dataesr/scientific-paragraphs-categorization huggingface dataset:
7 ). With this dataset the crocodyliform diversity decline at the end of the Paleocene, and the Maastrichtian-Danian transition is marked by a North Tethyian gentle diversity fall (Fig. 7C, D ). This is probably due to the poor Maastrichtian and Danian European sampling. If numerous remains are known for both interval of time, from 5 Maastrichtian and four Danian localities (Appendix 1 of Supplementary data), most of them are to poor to be attributed to a species and are considered as Thoracosaurus sp. The localities are extended from Ukraine to Spain, and it is probable that the gavialoid diversity was biased, underestimated for both these periods of time, questioning the reliability of the evaluation of the North Tethyian diversity. Moreover, the high Campanian and Maastrichtian diversity is clearly here the result of the overestimation of diversity in older time bins, related to the phylogenetic estimates only constructing backwardextending ghost lineages  (Tennant, 2016).
With all the phylogenetic diversity estimate datasets, the Maastrichtian and Danian diversities rise and decrease during the Selandian-Thanetian for global and Gondwanan diversity (Fig. 7C , D, 8C, D, 9C, D). It is also the case considering the dyrosaurid and gavialoid diversities in Gondwana (Fig. 7B, 8B, 9B ). On the contrary, the Laurasian diversity is relatively constant , and the corrections have the strongest impact on the North Tethyian diversity curves (Figs. 7 8 9 ), with larger variations between datasets (Fig. 7A, 8A, 9A ). This could be explained by the low number for Paleocene formations available in Laurasia compared to the number of Maastrichtian ones, and the high number of unidentified remains attributed to Thoracosaurus sp. in Europe and East Asia (Appendix 4 of Supplementary data).
Acknowledgments I received support from the  SYNTHESYS  Project (http://www. synthesys. info/),which is financed by  European Community Research Infra-structure Action  under the  FP7 'Capacities' programme  to collections ( GB-TAF-3366 ). I thank  Hesham Sallam  for providing information on the Quseir Formation, and  France de Lapparent de Broin  for the information on the Maastrichtian Nigerien field. I am grateful to anonymous reviewers, and editor  J. G. Meert  for their comments and constructive criticisms that greatly helped to improve the manuscript. I also thank  John Jones  for reviewing the English grammar in the paper.
Whole-genome sequencing To gain in-depth insight into the evolutionary history of L4. 3/ LAM prevailing in Tunisia, 17 and 11 isolates representative of TUN4. 3_CC1 and TUN4. 3_CC2, respectively, and three strains with an intermediate position between the two CCs, as revealed by the 24-loci MIRU-VNTR-based MST, underwent WGS. Genomic DNA was extracted from M. tuberculosis colonies cultured in LJ medium, and subjected to Illumina paired-end (PE) sequencing using NextSeq. The data generated were analysed using an in-house automated pipeline, including open source software. Briefly, FASTQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc) was used for quality control of raw sequencing data. Both adapters and low-quality bases with a Phred quality score <20 were clipped using Trimmomatic  (Bolger et al.
, 2014) , and only reads with a minimum length of 36 bases were used in the following analysis. Three different aligners-Burrows-Wheeler Alignment Tool  (Li and Durbin, 2009) , Novoalign (http://www. novocraft. com/products/novoalign/) and SMALT (http://www. sanger. ac. uk/resources/software/SMALT/)-were used to map PE reads to the genome sequence of M. tuberculosis H37Rv reference strain (Genbank: AL123456. 3). Local re-alignment and de-duplication of alignment files were performed using the Genome Analysis Toolkit (GATK)  (McKenna et al. , 2010)  and Picard (https://broadinstitute. github. io/picard/). VCF files, including single nucleotide polymorphisms (SNPs) and insertions/ deletions, were generated from each alignment file using GATK. Variants were then annotated referring to the Tuberculist server  (Lew et al.
, 2011). SNPs located in repetitive sequences, such as PE/ PPE genes and mobile elements, and those up to five base pairs from each other were excluded from the analysis. Only SNPs that were sequenced at a depth !10 and present in !70% of the reads were included in this study. SpolPred  (Coll et al. , 2012)  was used to predict spoligotypes in silico. Raw data were deposited in the European Nucleotide Archive (Accession No. PRJEB39509).
Genomic analyses Phylogenetic relationships between the Tunisian isolates were inferred by constructing an MST based on 1719 polymorphic sites using a temporary BioNumerics 7. 6. 3 evaluation licence (Applied Maths). Pairwise SNP distances were calculated using the ape package and the dna. dist function in R Version 3. 6. 3. For comparison of pairwise numbers of SNPs between L4. 3/LAM strains, the SNP distance between each pair of isolates was calculated, and a distribution of the pairwise distance was plotted. Wilcoxon rank sum test and Kruskal-Wallis test were used to test for differences as the data did not show a normal distribution.
Data availability statement General characteristics and genotypic data (24-loci MIRU-VNTR) of the 252 Tunisian L4. 3/LAM strains included in this study are provided in Table  S1  (see online supplementary material). The WGS of the 31 L4. 3/LAM Tunisian isolates has been deposited in GenBank (Accession No. PRJEB39509).
Acknowledgements This research was supported by the  Universitede Recherche Paris Sciences et Lettres (PSL) , the  ESPCI-Paris , the  Universitede Paris , the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Santeet de la Recherche Medicale (INSERM) , the  Ligue Nationale Contre le Cancer  ( LNCC, Program ''Equipe labelisee LIGUE ''; no. EL2016. LNCC/VaT ), and the  European Research Council  (grants  647275  ProFF, 780519 DeepMiR and 949493  MoP-MiP ). T. J. received a PhD fellowship from the  Conseil Regional d'Ile de France  (grant ARDoc  17012687 ). We thank the  SIRIC CARPEM  and the  Physicancer program  (no. PC201423 ) fundings.
Locomotor activity recording Females from all subgroups were individually introduced into Plexiglass Drosophila tubes (L = 9. 3 cm, D = 2. 5 cm) provided with an ad libitum 5% glucose solution (a cotton plug soaked with 5-7 ml 5% sterile glucose was placed at the bottom of the tube). Tubes were inserted into the holding grid of seven Locomotor Activity Monitors (LAM, TriKinetics, Waltham, MA) to detect and measure spontaneous activity automatically. LAMs are routinely employed in chronobiology  [8, 10, [49] [50] [51] [52]  and have been used in studies of mosquito activity  [10]. LAMs automatically record activity on a connected computer when a mosquito breaks an infrared beam across the centre of each holding tube. Each LAM received two control tubes (empty tubes lacking the mosquito within), and in all four treatment arms, treatment tubes and their positions in the LAM grid arrangement were chosen at random using the function "random" in the "Excel" software.
Because some artefacts were detected in control tubes from three out of the seven LAMs (originating from the same batch order), the external surface of all tubes was covered with a white rubber band, except for a 2-cm gap centred on the IR emitter/detector system. This decreased artefacts without impacting activity recording (data not shown). Each LAM was placed inside a hermetic wooden box (50 × 50 × 50 cm) in the insectary to prevent contamination from potential light pollution from the surroundings. Environmental conditions and photoperiod were identical to those previously described for rearing, except for two LAMs that were submitted to a constant dark (DD) regimen to measure free-running circadian parameters. Activity was recorded every minute (i. e. at a frequency of 60 -1 Hz) for 7 consecutive days.
Insemination status and body size At the end of the recording period, mosquitoes were killed by placing the holding tubes at -20 °C for 30 min. To control for the potential confounding effects of body size upon differences in locomotor activity among treatments, we used wing size as a proxy of body size  [53]. The right wing from each female was dissected under a binocular, and digital photographs of the wings of all tested mosquitoes were taken. The distance from the axillary incision (alula) to the apical margin  [54]  was measured using ImageJ software  [55]  and this measure used as a proxy of wing size. Females from the inseminated treatment arms were further processed according to the protocol described by Tripet and collaborators  [56]  to characterise their insemination status. Individual mosquitoes were stored in microtubes containing 70% (v/v) ethanol for 4 days; thereafter, their 6th abdominal segment was dissected under a binocular and separated from the rest of the carcass; the spermatheca was isolated and its envelope delicately torn apart to release the sperm bundle, which was observed under 35 × magnification.
For this task, we modelled individual activity scores recorded between 16:00 h (ZT10 or CT10 in DD regimen) and 07:00 h (ZT1 or CT0 in DD regimen) and expressed at the recording frequency of 300 -1 Hz (i. e. per 5 min) using negative binomial GLMMs (generalised linear mixed models). For mean nightly activity analysis, we analysed LD and DD activity score data separately according to the insemination status (virgin vs inseminated), food source (glucose vs blood), recording day (coded as a factor value), and their interactions. For hourly activity analysis, due to convergence issues, we split the dataset to analyse the effect of insemination in glucose-fed mosquitoes and then the effect of bloodfeeding in inseminated females in both the LD and DD regimens on day 3. Therefore, the explanatory variables of the GLMMs were the insemination status or the feeding source, the hour of the night (coded as a factor value), and corresponding interactions.
Onset and time of peak activity were analysed separately between LD and DD using mixed-effect linear models with food source, insemination status, days of recording, and interaction between food source and insemination status as explanatory variables and individuals as random intercepts. Actograms were made using the "dplyr" package  [59]. Chi-square periodograms were calculated using the "xsp" package  [60]. Mixed-effect models were fitted using the "glmmTMB" package  [61]  in R software  [62]. Adequacy of model residuals was checked using the "DHARMa" package  [63]. Estimated marginal means were estimated and compared using the "emmeans" package  [64]. Post-hoc Tukey's procedures with correction for multiple comparisons were run, when appropriate, to compare the levels of significant factors.
Conclusions In this study, we have measured under controlled laboratory conditions the spontaneous locomotor activity of early generations of field-collected A. coluzzii and experimentally created different physiological groups with the purpose to capture the natural diversity of daily and circadian activity phenotypes, while limiting at the same time the impact of confounding environmental and physiological factors. To capture such diversity, we have applied a statistical framework that controls for sources of interdependence in the data due to repeated measures on the same individuals and at the same time allows investigation of individual contributions to population-level effects. This approach generates richer information that can be exploited towards a more refined understanding of phenotypic diversity and the evolution of circadian activity rhythms.
Acknowledgements The authors wish to acknowledge the financial support of the  "Région Auvergne-Rhône-Alpes"  for their financial support through the " Pack Ambition Recherche ". This work was performed within the framework of the  EUR H2O ' Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon (UdL) , within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR). This work was also supported by the " Federation des Recherches en Environnement " through the  CPER "Environnement"  founded by the  "Région Auvergne ," the  French government and FEDER from European community. The authors thank  Muriel Joly  for her involvement and her help in conducting the A-YES assays.
The main nuclear capture quantities that serve as the input for s-process nucleosynthesis models are the MACS, i. e. , the convolution of the capture cross-sections and the Maxwellian energy distribution of neutrons for a given temperature kT. The results presented in  [9]  were obtained by using the MACS provided by the database KADoNiS0. 3 [13] , which is a (on-line) database for cross-sections relevant to the s-and p-processes. The MACS reported in KADoNiS comes from activation measurement  [14]  of a natural cerium sample using a quasi-stellar neutron spectrum of kT = 25 keV. Theoretical models, based on the Hauser-Feshbach (HF) theory, permitted extrapolating the experimental MACS for other values of kT. The HF calculations of the 30 keV MACS reported in  [14]  overestimated the experimental MACS by 30% in the case of 140 Ce.
Experimental Apparatus and Data Analysis The n_TOF facility represents a unique site where high-precision measurements of neutron-induced cross-sections can be performed. Since its operational start in 2001, the n_TOF collaboration  [21]  has largely contributed to the improvement of nuclear data of interest for the nuclear astrophysics community, in particular performing many accurate neutron capture cross-section measurements (see, e. g. , the two recent works  [22, 23] ). The n_TOF white pulsed neutron beam is produced by spallation reactions on a cylindrical lead target induced by a 20 GeV/c proton beam accelerated with the CERN Proton Synchrotron (PS). The kinetic energy of the neutrons reaching the two experimental areas is measured with the time-of-flight technique. The 140 Ce(n,γ) measurement required the high-energy resolution of the beam present in Experimental Area 1 (EAR1), thanks to its long flight path of ≈185 m.
The latter was obtained from fitting the gold capture resonances in the energy interval from 100 eV to 2 keV. In order to evaluate the different sources of background data, an empty sample was used to measure the component related to the beam, while a lead sample was employed to measure the sample-related background. Finally, the detectors were calibrated, acquiring data on a weekly basis with four γ sources: 137 Cs, 137 Y, Am-Be, and Cm-C. The neutron captures were observed by detecting the γ-rays produced by the decay of the compound nucleus 141 Ce. The experimental apparatus was made of four deuterated benzene (C 6 D 6 ) liquid scintillator detectors  [25]  encapsulated in a cylindrical case made of carbon fiber, to guarantee a very low neutron sensitivity. A relatively high neutron sensitivity is one of the difficulties encountered with C 6 F 6 detectors employed in the measurement by  [15].
The data allowed performing a reliable RSA of approximately 80 resonances, a large fraction of which are p-waves. These are of particular interest for the s-process, since they provide a larger contribution to the MACS between 8 keV and 30 keV. Direct experimental information will largely determine the MACS at 8 keV and significantly tighten the constraints of the statistical model to calculate the MACS at about 30 keV. The new neutron capture rate, once included in the nucleosynthesis stellar models, will shed more light on the discrepancy with the cerium abundance measured in the M22 globular cluster.
Furthermore, some genes were differentially expressed between multiple analyzed sample groups (Table  2 ). Moreover, the differentially expressed gene of interest sets were uploaded to the STRING database to analyze their predicted interactions. The results of this analysis are presented in Figure  8. As it is presented, different extents of predicted interactions were indicated among the analyzed sample groups (the highest in group C and the lowest in group B). Furthermore, a varying number of genes in each group were excluded from the analysis due to having no predicted interactions (10/20 on day 14 vs. day 1; 14/20 in differentiated osteoblasts vs. day 1; 8/12 in differentiated osteoblasts vs. day 14).
In Vitro Cell Culture The initial in vitro cultures (IVC) were conducted for a period of 3 days, or until ~90% confluency was observed, with the exception of early control samples that were harvested for RNA isolation after 1 day of culture. Afterwards, the cells were detached from culture bottles using 1× Trypsin solution (Sigma-Aldrich, Saint Louis, MO, USA) and passaged onto 6-well culture plates. There, they were maintained in DMEM until the 14th day of culture, with medium changed every 72 h. Furthermore, photographs of the cultures were taken, using an inverted microscope and proprietary software (Ixplore Standard, Olympus, Tokyo, Japan), every 24 h to assess morphological changes of the analyzed cells.
Each 6-well culture plate consisted of material collected from a separate canine specimen. One well out of each technical triplicate was stained with Alizarin Red. The resulting staining and morphological differences of control and differentiated samples were visually confirmed using an optical microscope and proprietary software (Ixplore Standard, Olympus, Tokyo, Japan) and the differentiation medium manufacturer's visual guidelines, in order to ensure the success of the differentiation process. Furthermore, the two remaining culture wells of both control and differentiated samples were pooled together, representing a single set of biological samples.
Bioinformatical and Statistical Analysis The raw counts obtained from the initial analysis contained the numbers of reads assigned to particular geneIDs. These datasets were subjected to normalization using the DESeq2 package (v. 1. 24) in R (v. 4. 0. 3). Normalized gene lists were then annotated with ENTREZ gene numbers using the org. cf. eg. db annotation package included in Bioconductor (v. 3. 12. 0), in order to provide reference for further analyses. The obtained ENTREZ annotated datasets were uploaded to the IDEP. 91 interface for further processing and visualization. IDEP is an integrated web application, providing a simple user interface for advanced bioinformatical analysis of RNAseq data  [90]. Principle component analysis (PCA) was used to visualize the relationship between samples.
p value presented on the y and x axes, respectively. For the purpose of this study, the 10 most upregulated and downregulated genes between the sample groups (day 14 vs. day 1; differentiated vs. day 1; differentiated vs. day 14) were selected. The expression of the genes of interest was visualized in the form of heatmaps, with the intensity of each sample's color indicating the extent of the change in expression between sample groups. Finally, all of the gene of interest lists were uploaded to the STRING (Search Tool for Retrieval of Interacting Genes/Proteins, STRING Consortium, Lausanne, Switzerland) database to determine the predicted interaction between the analyzed genes. The STRING database contains data on protein/gene interactions that have been obtained through experimentation, computational forecasting methods and analyses of the available public literature  [92].
ACKNOWLEDGMENTS This work was supported by the  University of Chicago  through a  FACCTS grant  (" Franceand Chicago Collaborating in The Sciences "), by the  CNRS  through the  PICS-USA-147504 , by a grant " Investissements d'Avenir" from LabEx PALM  ( ANR-10-LABX-0039-PALM ) and from the  ANR  grant " NonEQuMat " ( ANR-19-CE47-0001 ). AC acknowledges support from the  Air Force Office of Scientific Research MURI program , under Grant No. FA9550-19-1-0399.
Funding: This research received no external funding. Cancers 2022, 14, 147 9 of 11 Acknowledgments:  Veeru Kasivisvanathan  is an Academic Clinical Lecturer funded by the  United Kingdom National Institute for Health Research (NIHR). The views expressed in this publication are those of the author(s) and not necessarily those of the  NHS , the  National Institute for Health Research  or the  Department of Health. YAU Prostate Cancer Working Group :  Alexander Kretschmer ,  Roderick C. N. van den Bergh ,  Alberto Martini ,  Giancarlo Marra ,  Massimo Valerio ,  Igor Tsaur ,  Isabel Heidegger ,  Veeru Kasivisvanathan ,  Claudia Kesch ,  Felix Preisser ,  Christian D. Fankhauser ,  Fabio Zattoni ,  Francesco Ceci ,  Jonathan Olivier ,  Peter Chiu ,  Ignacio Puche-Sanz ,  Constance Thibault ,  Giorgio Gandaglia  and  Derya Tilki.
Zeta potential was measured by DLS as well and calculated with the Smoluchowski's equation. All measurements were performed in triplicate on fresh samples (1. 0 mg/mL in 1. 5 mM NaCl pH 7. 4) at 25 • C. The morphology of CDs was observed by transmission electron microscopy (TEM) using a bench top LVEM5 microscope (Delong Instruments, Brno, Czech Republic) operating at 5 kV. CD samples (0. 5 µL, 1. 0 mg/mL) were deposited on glow-discharged (90 V and 2 mA for 15 s) carbon-coated grids (Cu-300HD, Pacific Grid Tech, San Francisco, CA, USA). After at least 2 h drying at room temperature, the grids were observed. The average size of the NPs was determined by image analysis using the ImageJ software (v 1. 50i, NIH, Bethesda, MD, USA), from a set of 687 particles. Elemental composition of CDs was determined by analyzing carbon, hydrogen and nitrogen content (expressed as mass ratio) on a Vario EL III instrument (Elementar, Langenselbold, Germany).
Besides, to assess cell viability, cells were stained with the cell death marker TO-PRO ® -3 (Invitrogen TM , Thermo Scientific, Illkirch-Graffenstaden, France) at 0. 08 µM just before the flow cytometry analysis. Living cells were discriminated from dead cells according to their fluorescence intensity collected using an APC (red laser) channel. Cellular mortality was expressed as percent of positive cells. To investigate CD uptake by BALF cells, cell fluorescence was measured using the BV421 (violet laser) channel. CD-positive cells were discriminated from CD-negative cells by intensity of the collected fluorescence. Internalization results were expressed as percent of positive cells. Fluorescence measurements were carried out on a LSRFortessa X 20 TM flow cytometer (BD Biosciences, Le Pont de Claix, France) and collected data were analyzed using FlowJo TM software (v 10.
Mitochondrial Membrane Potential Assay Mitochondrial membrane potential was assayed using the JC-10 fluorescent probe (Sigma-Aldrich, St. Louis, MO, USA) according to the manufacturer's instructions. Cells were seeded into 96-well culture plates at a density of 10 5 cells/well, differentiated into macrophages, and incubated with increasing concentrations of CDs for 4 h. After the CD treatment, the cell culture supernatant was removed and the JC-10 probe (100 µL) was added to the cells for 1 h. Then, fluorescence of the samples was measured (functional mitochondria: λ ex = 540 nm, λ em = 590 nm; non-functional mitochondria: λ ex = 490 nm, λ em = 525 nm). The ratio of fluorescence intensity at 525 nm to fluorescence intensity at 590 nm was calculated for each sample. Data were expressed as the fold change of CD-exposed cells relative to the non-exposed control cells.
Statistical Analysis Experiments were carried out on n = 3-9 biological replicates (in vitro experiments) or on n = 3-6 mice (in vivo experiments). Data are presented as mean ± SEM. Statistical differences between groups were determined by Student t-test or analysis of variance (ANOVA) followed by Dunnett's test, using the GraphPad Prism software (v 6. 0, San Diego, CA, USA). Data were considered as significantly different when p < 0. 05.