dataesr/scientific-paragraphs-categorization huggingface dataset:
D. G. Leibovici et al. : Spatio-temporal  variations in LSMs at high latitudes Code and data availability. The analyses were performed using the R package PTAk (https://CRAN. R-project. org/package=PTAk, last access: 2 March 2020,  Leibovici, 2010). The multiway data tables used in the paper can be requested from the first author. CLM5. 0 is publicly available through the Community Terrestrial System Model (CTSM) git repository (https://github. com/ESCOMP/ctsm, last access: 2 March 2020,  Lawrence et al. , 2019) ; all model data are archived and publicly available at the UCAR/NCAR Climate Data Gateway, https://doi. org/10. 5065/d6154fwh, last access: 2 March 2020, Oleson, 2018.
Aim: We aim to identify driving genes of colorectal cancer (CRC) through multi-omics analysis. Materials & methods: We downloaded multi-omics data of CRC from The Cancer Genome Atlas dataset. Integrative analysis of single-nucleotide variants, copy number variations, DNA methylation and differentially expressed genes identified candidate genes that carry CRC risk. Kernal genes were extracted from the weighted gene co-expression network analysis. A competing endogenous RNA network composed of CRCrelated genes was constructed. Biological roles of genes were further investigated in vitro. Results: We identified LRRC26 and REP15 as novel prognosis-related driving genes for CRC. LRRC26 hindered tumorigenesis of CRC in vitro. Conclusion: Our study identified novel driving genes and may provide new insights into the molecular mechanisms of CRC.
Data download & preprocessing The somatic mutation information (reads were aligned against the human genome [hg19]), copy number segment information (Affymetrix genome-wide human SNP array 6. 0), methylation information (platform: Illumina 450K DNA methylation array) and mRNA expression profile data related to CRC and the relevant clinical data were obtained from the The Cancer Genome Atlas dataset (TCGA) database. Considered that array is dependent upon the signals generated via hybridization, the sensitivity for score in gene expression could not reach to the levels of RNA-sequence, the score of which shows directly expressing levels of a specific mRNA, we exploited RNA-sequence data from TCGA in this study. A total of 367 samples had multiple sets of data with somatic mutations, CNVs, methylations and mRNA expression profiles.
Identification of differential expression RNA expression profile data (level 3) of CRC and adjacent normal tissues were obtained from TCGA. LIMMA was used to identify DEGs in the normal and CRC tissues. Genes with log2|FC| ≥ 1 and p ≤ 0. 05 were identified as DEGs. We also employed LIMMA to analyze differentially expressed microRNAs (DEmicroRNAs) and differentially expressed lncRNAs (DElncRNAs), applying a threshold of p ≤ 0. 05 and log2|FC| ≥ 1.
WGCNA analysis Weighted gene co-expression network analysis (WGCNA) is a systematic biological method that focuses on the relationships of patterns and gene expression profiles in a scale-free network  [14]. The R package WGCNA was used for the co-expression network analysis using the expression values of the genes. First, a similarity matrix of gene expression was constructed, and a suitable soft threshold β was selected to ensure a scale-free network. Second, the expression matrix was transformed into an adjacency matrix and then into a topology matrix. Based on topological overlap measure, we clustered the genes into different modules through average linkage hierarchical clustering analysis. At last, we calculated the eigenvector value of each module in turn and assessed the correlation between modules and phenotypes.
Cell lines & cultures The CRC cell lines SW480, LoVo, HCT116, SW620, HT29 and Rko were purchased from the American Type Culture Collection (USA) and cultured in RPMI 1640 supplemented with 10% fetal bovine serum (Invitrogen). All cell lines were maintained in a 5% CO 2 atmosphere at 37 • C. Certifications for cell lines were shown in Supplementary data 1-6.
Identification of SNV genotypes in CRC MutSigCV was used to predict cancer-related genes in 367 CRC samples, and 81 candidate genes with a significance threshold of q < 0. 05 were identified (Supplementary Table  1 ). We then divided the mutations into 96 types according to the bases that were located 1 bp upstream and downstream of the mutation sites and determined the frequency distribution of these mutation types in the 367 CRC samples (Figure  1A ). At last, we assessed the frequency of the 96 mutation types in each sample through non-negative matrix decomposition and extracted three mutational signatures (Figure  1B ). We analyzed the similarity between these three mutation signatures and COSMIC signatures through R package maftools. The result illustrated that these mutation signatures were similar to signature 6, 1 and 10 in COSMIC, with cosine similarity 0.
Comprehensive analysis of CNV-disrupted genes & DNA methylation in CRC The CNV data of the CRC samples were analyzed by GISTIC. A total of 18 amplifications and 26 loss copies of minimal common regions were detected (Supplementary Table  5 ). Figure  2A  & B show that 13q12. 2 and 8q24. 21 were the most significant amplified regions, and 16p13. 3 and 8p21. 3 were the most significant loss regions. Unsupervised hierarchical clustering of the CNV results from these samples divided the CRC samples into two subgroups, one of which had obvious CNV changes (Figure  2C  & Supplementary Table  6 ).
C > A C > G C > T T > A T > C T > G C > A C > G C > T T > A T > C T > G S1 Next, we compared the classification of SNP data with that of CNV data and found that the classification results from the different platforms were consistent. Most of the samples of CNV subgroup 1 were categorized into SNP subgroup 1, and SNP subgroup 2 was categorized into CNV subgroup 2 (Figure  2D  & Supplementary Table  7 ). Furthermore, we downloaded 367 CRC samples and 45 normal samples from the TCGA database to screen the differentially methylated sites. We screened out 2719 differentially methylated sites with p ≤ 0. 05 and |logFC| ≥ 0. 4. Hierarchical cluster analysis of differential DNA methylation sites in all CRC samples and normal samples showed that these methylation sites were clustered into two subgroups (Figure  2E  & Supplementary Table  8 ).
Furthermore, a hypergeometric distribution test showed that DEGs were significantly enriched within the significant CRC-related gene set analyzed by MutSigCV (p = 0. 011). We then obtained CRC-related genes from the NCBI Gene database, the Online Mendelian Inheritance of Man database, and the Kyoto Encyclopedia of Genes and Genomes database (Supplementary Table  17 ) and identified the overlap between the DEGs and the CRC-related genes. The relationship between DEGs and CRC-related genes is outlined in Supplementary Figure  1B. Three genes were distributed in all four platforms. In addition, 1316 genes were identified only as DEGs, GO analysis showed that they were enriched in biological process such as regulation of cell population proliferation, regulation of cell migration and regulation of cell motility, which might be correlated with CRC tumorigenesis, so these genes may be related to tumorigenesis of CRC (Supplementary Table  18 ).
WGCNA analysis to identify hub genes in the key module The R package WGCNA was used to create the co-expression network of genes. The clustering was based on the expression data of 4291 genes mentioned above. In sample cluster dendrogram and trait indicators, red color represented survival and white color represented death. We found no outlier samples were detected and removed (Figure  3A ). To achieve a scale-free topology (R 2 > 0. 8), we set β = 7 for the adjacency matrix (Figure  3B ). The expression matrix is transformed into adjacency matrix, and then the adjacency matrix is transformed into topology matrix. Based on topological overlap measure, we exploited average linkage hierarchical cluster method to get gene clusters. According to the standard of hybrid dynamic cut tree, we set 20 as minimum gene number for each gene network module.
In the TCGA database, we identified 263 microRNAs and 3886 lncRNAs that were differentially expressed between the CRC and normal samples. We then investigated the co-expression of lncRNAs to find the functional clusters of CRC samples through WGCNA. A soft threshold of β = 5 was chosen to ensure a scale-free network, and this generated six modules-green, turquoise, yellow, brown, blue and red. In addition, the gray module represented a collection of uncorrelated genes (Figure  6A  & B ). We further analyzed the correlation between modules and phenotypes to infer the relationships between genes and phenotypes. The integrated analysis results showed that the green, turquoise, brown, blue and red modules were significantly associated with CRC (Figure  6C  & D ). We calculated the correlation between gene expression and their modules according to the eigenvectors of each module and chose lncRNAs with R > 0.
For example, Chang et al. [37] integrated and analyzed RNA sequence and somatic CNV data to demonstrate dosage-sensitive genes in CRC, which provided a new reference for cancer therapy. In this study, we investigated genes associated with CRC incidence and prognosis. By applying multi-omics analysis, we screened CRC prognostic-related genes and validated their prognostic significance in clinical samples. We downloaded multi-omics data from TCGA and identified significant mutation genes, differential methylation sites and DEGs. We then mapped the DEGs to certain CRC-related genes from the NCBI Gene database, the Online Mendelian Inheritance of Man database, and the Kyoto Encyclopedia of Genes and Genomes database. To obtain the candidate gene sets, the key gene sets were further expanded by adding the significant mutation genes from the mutation data analysis and the genes annotated with differential methylation sites.
Polyacrylamide Gel Electrophoresis (PAGE) Products of PCR amplifications, with or without gel purification (Qiagen) were run on denaturing urea (8 M) polyacrylamide gel (8% w/v) in 1X TBE buffer following heat denaturation of samples at 95 • C for 5 min and subsequent snap-cooling on ice. The electrophoresis was carried out at 25 W after pre-running the gel for 1 h. Typically, the gel was stained in 1X TBE containing 0. 02% (v/v) SyberSafe (Invitrogen) and then imaged using ChemDoc (Bio-rad). Band intensity was analyzed using Image Lab software (Bio-Rad). When required, bands were eluted with a solution of 0. 3M NaCl and ethanol precipitated.
Native Agarose Gel Electrophoresis (NAGE) Products of amplification were analyzed on 4% (unless otherwise stated) native agarose gel electrophoresis in 1X TAE buffer. SyberSafe dye (Invitrogen) was added to agarose at pre-casting step with a volume ratio of 1:10,000. DNA was detected using ChemDoc (Bio-rad) and band intensity was measured using Image Lab software (Bio-rad).
FUNDING This work was supported by  King Abdullah International Medical Research Center (KAIMRC)   Institutional Research Grant  (grant number  RC17/006/R)  to AN and  Natural Sciences and Engineering Council of Canada (NSERC)  ( 418240  to JP). JP was a junior 2 research scholar of  Fonds de Recherche du Québec en Santé (FRQS) , VA-S received a  Mitacs Globalink fellowship  and EY received a fellowship from  Fonds de Recherche du Québec -Nature et Technologies (FRQNT).
To build the final statistics, we considered a list of predicted events over a long time span, and stellar astrometry from Gaia data release 2. Results. We derive an acceptable range of observability of the events, with clear indications of the expected errors in terms of timing uncertainties. By converting the distribution of such errors to astrometric uncertainties, we show that the precision on a single chord can reach levels equivalent to the performance of Gaia (sub-milli-arcseconds). The errors on the asteroid position are dominated by the uncertainty on the position of the occultation chord with respect to the barycentre of the object. Conclusions. The limiting factor in the use of occultation astrometry is not the light curve uncertainty, but our knowledge of the asteroid's shape and size.
Although these have been obtained without corrections for the interstellar reddening, they are expected to be accurate enough for an estimate at ∼20% level  (Andrae et al. 2018 ). In the data set, we find a maximum apparent diameter of ∼0. 2 mas at V * = 11, which is too small to become a serious concern. We adopt a conservative transmission efficiency (telescope reflections, correcting optics, and camera included) of 0. 3. Some observations performed at the site of the UniversCity telescope with a ∼1 m instrument, with an Andor iXON 888 camera and a Johnson-Cousin standard R filter, have allowed the determination of a typical background value used for our simulations of N bg = 7. 400 ADU s -1 pixel -1 , (ADU -analog-to-digital unit) per unit square metre of the mirror surface. These are re-scaled to the size of the UniversCity telescope, by assuming the same transmission factors, as well as the same camera and filter (R).
Regression procedures on the simulated data To retrieve the light curve parameters from the simulated data sets described above we adopt the Bayesian inference method (BIM) by a nested sampling Monte Carlo algorithm as implemented in the DIAMONDS 1 package  (Corsaro & De Ridder 2014a,b; Corsaro et al. 2018). To apply the BIM, we implement a light curve function as: F(t) = F 0 -F A S (σ, t 0 , L) (1) 1 https://github. com/enricocorsaro/DIAMONDS where S is a "supergaussian" function composed of three branches: S(σ, t 0 , L) =          N(t, t 0 -L/2, σ) t ≤ t 0 -L/2 1 t 0 -L/2 ≤ t ≤ t 0 + L/2 N(t, t 0 + L/2, σ) t ≥ t 0 + L/2 In the equation above, N(x, x c , σ) is a normal distribution function of x, centred at x c , of width σ. With the small value for σ that we adopt (1/10 of the exposure time) the supergaussian becomes similar to a "gate" function, but it preserves its properties of being continuous with continuous derivatives.
Derived uncertainty on asteroid astrometry By a simple procedure, we can convert the precision that we expect on the centre epoch of the occultation chord (Fig. 4 ) to the corresponding along-track astrometric precision. For the asteroids from the orbit database at the Minor Planet Center, we proceed by computing the predicted circumstances for occultation events from a single arbitrary site (in our case Nice observatory, MPC code 020) over a full year 3. We consider a sample of Gaia DR2 target stars with V<14, and include asteroids with a diameter >5 km. For each predicted event, we compute the expected DNR with our telescope configuration, and the corresponding apparent angular topocentric velocity vector v a of the asteroid, in the equatorial frame, projected on the sky. The apparent velocity is the scale factor that allows us to convert the uncertainty on timing, given by the average shown in (Fig.
1 s, the corresponding physical chord length is ∼1. 5-3 km. This represents a practical limit, which can be overcome only by increasing the sampling frequency, if the available flux allows. For larger objects, the overall error budget seems to be dominated by shape and size effects, increasingly for larger asteroids, and one should note that if these physical properties have been independently measured, they can be exploited to correct the astrometry derived by the occultation. Today, some thousands of shapes and rotational parameters (period, spin axis) are known, mainly from rotation light curve inversion. The largest database of asteroid sizes (∼10 5 ), is produced by the cited NEOWISE survey. As the number of asteroids for which shape and size is known will increase in the future, the possibility to determine the position of the centre of mass with respect to the observed chords will be more and more common, permitting us to get closer to the limit represented by our single-chord astrometric accuracy.
This is clearly a small sample, but despite a growing number of reports, only a fraction of observers provide the photometric data and satisfy at the same time our selection criteria. The selected sample consists of 25 events. The DNR is estimated directly from the light curve, in the absence of the required information for more accurate modelling. Drop-to-noise ratio computation shows that 18 events fall in the range of interest to us (Table  1 ). We notice (Fig. 7 ) that the overall trend is satisfactorily respected. A larger dispersion is present, which is probably the result of the large variety of instruments and observational con- ditions, certainly much less homogeneous than in our simulated sample. We consider that within the limit of this comparison our model appears to be close enough to the reality to be usable for an approximate estimate of the performance of our general survey.
Acknowledgements This research has been supported by the  Pessoa program for science cooperation between Portugal and France , the  Programme Nationale de Planetologie in France , the  BQR programs of Laboratoire Lagrange and Observatoire de la Côte d'Azur. E. C. is funded by the  European Union   Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No. 664931. P. M. acknowledges project  FCT 395 P-TUGA PTDC/FIS-AST/29942/2017. A special thanks to  Rui Agostinho  (IA) and  Jean-Pierre Rivet  (OCA), for their help on some critical aspects of our light curve modelling, and to the anonymous referee who helped to considerably improve the manuscript. We acknowledge the use of the Linoccult package by  A. Plekhanov  to compute the predictions of asteroid occultations described in Sect.
Vitrified samples suffer no fixation artefacts and cellular structures are preserved faithfully in their pre-vitrification state  29. Fortuitously, fluorescence imaging at cryogenic temperatures is characterised by enhanced photo-stability of dyes and sharper emission spectra as compared to room temperature imaging leading to data with better signal to noise ratios  14, [31] [32] [33]. However, numerical aperture (NA) in cryo-FM systems is limited because there are currently neither suitable immersion fluids nor robust dipping objectives. As a result, cryo-fluorescence imaging is done using air objectives with a maximum NA of circa 0. 9, limiting the achievable optical resolution. Moreover, it remains technically challenging to prevent ice crystal formation and sample damage while imaging under cryogenic conditions.
Results/Discussion To address the current limitations of cryo-imaging relatively thick biological samples at a nearphysiological state to tens of nanometres resolution we have developed a correlative imaging platform for data collection on cryo-preserved vitrified samples (primarily cell populations, adherent or in suspension) deposited on gold 3mm EM grids (see Methods; Supplementary Figure  1 ). Grids are first mapped using conventional brightfield cryo-fluorescence imaging where the areas of interest are identified before 3D data is collected, first using our newly developed cryo-3D-SIM system and then at a soft X-ray full-field transmission microscope. Data are then processed and correlated to produce 3D imaging volumes that contain both chemical localisation and ultrastructural organisation information enabling the meaningful and unambiguous interpretation of biological features.
The system's optical features are incorporated under the label 'cryoSIM' in the available-online Spekcheck tool  42  where the efficiency and performance of different fluorophores can be assessed in silico with respect to features in our setup, thereby enabling the intelligent design of fluorescence-dependent imaging experiments (Figure  1d ). 3D-SIM requires 30 times as many images as widefield fluorescence, 15 images per Z-plane, and twice as many Z-planes to satisfy Nyquist-Shannon sampling  7. At the cryoSIM, each Z slice is recorded 15 times (five phases for each of three angle in the SI pattern) at 125nm increments along the Z axis as 512x512pixel images with 125nm per pixel and reconstructed to 1024x1024pixel image stacks with voxel size of 62. 5x62. 5x125nm (see Methods). Point spread functions for reconstruction purposes at cryogenic temperatures are generated before data collection using 175nm PS-Speck nanobeads (ThermoFischer) at emission wavelengths of 440, 525, 605 and 647nm.
cells. es/Beamlines/XM]). Once at the TXM, grids are examined and mapped with visible-light to produce a new mosaic at the sample orientation (moving from one holder to the other results in random sample rotation in each microscope). The two mosaics can be aligned with a semi-automated plugin at the beamline and the established ROIs from SIM data collection are recovered to allow X-ray data collection in the same regions. The samples are further evaluated for radiation damage and possible freezethawing (semi-crystalline ice areas can be readily identified in X-ray projections) and a data collection strategy is devised based on trial exposures in areas away from established ROIs in conjunction with the imaging requirements of each project. 2D X-ray mosaics are acquired at all ROIs covering single grid squares (imaging of 7x7 adjacent FOV at 16x16μm will image a whole grid square in a standard EM grid) and these are used to decide on data collection areas.
3D X-ray data is collected as a tilt series as described previously. They are relayed in real-time to an automated pipeline using IMOD Batchruntomo  51  and, provided a sample has good contrast and fiducials (ideally >3 and well dispersed in the FOV), the data are reconstructed to tomograms using IMOD's weighted back projection, serial iterative reconstruction and batch processing options. The resulting tomograms can then be used to further refine data collection strategy. Processed data from both high-end microscopes can then be used in eC-CLEM  52  for the in-silico 3D correlation of associated features, 3D overlay and further analyses (see Methods; Figure  3 ; Supplementary Figures  3  and 4 ). microscope with the 40nm objective). e, The combined 3D stacks can now be further analysed with image analyses packages such as Fiji  43  or segmentation algorithms such as SurVoS  53  to extract statistical data on cellular structures or further combined with tomograms from adjacent ROIs to increase the 3D space imaged or segmented.
uninfected (d  and e ); 1h PI (f and g); 2h PI (h and I); 4h PI (j and k). l, Superposition of SIM processed data of a 4h PI sample on the 2D X-ray mosaic of the same area and expansion of adjacent FOVs where two adjacent X-ray tomograms were collected and stitched. m, Expanded region of interest (highlighted purple in l). n and o, same as n, with Gal3 signal in red and reovirus signal in green respectively. p, q and r, ROI from 2h PI: single Z-axis slice from the corresponding tomogram with Gal3 (red) and MRV (green) localisation. Multi-vesicular bodies (MVPs) are clearly identifiable with distinct sub-compartments where Gal3 is concentrated (presumably the result of virus-carrying vesicles fusing with virus free late endosomes). Novel virus-induced carbon-rich substructures are marked by yellow arrows and vesicle areas within MVBs that remain impermeable to Gal3 marked by blue arrows.
9NA, 2mm working distance). Emission path: Fluorescence signal from the sample is collected by the objective and transmitted back from the 45º mirror, through a 1:1 telescope and reflected by the silver-coated mirror upstream then separated from the excitation light by the primary dichroic. The emitted light is then reflected towards the cameras (iXon Ultra 897, Andor) by a broadband dielectric mirror and a final telescope which magnifies the image to optimise the camera pixel size to the optical resolution. There is a secondary dichroic, that splits the light before the detectors. Each detector has a dedicated filter wheel to allow selection of different emission channels. All hardware is controlled by two open source software packages, Cockpit and pythonmicroscope. These packages allow control of complex microscope systems in real time and provide a simple, user-friendly interface.