dataesr/scientific-paragraphs-categorization huggingface dataset:
SCNA and methylation analyses We downloaded gene-level SCNA data identified by the GISTIC2 package  [131]. To quantify the methylation level, we processed the value of each probe. Specifically, we excluded probes with more than 5% of samples harboring missing data (NA value) within a cancer type due to technical issues (e. g. , multiple mapping probes). We focused on promoter methylation and analyzed probes that mapped to the 2000 bp assumed promoter region (1500 bp upstream and 500 bp downstream of the transcription start site, TSS)  [132]. Herein, we defined the TSS of each gene based on the functionally most important transcripts or principal transcripts annotated by the APPRIS database  [133]. In the absence of a principal transcript, we used the longest transcript. For a gene with multiple probes, we retained only the one with the strongest negative correlation between the methylation level and gene expression  [134].
We implemented the Spearman correlation-based analysis to evaluate how strongly gene expression is affected by SCNA or promoter methylation. For each gene, we calculated the correlation between gene expression and SCNA/methylation within each cancer type. According to the genome-wide distribution of the correlation coefficient, 0. 3/-0. 3 was selected as the cutoff to define significantly correlated genes for SCNA/promoter methylation (Fig. 3 ). We also tested a more stringent threshold of 0. 4/-0. 4 and largely reproduced the patterns generated with the 0. 3/-0. 3 cutoff (Additional file 1: Fig. S3c ). In addition, we examined whether genes upregulated in tumor compared to normal samples show lower normal expression compared to those downregulated in tumor. We recorded the mean expression values of normal samples within each cancer type and then pooled all data across the 13 cancer types.
S2d. Since correlation-based analyses including survival and coexpression analyses depend on expressional variance, they are not appropriate for genes unexpressed in too many samples within a cancer type. Therefore, we followed  [130]  and excluded genes without any reads mapped across at least 20% of samples in a cancer type of interest. We implemented the Cox proportional hazards model with the survival package v2. 43. 3 in R to define prognostic genes as those whose expression levels were significantly associated with the survival time of patients after controlling for clinical variables (age/stage/gender) if applicable (Additional file 2: Table  S3 ). Among multiple clinical endpoints, overall survival (OS) and PFI have been recommended for high-quality survival analyses  [121]. Note that PFI data is recommended for all 13 cancer types covered in this study, and OS data is problematic for three cancer types, where the endpoint needs a longer follow-up or the number of events is not sufficient.
The pattern (Fig. 4a ) remained robust (Additional file 1: Fig. S4b ). Notably, in Additional file 3 and Additional file 1: Fig. S4a /c, we also showed the hazard ratio together with the 95% confidence interval. The hazard ratio is similar to the odds ratio and both of these values are often perceived as relative risk  [137]. The major difference is that the hazard ratio is not constant while the odds ratio is. Therefore, the hazard ratio is more suitable for survival data analyses  [137]. To infer the functions of PSGs, we performed expression correlation-based enrichment analyses. For a given gene, we used the "ppcor" in the R package psych v1. 8. 12 to calculate the Spearman correlation coefficient between its expression and that of other protein-coding genes while controlling for tumor purity within each cancer type.
To represent the general scenario, the later list includes eight randomly sampled MSigDB gene sets, which covers all major categories of biological processes except cell proliferation and DNA damage (these two categories being related with cell cycle, Additional file 1: Fig. S4d ): metabolic (bile acid metabolism), cellular component (apical junction), immune (complement, coagulation), pathway (protein secretion), signaling (androgen response, mTORC1 signaling) and development (epithelial-mesenchymal transition). We then used the RDAVIDWebService package v1. 28. 0  [138]  to enable enrichment analyses for 1356 (552+804) times. To validate the excess of cell cycle-related PSGs based on MSigDB or DAVID, we further analyzed an independent cell cycle gene list, which is curated via a literature survey and experimental analyses in normal cells  [70].
This list includes 701 genes involved in a dozen distinct processes, such as cell cycle progression, DNA damage/replication, or spindle assembly  [70]. Despite of database version changes, the majority (666 or 95. 0%) of these genes were covered in our analyses. For CRISPR/Cas9 screening data analyses, we downloaded the single guide RNA (sgRNA) abundance fold changes file (2019_Q4 version) from the DepMap data portal  [71]. To determine the correspondence between TCGA cancer types and Dep-Map cell lines, we downloaded the cell line annotation file from the Cancer Cell Line Encyclopedia (CCLE) project  [139]  and retained cell lines that corresponded to the 13 cancer types covered in this study. Considering within-tumor heterogeneity  [140] , we required at least five cell lines to represent a cancer type.
Evolutionary analysis of DDX11 To dissect the evolutionary history of DDX11, we examined the syntenic alignment at both loci (LOC642846/DDX12P, DDX11) provided by the UCSC genome browser  [144]. We chose five primates, namely, humans, chimpanzees, gorillas, orangutans, and rhesus monkeys, considering both phylogenetic relationships and the quality of genome assemblies. Given the presence and absence of orthologous loci across these species, we reconstructed the evolutionary history of this gene family by following the parsimony rule. To infer the selection force acting on DDX11 homologs, we extracted the homologous sequences of 10 phylogenetically representative mammals from the Ensembl database  [126]. LOC642846/DDX12P in humans and DDX12P in chimpanzees were conceptually translated with the DDX11 protein of humans as the template.
We implemented PRANK v170427 to perform protein-level alignment since this software generates fewer alignment errors  [145]. Protein alignment was transformed to codon level alignment with PAL2NAL v14  [146]. As described in previous studies  [79, 147] , the DDX11 protein sequence was divided into five functional regions (T, Arch, Fe-S, Hel, and Linker), in which Hel denotes all known small motifs. We extracted the alignment for each region and then conducted a branch test with the codeml program in the PAML v4. 9h package to detect the signal of natural selection  [84]. For each domain, the significance test was performed by comparing two-ratio and three-ratio branch models, where ω 0 , ω 1 , and ω 2 denote the rates (K a /K s ) of the outgroups, pseudogenic copies and derived functional copies, respectively (Additional file 1: Fig.
6. 1d [150] , and performed the quantification with RSEM v1. 3. 1  [151]. Fragments per kilobase million (FPKM) values were log 2 transformed after adding one. We used the STAR/RSEM pipeline here rather than kallisto because this dataset is small and the STAR/RSEM pipeline has a slightly better performance despite slower running speed  [97]. To account for between-species heterogeneity in gene annotations and sequencing libraries, we implemented the following procedures. On the one hand, since both the N-terminal and C-terminal domains of DDX11 protein are required for its function  [76, 152] , we used only annotated protein-coding transcripts with protein products of at least 800 amino acids across species (Additional file 2: Table  S6 ). On the other hand, we downloaded orthologous information from Ensembl BioMart v98  [153]  and retained one-to-one orthologous genes across species.
Developmental transcriptome data analyses To examine the expression dynamics of the developing brain, we took advantage of a strand-specific RNA-seq dataset  [90]. We reprocessed the raw sequencing data by following the aforementioned TCGA gene quantification pipeline except that the "strandspecific" mode in kallisto v0. 43. 1 was enabled. We divided 52 samples of cerebrum (forebrain) into 12 stages of development according to  [113]  with the requirement of at least two replicates for each stage (Table  S7 ). Medians of gene expression values within each stage were subsequently used. We removed weakly expressed genes (median expression level lower than 0. 2 across all 12 stages). Here, weeks after conception conventionally refers to completed weeks  [154] ; thus, the end of the embryonic stage, i.
05 were selected. Notably, numerous lists of noncoding regions under positive selection or human/primate-specific regulatory sequences have been published  [108, [157] [158] [159] [160] [161]. However, these lists were derived from functional genomics data of fetal or postnatal brains and thus could not be directly transferred to the embryonic brain. This is why we ultimately used the list generated in  [156] , which was computed based on the human genome directly and thus not anchored to a specific developmental stage. For all these meta-analyses, we used the Ensembl ID or gene symbol to cross-reference different datasets.
Availability of data and materials No large-scale experimental data has been generated in this project. The age data used in this article were downloaded from the previous literatures. In brief, the synteny-based gene age data was from GenTree database  [162]  and the phylostratigraphy data was from Supporting Information (Dataset S1) of one previous work  [163]. Additional file 3 and the R code for Figs. 1, 2, 3, 4, 5, and 6 are available at both GitHub  [164]  and Zenodo  [165].
Whereas Kepler observed only 3 known Be stars  (Rivinius et al. 2016) , TESS is observing over 1000. Another benefit is that the TESS mission was designed for brighter stars (V 12), which means that the systems viewed by TESS have more comprehensive historical datasets and are more practical to observe from the ground. Capitalizing on the strengths of TESS, the main goal of this work is to provide an overview of the variability seen in the population of over 400 Be stars observed in the first year of the TESS mission at a high precision and at short timescales (tens of minutes to weeks). Variability characteristics are ascribed to every star in the sample, providing insight to the behavior of the population as a whole and bringing to light patterns that exist according to spectral type and correlations between the different variability characteristics.
Extracting light curves from TESS FFIs Light curves are extracted from the TESS FFIs using three different methods. This is done to increase our confidence in the results when these methods produce LCs that agree, and also decreases the incidence of false-positive detections of signals if they exist in only one version of the extracted LC, being perhaps caused by imperfect removal of systematic trends or blending from neighboring objects. The first method used to extract LCs begins by using the lightkurve package  (Lightkurve Collaboration et al. 2018)  and TESScut  (Brasseur et al. 2019)  to download a target pixel file (TPF) with a 50 × 50 pixel grid centered on the target star's coordinates for every available TESS sector. An aperture threshold of 10 sigma relative to the median flux level is used as a first step to automatically determine the aperture mask for the target star.
1 degree and an outer radius of 0. 35 degrees (although this is allowed to vary if there are too few stars of sufficient brightness within the original annulus). TESScut is then used to extract a light curve for the target star and all of the identified neighboring stars using a 3 × 3 grid of pixels. The trend filtering algorithm (TFA) in the VARTOOLS light curve analysis package  (Hartman 2012 ) is then used to identify and remove trends that are common to the set of the target star and its neighbors. This aperture is almost always smaller than that used in the PCA method, and is therefore less susceptible to blending (at the cost of achieving a lower SNR due to excluding some flux-containing pixels). In some cases one of the above methods will fail partially or completely (most often when a target is close to the edge of a detector, or in the most crowded fields).
Sample selection The Be Star Spectra (BeSS) database 1  (Neiner et al. 2011)  was used to create a list of Be stars between V-mag of 4 -12. The sample of Be stars from  Chojnowski et al. (2015)  was also included. Only targets in the southern ecliptic hemisphere were chosen, in order to match the fields observed by TESS in its first year of operations. This initial list of 539 stars was reduced to 432 by rejecting systems known or strongly suspected to be something other than a classical Be star (e. g. interacting binaries or B[e] stars), and stars for which there are no TESS data or the data is of insufficient quality (i. e. when a star falls into a gap between sectors, or systematic effects or unusually high noise levels severely hamper our ability to analyze a given LC). Of these 432 classical Be stars, 218 (50%) are of early type (B3 and earlier), 79 (18%) are mid type (B4, B5, and B6), 112 (26%) are late type (B7 and later), and 23 (5%) are of unknown spectral type.
Lomb-Scargle periodograms and pre-whitening Because of the generally complex nature of photometric variability of Be stars, there are signals with a wide range of timescales and behavior that we are interested in measuring which requires extra analysis steps. For example, in a system with significant aperiodic low-frequency variability, it is prudent to detrend against these signals when analyzing the data for higher frequency signals. Through iterative prewhitening, each sector of data for each object was detrended against all signals lower than 0. 5 d -1 up to a false alarm probability of 10 - 2  (in a similar fashion as  Rivinius et al. 2016). A frequency analysis was then performed for the entire set of TESS observations for each star, separately for the original and detrended versions of the light curves.
The timeseries. LombScargle package  (VanderPlas et al. 2012; Van-derPlas & Ivezić 2015)  of Astropy  (Astropy Collaboration et al. 2013 , 2018)  was used to compute these Lomb-Scargle Periodograms (LSPs). The above procedure was applied to both the 2-and 30-minute cadence data, when available. To determine the individual frequencies present in the data, the Vartools light curve analysis software with the Lomb-Scargle routine  (Zechmeister & Kürster 2009; Press et al. 1992 ) was used to detect and iteratively pre-whiten the data against each recovered signal (up to a false alarm probability of 10 -2 ), recording the frequency, phase, amplitude, SNR, and false alarm probability. The frequency and amplitude of these signals are used to plot the pre-whitened periodograms, and are also used to re-construct each light curve based on the recovered signals, which are visually compared to the original photometric data to ensure a good fit from the Fourier analysis.
Wavelet plots It is common for Be stars to show photometric signals that vary in amplitude over time. Wavelet plots are a convenient way to visualize this, as they depict the frequency spectrum as a function of time (at the cost of a degraded frequency resolution). The Python package scaleogram 2 was used to perform a wavelet analysis for each star, considering the original and low-frequency detrended (signals with frequencies < 0. 5 d -1 removed) versions of the data separately.
Panel 8 of Fig. 5  shows one such example, where the frequency spectrum does include two groups following the typical pattern ( f g1 ≈ 2. 45 d -1 , and f g2 ≈ 4. 94 d -1 ). There is also a strong isolated signal ( f ≈ 1. 30 d -1 ) and its sub-harmonic, which are seemingly unrelated to the two frequency groups. Situations like this may reflect "composite frequency spectra," where the lower frequency group and its sub-harmonic may indicate binarity or rotation (perhaps of a companion star or system), while the pair of higher frequency groups are formed by families of pulsation typical of Be stars. This example is purely illustrative -such interpretation requires further data and analysis and is beyond the scope of this work. All cases where the typical group configuration is realized (and when it is not) and the relative strength and central location of g1 and g2 were recorded.
Comparison to other studies Flickers on the short timescales that are seen in TESS data have been captured in other Be stars with space photometry. In Kepler data for 3 Be stars, flickers are found in two systems with timescales of about 10 -50 days  (Rivinius et al. 2016). Using data from CoRoT,  Semaan et al. (2018)  analyzed light curves for 15 Be stars, highlighting 6 with flicker events with total durations between about 10 -60 days. However, until this work, there has been a lack of analysis of a large set of space photometery for Be stars. The large dataset provided by TESS opens a window into studying these events in a larger number of individual systems, providing insight into the types of systems showing small-scale flickers and how frequently they occur. A detailed analysis of the flickering behavior of Be stars will be the subject of a forthcoming paper.
2019). Although stochastic variability does not contain any coherent periodic signals, there is still valuable diagnostic potential in the profile and amplitude of the frequency spectrum of these stochastic signals. For example,  Bowman et al. (2020)  analyzed TESS data for 70 OB stars with TESS, concluding that there is strong evidence for internal gravity waves by comparing measurements of the stochastic frequency spectrum to models of wave propagation in stellar interiors which originate at interface of the convective core and radiative envelope. These internal gravity waves may be an important aspect of Be stars, since they are efficient at transporting angular momentum from the stellar interior outward  (Rogers et al. 2013; Edelmann et al. 2019 ). While it is beyond the scope of this work to quantitatively analyze the stochastic variability seen in the TESS Be star sample, it is important to note that these features, although appearing random and incoherent, can still be used as a diagnostic to infer the physical origin(s) of the signals.
DISCUSSION OF OVERALL RESULTS Virtually all of the Be stars observed by TESS in sectors 1 -13 are variable. Their photometric signals carry information about the underlying physical processes causing this variability, and a careful study of these observations can elucidate the nature of Be stars as a population especially in regards to pulsation and mass ejection episodes. While there remains much to be explored with these data, the overview of Be star variability seen by TESS presented in this work provides a solid background of their photometric behavior as a population on short timescales and is a first step towards fully leveraging the unique dataset provided by TESS for these and similar objects. A discussion for each characteristic variability pattern is given in Section 5. The remainder of this section highlights correlations between different classes of variability.
Especially exciting, given the brightness of TESS targets, is the relative ease of monitoring these stars with other observational techniques (spectroscopy, polarimetry, multi-band photometry, and interferometry) from the ground which, when combined with TESS data, can greatly improve our understanding of these objects. (grant 311446/2019-1) and FAPESP (grant 2018/04055-8). T. A. acknowledges support from FAPESP (grant 2018/26380-8 ). A. R. acknowledges support from CAPES (grant 88887. 464563/2019-00). A. L. acknowledges support from CAPES (grant 88882. 332925/2019-01). P. S. acknowledges support from FAPESP (grant 2020/04445-0). The authors are grateful to the amateur spectroscopy community, whose observations have directly supported this and forthcoming works, in particular Stephane Charbonnel, Christian Buil, Paolo Berardi, Tim Lester, Alain Maetz, Robin Leadbeater, Erik Bryssinck, Olivier Garde, Franck Houpert, Olivier Thizy whose data was used in this publication.
This work makes use of observations from the LCOGT network. This paper includes data collected by the TESS mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided by NASA's Science Mission directorate. TESS Guest Investigator program G011204 provided the 2-minute cadence data. This project makes use of data from the KELT survey, including support from The Ohio State University, Vanderbilt University, and Lehigh University, along with the KELT follow-up collaboration. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC https://www. cosmos. esa. int/web/gaia/dpac/ consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
This research has made use of NASA's Astrophysics Data System. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. This work has made use of the BeSS database, operated at LESIA, Observatoire de Meudon, France: http://basebe. obspm. fr. This research made use of Lightkurve, a Python package for Kepler and TESS data analysis  (Lightkurve Collaboration et al. 2018). This research made use of Astropy, 5  a community-developed core Python package for Astronomy  (Astropy Collaboration et al. 2013 , 2018). This work used the BeSOS Catalogue, operated by the Instituto de Física y Astronomía, Universidad de Valparaíso, Chile : http://besos. ifa. uv. cl and funded by Fondecyt iniciación No 11130702, using the PUCHEROS instrument  (Vanzi et al. 2012 ). The page is maintained thanks to FONDECYT No 11190945.
This signal also seems to have a beat envelope, in both KELT + TESS. Also a signal at 2. 33 d -1 , which is strongest when the beat envelope of the low-freq signals are low, but is present throughout, and does seem relatively strong near peak brightness. TIC 439164152 = CPD-63 2495:. O9. 5Ve. Has one eclipse near the end of the dataset, reaching a depth of about 0. 5%. No optical BeSS spectra, but 3 IUE spectra exist. High Mass X-ray Binary (HMXRB,  Lutovinov et al. 2013 ). Chernyakova et al. (2014)  gives an interesting study of the object from its 2010-2011 periastron passage. Known to have an orbit of P orb = 1236. 9 d, e = 0. 87  (Johnston et al. 1992 (Johnston et al. , 1994)). Seems like a real classical Be star, and not a supergiant. Probably the eclipse is unrelated to the HMXRB and may be a blend or some other source.
With an orbital period of about 3. 4 years, an eclipse is extremely unlikely. TIC 468095832 = 2E 1118. 7-6138 = WRAY 15-793: HMXRB and a gamma-ray source, with a spectral type of O9. 5III/Ve  (Janot-Pacheco et al. 1981). Dramatic variability is seen in KELT, with events having amplitudes up to 2 magnitudes, and duration of ∼100 days. TIC 21249978 = V868 Ara: See Sec. B. 4. The TIC numbers, common ID, and spectral type (as listed on the BeSS database) are given, along with the following information. qual. : Indicates the availability of TESS data of reasonable quality. Values of 0 mean the system was either not observed by TESS in Cycle 1, or there were significant problems with the data. Be: A value of "Y" means this a classical Be star, "S" means the classical Be designation may be suspect, but there is insufficient evidence to reject it, "U" means this is rejected as a classical Be star but the nature of the system is not necessarily known, and "N" marks systems known to be something other than a classical Be star.