dataesr/scientific-paragraphs-categorization huggingface dataset:
This constellation consists of six low-orbit microsatellites was launched on April 15, 2006 (UTC) to collect atmospheric remote sensing data for operational weather prediction, climate, ionospheric (space weather monitoring), and geodesy research. The COSMIC constellation could provide about 2,000-2,500 global distributed electron density profiles when the system was fully operated (http://cdaac-www. cosmic. ucar. edu/cdaac/index. html). The FY-3 series of CMA/NSMC (China Meteorological Administration/National Satellite Meteorological Center) represents the second generation of Chinese polar-orbiting meteorological satellites (follow-on of FY-1 series), and it is operated by the National Satellite Meteorological Center (NSMC) of the China Meteorological Administration. The key aspects of the FY-3 satellite series include collecting atmospheric data for intermediate-and long-term weather forecasting and global climate research (https://directory.
The bottom-side electron density profiles observed by the GIRO network are downloading using the Standard Archive Output (SAO-X, version 3. 5. 3) Explorer from the Digital Ionogram Data Base (DIDBase) web portal (http://umlcar. uml. edu/ DIDBase/). The spatial locations of daily COSMIC, FY-3C RO events, and the Digisondes used in this study are shown in Figure  1. Sometimes the space-borne and ground-based measurements are affected by instrument failure and severe solar-terrestrial events, therefore the RO profiles and Digisonde observations should be checked for quality control. In this study, for space-borne technique, first the RO profiles are simulated by the Chapman function  (Lei et al. , 2006) , and the correlations between observed profiles and simulated profiles are computed. The RO profiles with the correlations of smaller than 0.
Moreover, high-speed solar wind streams usually have a remarkable impact on the ionospheric electron density, sometimes the sudden ionospheric perturbations can not be attributed to the severe geomagnetic filed motivated by coronal mass ejection or solar flare  (Hajra et al. , 2017). Therefore, it is believed the index solar wind speed (Vsw) play an important role in improving the performance of the ANN-TDD model. For geomagnetic filed, the indices Dst, Kp, and Ap are used to represent the level of geomagnetic storm. These parameters are obtained from the Space Physics Data Facility of NASA (https://omniweb. gsfc. nasa. gov/).
, 2019; Tulasi Ram et al. , 2018). In the training process, the neural network stops to update the weights and biases when the cost of the validation set increasing for several consecutive steps, in this study the step is set as 6. In this study, the back propagation (BP) feedforward neural network is used to develop an ionospheric electron density model, namely artificial neural network-based three-dimensional density model (ANN-TDD). During the training process, the entire data set is divided into 10 batches using the mini-batch strategy, and each batch contains about one million vectors. The input parameters include day of year (DOY), local time (LT), geomagnetic latitude (MLAT), geographic longitude (LON), and altitude. Besides, the electron density is also dependent on solar radiation and geomagnetic activity.
the training stage, the whole samples are divided into the training set, the validation set and the test set, which contain 70%, 15% and 15% of the samples, respectively. A training data set is a set of examples used to fit the parameters (e. g. , the weights of the connections between neurons) during the learning process. Usually, a training data set consists of pairs made up of an input vector and the corresponding output vector. A validation data set is a set of examples used to tune the hyper-parameters of a classifier. The validation datasets can be used for regularization by early stopping, which is helpful to avoid overfitting during the training process. A test data set is independent of the training data set, but it follows the same probability distribution as the training data set.
Based on the success of the ANN model in predicting ionospheric peak parameters foF2 and hmF2, this study uses the profiles obtained from multiple RO systems (COSMIC-1, FY-3C) and the observations of global Digisondes to develop a completely global three-dimensional electron density model with an artificial neural network, namely ANN-TDD. In the training stage, the correlation coefficients of the datasets all exceed 0. 96 in training, validation and test sections, and the RMSE of the predicted residuals of the ANN-TDD is 7. 8 × 10 4 el/cm 3 , which reveals that the ANN-TDD model has perfect performance in learning ionospheric nonlinear characteristics and a strong capability in predicting the electron density with high accuracy. The predicted capability of the ANN-TDD is thoroughly validated with the profiles derived from the Millstone Hill and Jicamarca ISR under high-low level solar activities and severe geomagnetic storms, alongside the well-established empirical model IRI-2016.
The bias also extends to global databases which tend to be in English but require information generated worldwide to be complete. Consequently, it is not surprising that country-level data for such global databases (e. g. , Global Biodiversity Information Facility, gbif. org) are more complete in countries with a higher proportion of Anglophones than those where English is rarely spoken  25. Importantly non-Anglophone policymakers and the broader public might miss relevant scientific discoveries which are only communicated in English. Several studies have shown that access to scientific information can be limited for certain groups if national languages are not used  3, 17, 26, 27. As a result, the transfer of scientific knowledge into local policies may be hindered  28. Furthermore, scientific discovery and its application can be slowed for non-Anglophones due to the linguistic burden of publishing in English  [29] [30] [31].
Existing resources that explicitly seek to assemble and share non-English-language sources can also help address these gaps. For example, the Conservation Evidence database systematically catalogues English-language journal articles, Non-English-language journal articles, and grey literature to identify conservation actions and the effects of these actions on biodiversity and ecosystem services  74. Assessment processes can solicit and search for relevant non-English-language studies and, where relevant, include them, as IPBES has recently done to solicit ILK materials in national and local languages  75. They can also facilitate searches for non-English-language literature in collaboration with native speakers of different languages  76  or with the aid of emerging technologies (e. g. , litsearchr package in R [elizagrames.
github. io/litsearchr] translates search strings into multiple languages). Additionally, the use of non-scientific databases that provide access to large volumes of non-English-language scientific literature (e. g. , SciELO in Brazil [scielo. br], Dialnet in Spain [dialnet. unirioja. es], HAL in France [hal. archives-ouvertes. fr], J-STAGE in Japan [jstage. jst. go. jp]) could be also actively encouraged. All of these actions can serve to increase the legitimacy of assessment processes, making them more inclusive, representative, and accurate  22, [77] [78] [79] [80]. Beyond scholarly literature, additional processes are needed to make clear how what is often referred to as grey literature can be evaluated appropriately  81. In IPBES, for example, the current criteria for evidence assessment speaks primarily (albeit not exclusively) to scientific literature  82.
Acknowledgments We thank all the  Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Fellows  for an enriching experience and discussions. We thank  Editor Andrew  and the three anonymous reviewers as well as  Logan E. Neu  ( U. of Minnesota ) for conducting an internal review of this manuscript for the  U. S. Geological Survey. ÁFLL is funded by the  Academy of Finland  (grant agreement nr. 311176 ). IP was supported by the  Programme d'Investissements d'avenir  ( 19-MPGA-0009 ). PJ was partially funded by the  InterAmerican Institute for Global Change Research. TA is funded by the  Australian Research Council   Future Fellowship  ( FT180100354 ) and the  University of Queensland  strategic funding. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U.
For the experiments shown here we have performed 15 iterations. At the end of the initialisation procedure, we use the last inferred basal-drag coefficient together with the corresponding thermal state to run a relaxation experiment of 65 years with a freely evolving grounding line. The simulated ice sheet after this relaxation experiment is used as the initial condition for the historical experiment (hist; see Sect. 2. 3). It should be noted that such an initialisation procedure produces an ice sheet in quasi equilibrium with the late-20thcentury mean climate state. By construction it does not simulate the accelerated mass loss observed in the last decades  (Rignot et al. , 2019). Our reference ice thickness and bedrock topography are from the Bedmap2 dataset  (Fretwell et al. , 2013). This dataset is used as the initial topography for the 65-year relaxation experiment used to define the initial state for the historical simulation.
The ice thickness in Bedmap2 is also used as a target for the iterative initialisation procedure. Our reference present-day surface mass balance comes from RACMO2. 3p2  (van Wessem et al. , 2018 (van Wessem et al. , ), averaged over 1979 (van Wessem et al. , -2016. The reference present-day oceanic forcing used to compute the sub-ice-shelf melt rates (more details available in Sect. 2. 2) is derived from a combination of observational datasets  (Jourdain et al. , 2020) , averaged over 1995-2017. These reference atmospheric and oceanic forcings are used during the initialisation procedure and for the relaxation and control experiments (ctrl and ctrl_proj; see Sect. 2. 3). The geothermal heat flux is taken from  Shapiro and Ritzwoller (2004). The model is run on a Cartesian grid at 16 km resolution, covering the Antarctic ice sheet and using a polar stereographic projection.
Since the ice shelves show a larger velocity magnitude, they also show the largest absolute velocity changes (a few hundred metres locally). 3. 2 Ice sheet evolution projections 3. 2. 1 Ice sheet evolution for CMIP5 models using RCP8. 5 The evolution of the total ice mass change for the different CMIP5 models under the high-emission scenario for greenhouse gases (RCP8. 5) and using the sub-ice-shelf melt parameterisation calibrated over the Antarctic-wide dataset (MeanAnt) is shown in Fig. 4. The total ice mass (Fig. 4a ) is decreasing for the six CMIP5 models, and for most models there is an acceleration of ice mass loss over the course of the century. HadGEM2-ES produces the largest mass loss (about 300 × 10 3 Gt in 2100), while CSIRO-Mk3 produces the smallest loss (lower then 50 × 10 3 Gt).
Ice sheet evolution using the Pine Island Glacier calibrated sub-ice-shelf melt parameterisation The computation of the sub-ice-shelf melt rate in ice sheet models is one of the largest sources of uncertainty. The standard approach in ISMIP6-Antarctica is a parameterisation tuned to reproduce a combination of observational datasets  (Jourdain et al. , 2020). However, the choice of the dataset used to calibrate the parameterisation can lead to substantial differences in the sub-ice-shelf-melt model. Figure  10  shows the simulated total ice mass change when using the sub-ice-shelf melt parameterisation calibrated to reproduce the mean Antarctic melt rate (reference, MeanAnt) or calibrated to reproduce the Pine Island grounding-line melt rate (PIGL). The PIGL calibration produces higher melt rates and much greater mass loss than the reference calibration.
5). However, this seems nonetheless in contradiction with the acceleration in mass loss reported by modern observational techniques  (Rignot et al. , 2019). One reason for this disagreement is that most models participating in ISMIP6, including GRISLI, use some kind of data assimilation procedure that produces an ice sheet initial condition in quasi equilibrium with present-day forcing. This methodology is thus not suited to reproduce the recent acceleration in mass loss, which is particularly large in West Antarctica, where it has been estimated to be 48 Gt yr -1 per decade for 1979-2017  (Rignot et al. , 2019). For example, a simple cumulative value of the observed 2012-2017 loss rate (219 Gt yr -1 ; The IMBIE team, 2018) from 2015 to 2100 will result in an Antarctic ice sheet contribution to sea level rise of 52 cm SLE.
Figure10. Simulated total ice mass change (a) and ice volume contributing to sea level rise (b) for projections under the different CMIP5 forcings using the RCP8. 5 scenario and the medium oceanic sensitivity. For the projections, the solid lines stand for experiments that use a sub-shelf melting rate parameterisation calibrated against the Pine Island Glacier area only (PIGL), while the dashed lines stand for experiments that use the sub-shelf melting rate parameterisation calibrated against the Antarctic-wide dataset (MeanAnt). The evolutions begin with the historical simulation hist (1995-2015), and the control experiments ctrl and ctrl_proj are depicted in grey (solid and dashed, respectively). For each projection experiment, the vertical bar shows the minimal and maximal changes associated with the oceanic-forcing sensitivity to temperature change (low and high).
Data availability. The GRISLI outputs from the experiments described in this paper are available in the Zenodo repository with digital object identifier https://doi. org/10. 5281/zenodo. 3784665 (Quiquet and Dumas, 2020). The outputs in the Zenodo repository are the standard GRISLI outputs on the native 5 km grid, and, as a result, they may slightly differ from the post-processed outputs available in the official CMIP6 archive of the Earth System Grid Federation (ESGF).
Colony Formation Assay Cells were seeded at a density of 500 cells per well in 6-well plates and grown for 48 h in standard medium. The culture medium was then replaced with charcoal-stripped FBS-containing medium for 24 h. Then, cells were treated with DMSO 0. 01% or 1 µM G-1 for 72 h, followed by 72 h without G-1 treatment to assess the impact of G-1 on colony growth. The colonies formed were subjected to a 10 min fixation in 4% paraformaldehyde and a 30 min staining in 0. 1% crystal violet. After removing the staining solution, the colonies were air-dried. The clones of at least 300 µm diameter were counted automatically with the ImageJ software (https://imagej. nih. gov/ij/, accessed on 10 May 2021).
GBM Patient Cohort and Survival Analysis Gene expression and patient data from GBM patients were downloaded from The Cancer Genome Atlas (TCGA) using TCGAbiolinks (R package) and further processed with R software (open access available online: https://cran. r-project. org/, accessed on 1 November 2021; version 4. 0. 2). Data from patients presenting primary GBM with wild type IDH and ATRX status and who received both chemotherapy and radiotherapy (n = 99) were selected. The normalization was performed using DESeq2 (R package). Boxplots of GPER expression level were generated for female (n = 30) and male (n = 69). Patients were assigned to a high-or low-GPER expression group using the optimal cut-off value obtained by using the maximally selected rank statistics by the surv_cutpoint function of the survminer R package.
Clinical Relevance of GPER Expression in GBM Tumors Exploration of the GBM project from TCGA database indicated that GPER expression was predictive of overall survival in patients who underwent radiochemotherapy treatment (Figure  1a ), but not in the whole patient population (data not shown). Moreover, high GPER expression was significantly of good prognosis for female GBM patients (Figure  1b ). Since this finding was not observed for male patients, this could indicate that GPER was differently expressed between both sexes. However, no difference was observed for GPER expression between males and females suggesting that not only the expression level but also the activity of the GPER receptor could be related to survival and better response to anticancer treatment (Figure  1c ). Therefore, GPER protein expression was assessed by Western blot in LN229 female and U251 male GBM cell lines.
G-1 acted in a GPER-independent manner. LN229 and U251 cells were pretreated for 24 h with 1 µM or 10 µM of the GPER antagonist G36 and then for another 96 h with a combination of 1 µM G-1 and 1 µM G36 or 1 µM G-1 and 10 µM G36. The number of metabolically active cells was determined using MTT assay. n = 3. Data are presented as the mean ± SD. The level of significance was determined at 96 h using Student's t-test with ns = non-significant.
Valdivia and coworkers  [50]  showed that the N-terminal domain residue Asn44 was critical for a mature and functional GPER protein in the plasma membrane. This suggested that the receptor might perform differential functionalities in U251 or in LN229 GBM cells. In silico analysis of GPER expression across TCGA samples indicated that GBM are one of the tumors that expressed the most GPER mRNA (UALCAN database,  [51] ). Thus, GPER-dependent signaling could appear favorable since TCGA-GBM data also indicated that a high GPER expression level could be of better prognosis for female (but not male) GBM patients treated with radio-chemotherapy. U251 and LN229 GBM cells derived from a male or a female tumor, respectively, were exposed to G-1, a synthetic agonist initially selected to specifically trigger GPER activity.
Finally, we confirmed that the protocol specifically purifies the exogenous LGN in the GFP-LGN-bead-bound fractions as compared to controls (Supplementary Fig. 1f ). The purified GFP-LGN protein complex was trypsin-digested and analysed using liquid chromatography-tandem mass spectrometry (LC-MS/MS, see Methods for the technical details). All mass spectrometry proteomics data obtained from interphase and metaphase cells in two independent experiments have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  29. Removal of proteins represented in the control beads, as well as common MS contaminants using the Contaminant Repository for Affinity Purification (CRAPome) database  30  and singletons identified an LGN/GPSM2 protein subnetwork of 18 proteins in metaphase cells (Fig.
Mass spectrometry and data analysis Peptide extracts were separated on an Ultimate 3000 RSLC nano system (Thermo Scientific) using a PepMap C18 EASY-Spray LC column, 2 μm particle size, 75 μm x 75 cm column (Thermo Scientific) over a 140 min (single run) linear gradient of 3-25% buffer B (0. 1% formic acid in acetonitrile (v/v)) in buffer A (0. 1% formic acid in water (v/v)) at a flow rate of 300 nL/min. Peptides were introduced using an EASY-Spray source at 2000 V to a Fusion Tribrid Orbitrap mass spectrometer (Thermo Scientific). Full MS spectra were recorded from 300 to 1500 m/z in the Orbitrap at 120,000 resolution with an automatic was performed using TopSpeed mode at a cycle time of 3 s. Higher-energy collisional dissociation (HCD) fragmentation was induced at an energy setting of 28 for peptides with a charge state of 2-4.
Fragments were analyzed in the orbitrap at 30,000 resolution. Analysis of raw data was performed using Proteome Discoverer software (Thermo Scientific) and the data processed to generate reduced charge state and deisotoped precursor and associated product ion peak lists. These peak lists were searched against the human Uniprot KB database protein database (42,186 entries, 2018-05-18). A maximum of one missed cleavage was allowed for tryptic digestion and the variable modification was set to contain oxidation of methionine and N-terminal protein acetylation. Carboxyamidomethylation of cysteine was set as a fixed modification. The false discovery rate (FDR) was estimated with randomized decoy database searches and were filtered to 1% FDR. Proteins present in the control were removed and further contaminants were also removed by searching against the Contaminant Repository of Affinity Purification (CRAPome) database  30.
The interactions network was constructed with the online STRING protein-protein interaction (PPI) database (v. 11) 79 using an interaction confidence score of ≥ 0. 6. The PPI networks were visualized and analysed using Cytoscape software (version 3. 8. 2)  80. Gene enrichment analyses were performed to identify Gene Ontology (GO) biological process as well as Reactome and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways associated with the PPI networks using ClueGO plugin of Cytoscape (version 2. 5. 7)  81.
Quantitative confocal microscopy Immunofluorescence images were captured with an inverted Leica TCS SP8 inverted laser scanning microscope (Leica Microsystems) using x40 oil immersion (40x HC Plan/Apo CS2 1. 30 numerical aperture (NA)) and x63 glycerol immersion (63x HC Plan/Apo CS2 1. 30 NA) objectives. Z-stacks at 2048 × 2048 pixels were collected at 0. 2 or 0. 3 μm (MCF-10A), 0. 5 μm (MECs) and 1 μm (mammary sections) intervals. Images were processed with Fiji software  82. Astral microtubule images were denoised using ND-Safir 3 software 83. ANXA1, LGN and Gαi1 profiles at the cell cortex of metaphase cells were measured in Fiji using a custom macro as described  84. Fluorescent intensity values were reported along the cortex starting (and finishing) from a point facing the metaphase plate.
5 or 3 min for 3 or 5h. Exposure times were 250 msec and 5% laser power for GFP, 80 msec and 2% laser power for labelled DNA and 80 msec and 2% laser power for labelled microtubules using the DAPI-FITC-mCh-Cy5 filter set. Images were deconvolved using the DeltaVision software SoftWoRx and further processed using Fiji. Mitotic spindle oscillations were calculated from time-lapse videos of MCF-10A cells labelled with SiR-tubulin and Hoechst 33342. Metaphase spindle angles in the z and xy plane were manually determined for every frame using Fiji. Measurements of the spindle angle axis in the z plane were performed as described above in fixed cells. Spindle angles were reported as positive values unless spindle poles changed direction. In this case, angles were displayed as negative values. Mitotic spindle orientation in the xy plane was measured by drawing a line crossing both spindle poles of the first frame of metaphase.
Acknowledgments We would like to thank  Dr Mark Willett  for valuable assistance with fluorescence microscopy;  Andrew Crocker  for mouse husbandry;  Dr Melissa Andrews  for providing the HEK293 cell line;  Dr David Bryant  for providing the pLKO. 4-Mem-Venuslentiviral plasmid. In addition, we thank  Dr Marcin Przewloka  for critically reviewing the manuscript. This work was supported by a  Wellcome Trust Seed Award in Science  ( 210077/Z/17/Z ) and  MRC   New Investigator Research Grant  ( MR/R026610/1 ) awarded to SE. MF was supported by a  Gerald Kerkut Trust PhD studentship.
The combined actions of R domain phosphorylation, ATP binding to the NBDs, and subsequent TMD conformational change result in CFTR activation and chloride secretion  (8). More than 2000 CFTR mutations have been identified to date, of which around 400 currently fulfill functional and clinical criteria for CF disease. Information regarding these mutations can be accessed from two databases: http://genet. sickkids. on. ca and the CFTR2 database at https://cftr2. org. Mutations can be broadly divided into six classes according to the specific defect in CFTR protein biology  (9, 10). Class I mutations arise from nonsense, frameshift, or splicing mutations that produce premature termination signals or no mRNA, leading to absent protein production. Class II mutations occur as a result of misfolded CFTR protein, endoplasmic reticulum (ER)-associated degradation, and reduced CFTR trafficking to the cell surface.