dataesr/scientific-paragraphs-categorization huggingface dataset:
ECHs shared with the ancient Ust'-Ishim male The sequence of the Ust'-Ishim male is available as part of the Simons Genome Diversity dataset. We computed pairwise sequence distances in 100kb windows between the Ust'-Ishim male and all present-day males. Windows, where the pairwise difference is based on fewer than 27,217 positions due to uncalled bases, are considered missing data. The Ust'-Ishim has more uncalled bases than the contemporary samples, and the cutoff is adjusted from 50,000 to 27,217 to ensure the average number of 100kb pairwise comparisons to Ust'-Ishim matches the average number of 100kb pairwise comparisons between all contemporary samples. The distribution of these sequence distances to Ust'-Ishim shows the same characteristic enrichment of short pairwise distances as present-day non-Africans.
Estimation of the population bottleneck for SGDP samples We used SMC++ on chromosome 7 to construct the autosomal population demography for the male samples analyzed. While the extensive population structure in the SGDP dataset will inflate population sizes, this is not the case for the initial out-of-Africa bottlenecked population. The minimum Ne of this shared demography thus provides a good estimation of the bottleneck Ne relevant for the frequency simulations in Figure  5. In the SMC++ analysis, we use one male individual from each non-African region as the ''dedicated individuals'': S_Greek-2 (WestEurasia), S_Korean-1 (EastAsia), S_Irula-2 (SouthAsia), S_Yakut-2 (CentralAsiaSiberia), S_Papuan-9 (Oceania), S_Pima-1 (America). The remaining individuals contribute information through the SFS.
Analysis of CEU population We use the phase3 version of the 1000 genome dataset and extract the 49 males from the CEU population. We then repeat our ECH inference procedure using a clique size of 50% instead of 25%, which we used in the main analysis of SGDP. The larger clique size accommodates the additional recent ancestry among males from the homogeneous CEU population not found among males sampled from the many separate populations in the SGDP. Using this clique size, the proportion of the X chromosome overlapping an ECH is 10%, similar to the 11% observed in our SGDP analysis. Fifteen of the ECH peaks in the CEU analysis match exactly one of the nineteen peaks in the SGDP analysis (Figure  S8 ). Four SGDP ECHs are not found in the CEU analysis, which in turn finds five ECHs not found in the SGDP analysis.
65 (African samples). For a realistic representation of recombination on the X chromosome, we use the sex-averaged deCODE recombination map derived from parent-offspring pairs. 14 This map is not confounded by differences in effective population size or selection along the chromosome. We use SLiM3  16  to simulate fifteen consecutive 10Mb segments of the X chromosome from position 4,000,000 to 154,000,000, which excludes the pseudoautosomal regions. From each simulation, we extract 49 male X chromosomes (matching the number of CEU males) from the population and perform ECH inference as described above. On each simulated chromosome, we mask the same regions as missing data as we do in our analysis of the CEU population. We do this to ensure that the analyzed chromosomal regions from simulations correspond to the analyzed chromosomal regions in the CEU analysis.
METHOD DETAILS Data processing and filtering We analyzed male X chromosomes from the fully public subset of genomes of the Simons Genome Diversity Project 10 as initially published. We use the SGDP-lite version of the dataset that is run-length compressed sequences representing whether each base differs from the hg19 reference. We exclude African individuals with evidence of gene flow from non-African populations: Mozabite and Saharawi show extensive non-African ancestry and Neanderthal admixture,  10  and Masai and Somali show a non-African component in STRUCTURE analysis,  10  Luhya and close populations Luo and BantuKenya, show a non-African component in Admixture analysis. 15 ollowing,  10  we also exclude five samples (S_Finnish-1, S_Finnish-2, S_Mansi-1, S_Mansi-2, S_Palestinian-2) "based on missing X chromosome data in the initial processing, for themselves or a second sample.
The PI3K/Akt/mTOR pathway is involved in adipocyte-mediated proliferation and migration of breast cancer cells  [48]  and can be disrupted by glucocorticoids  [49, 50]. It is likely, therefore, that the net outcome for breast cancer development depends on the balance between the opposing actions of insulin, oestradiol, progesterone, glucocorticoids, and other alternative pathways. A different balance of ABSI-related factors within different populations and datasets may explain why the inverse association with ABSI was more prominent for the subset of cancers with available ERPR status in our study and why other studies have either failed to find evidence for association, or have reported a positive association with ABSI. A major strength of our study is the availability of waist and hip size measurements from a large multicenter cohort.
Acknowledgements The authors thank the  National Institute for Public Health  and the  Environment (RIVM) ,  Bilthoven , the  Netherlands , for their contribution and ongoing support to the EPIC study. The authors acknowledge the use of data from the  EPIC-France  cohort (principal investigators (PIs)  Dr Gianluca Severi  and  Dr Marie-Christine Boutron-Ruault );  EPIC-Bilthoven cohort (PI Prof Monique Verschuren) ; and  EPIC-Cambridge  cohort (PI  Prof Nicholas J Wareham ).
Data Availability The dataset analysed in the current study was used under license and cannot be made freely available in a public repository or obtained from the authors due to restrictions related to privacy regulations and informed consent of the participants. Access to the data, however, can be obtained by bona fide researchers from the central EPIC repository at IARC, subject to approval of the research project by the EPIC Steering Committee and a material transfer agreement. For information on how to submit an application for gaining access to EPIC data and/or biospecimens from the EPIC repository at IARC, please follow the instructions at https://epic. iarc. fr/access/index. php For further queries related to the data used in this study, contact the corresponding author Dr Sofia Christakoudi s.
S1 ). Cryo-TEM. A 5 μL droplet of nanosized bubble dispersion was deposited onto a plasma-treated lacey carbon-coated copper grid (Electron Microscopy Sciences). The droplet was blotted with filter paper, plunged into liquid ethane (-170 °C) using an automatic plunge freezer (EM GP2, Leica), and stored in liquid nitrogen. The samples were inspected using a JEM-3200FSC (JEOL) microscope with an accelerating voltage of 300 kV, and images were acquired using Digital Micrograph software (Gatan, version 1. 83. 842). AFM. The AFM measurements were carried out using a Dimension Icon AFM (Bruker AXS, France; formerly Veeco) with ScanAsyst-air cantilevers (sharp silicon nitride tips with a nominal radius of 2 nm for PeakForce Tapping in air). The scan size was set to 100 nm × 100 nm with a resolution of 256 pix × 256 pix, and the scanning was performed with a scan rate of 1 kHz.
Finally, the sample was imaged. SI Appendix, Fig. S2  shows the sample preparation procedure. Surface Tension and Surface Dilatational Rheology. Measurements of the surface tension and mechanical properties of the HFBI adsorbed layer were carried out using an optical tensiometer (Theta tensiometer, Biolin Scientific, Finland), and the obtained data were analyzed using the instrument software (OneAttension). A hooked needle filled with C 4 F 10 using a Hamilton syringe was immersed in a solution of HFBI (0. 01 mg mL -1 ). Then, a 10 μL gas bubble was created on the needle tip, and the bubble was monitored for about 3 h until the surface tension curvature reached a steady state. At that point, the static surface tension was measured by applying a sinusoidal volume disruption using an integrated pulsated droplet module (PD-200, Biolin Scientific, Finland).
I. 7, Supporting Information). The delay in t 0,i might be associated with the early charge-transfer (CT) character of the Franck-Condon state, while differences in  i may be due to competing intersystem crossing (ISC) and structural dynamics. The elaboration of a complete model for the nonadiabatic formation of the quartet state requires the acquisition of extended datasets in several solvents of different properties (e. g. , polarity). On a longer timescale, the K 1 and K kinetics shown in Figure  6c  decay with a time-constant of 7. 5 ± 1. 3 and 5. 4 ± 0. 6 ps, which are close to the lifetime obtained from the transient OAS measurements (Figure  2b ). The time constants of the non-adiabatic formation of the quartet HS and its decay are both indicated in the schematic photocycle shown in Figure  6d.
Acknowledgements The authors gratefully acknowledge European XFEL in Schenefeld, Germany, for provision of X-ray free-electron laser beamtime at FXE and thank the instrument group and facility staff for their expert assistance. S. E. C. acknowledges financial support from European XFEL. T. -K. C. received financial support from the European XFEL. W. G. acknowledges funding from  Spanish MIU  through " Ayudas Beatriz Galindo " ( BEAGAL18/00092 ),  Comunidad de Madrid  and  Universidad Autónoma de Madrid  through " Proyecto de I + D para Investigadores del Programa Beatriz Galindo " ( SI2/PBG/2020-00003 ),  Spanish MICIU  through  Proyecto de I + D + i 2019  ( PID2019-108678GB-I00 ) and IMDEA-Nanociencia support from the  Severo Ochoa Programme for Centers of Excellence in R&D (MINECO , grant  CEX2020-001039-S ).
W. G. further acknowledges partial support from the  National Science Centre in Poland  under  SONATA BIS 6 Grant 2016/22/E/ST4/00543. This work received funding from the  Danish Council for Independent   Research Grant  Nos. 4002-00272  and  8021-00347B  (M. P. and K. B. M. ) and the  Hungarian National Research, Development and Innovation Fund  under Grant No. NKFIH PD 134976  (M. P. ). M. P. acknowledges support from the  János Bolyai Scholarship of the Hungarian Academy of Sciences. This work received funding from the  Danish Council for Independent Research  No. 7026-0037B , and  Swedish Research Council  No. 2017-05337  (K. Z. ). This work was performed, in part, at the  Center for Nanoscale Materials , a  U. S. Department of Energy Office of Science User Facility , and supported by the  U.
The MPL lidar The NASA Micro-Pulse Lidar Network is a federated network of Micro-Pulse Lidar systems designed to measure aerosol and cloud vertical structure, and boundary layer heights  (Welton et al. , 2001). All sites in MPLNET currently use the MPL, which was developed at NASA Goddard Space Flight Center (GSFC) in the early 1990s. The MPL was patented and subsequently licensed to industry for commercial sales beginning in the mid 1990s. The data collected by MPL instruments are continuously, day and night, over long time periods from sites around the world. Most MPLNET sites are co-located with sites in the NASA Aerosol Robotic Network (AERONET). MPLNET data have contributed to many studies and applications, such as: domestic and international aerosol and cloud research  (Welton et al.
, 2002; Lewis et al. , 2016). Data are centrally processed at NASA GSFC through MPLNET version 3 (V3, released in 2021) algorithm and level 1. 5 (L15, near real time, quality assured) data  (Welton et al. , 2018). In particular, we used the MPLNET Normalized Relative Backscatter (NRB) product, provided with 1-min temporal resolution and at 75m vertical resolution. This product includes https://doi. org/10. 5194/amt-2023-134 Preprint. Discussion started: 21 August 2023 c Author(s) 2023. CC BY 4. 0 License. correction of deadtime, darkcount, afterpulse, background, overlap  (Campbell et al. , 2002; Welton and Campbell, 2002)  and polarization calibration  (Welton et al. , 2018). Cloud base height and cloud top height, as well as cloud optical depth and extinction coefficient profiles, linear volume depolarization ratio and cloud phase belong to MPLNET Cloud (CLD) product, described by  (Lewis et al.
1  (Cadet et al. , 2005)  or 0. 05  (Chen et al. , 2002) ), for thick clouds because the lidar signal does not penetrate the whole cloud, for very noisy lidar signals or for small lidar signal values. In spite of all these disadvantages, it is common to find this method combined with other ones, to make a first estimation of the cloud optical depth, due to its low computational cost. This first estimation of the cloud optical depth is usually used as a constraint in other methods. For example, CALIPSO algorithm applies the transmittance method under certain situations. When a molecular region is found immediately above and below the cirrus cloud, the Hybrid Extinction Retrieval Algorithm (HERA) implemented with CALIPSO data uses the two-way transmittance method to obtain the cloud optical depth directly from the ratio of the mean attenuated scattering ratios, without multiple scattering correction  (Young and Vaughan, 2009).
Cirrus geometrical properties After having carried out the identification of 367 high-altitude cirrus clouds, measured in Barcelona, through MPLNET products and radiosonde data from November 2018 to September 2022 (only at 00 and 12 UTC, when radiosondes are available), the two-way transmittance method has been applied successfully to 203 of them, i. e. to 55% of all cases. Note that 39% percent of the 203 high-altitude cirrus cases have another cloud below the cirrus cloud. The elimination of some cases has been carried out on the basis of the noise associated to the lidar signal and/or the possibility to guarantee a cloud/aerosol-free zone both above and below the cirrus cloud. In this section, the geometrical and optical properties of high-altitude cirrus scenes are presented and discussed.
The monthly distribution of sub-visible cirrus clouds does not vary considerably, remaining the category with the lowest occurrence over the year. In contrast, the distribution of visible and opaque cirrus varies slightly. It can be said that in the warmer seasons, opaque cirrus are more frequent than visible cirrus. As shown in Table  3 , these observations vary considerably depending on the latitude. The frequency of cirrus detection seems to be highly variable, a more extended database is needed to state a tendency. It is also observed that at latitudes close to the one of this study (41. 38ºN, Barcelona), the occurrence of visible cirrus clouds predominates. In general, it could be said that the occurrence of each cirrus depends on the weather pattern of each site. The probability distribution of cloud base and top heights and mid-cloud temperature of cirrus clouds are shown in Fig.
Types In Table  4 , it can be seen that the cloud top height do not vary considerably depending on the type of cloud. The cirrus clouds might reach to/near the tropopause, since the average tropopause height calculated with radiosondes  (World Meteorological Organization, 1957)  on the days of cirrus scenes analysed is 11± 1 km. In contrast, the other geometrical, thermal and optical properties do vary with cloud type. For example, subvisible clouds are the highest, coldest and thinnest clouds. Also, their thickness is 0. 9 km less than the average thickness calculated with the whole cirrus dataset and its temperature is 4ºC colder than the mean temperature. These results are consistent with other studies of SVC cirrus from space-borne lidar observations  (Martins et al. , 2011). Their COD is within the value selected to make this classification and the LR is lower than 30 sr, and their mean linear cloud depolarization ratio is 0.
27, being the lowest value of all categories. Visible cirrus clouds are the most predominant type in this study. Their geometrical properties are very similar to those of the whole cirrus dataset, but the average of the optical properties are slightly lower. Opaque cirrus clouds have the highest value of LR, which may be due to the fact that these clouds contain the greatest richness and variety of ice crystals. On the other hand, opaque clouds contribute the most to the total radiative forcing  (Kienast-Sjögren et al. , 2016) , being the lowest, warmest and thickest clouds in the whole cirrus dataset. Also, their thickness is 0. 6 km higher than the average thickness calculated with the whole cirrus dataset and its temperature is 4ºC warmer than the mean temperature.
Cirrus correlation In this section the correlations between the different cirrus products obtained with the two-way transmittance method, radiosonde and MPLNET data are analysed. First, the linear correlations between the temperature and height of the cirrus base and the lidar ratio with  temperature and contrary to this, a negative tendency with the cloud base height. This means that as the cloud base temperature increases, the cloud base height decreases, being characteristic of the troposphere. As the cloud base height is lower, it is observed that the cloud optical depth increases. This could be due to the fact that as an air mass loaded with water vapour ascends vertically, the water vapour gradually condenses. Thus, clouds located at higher altitudes are formed from air masses with a lower water vapour content and, therefore, both their geometric and optical thickness are smaller.
32± 0. 13, with the highest occurrence in spring. It could be said that in the warmer seasons, opaque cirrus are more frequent than visible cirrus. In addition, these properties were compared to the literature, obtaining similar properties in nearby latitudes, with a majority of visible and opaque cirrus clouds being present. It was also found that the occurrence of each cirrus depends on the weather pattern of each site and the lidar ratio seemed to have a generally increasing trend towards the poles and a decreasing trend towards the equator. Forth, it was found that the efficiency of the two-way transmittance method and the properties of the cirrus clouds were not dependent on the hour of day and their properties were analysed according to the COD. Resulting in that the subvisible cirrus clouds were the highest, coldest and thinnest clouds; the visible cirrus clouds were the predominant and the opaque cirrus clouds were the lowest, warmest and thickest clouds in the whole cirrus dataset.
Acknowledgements. The authors acknowledge the funding of this research by the  Spanish State Agency for Research (AEI)  for the project  PID2019-103886RB-I00  and its support to ACTRIS ERIC. The authors also acknowledge the support of the  European Commission  through the  Horizon 2020 Research and Innovation Framework Programme projects ACTRIS IMP  (grant agreement No  871115 ),  ATMO-ACCESS  (grant agreement No  101008004 ),  GRASP-ACE  (grant agreement No  778349 ) and  REALISTIC  (grant agreement No  101086690 ).
Data availability. The MPLNET products are publicly available on the MPLNET website (https://mplnet. gsfc. nasa. gov/download_tool/) (MPLNET, 2023) in accordance with the data policy statement. The CALIPSO product is provided by the NASA Langley Research Center's (LaRC) ASDC DAAC and is managed by the NASA Earth Science Data and Information System (ESDIS) project. NASA data are freely accessible and available on the Atmospheric Science Data Center website (https://asdc. larc. nasa. gov/) (NASA, 2023). Radiosoundings data are available upon request from the authors or Meteocat.
This analysis was performed by using the genetic profiles of 14,883 genes generated by the RNA-seq experiment. Among them, 346 DEG were detected in the four comparisons: control versus BPA, control versus BPS, BPA versus BPS and control versus G1, with 259, 12, 69 and 75 DEG, respectively (Fig. 2A  and Supplementary Table  3 ). Only three DEG, SREBF1, SPRY2 and TXNIP, were common between the control versus BPA, control   S3. Principle component analysis of gene expression in GC treated or not with BPA, BPS or G1 performed on expression values of 24 differential genes C versus BPS and control versus G-1 comparisons. The control versus BPA and control versus G-1 comparisons revealed 11 common DEG: TXNIP, JUN, NR4A1, SPRY2, C15orf39, ENSOARG00000018728 novel gene, TUBB, PPP1R3C, GADD45B, SREBF1 and STAR.
However, the majority of DEG induced by BPA (200/259, 77%) and G-1 (69/75, 92%) were specific modifications, not found in other comparisons. Moreover, nine DEG (TXNIP, ENSOARG00000022521 novel gene, SPRY2, ENSOARG00000024133 novel gene, FAM217A, ANKRD1, DDIT4, MAPS2, SREBF1) were common between the control versus BPA and control versus BPS comparisons. Thus, the majority of DEG found in the control versus BPS comparison (9/12, 75%) were with the same DEG found in the control versus BPA comparison (Fig. 2A ). A hierarchical clustering showed that BPA modified the expression of the genes in an opposite direction compared with G1. Indeed, cluster 1 regrouped the genes, which were upregulated by BPA, whereas cluster 2 showed the genes that were upregulated by G-1 (Supplementary Table  3  and Supplementary Table  4 ).
Regarding BPS, only four genes were downregulated (RHOB, ANKRD1, SPRY2 and TXNIP) and eight were upregulated (RASL11B, PDK4, SREBF1, MASP2, DDIT4, FAM217A, ENSOARG00000024133 novel gene and ENSOARG00000022521 novel gene) compared with the control. In addition, TXNIP and SPRY2 were downregulated and six genes were upregulated (FAM217A, SREBF1, DDIT4, MAPS2, ENSOARG00000024133 novel gene, ENSOARG00000022521 novel gene) by both BPA and BPS compared with the control.
Interestingly, they are involved in cell growth, differentiation and apoptosis in ovine and porcine follicular cells  [82, 83]. Several genes involved the biosynthesis of steroids in GC, such as STAR and CYP11A, exhibit Sp1-like sites, which would allow binding with KLF proteins. Thus, inhibition of KLF could increase the transcription of steroidogenesis-related genes, and ultimately the secretions of progesterone and oestradiol, which is opposite to what we observed here. Nevertheless, different KLF are already reported to act through separate pathways and therefore to not have the same effect on steroidogenic actor mRNA  [84]. Finally, the genome of the O. aries has not yet been fully annotated. Thus, future studies could focus on the 48 DEG that remained to be identified. Indeed, among the genes exhibiting the lowest p-value, ENSOARG00000022521 and ENSOARG00000024133, are likely non-coding RNA.
Live/Dead staining was performed according to the manufacturer's instructions (Thermo Fischer Scientific). Thus, living cells (green coloured) and dead cells (red coloured) were counted (300 cells minimum per chamber) with ImageJ software (ImageJ-win64). The results are expressed as the percentage of living and dead cells from four and seven independent cultures, at 1 and 48 h of treatment, respectively. Regarding the dehydrogenase activity assay, GC (100,000 viable cells in 150 µL of complemented serum-free McCoy's 5A media per well) were cultured on 96-well plates (Thermo Scientific Biolite). Cell viability was determined by using the CCK-8, based on the reduction of water-soluble tetrazolium salt (WST-8) to formazan (coloured yellow) by dehydrogenases in living cells, according to the manufacturer's instructions (Thermo Fischer Scientific).
The number of viable cells was determined by colourimetric assay by measuring the absorbance by at 450 nm (Thermolabsystems plate reader, Thermo Fischer Scientific) and Ascent Software version 2. 6 for Multiskan. The results are expressed as the mean ± standard error of the mean (SEM) from six and 11 independent cultures at 1 h and 48 h of treatment, respectively. Each condition was performed in triplicate, and the measured values were normalised by the mean value of the control condition. For the lactate dehydrogenase activity assay, GC (100,000 viable cells in 150 µL of complemented serum-free McCoy's 5A media per well) were cultured on 96-well plates (Thermo Scientific Biolite). The activity of lactate dehydrogenases released by damaged cells was assessed with the Lactate Dehydrogenase Activity Assay Kit, according to the manufacturer's instructions.