dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Given the prevalence of ESR1 mutations in MBC and the benefit of their early detection by a sensitive technique (as shown in the PADA-1 trial), the development of new endocrine treatments that will directly target and/or circumvent ESR1 mutation, and the better understanding of the role played by these mutations (it has recently been shown that patients harbouring ESR1 mutation could be good candidates for immune therapies  [29] ), it is likely that ESR1 mutation testing will become a standard practice in follow-up with MBC patients in the coming years. We successfully developed highly sensitive and robust dPCR assays for the qualitative and quantitative clinical detection of ESR1 mutations in plasma, which we have shown to be very suitable for this purpose. and D538G mutated (MUT) ctDNA (in copies/ml of plasma) concentrations measured during follow-ups with five patients (Supplementary Dataset).
Bioinformatic and statistical analyses Bioinformatic processing was performed following the DAMe pipeline  (Zepeda-Mendoza et al. , 2016 , as in Sire et al. , 2022). A various number of PCR replicates were investigated to retain shared MOTUs with a minimum of two reads in collection medium metabarcoding (i. e. , in at least 1/6 PCR replicates, standing as additive combination of sample replicates; or 2/6; 3/6 and 4/6 for relaxed restrictive combinations). For homogenate metabarcoding two PCR replicates (2/3) with two reads minimum per MOTU were retained to discard singletons. MOTU clustering was performed using a 97% similarity threshold and taxonomic assignment was performed with the BOLD DNA reference database  (Ratnasingham & Hebert, 2007)  using BOLDigger tool with BOLDigger option (Buchner & Leese, 2020).
MT homogenate metabarcoding results of 127-bp amplicons from Uni-Minibar primers were also compared with homogenate metabarcoding of 313-bp amplicons of the same traps  (Sire et al. , 2022). To do so, we checked for homoscedasticity of variance and normality of data using 'descdisc' and 'fitdist' functions from the fitdistrplus v1. 1-6 package and assessed with Levene test. If data were normally distributed, an anova test was applied, followed when significant by a pairwise T -test with Bonferroni correction using R built-in functions. If non-parametric analyses were needed, Kruskal-Wallis test was applied, along with unpaired Wilcoxon rank-sum test with Bonferroni correction to assess the direction of the significance when needed. Similar analyses were performed to account for the difference in species richness across dieback level gradient and stand types.
Among these, 12 species were also found in the morphological dataset, of which only five were found by metabarcoding and morphology treatments of the same traps. However, these observations had low reliability since these five species remained undetected by metabarcoding in most of the traps in which they had been identified morphologically, and multiple detections in metabarcoding samples were conversely not verified via morphological sorting (e. g. , potential cross-contaminations). Similarly, for the three Coleoptera from 2/6 combination parameter that were all identified down to species level: Cis festivus (Panzer, 1793), Pyrochroa coccinea (Linnaeus, 1761) and Quedius lucidulus (Erichson, 1839). P. coccinea was not found in the morphological dataset and the other two also corresponding to the Coleoptera MOTUs found in 3/6 and 4/6 combination parameters were present but not detected concurrently in the morphological and metabarcoding treatments of the same traps (Tables SV, SVI).
Comparing both 127-bp combination parameters, 40 MOTUs with 100% similarity to 313-bp dataset were shared. The additional 27 MOTUs from the additive combination are identified as Diptera (16), Lepidoptera (six), Hemiptera (two), Coleoptera (two) and Hymenoptera (one). While 1/6 additive combination allows a slightly better recovery of insects from collection medium metabarcoding of MT samples, no improvement was highlighted under that parameter for WFTs. As this led to little increase in MOTUs number, and in order to reduce the risks of dealing with false positive MOTUs from 1/6 PCRs additive combination, hereafter results focus on the filtered dataset from the 2/6 PCR replicates relaxed restrictive combination only. The 27 EtOH-MPG (MT) samples gave a total of 238 arthropod MOTUs and a number ranging from three to 46 (  for the 233 MOTUs detected in the MT collection medium (EtOH-MPG), 118 (51%) were unambiguously assigned to species (Fig.
2A ; Table  SVII ). Sequencing of MT tissue homogenate targeting the 127-bp amplicon resulted in 3,728,546 reads in total, reduced to 406,776 for 169 MOTUs after applying a relaxed restrictive combination parameter of 2/3 PCR replicates. Filtering of negative and positive controls generated 75% reads drop (from 406,776 reads to 101,655 for a three MOTUs loss). Two traps yielded no result with homogenate metabarcoding and corresponded to samples with 29 and 46 MOTUs detected in collection medium. Each of the 25 remaining traps harboured one to 50 MOTUs and an average number of reads per sample of 10,982. 3 (± 4,139. 802 SE). For ecological analyses, 15 traps did not meet the >10k reads threshold and were discarded, leading to a final dataset for homogenate metabarcoding from MT samples comprising 146 arthropod MOTUs for 10 traps (Table  SVIII ).
CONCLUSION We evaluated the feasibility of detecting terrestrial arthropod species from collection media of two mass-trapping systems using metabarcoding. We tested metabarcoding of monopropylene glycol (MPG)-water collection medium from window-flight traps for the first time, but show that it failed to recover significant numbers of species. In contrast, metabarcoding of MPG-ethanol collection medium from Malaise traps showed a high species recovery and higher number of small and soft-bodied arthropod species compared to destructive metabarcoding of homogenates. However, methodological choices prevent us from making meaningful comparisons of the invertebrate communities found for each treatment, and highlight the need for further protocol optimization. Indeed, analyzing the collection/preservation medium takes metabarcoding away from ideal experimental conditions and we call for caution in considering the potentially significant impact of fieldwork conditions (DNA degradation, inhibitors, collection medium composition), laboratory processes (DNA filtering and extraction methods, primer affinity) and data analysis (sequence length, sequencing depth) on the results.
ACKNOWLEDGEMENTS We are thankful to  Wilfried Heintz ,  Laurent Burnel ,  Jérôme Molina  and  Jérôme Willm  for the field work,  Sylvie Ladet  for her SIG assistance,  Carl Moliard  and  Guilhem Parmain  for the sorting and morphological identification of the Coleoptera from WFTs. We also want to thank  Florent Figon  and  Sophie Van Meyel  for helpful discussions on the manuscript. Part of this work has been carried out with the technical support of the  Genomic Facilities (PST 'Analyse des Systèmes Biologiques ') at the  University of Tours. We are thankful to  Dr. Caroline Chimeno  and an anomynous reviewer for their insightful comments that greatly improved the manuscript.
Funding This work is part of the international project  CLIMTREE ''Ecological and Socioeconomic Impacts of Climate-Induced Tree Dieback in Highland Forests'' anchored in the Belmont Forum Call ''Mountains as Sentinels of Change' '. The French team ( Lucas Sire ,  Annie Bézier ,  Béatrice Courtial ,  Laurent Larrieu ,  Christophe Bouget ,  Elisabeth A. Herniou ,  Rodolphe Rougerie  and  Carlos Lopez-Vaamonde ) was funded by the  French National Research Agency (ANR)  ( ANR-15-MASC-002-01 ). Lucas Sire ,  Annie Bézier ,  Elisabeth A. Herniou.
Data Availability The following information was supplied regarding data availability: All supplementary information, analytical script and sequencing data are available at Figshare: Sire, Lucas (2023). Supplemental Data -Non-destructive DNA metabarcoding of arthropods using collection medium from passive traps. figshare. Dataset. https: //doi. org/10. 6084/m9. figshare. 22094036. v4. The raw sequencing data are also available at NCBI CLIMTREE_EtOH: PRJNA927244.
Materials Losartan and valsartan drug substances and drug products used were either acquired from the marketplace by the study organizer (US FDA) or submitted by pharmaceutical manufacturers to the organizer (US FDA) for testing. Nitrosamine standard solutions in methanol, including NDMA (100 mg/mL), NDEA (100 mg/mL), NDIPA (100 mg/mL), and NDBA (100 mg/mL) were purchased from Chem Service (West Chester, PA, USA). NMBA (200 mg/mL) and NEIPA (100 mg/mL) were purchased from Toronto Research Chemicals (Toronto, ON, Canada). These standards were distributed to the participating laboratories with their respective certificate of analysis and safety data sheet. They were intended to be spiked into samples and used as reference standards. Isotope-labeled nitrosamine standards were purchased by each individual participating laboratory as needed.
4c ). Most of the %difference values for NMBA were below 0 with 25% of these values less than À20 and a few outliers with values below À50. By compiling and evaluating the large dataset from the analyses of spiked samples, important insights can be obtained regarding the performance characteristics of an analytical procedure for the quantitative analysis of trace level nitrosamines in a sample solution, including precision, accuracy, and limit of detection (LOQ) as discussed below. In terms of repeatability and reproducibility, the %RSD values generally decreased with increasing spiked concentrations of nitrosamines. At the lowest spiked concentration of 0. 01 ppm, the largest %RSD values for repeatability and reproducibility were 18. 4 and 29. 3 (Fig. 3 ) (excluding outliers), respectively.
Tissues were stained for amylase (acinar marker), SOX9, and ST6GAL1 (Figure  7B ). No detectable SOX9 or ST6GAL1 was observed in healthy WT acinar cells (saline control), whereas numerous cells within WT pancreatitis tissues (cerulein) coexpressed amylase, SOX9, and ST6GAL1. These data show that pancreatitis induces an upregulation in endogenous ST6GAL1 and that ST6GAL1 expression is found specifically in the ADM-like cells. In SC mice, SOX9 was highly expressed in the acinar cells of both the healthy and inflamed pancreas, indicating that ST6GAL1 activity is sufficient to induce SOX9 expression. The induction of pancreatitis by cerulein was verified by measuring serum amylase levels (Figure  7C ). The number of ADM-like cells was quantified in WT and SC mice by staining dissociated pancreatic cells for surface markers of ADM.
On the other hand, EGFR was strongly activated in the PanINs of both KC and KSC mice (Figure  8D ), in line with other reports  (55). Finally, we examined EGFR activation in organoids. Pronounced activation of EGFR was detected in SC and KSC organoids but not in WT and KC organoids (Figure  8E ). Collectively, these results suggest that the sialylation-dependent activation of EGFR may constitute an important mechanism by which ST6GAL1 activity reprograms acinar cells into a more ductal, progenitor-like state. and 2,000 cells seeded into fresh organoid culture. The number of organoids formed at day 3 was enumerated (n = 3 independent experiments, with 3 wells per genotype counted). Data analyzed by 1-way ANOVA, *P < 0. 05. (C) Cells were dissociated from organoids, then seeded into fresh organoid culture, and organoid growth was monitored over time.
Scale bar: 100 μm. (D) At days 3, 6, and 9 following the seeding of 2,000 organoid-derived cells into fresh organoid culture, organoids were dissociated, and the total number of cells was quantified (n = 4). Data analyzed using a Student's t test. *P < 0. 05. (E) Sox9, Hes1, and Ptf1a mRNA was quantified by qRT-PCR (n = 3). Data analyzed by 1-way ANOVA. *P < 0. 05. (F) Organoids were lysed and immunoblotted for SOX9 and ST6GAL1. Densitometric analyses were conducted on blots from 4 independent organoid lysates. Data analyzed by 1-way ANOVA. *P < 0. 05. Relative D. U. , densitometric units normalized to β-tubulin. (G) RNA-Seq was conducted on organoid lines generated from 3 distinct mice per genotype. The heatmap depicts canonical acinar and ductal genes. Color key indicates z score.
Unstained and single-color controls were used for compensation and gating, and only viable singlets were included. A BD LSR II flow cytometer (BD Biosciences) was used to acquire data, and FlowJo V10 was used for data analysis. Experiments were conducted on 3 mice per group. JCI Insight 2023;8(19):e161563 https://doi. org/10. 1172/jci. insight. 161563 For survival analyses, mice were euthanized when they lost 20% of body weight and/or presented with low body score (n = 10 mice/genotype). Tissues were evaluated for macro-metastases in lungs, liver, intestines, peritoneum, and other organs, and micro-metastases were identified by examining H&E-stained sections.
RNA-Seq analysis. STAR (version 2. 7. 10a) was used to align the fastq sequences to Gencode's mouse genome (GRCm39 Release M32)  (82). HTSeq-count (version 2. 0. 2) was used to estimate transcript abundances  (83). DESeq2 (version 1. 38. 3) normalized and calculated differential expression following their vignette  (84). IPA. A data set containing gene identifiers and corresponding expression values was uploaded into IPA  (85). A fold-change cutoff of ±2 and P < 0. 05 was set to define network-eligible molecules. GSEA. GSEA (version 4. 1. 0) was performed on the normalized gene expression data using the Molecular Signatures Database (version 7. 2)  (86, 87). Default parameters were used, except the permute parameter was set to gene set, and plot graphs for the top sets of each phenotype were set to 50.
Acknowledgments This study was supported by  NIH  grant  U01 CA233581 ,  Alliance of Glycobiologists for Cancer Research  (to SLB), and grants from the  Richard A. Elkus Foundation  and the  UAB O'Neal Comprehensive Cancer Center  ( O'Neal Invests program ) (to SLB). NB was supported by a  Predoctoral Scholar Award  from the  UAB O'Neal Comprehensive Cancer Center. MPM was supported by  NIH  grant  F32 CA264906. ADS and CMB were supported by the  NIH   T32 Training Program   in Cell, Developmental and Integrative Biology  ( T32 GM008111 ). The authors gratefully acknowledge assistance from the following  UAB Shared Facilities : Small  Animal Imaging Shared Facility, Transgenic Mouse Core, Flow Cytometry Core, Heflin Genomics Core Laboratory , and  Stem Cell Organogenesis Program. Address correspondence to:  Susan L.
Data analyzed using a Student's t test. *P < 0. 05. (E) Sox9, Hes1, and Ptf1a mRNA was quantified by qRT-PCR (n = 3). Data analyzed using a Student's t test. *P < 0. 05. (F) ST6GAL1 was overexpressed (OE) or knocked down (KD) in the 266-6 pancreatic cancer cell line (EV, empty vector control). The expression of SOX9, HES1, and PTF1A was measured by immunoblotting. Graphs depict densitometric analyses of blots from 4 independent lysates. Data analyzed using a Student's t test. *P < 0. 05.
Ectopic expression of Rspo1 leads to female-specific ectopic proliferation The striking sexual dimorphism in our Sf1-Rspo1 GOF model is reminiscent of the female bias observed in human adrenal diseases. To further investigate its molecular underpinnings, we conducted mRNA sequencing and differential expression analysis of whole adrenals from control and Sf1-Rspo1 GOF mice of both sexes during puberty (at 4 weeks), a timepoint before the occurrence of male-specific degeneration of adrenals (Fig. S4A ) [data available at NCBI's Gene Expression Omnibus  (Barrett et al. , 2013)  with the accession number GSE178958]. As expected, principal component analysis (PCA) identified the presence of the Rspo1 GOF allele as a major component in our RNA sequencing (RNA-seq) experiment. Strikingly, sex was responsible for 26.
3% of the variation in gene expression patterns among our experimental groups (Fig. 2A ). To gain insights into the molecular changes occurring in different subgroups, we next performed gene set enrichment analysis (GSEA) using the Molecular Signature Database (Broad Institute, Cambridge, MA, USA). Genes enriched in Sf1-Rspo1 GOF animals of both sexes compared to controls (corresponding to cluster 1 of the heatmap in Fig. S4B ) were related to β-catenin upregulation  (Fevr et al. , 2007; Sansom et al. , 2007)  or targets of the β-cateninassociated transcription factor LEF1  (Xie et al. , 2005) , a finding consistent with an hyperactivation of canonical WNT signaling by Rspo1. Genes specifically upregulated in female Rspo1 GOF adrenals (corresponding to cluster 3 of the heatmap in Fig. S4B ) were found to be primarily related to cell cycle regulation, DNA replication and repair, and cell division (Fig.
Given the slower recombination rate compared to that of Sf1-Cre, we analyzed adrenals at 20 weeks of age. Similarly to Sf1-Cre, Cyp11b2-Cre activation of Rspo1(AS-Rspo1 GOF ) resulted in female adrenal hyperplasia, whereas growth of the male adrenals remained comparable to that of wild-type controls (Fig. 5B ). Strikingly, simultaneous deletion of the Ar allele caused a significant increase of male adrenals to a weight comparable to that found in female counterparts (Fig. 5B ). Moreover, male AS-Rspo1 GOF /Ar KO and female AS-Rspo1 GOF adrenals appeared similar on the histological level, characterized by cortical hyperplasia (Fig. S9A ). BrdU-based Fig. 2. Transcriptomic analysis of Sf1-Rspo1 GOF adrenals during puberty reveals sex-specific regulation of cell cycle and immune responses. (A) Principal component analysis (PCA) plot of gene expression data in control and Sf1-Rspo1 GOF male versus female adrenals during puberty (4 weeks of age).
RNA-seq and analysis RNA was extracted from mouse right adrenals using the RNeasy mini kit (QIAGEN) according to the manufacturer's instructions. Four biological replicates were analyzed for each group (control male, control female, Sf1-Rspo1 GOF male and Sf1-Rspo1 GOF female). The sample quality was assessed using a Bioanalyzer 2100 (Agilent Technologies) and a RNA integrity number (RIN) cut-off value of 7. 0 was applied. Library preparation and sequencing, as well as differential expression analysis were conducted by Novogene. Briefly, library preparation was conducted using a NEBNext Ultra RNA Library Prep kit for Illumina (New England Biolabs). After cluster generation, the library preparations were sequenced on an Illumina platform and 125/150 bp paired-end reads were generated. Raw data (raw reads) of FASTQ format were firstly processed through in-house Perl scripts.
In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing poly-N and low-quality reads from raw data. All the downstream analyses were based on the clean data with high quality. The annotation was performed based on the GRCm38 genome assembly and downloaded from Ensembl. The index of the reference genome was built using Bowtie v2. 2. 3 and paired-end clean reads were aligned to the reference genome using TopHat v2. 0. 12. To count the reads numbers mapped to each gene, HTSeq v0. 6. 1 was used. The fragments per kilobase of transcript per million mapped reads (FPKM) value of each gene was calculated based on the length of the gene and read count mapped to this gene. Differential expression analysis was performed using the DESeq R package (v1.
log 10 (FPKM+1) expression values from differentially expressed genes as templates. Enriched gene sets were calculated using the Molecular Signatures database with an FDR q-value threshold of 0. 05. GSEA  (Subramanian et al. , 2005)  was conducted after DESeq2 was used via the GenePattern platform  (Reich et al. , 2006)  to calculate differentially expressed genes between male and female GOF adrenals.
Acknowledgements We thank the staff of the animal facility for their help with animal husbandry and genotyping. We also thank  Samah Rekima  (histology platform) for help in analyzing RNA Scope quantification. We are grateful to  S. Sacco ,  V. Vidal  and  R. Bandiera  for initial analysis of the Rspo1 GOF phenotype, to  D. Breault  ( Harvard University, USA ) for the AS-cre,  K. Parker  for the Sf1-Cre and Ar and  J. Hilkins  for the Rspo3 GOF alleles.
Funding This work was funded by the  Ligue Contre le Cancer (Equipe Labelliseé   2018  to A. S. ), the  Agence Nationale de la Recherche  ( ANR-11-LABX-0028-01  and  ANR-18-CE14-0012  to A. S. ),  Worldwide Cancer Research  ( WWCR 18-0437  to A. S. ), the  Fondation pour la Recherche Medicale  ( FRM SPF201809007141  to R. L. ) and the  Deutsche Forschungsgemeinschaft  ( CRC/Transregio 205/1 , project no. 314061271 , to A. S. , M. P. and  N. B. ). Open Access  funding provided by  UniversiteĆ ôte d'Azur. Deposited in PMC  for immediate release.
The main objective of the present paper is to develop original analytical solutions for the elastoplastic lateral-torsional buckling of perfectly straight beams. A general formulation based on a previously developed 3D plastic bifurcation theory is first proposed and then applied to the cases of two specific cross-section geometries, namely rectangular and I profiles. For the sake of simplicity, transverse shear strains are neglected in the analysis. The von Mises yield criterion with a linear isotropic hardening is adopted. In the case of a rectangular cross-section, closed-form solutions are obtained for the elastoplastic critical buckling moment, which is shown to depend on the geometric and material parameters of the beam, including the yield stress. For validation purposes, all the analytical solutions are compared against the results of numerical computations performed with an in-house program based on a shell finite element formulation.
, 2020). In Section 3, two particular cases are investigated: a beam with a thin rectangular cross-section and an I-beam. In some particular conditions, closed-form solutions are derived for the elastoplastic critical bending moments. Next, in Section 4, an in-house shell finite element program is employed to validate the previous analytical solutions. Some special features of the finite element tool are first recalled and numerical computations are performed for the two specific geometries defined above. Numerical computations are also carried out using a beam finite element formulation in Abaqus software, for comparison purposes. The analytical and numerical results are generally proved to be in very good accordance. Lastly, general conclusions and prospects are proposed in Section 5.
Finite element modeling The previous theoretical solutions will be now validated thanks to finite element computations. For that purpose, use is made of an in-house shell finite element program. Developed within a total Lagrangian framework, the program can handle plasticity and geometric non-linearities (finite displacements and rotations) in order to deal with the plastic buckling and advanced post-critical behavior of general structures. The use of such an advanced program brings many advantages, among which: • it is based on a shell finite element model which will validate the beam formulation developed in this paper in a more comprehensive way (conversely, a beam finite element model would have validated the analytical expressions only, but not the general beam assumptions); • it is provided not only with arc-length solution methods, but also with branch-switching algorithms, that enable one to deal with non-linear plastic bifurcation analyses, without resorting to any initial imperfections.