dataesr/scientific-paragraphs-categorization huggingface dataset:
The corresponding predictions of water film thickness as a function of relative humidity (RH) yield identical trends for all systems within the precision of our results (Fig. 4c ). Our results on RH vs. film thickness averaged across all six systems yield RH = 53  9, 85  7 and 97  5% at water coverages of 1, 2, and 3 ML, respectively. Despite the nonnegligible uncertainties, our simulation predictions are in excellent agreement with the experimental results of Cantrell and Ewing  39  and Balmer et al. 41  (shaded gray and green areas in Fig. 4c , respectively) at RH > 60%, where these two studies reported identical results. At RH < 60%, where the two experimental datasets diverge, our results match those of Balmer et al. 41. Finally, at film thicknesses  2 ML, we also report in Fig. 4c  a prediction of RH vs.
ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. It includes simulation details of the hybrid GCMC+MD technique; additional details of the XRR data analysis; supporting figures showing density profiles, adsorption energetics, and MD simulation snapshots, as well as an excel file containing differential electron density data. (AK and ICB for computational simulations, experimental data collection, and analyses) and Contract DE-AC02-06CH11357 to UChicago Argonne, LLC as operator of Argonne National Laboratory (SSL and PF for measurements and analyses of XRR data). Molecular dynamics simulations were carried out using resources of the National Energy Research Scientific Computing Center (NERSC), which is supported by the U.
Population of cores in the ALMA-IMF protoclusters The star-formation activity of the ALMA-IMF massive clouds, and therefore the richness of the ALMA-IMF core database, can be assessed by the number of cores one can detect. In Paper V,  Louvet et al. (in prep. ) extracted sources from the cleanest continuum images (see definition in Sect. 3. 2) and identified about 840 compact sources. Moreover, we showed that using both the bsens and cleanest images (see definitions in Sect. 3. 2), the number of robust core detections could further increase by a factor of up to ∼2 (companion paper: Paper III,  Pouteau et al. 2022). Extracting these additional sources requires careful treatment of the line contamination to exclude emission peaks associated with line rather than continuum emission. The additional sources are predominantly lower-mass cores and the fluxes of common sources are consistent between the two approaches.
One can also gain in source detections using images where the cirrus noise is reduced by the Multiscale non-Gaussian Segmentation (MnGSeg) technique  (Robitaille et al. 2019). MnGSeg separates these cloud structures, which are incoherent from one scale to another and referred to as Gaussian, from the filaments and cores, which are coherent structures and are associated with star formation. Thus, the core database of the ALMA-IMF Large Program can potentially contain about 1 500 objects. To focus on cores that are real density peaks,  Louvet et al. (in prep. ) excluded millimeter sources that could correspond to free-free continuum peaks, that is, sources associated with inhomogeneities of H II regions that develop in the ALMA-IMF protoclusters. This exclusion marginally reduced the number of cores by ∼5% in Young regions, ∼13% in Intermediate regions and reduced it further, by ∼27%, in Evolved clouds.
2013; Nguyen-Luong et al. 2013; Louvet et al. 2016). Such emission could emerge as a consequence of the formation of high-mass star-forming regions through cloud-cloud collision. The ALMA-IMF line data cubes contain several emission lines necessary to trace the gas kinematics (velocity gradients, infall, rotation, turbulence level, and shocks) of cloud structures from the average 10 4 cm -3 gas density of massive clouds up to the 10 7 cm -3 density of cores. The Large Program setup indeed notably covers, with increasing critical density (n crit = 1 × 10 4 -5 × 10 6 cm -3 ), the C 18 O (2-1), N 2 H + (1-0), DCO + (3-2), DCN , N 2 D + (3-2), and 13 CS  emission lines (see Table  3 ). The constraints obtained for these different lines, along with shock lines such as SiO , will be combined in future work to quantify the kinematics of molecular clouds and protoclusters over two orders of magnitude in physical scale, from one parsec to one hundredth of a parsec.
In parallel, a detailed characterization of the outflows through their CO (2-1) and SiO  emission will allow us to reach three objectives: reveal the episodicity of the accretion-ejection processes, as done by, e. g. ,  Nony et al. (2020) , constrain the turbulence level injected by the outflows into ambient molecular gas on cloud scales (e. g. ,  Li et al. 2020 ) at the different evolutionary stages, and search for molecular outflows of peculiar morphology (e. g. ,  Tafoya et al. 2021). Due to the higher sensitivity of future measurements using the wealth of spectral lines (see Table  3  and Sect. 4. 5), including the entire N 2 H + (1-0) line multiplet in combined 12 m plus 7 m plus Total Power data cubes, the conditions (optical depth, excitation temperature), radial and line-of-sight velocities, and line widths in the ALMA-IMF protoclusters will be determined in detail in future work.
Hot cores and molecular complexity in high-and low-mass protostars Emission lines of COMs originating from bright hot cores or chemically rich protostars are present over multiple, if not all, ALMA bands. With a 6. 4 GHz noncontinuous bandwidth, corresponding to 2. 9 GHz covered by four spectral windows at 3 mm plus 3. 5 GHz covered in eight spectral windows at 1. 3 mm, the ALMA-IMF data will reveal the molecular content of a large population of objects (see Sect. 3. 2 and transitions in Fig. 8  and Table  3 ). Cores extracted in the ALMA-IMF protoclusters cover a broad range of masses, 0. 15-250 M , and all evolutionary stages from pre-stellar cores, to protostars, to HCH II regions (see Sect. 4. 2 and Fig. 5 ). The brightest cores frequently display line-rich spectra associated with hot molecular gas (see, e.
g. , Fig. 8 ). Indeed, several well-known hot cores are within the ALMA-IMF images, especially in all the Evolved clouds, G010. 62  (Liu et al. 2010; Law et al. 2021)   et al. 1998; Rivilla et al. 2017 ) and one in the Young G327. 29 cloud  (Wyrowski et al. 2008; Bisschop et al. 2013). A first-look analysis of ALMA-IMF data cubes concerning hot molecular gas indicates that all the targeted protoclusters, even the youngest ones, contain at least one core with line-rich spectra. In particular, the ten brightest continuum sources from Table  5 , corresponding to cores with masses >25 M assuming T dust = 75 K (see Sect. 4. 2), all exhibit numerous transitions from COMs in all spectral bands and thus are potential hot cores. This trend suggests that the hot core phase would appear very early in the evolutionary sequence of the formation of high-mass stars and massive clusters.
g. ,  Lefloch et al. 2017; De Simone et al. 2020) , in externally heated regions (such as Orion-KL and photodissociation regions;  Favre et al. 2011; Pagani et al. 2019; Le Gal et al. 2017) , and through accretion shocks (as observed toward G328. 25, Csengeri et al. 2019). Beyond the emergence of COMs, ALMA-IMF also covers several deuterated molecules as well as isotopologues from abundant species such as CH 3 CN, CH 3 OH, OCS, and H 2 CO. Measurements of the deuteration and isotopic fractionation provide indications of physical conditions that may impact the chemistry. Therefore, the physical processes and the chemistry driving the emergence of molecular complexity in high-mass protostars and dynamically evolving clouds could be statistically A8, page 21 of 27 A&A 662, A8 (2022) Fig. 8. ALMA-IMF spectrum, consisting of the 12 spectral windows of the 12 m array data cubes, observed for the brightest 1.
Conclusion ALMA-IMF 9  is a Cycle 5 Large Program carried out with ALMA 12 m, 7 m, and Total Power arrays. Its potential and first highlights based on 12 m array data can be summarized as follows: 1. We selected 15 massive (2-33 × 10 3 M ), nearby  (2. 5-5. 5 kpc)  protoclusters that span early protocluster evolutionary stages (see Sect. 2 and Table  1 ). ALMA mosaics cover total noncontiguous areas of ∼53 pc 2 at 1. 3 mm and ∼122 pc 2 at 3 mm in the typical yet extreme environments of the Milky Way clusters in formation (see Fig. 1 ). The ALMA-IMF spectral setup was carefully designed to focus on lines tracing gas motions from clouds to cores. In addition, the ALMA-IMF setup covers a 6. 4 GHz noncontinuous bandwidth at 3 mm (Band 3, 99. 66 GHz) and 1 mm (Band 6, 228. 4 GHz) used to survey lines from COMs.
2. The ALMA-IMF data set is homogeneous, with approximately matched point mass sensitivity and physical resolution across the protocluster sample that spans a factor of ∼3 in distance. Hence, the key feature of our approach is the lack of significant distance bias, which enables robust, synergistic science on the emergence of the IMF and star clusters. The ALMA-IMF database consists of 1 mm and 3 mm continuum images that are sensitive to ∼0. 18 M and ∼0. 6 M point-like cores, respectively, at a matched spatial resolution of ∼2100 ± 400 au (see, e. g. , Fig. 2 ). Moreover, the ALMA-IMF data set contains many emission lines that trace dense molecular gas, outflows, shocks, COMs probing hot cores, and recombination lines that trace the ionized gas (see Sect. 3 and Table  3 ). The whole ALMA-IMF data set is processed with a pipeline 10 described in Paper II  (Ginsburg et al.
4. 4). The constraints obtained for these different lines, along with shock tracing lines such as SiO , will be combined to quantify the kinematics of molecular clouds and protoclusters over two orders of magnitude in physical scale, from one parsec to one-hundredth of a parsec. In particular, the N 2 H + (1-0) line shows networks of filaments that may trace inflow gas streamers (see, e. g. , Fig. 7 ). They will be traced down to the scale of cores to potentially quantify 10 see https://github. com/ALMA-IMF/reduction the growth of core masses and the evolution over time of the shape of the CMF. 7. ALMA-IMF has the potential to identify several tens of cores that exhibit line-rich spectra potentially corresponding to hot cores (see, e. g. ,  Fig. 8 and Sect. 4. 5). Beyond the well-known hot cores hosted in Evolved protoclusters, we also cover several regions where completely new, bright hot cores can be recognized.
The detection of COMs toward the brightest sources suggests that the hot core phase appears early in the emergence of high-mass protostars. Thanks to the similar sensitivity and spatial resolution toward each protocluster, we will be able to perform a homogeneous characterization of their molecular content and use spectroscopic tracers to constrain the emerging molecular complexity in protostars of high to intermediate mass. The ultimate objective of ALMA-IMF is to push forward our understanding of the IMF of stars and stimulate improvements to star-formation models, taking the effects of cloud characteristics and evolution into account. To this end, we will provide the community with a high-legacy database of protocluster clouds, filaments, cores, hot cores, outflows, and inflows at matched sensitivity.
Acknowledgements. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2017ALMA#. 1. 01355. L, # 2013ALMA#. 1. 01365. S, and # 2015. 1. 01273. 1. 01273. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. This project has received funding from the  European Research Council (ERC)  via the  ERC Synergy Grant  ECOGAL (grant  855130 ), from the  French Agence Nationale de la Recherche (ANR)  through the project  COSMHIC  ( ANR-20-CE31-0009 ), and the  French Programme National de Physique Stellaire and Physique et Chimie du Milieu Interstellaire (PNPS and PCMI) of CNRS/INSU  (with  INC/INP/IN2P3 ).
S. B. acknowledges support from the  French Agence Nationale de la Recherche (ANR)  through the project  GENESIS  ( ANR-16-CE92-0035-01 ). T. C. and M. B. have received financial support from the  French State  in the framework of the  IdEx Université de Bordeaux Investments  for the future Program. Y. P. , A. L. S. , G. B. , and B. L. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , for the Project "The  Dawn of Organic Chemistry" (DOC) , grant agreement No. 741002. F. L. acknowledges the support of the  Marie Curie Action of the European Union  (project  MagiKStar , grant agreement number  841276 ). A. S. gratefully acknowledges funding support through  Fondecyt Regular  (project code  1180350 ) and from the  Chilean Centro de Excelencia en Astrofísica y Tecnologías Afines (CATA) Basal  grant  AFB-170002.
R. G. M. acknowledges support from  UNAM - PAPIIT  project  IN104319  and from  CONACyT Ciencia de Frontera  project  ID 86372. Part of this work was performed at the high-performance computers at  IRyA-UNAM. We acknowledge the investment over the years from  CONACyT  and  UNAM , as well as the work from the  IT  staff at this institute. A. G. acknowledges support from the  National Science Foundation  under grant No. 2008101. G. B. also acknowledge funding from the  State Agency for Research (AEI)  of the  Spanish MCIU  through the  AYA2017-84390-C2-2-R grant. P. S. and B. W. were supported by a  Grant-in-Aid for Scientific Research  (KAKENHI Number  18H01259 ) of the  Japan Society for the Promotion of Science (JSPS). P. S. and H. -L. L. gratefully acknowledge the support from the  NAOJ   Visiting Fellow Program  to visit the  National Astronomical Observatory of Japan  in 2019, February.
De Novo Assembly of Transcriptomes Libraries within each species (Table  S1 ) were merged for mapping and de novo assembly. First, reads were mapped to the corresponding reference genomes of each species using STAR v2. 5. 0  [39]  with a maximum intron length of 900 (-alignIntronMax), maximum mismatches of 2 (-outFilterMismatchNmax), maximum multi-mapping locations of 10 (-outFilterMultimapNmax) and minimum overhang of 25 (-alignSJoverhangMin) for spliced alignments. Mapped reads were split into plus and minus strand-sets with Samtools v. 1. 13  [40]. Then, de novo assembly of transcript fragments (i. e. , contigs) were performed on each strand-set using Trinity  [41] , with a kmer size of 15 nt (-KMER_SIZE 15), a maximum intron size of 900 nt (-genome_guided_max_intron 900), a minimum coverage of 20 (-genome_guided_min_coverage 20) and a minimum length of 150 nt (-min_contig_length 100).
Lastly, resulting transcript contigs were mapped on the corresponding reference genome using Gmap  [42]  with a maximum intron length of 900 nt (-K 900). Transcript contigs mapped to the opposite strands of coding genes (with a minimum 10% contig length, detected using featureCounts software  [43]  with -fracOverlap 0. 1) were defined as "NAT fragments". Genes were defined as "NAT genes", when at least 1 NAT fragment mapped. In order to specify parts of genes preferentially covered by NAT fragments, we split the genes in 5 equal regions and counting was performed on each region as well.
Analyses of Small RNAs The small RNA data were processed as previously described  [44]. Briefly, deduplicated reads were removed using fqCleanER (https://gitlab. pasteur. fr/GIPhy/fqCleanER, accessed on 10 August 2021). The remaining unique reads were then mapped to the corresponding reference genomes using Bowtie v. 0. 12. 7  [45]  with maximum 1 end-to-end mismatch (-v 1) and all mapped locations were reported (-all). Genes with ≥20 small RNA reads were defined as sRNA targeted genes.
Differential Expression Analysis Firstly, reads of each replicate were mapped to the reference genome of E. histolytica, using STAR v. 2. 5. 0  [39] ) with a maximum intron length of 900 (-alignIntronMax), 2 mismatches maximum (-outFilterMismatchNmax), 10 locations maximum for a read mapping (-outFilterMultimapNmax), minimum overhang of 25 nt for spliced alignments. Secondly, read counts for each gene in each sample were computed with featureCounts  [43]  separately on the direct (-s 1) and opposite strand (-s 2), allowing multimapping reads (-M). In order to take into account length differences between orthologous genes across species, we first applied a Transcript Per Million (TPM) quantification  [51]. The DESeq2 normalization was used: size factors were computed on sense counts and were then applied to both sense and antisense counting.
Finally, transcript differential expressions were calculated on the merged normalized counts (sense and antisense) using DESeq2 v. 1. 24. 0  [52]  within the SARTools pipeline v. 1. 7. 2  [53]. False discovery rates were corrected using the Benjamini-Hochberg procedure. The analysis was conducted in R  [54]  and figures were produced using the ggplot2 package  [55].
eggNOG Annotations and GO Terms Enrichment To combine gene datasets the Venn diagram approach was used (https://bioinfogp. cnb. csic. es/tools/venny/, accessed on 15 December 2021). Predicted proteins were retrieved using UniProt (https://www. uniprot. org/, accessed on 15 December 2021) using gene name and the Uniprot function. For annotation, predicted proteins of Entamoeba were assigned to the orthologous groups of the EggNOG database v. 5  [56]  using the eggNOG-mapper. V. 2 (http://eggnog-mapper. embl. de/, accessed on 27 August 2021) according to their defined parameters (hit e-values ≤ 1e-3). One-letter abbreviations for the functional categories' correspondence are at https://www. ncbi. nlm. nih. gov/research/cog#, accessed on 27 August 2021. Further identification of protein classes and gene ontology enrichments corresponding to genes harbouring sense and antisense transcripts were performed with Amoeba DB tools: search for gene ID, data analysis for GO terms (biological processes and molecular functions) and synteny search in the setting pre-configured table, orthologs and paralogs within VEuPathDB (https://amoebadb.
histolytica core NAT)  [32] , including invasion of the human colon. From those 452 genes, in this work (Table  S3 , Sheet 5), 98% of them (n = 444) are present in that dataset of E. histolytica genes presenting NATs. Next, we examined in a Venn diagram the overlap between these E. histolytica core NATs and the inter-species coreNATcore defined above and 86 genes were found to be present in both datasets (Table  S8 , Sheets 1 and 2). In the four species, there is no significant difference between the level of expression of these 86 genes and the level of expression of the other genes according to the comparison test of Wilcoxon (p-value = 0. 1099, 0. 1425, 0. 1727 and 0. 4273 for E. histolytica, E. dispar, E. moshkovskii and E. invadens respectively), (Supplementary Figure  S2 ). They map into 57 contigs (Table  S8 , Sheet 3) and are not clustered in any particular region within these contigs.
Data preprocessing and quality control Quality control and preprocessing of data were performed by SciLicium (Rennes, France). The first read contains 16 bases that must have a quality score higher than 10. The first 6 bp correspond to a unique sample-specific barcode and the following 10 bp to a unique molecular identifier (UMI). The second reads were aligned to the rat reference transcriptome from the UCSC website (release rn6, downloaded August 2020) using BWA version 0. 7. 4. 4 with the parameter "-l 24". Reads mapping to several positions in the genome were filtered out from the analysis. The pipeline is described in  (Giacosa et al. , 2021). After quality control and data preprocessing, a gene count matrix was generated by counting the number of unique UMIs associated with each gene (lines) for each sample (columns).
The UMI matrix was further normalized with the regularized log (rlog) transformation package implemented in the DeSeq2 package  (Love et al. , 2014). Raw and preprocessed data were deposited at the GEO repository under the accession number GSE199234  (Edgar et al. , 2002). The resulting transcriptomic signatures were also deposited at the TOXsIgN repository under the accession number TSP1267 (https://toxsign. genouest. org/) (Darde et al. , 2018).
B) Significantly enriched biological process terms (Supplementary Table  S2 ) are given followed by the total number of genes associated with the term and the p values. N = 11-12 samples/group. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article. ) GD17 phallus controls vs GD17 perineum controls, GD21 phallus controls vs GD21 perineum controls, GD17 triticonazole-exposed samples vs GD17 controls, and GD21 triticonazole-exposed samples vs GD21 controls (Fig. 1C ). Briefly, genes showing an expression signal higher than a given background cutoff (corresponding to the overall median of the rlog-transformed UMI dataset, 0. 0) and at least a 1. 5-fold change between the group expression values were selected. To define a set of 2756 genes with significant statistical changes across comparisons, the linear models for microarray data (LIMMA) package was used (F-value adjusted with the false discovery rate (FDR) method, p ≤ 0.
Temporal changes to the transcriptomes of rat perineum and GT between GD17 and GD21 We analyzed the transcriptome of the developing perineum and GT during fetal life, at GD17 and GD21 in both triticonazole-exposed and unexposed control rats (Fig. 1A ). The two first dimensions of a principal component analysis (explaining 43. 5% and 27. 4% of variances) on the resulting gene expression dataset clearly discriminated tissues and developmental stages, but independently of their exposure (Fig. 1B ). We next identified a set of 2703 statistically significantly differentially expressed genes (DEGs) comparing dynamic transcriptional variations between developmental stages (GD17 vs GD21) in perineum (1087 DEGs) and in GT (1590 DEGs), and when comparing perineum and GT at GD17 (1113 DEGs) and GD21 (951 DEGs) from control animals (Fig.
Among the DEGs identified in these analyses were several genes with known associations with hypospadias in humans, including SOX9 and its direct target gene NEDD9  (Sreenivasan et al. , 2017; Yang et al. , 2019). We also saw a significant increase in ACTA1 which, together with LMOD3, are essential for correct craniofacial patterning, palate fusion and urethral closure  (Avasthi et al. , 2019). TNNC1 represents another interesting DEG in our dataset. It is a known target of GATA4 which, when mutated, is associated with hypospadias in humans  (Yu et al. , 2011). Finally, MAU2 was downregulated in both phallus and the perineum, which is of interest in that it is an essential component of the cohesion complex along with ATRX, both of which are implicated in hypospadias  (Piché et al. , 2019; Stevenson, 2000).
Through sperm alteration, semen microbiota tend to be recognized as a cause of infertility, but due to the limited number of studies focusing on this ecological niche, this hypothesis remains controversial. This study aimed to characterize and compare the bacterial communities of sperm samples from patients undergoing couple infertility treatment at the time of diagnosis. The study was prospective (September 2019 to March 2020), monocentric, and focused on alterations of spermatic parameters: count, motility, and morphology. After the amplification of the 16S rDNA (V1 to V3), libraries (n = 91, including 53 patients with abnormalities) were sequenced using the MiSeq platform (Illumina). After quality control processing using a homemade pipeline (QIIME2 modules), the main genera were: Prevotella, Finegoldia, Pseudomonas, Peptinophilus, Streptococcus, Anaerococcus and Corynebacterium.