022, in accordance with the PRISMA methodology  (Liberati et al. , 2009)  in two databases: SCOPUS and Emerald Insight databases. The choice for these databases relies on three arguments: (i) their scientific quality; (ii) the type of articles published (scientific and empirical articles), and; (iii) the thematic field covered in relation to management or transmission of knowledge. The keywords are related to the definition of the scope of investigation, determined in the previous section. Four keywords are used to cover this perimeter: "knowledge retention program" OR "knowledge transmission program" OR "knowledge transfer program" OR "knowledge management program" OR "knowledge sharing program. " The option to consider the word "program" was related to the need to only find articles that could illustrate a set of activities or practices followed for a particular purpose (in this case, the management or transmission of knowledge in a particular sector, team or company), and not to collect and analyse the vast scientific content about knowledge management or knowledge transmission.
Therefore, these were the references taken into consideration to assume this period (  2004 ) as the time frame from which articles could be found to match the needs of this research. To operationalise the search, the inclusion criteria defined for the article search were: (i) articles published in peer-reviewed journals between 2004 and 2022, written in English or French; (ii) empirical articles, with a qualitative approach (by privileging the process, the description of activities and practices) that presents management or transmission programs (or similar, such as management or transmission initiatives, actions, practices); (iii) articles from the databases whose title or abstract included the search keywords; (iv) articles whose participants were workers in a professional context. For the search of the keywords, the specificities of the search fields of both databases were also considered.
That is, in the case of Emerald Insight, the available filters in the database for selection were: the type of material to search (journal articles); year of publication (2004-2022); and "access type" (all content). In the case of SCOPUS, since the platform allows the selection of other types of filters, the following were selected in order to refine the search: year of publication  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020) (2021) (2022) ; disciplines (social sciences, business, psychology); type of document (journal/articles); keywords (the selection included all the available keywords that relate in some way to the keywords of the search, such as "knowledge management, " "knowledge transfer, " "knowledge, " "program development, " "qualitative analysis"); language of article (English, French); articles in open access.
Number of selected articles and categories defined for analysis Based on the PRISMA methodology (Figure  1 ;  Liberati et al. , 2009) , the search allowed us to identify 2,196 articles through database searching and other sources. The articles were found in the databases defined for the search (n = 2,190), as well as through hand screening and other websites (like Work; Management and Avenir; and other websites like research centers related to work issues) (n = 6). After the elimination of duplicates and a first reading of the abstracts, method and results of the articles, 2,189 articles were eliminated (for not meeting the inclusion criteria), leading to a total of 39 articles analyzed for eligibility. After this analysis, 11 studies were excluded: although in the screened records, they appeared to meet the inclusion criteria, it was clear from the full reading of the articles that their content made it unfeasible to analyse the articles for the intended purpose (e.
In order to do so, the following data collection techniques were used to identify the knowledge: analysis of internal documentation (six articles), observations of work (three articles), semi-structured interviews, narrative and retrospective interviews, and collective interviews with actors with critical knowledge, focus groups for validation of content to be transmitted or identified and for analysis of implemented initiatives (12 articles); video records of the real activity and professional gestures (one article). These results show that knowledge is not just procedural knowledge, but it is essentially secular/non-expert knowledge that links the questions of the effectiveness of professional gestures and of their safety, from the point of view of occupational health. Regarding the duration of the mentioned implemented practices (research question 5), the majority of the studies indicate a 1-year period, approximately (six articles-year; one articlemore than 2 years; one article-years) and one article indicates a duration of a few months; in the remaining articles, it was not possible to find this information.
g. , static digital platforms; documents) do not contemplate the possibility of revisiting and updating the available contents, thus putting them at risk of becoming obsolete and of little use for learning and work performance. While in the studies from the records identified through database searching the focus is mainly on a macro (organizational) level, seeking impact for the company and for productivity; in these disciplines the focus is mainly on a micro (individual and their collective) level. The analysis ends up being more focused on the workers, on the role they assume, on the valorisation of their experience and active participation in the process (e. g. ,  Cloutier et al. , 2012). With regard to the methodologies used to prepare the initiatives and identify knowledge (e. g. , implicit/critical), there are several techniques that are common among the different approaches exposed in this article, such as the use of communities of practice, holding training sessions, interviews, to name but a few.
It is oriented towards companies and, in particular, HR Departments, regarding the design and implementation of initiatives within this subject. Although this topic is not new for researchers or practitioners, no literature has been found on this point of view. The innovative character of this article lies in the fact that it systematizes the existing programs that have been put into practice and how it has been done. In addition, it conceptualizes the dimensions to consider (and relations between them) when designing and implementing a program of this nature, bringing different scientific perspectives into the discussion. However, the main limitation of the article is the number of databases in which the systematic review was conducted and the year option assumed for the articles search (  2004 ), which may have excluded other articles that could complement the performed analysis.
Study population and sample collection We analyzed the samples collected during the CEREMI trial (ClinicalTrials. gov identi er NCT02659033), a prospective, randomized open-label clinical trial conducted at the Clinical Investigation Center of the Bichat-Claude Bernard Hospital (Paris, France) from March 2016 to August 2017. The trial was sponsored by Assistance Publique -Hopitaux de Paris and approved by French Health Authorities and by the Independent Ethics Committee Ile-de-France-1. All procedures were conducted in compliance with good clinical practice and the Declaration of Helsinki. Full details of the trial have been reported elsewhere  [13]. Brie y, healthy volunteers of both genders aged between 18 and 65-year-old without exposure to antibiotics in the preceding 3 months nor the history of hospitalization in the last 6 months were prospectively included after obtention of their informed consent.
Fluorescence events were monitored using the FITC lter 505LP 530/30 nm and perCP lter 635LP 695/40 nm optical detectors. Forward and sideways-scattered light was also collected. The BD Accuri CFlow software was used to gate and separate the microbial uorescence events on the FL1-FL3 density plot from the faecal sample background. A threshold value of 200 was applied to FSC/SSC light. The gated uorescence events were evaluated on the forward-sideways density plot, to exclude remaining background events and to obtain an accurate microbial cell count.
Microbial gene count table To create the gene count table, the METEOR software was used [36]: rst, reads were ltered for low-quality by AlienTrimmer  [37]. Reads that aligned to the human genome (identity > 95%) were also discarded. The remaining reads were trimmed to 80 bases and mapped to the Integrated Gut Catalogue 2 (IGC2) [38], comprising 10. 4 millions genes, using Bowtie2  [39]. The unique mapped reads (reads mapped to a unique gene in the catalogue) were attributed to their corresponding genes. The shared reads (reads that mapped with the same alignment score to multiple genes in the catalogue) were attributed according to the ratio of their unique mapping counts of the captured genes. The resulting count table was further processed using the R package MetaOMineR v1. 31  [40]. To decrease technical bias due to different sequencing depths and avoid any artifacts of sample size on low-abundance genes, read counts were 'rare ed' using 20M high-quality reads (a threshold chosen to include all samples) using a random sampling procedure without replacement.
Determination of the β-lactamasome The IGC2 and the oral catalogues were annotated for the Antibiotic Resistant Determinants (ARD) using a two steps approach. First, potential ARD homologs were selected among catalogue genes using BLASTP against Mustard antibiotic resistance determinant database (http://www. mgps. eu/Mustard)  [15]. Genes with ≥ 50% identity for ≥ 90% alignment coverage were selected and tested using pairwise comparative modelling (PCM), a 3-dimensional modelling-based approach  [15]. This allowed the identi cation of a non-redundant list of 19061 ARD from 21 families of which 5 beta-lactamase families: 627 blaA genes, 463 blaB1, 463 blaB3, 181 blaC and 89 blaD. The richness of the resistome was evaluated as the number of copies of genes mapping to one of the identi ed ARD.
We used viralVerify 1. 1  [47]  to classify the non-redundant contigs as viral or non-viral, and only viral contig were selected for further analysis. Then, we mapped each sample read on the "viral non-redundant contigs catalog" using bowtie2 (v2. 4. 2 local -very-fastlocal options)  [39]  and exploited SAM les with samtools (v1. 3. 1 with the following commands: views, sort, index, idxstat)  [50]. As a result, we obtained a matrix (matrix count) representing the number of reads of a sample (columns) mapping each contig reference catalog (rows) in the dataset. All the matrix counts were rare ed at 3 003 762 reads with the "rarefy" function of the vegan package in R  [51]. The phage microbiome richness was computed as the number of phage contigs identi ed in each sample. Determination of fungal load and Candida albicans DNA concentration fungal loads and Candida albicans DNA concentration were analysed in all available samples.
AMPure XP (Beckman Coulter, Brea, CA, USA) was used to purify the PCR products and a Bioanalyzer DNA 1000 chip allowed their veri cation and their quanti cation. Samples concentrations were normalized at 4 nM and 5 µL of each diluted sample were pooled into a library and a PhiX sequencing control was prepared by the manufacturer's guidelines. Libraries were sequenced on Illumina MiSeq platform (Illumina, San Diego, CA, USA) with the MiSeq Reagent Kit V3 in 300bp paired-end. The sequencing allowed the recovery of 8'819'635 amplicons from ITS1 region. The SHAMAN pipeline was used to remove the singletons and chimera amplicons, resulting in a total of 56'634 amplicons  [59]. The remaining amplicons were clustered in 4648 OTUs using a cut-off value of 97% identity. 551 OTUs could be associated to fungal sequences using the Unite database and on these OTUs, 340 were present in at least two faecal samples and were kept for further analysis.
A rst round of annotation was performed on SHAMAN against the UNITE database (rev. 8. 0) and then a second round was performed against a more recent release of UNITE (rev. 8. 2). The OTUs that could not be annotated after these two rounds submitted to a classic BLASTN and only hits matched with a similarity above 97% to reference genomes were conserved. The abundances and weighted non-null normalized counts tables were generated with SHAMAN  [59]. The richness of the fungal microbiome was computed as the number of unique fungal OTUs identi ed in each sample.
Pellets were diluted 3-fold and reconstituted with H2O/ACN (20/80). Non-targeted approach experiments were performed using a HILIC phase chromatographic column, ZIC-pHILIC 5µm, 2. 1 × 150 mm at 15•C (Merck, Darmstadt, Germany), and on a UPLC Waters Acquity (Waters, Milford, MA, USA) coupled to Q-Exactive mass spectrometer (Thermo Fisher Scienti c, Waltham, MA, USA). Processing steps were carried out using the R software [62]. Peak detection, correction, alignment and integration were processed using XCMS R package with CentWave algorithm  [63, 64]  and work ow4metabolomics platforms  [65]. The resulted datasets were log10 normalized, ltered and cleaned based on quality control (QC) samples [66]. The features were then putatively annotated based on their mass over charge ratio (m/z) as well as retention time using a local database as described previously [67] and then validated based on MS/MS experiments.
Acknowledgments Composition of the PrediRes study group: Scienti c council:  Charles Burdet  ( INSERM, Univ Paris Diderot, APHP -Bichat Hospital ),  Erick Denamur  ( INSERM, Univ Paris Diderot ),  Xavier Duval  ( INSERM, Univ Paris Diderot ,  APHP -Bichat Hospital) ,  Dusko Ehrlich  ( INRA Metagenopolis ) ,  France Mentré  ( INSERM, Univ Paris Diderot ,  APHP -Bichat Hospital) ,  Eduardo P. C. Rocha  ( Institut Pasteur ). Metagenomic analyses :  Laurie Alla  ( INRAE Metagenopolis ),  Dusko Ehrlich  ( INRAE Metagenopolis ),  Emmanuelle Le Chatelier  ( INRAE Metagenopolis ),  Florence Levenez  ( INRAE Metagenopolis ),  Nathalie Galleron  ( INRAE Metagenopolis ),  Nicolas Pons  ( INRAE Metagenopolis ),  Benoît Quinquis  ( INRAE Metagenopolis ). E. coli analyses:  Khadija Bourabha  ( INSERM ),  Antoine Bridier Nahmias  ( INSERM, Univ Paris Diderot ),  Olivier Clermont  ( INSERM, Univ Paris Diderot ),  Erick Denamur  ( INSERM, Univ Paris Diderot ),  Mélanie Magnan  ( INSERM, Univ Paris Diderot ),  Olivier Tenaillon.
Funding The  CEREMI  clinical trial was funded by the  Contrat de Recherche Clinique 2013 (Assistance Publique-Hôpitaux de Paris ,  Département de la Recherche Clinique et du Développement ,  CRC13-179 ). The  PrediRes  project was funded by a grant from the  French National Research Agency  ( ANR-16-CE15-0022-01 ). Analysis of the  fungal microbiome  was funded by grants from the  French National Research Agency  ( FunComPath   ANR-14-IFEC-0004 ) and the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie action, Innovative Training Network (FunHoMic ; grant  N° 812969 ). High-throughput sequencing of phage DNA was performed on the  Institut Pasteur Genomics Platform , member of " France Génomique " consortium ( ANR10-INBS-09-08 ). Analysis of the microbiome and resistome was partly funded by the  Metagenopolis grant  ( ANR-11-DPBS-0001 ).
Availability of data and material The metagenomic shotgun sequencing data of bacterial microbiome are available from the European Nucleotide Archive (EMBL-EBI) under accession number PRJEB58157. The metagenomic shotgun sequencing data of phage microbiome are available from the European Nucleotide Archive (EMBL-EBI) under accession number PRJEB58823. The other datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.
This phase shift was corrected in the (commercial) software but, as we used the raw data to fully control the data processing and to synchronize these signals with the IR camera acquisition, an electronic phase shift correction has been done on the raw data. This phase shift has been evaluated at all the measured frequencies and at two temperatures (28 °C and 60 °C) using a sample of steel (no material viscosity) sheet. This steel sheet was of the same length as the polymer samples but with a cross-section area such that it has a stiffness equivalent to that of PS samples. Finally, regarding the real sample elongation, it was considered that components of the DMTA machines (especially the load cell) induced a non-negligible spurious displacement that induced an important bias. Therefore, the machine stiffness was measured using a very stiff sample.
This type of control was performed on all samples, regardless of the loading frequency  0 or the ambient temperature  0 at which the test was performed. Strain controlled tension-compression symmetric cyclic measurements were conducted on specimens at several frequencies (e. g. , 0. 01, 0. 1, 1 and 10 Hz) and several temperatures. The following sets of  0 have been particularly analyzed: (313 K, 348 K, 363 K) for the PS samples and (313 K, 323 K, 333 K) for the PA6. 6 samples. Note that a new sample was used after each frequency sweep, i. e. , for each new temperature  0 , to reduce aging effects or change in water content. The recordings were made over several tens of cycles to get a stabilized cyclic regime and ensure a sufficient set of mechanical and thermal data to perform relevant filtering.